480 Final Paper

The Direct and Indirect Financial Burden of Utilizing Canadian Health Care Services
By: Istvan Kery

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Abstract
The topic of this research paper is the financial burden of utilizing health services based on limited
public insurance and the financial cost of supplemental insurance. The paper will explore how this
financial burden affects low-income households’ health care utilization in Canada. It will attempt to
find evidence that low-income households face a ‘double disadvantage’ where low-income
households directly face a barrier to pay for health services without public insurance and indirectly
face a barrier to pay for health services from the financial cost of supplemental health care
insurance. The research finds low-income Canadian residents are unable to pay for health services
directly and are unable to pay for supplemental health insurance, the owning of which would reduce
health service cost. Ultimately, the research does provide evidence that Canadian residents face a
financial barrier based on household income that limits their use of health services and suggests
they face a financial double disadvantage.

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1. Introduction
The provision, financing, and delivery of health care services in Canada have undeniable economic
and social significance. Canadians’ general well-being derives from their ability to utilize these health
care services. The Canadian Health Act (CHA) is a foundational legislation that attempts to provide
universal public coverage for physician and hospital services, regardless of ability to pay. Accessibility
is one of the CHA’s five pillars, which states residents of Canada will be provided uniform insurance
that grants reasonable access to medically necessary health services. The public insurance compensates
patients for the financial cost of these health services.
The limitation to this pillar is there are important health services that are not considered ‘medically
necessary’ and thus are not included in the universal public insurance plan. Specifically, Canadian
residents are not compensated for the cost of prescription drugs, dental services, and eye care services.
Without public insurance, these services can be a financial burden on Canadian residents because it is
highly likely that residents will require these services on multiple occasions. This burden creates a
demand for supplemental health care insurance as a complement to the CHA public insurance. In
Canada, the federal government, employers, or private services provide the supplemental insurance.
These forms of insurance are not considered universal because there is a financial cost to holding the
insurance and this cost can be a barrier to obtaining the insurance. Thus, holding supplemental insurance
can also be a financial burden for Canadian residents.
The topic of this research paper is the financial burden of utilizing health services based on limited
public insurance and the financial cost of supplemental insurance. The paper will explore how this
financial burden affects low-income households’ health care utilization in Canada. It will attempt to find
evidence that low-income households face a ‘double disadvantage’ when utilizing health services
excluded from the CHA. The ‘double disadvantage’ implies low-income households directly face a
barrier to pay for health services without public insurance and indirectly face a barrier to pay for health
services from the financial cost of supplemental health care insurance. Ultimately, these barriers could
force Canadian residents to utilize fewer health services than they actually require.
The paper will ask two parallel research questions:
1. Do high-income households utilize more health care services than low-income households?
2. Do residents with supplemental health insurance utilize more health care services than those
without supplemental health insurance?

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The intuition underlying these questions is formed from two hypothesized relationships. First, it is
hypothesized there is a positive relationship between health service utilization and income, where higher
income leads to higher utilization. High-income households are able to afford more services excluded
from the CHA. Second, it is hypothesized there is a negative relationship between health service
utilization and the price of health services, where lower prices lead to higher utilization as a decrease in
price increases demand. Supplemental insurance enters the relationship because insurance reduces the
price for health care services for households, which increases utilization. Therefore, two hypotheses will
be made:
Hypothesis 1: Households with high income will utilize more health care services than households
with low income.
Hypothesis 2: Residents from high-income households are more likely to hold supplemental health care
insurance.
The research will explore the direct and separate effects that household income and
supplemental health insurance have on health care utilization. The intent of this research is find
evidence for hypothesis 1. The research will then explore the direct effect household income has on
supplemental insurance. The intent of the research is to provide evidence for hypothesis 2. These
hypotheses will be used to find evidence for the double disadvantage that low-income households
experience. The research will attempt to find evidence that low-income residents directly utilize
fewer health services due to the financial burden of the services, and low-income residents
indirectly utilize fewer health services due to the financial burden of supplemental insurance, the
owning of which tends to induce health service utilization.
2. Literature Review
After review and research of related literature, there is evidence to support that there are
financial barriers restricting Canadian residents from utilizing the full range health services in
Canada. These barriers are present in both level of household income and access to supplemental
health insurance. The review provides evidence for both of these literatures and demonstrates how
exploring the double disadvantage contributes a new perspective to the existing literature.

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2.1 Household Income and Health Care Utilization
Allin and Hurley (2009) found evidence for the hypothesis on household income by
demonstrating there is an income gradient present in the utilization of health services. Specifically,
private financing of prescription drugs demonstrates households with higher incomes can purchase
more prescription drugs. The authors used the Canadian Community Health Survey (CCHS) to find
income creates a ‘pro-rich’ inequity, where those with higher incomes are more willing to pay out-
of-pocket for health services. The authors also found there is a pro-rich inequity with physician
visits because prescription drugs are private cost complements to a visit. This implies individuals
who can afford to pay for prescription drugs will also utilize more physician visits, assuming
individuals use these visits to gain access to prescription drugs. Therefore, Allin and Hurley
suggested a more universal and accessible health care system would benefit more Canadian citizens
by reducing the cost of private cost complements.
Additionally, Asada and Kephart (2007) also found an income gradient by using a two-part
model to provide evidence that lower income is correlated with fewer physicians and specialist
visits. The authors used the CCHS and attempted to control for medically necessary services in their
methodology in order to isolate the potential scenario that individuals were not using other
important services because they could not afford them. In essence, the authors uncovered a crucial
question about income inequities that must be considered in the Canadian health system and in this
paper.
Williamson and Fast (1998) conducted a specific survey-based research study in Edmonton.
The goal of the project was to determine whether there was a relationship between poverty and
medical treatment in the city. Their survey found 38% of respondents failed to obtain physician
services when they were ill, and 40% did not fill out prescriptions because they could not afford the
cost. The value in this approach was they divided the impoverished demographic into two different
categories: working poor and social assistance beneficiaries. Their distinction is important to
understanding the financial barriers low income creates in the health system. The surveys showed
93% of the working poor demographic could not afford to fill out prescriptions, and they were not
even able to afford the reduced Blue Cross health insurance. The social assistance beneficiaries
were less restricted for purchasing the prescription drugs because additional public health insurance
is included in their assistance. The limitation to this research study is the geographic specificity.
There are undeniable differences in the social and economic circumstances of each province and

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city, which limits the credibility of extrapolating these research results nation-wide. However, the
research study still contributes evidence that low income creates a financial barrier to health
services.
2.2 Supplemental Insurance and Health Care Utilization
Stabile (2001) contributes to this literature with his paper on the propensity to purchase
supplemental insurance if tax exemptions can be offered on these plans. The variation in provincial
tax laws, specifically Quebec’s taxation of employer provided health insurance, offers a point of
analysis because Stabile was able to consider the effect of various sized tax exemptions. Stabile
found this process allowed him to isolate the moral hazard effect, where individuals utilize more
health services because they hold insurance, not because their need has changed. This does not
contribute to adverse selection because the motivation for adverse selection is being sicker, not
experiencing increased tax exemptions. Stabile found almost half of the increase in utilization based
on supplemental insurance is a result of the moral hazard effect. This effect provides further
evidence that lack of supplemental insurance is a barrier to utilizing more health services.
Devlin et al. (2011) provide further research to the literature by considering the effect of
supplemental insurance on the utilization of prescription drugs. The paper ultimately concluded the
reimbursement from insurance packages for private health costs encourage the utilization of these
services. With the 2005 CCHS, the authors used a latent class model for the analysis where the
population is divided into two categories: high service users and low service users. This implied
high service users are those individuals who are sick or have chronic diseases and low service users
are those individuals who are healthy. The analysis showed the high service users’ propensity to
utilize services is unaffected by obtaining insurance, which intuitively implies those who are sick
will still be willing to utilize services without insurance. However, the propensity for low users to
increase their utilization increases when insurance can be obtained. This heterogeneous result is
valuable for the literature because it isolates lack of insurance as a barrier to utilizing health
services.
Kapur and Basu (2005) further contribute to the literature by examining how combined
forms of insurance coverage for prescribed drugs affect their purchase. The authors compiled a
micro-dataset from multiple national health surveys. For this examination, the authors stated two
broad categories of prescribed drug insurance coverage: catastrophic and conventional insurance.

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Catastrophic plans are for households that have high drug expenses relative to their household
income, and conventional plans are the packages that include sizeable deductibles for the prescribed
drugs. Kapur and Basu (2005) found when the forms of coverage overlap, 96% of Canadians have
some coverage for prescribed drugs. However, when this overlapping coverage was decomposed,
only 25% of these residents were insured publicly. This suggested the majority of respondents
solely held private prescription drug insurance, providing evidence that prescription drug insurance
is predominantly private. Thus, if a Canadian resident is unable to hold private supplemental health
insurance, it is likely the individual is not holding public insurance either. This lack of insurance
can be a strong barrier to utilizing prescription drugs.
Nyman (1999) offered a unique perspective on the relationship between supplemental
insurance and health care utilization by suggesting demand for supplemental health insurance is for
its access motive. Nyman found supplemental health insurance was not only about reducing
financial risk, but also about gaining access to crucial services for which an individual would
otherwise pay. He found by overlooking the access motive, economists will be missing an
important, albeit intangible, benefit to supplemental health insurance. This research demonstrated
supplemental health insurance not only protects individuals in medical emergencies, but can also
remove barriers to accessing other crucial health services by reducing or covering the immediate
expense. This research paper builds from this idea by highlighting the positive relationship between
insurance and health care utilization, ultimately demonstrating an individual is at a disadvantage to
utilizing health services without insurance.
2.3 Income-Insurance Relationship
The literature demonstrates socioeconomic status of Canadian citizens is a common thread
between income and insurance. In most of the research there was evidence suggesting both low
income and lack of supplemental insurance are characteristics of low socioeconomic status. Also, it
is found low socioeconomic status also contributes to lower health service utilization. Curtis and
MacMinn (2008) contribute to this relationship by examining the post CHA years to determine
whether there had been a change in the access to medical services, and specifically, if the change in
access had been a result of inequity caused by socioeconomic status. Consistent with previous
findings, the authors demonstrated there is a positive relationship between health care utilization
and holding supplemental insurance. It should be recognized the findings demonstrated low

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education levels also contribute to lower utilization levels as compared to wealthier individuals with
higher education.
In another study completed in Sweden, Lundberg et al. (1998) found similar results where
low socioeconomic status individuals, characterized by low income and education, were more
sensitive to price changes in prescription drugs. This implies having low socioeconomic status
makes prescription drugs less accessible should the prices increase. Lundberg et al. (1998) further
explained policy decisions should not only encourage increased public funding, but should
encourage increased education for this demographic, while simultaneously developing the
neighbourhoods and regions to increase socioeconomic status.
This research paper will contribute to the literature on household income, supplemental
health insurance, and health care utilization by providing further evidence about the relationship
between income and supplemental insurance. Specifically, the paper will demonstrate household
income affects health service utilization directly through service payment and indirectly through the
financial burden of paying for supplemental insurance. The analysis will show income and
insurance separately affect health care utilization, but the contribution to the literature will be
demonstrating how income affects utilization indirectly via the relationship between household
income and supplemental insurance.
3. The Data Set
The data comes from Statistics Canada’s Canadian Community Health Survey (CCHS),
cycle 3.1. The CCHS is a national and cross-sectional survey conducted every 2-years. Cycle 3.1
was collected between January 2005 and December 2005. The CCHS collects information through
telephone survey and self-administered questionnaires that include a wide range of health-related
questions targeting household and individual level information. Cycle 3.1 was chosen because it
includes detailed information about supplemental health insurance and includes multiple measures
of health care utilization. Specifically, this paper will use general practitioner (GP) visits, dental
visits, and eye exams, as the measures of utilization for the research analysis.

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3.1 Sample Restrictions
Geography Restriction: The sample is restricted to respondents who are residents of Ontario. Cycle
3.1 only collected insurance coverage data for residents in Ontario, so a national analysis cannot be
completed. Also, there would likely be variations of supplemental insurance plans inter-
provincially, which would complicate the regression analysis. Therefore, the most effective analysis
will occur with sample data from Ontario residents. The CCHS sample of 132,221 respondents
decreases by 62,908 with this restriction.
Age Restriction: The sample was restricted to respondents 18-64 years of age. Respondents who are
65 or older complicate the analysis because this resident demographic has public insurance
coverage for prescription drug use. Supplemental prescription drug insurance is one of the main
independent variables being analyzed for the hypotheses of this paper. Respondents who are 18 or
younger are more likely to live with a parent or guardian. The respondents would likely be
beneficiaries of a parent’s insurance, if the parent held insurance, which complicates the health care
utilization patterns of the demographic. Ultimately, utilization patterns still centre on the
respondents who hold supplemental insurance, which is why this restriction is applied. The CCHS
sample of 132,221 respondents decreases by 40,431 with this restriction.
3.2 Dependent Variables
Health care utilization will be measured in three forms: visits to GP, visits to dentist and
receiving an eye exam. Regular dentist visits and eye exams are health services excluded from the
CHA, making the services a valuable point of analysis. GP visits are covered under the CHA and
purchasing prescription drugs is a complementary health service. However, prescription drugs are
excluded from the CHA, so GP visits is used to make inference about prescription drug utilization.
Table 1 – Summary Statistics: Utilization Variables
Variable Obs Mean Std. Dev. Min Max
# of GP visits in last 12m 28814 3.128 4.378 0 31
Dentist visitin last 12m 28882 0.674 0.469 0 1
Eye exam in last 12m 28882 0.353 0.478 0 1

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Table 1 shows, on average, respondents in the sample make 3.12 visits to the GP. Dentist visits and
eye exams are measured by the probability a respondent made a visit or had an exam in the last 12
months. Table 1 shows approximately 67% of the sample made a dentist visit and 35% of the
sample had an eye exam in the last 12 months.
3.3 Independent Variables – Socio-demographic
The socio-demographic independent variables are: age, sex, highest level of education, and
household income. Each variable was converted to a binary variable to isolate respondents in the
sample to a specific category. Table 2 shows the percentage respondents in each category.
Table 2 – Summary Statistics: Socio-demographic Variables
Income Less than $15k 28882 0.057 0.232 0 1
Income $15k - $30k 28882 0.093 0.291 0 1
Income $30k-$50k 28882 0.173 0.379 0 1
Income $50k-$80k 28882 0.245 0.430 0 1
Income greater than $80k 28882 0.316 0.465 0 1
Income not stated 28882 0.116 0.320 0 1
Age: 18-19 28882 0.043 0.204 0 1
Age: 20-24 28882 0.083 0.276 0 1
Age: 25-29 28882 0.096 0.295 0 1
Age: 30-34 28882 0.114 0.317 0 1
Age: 35-39 28882 0.119 0.324 0 1
Age: 40-44 28882 0.127 0.333 0 1
Age: 45-49 28882 0.098 0.297 0 1
Age: 50-54 28882 0.105 0.306 0 1
Age: 55-59 28882 0.113 0.317 0 1
Age: 60-64 28882 0.102 0.303 0 1
Female 28882 0.533 0.499 0 1
Male 28882 0.467 0.499 0 1
No high school diploma 28882 0.060 0.238 0 1
High school diploma 28882 0.115 0.319 0 1
Some Post Sec. Education 28882 0.053 0.224 0 1
Uni/College Degree 28882 0.695 0.460 0 1
Education level not stated 28882 0.077 0.266 0 1

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The respondents report household income levels ranging from under $15,000 to $80,000 or above.
Approximately, 55% of the sample have household income over $50,000 and 15% have
income under $30,000. The sample includes households that tend to have a higher income.
The age distribution of respondents is relatively even as each age category contains approximately
10% of the sample. Age categories 18-19 and 20-24 represent the smallest percentage of the
sample at 4.3% and 8.3%, respectively.
Table 2 shows approximately 47% of the sample is male and 53% of the sample female.
The respondents report education level as having no high school diploma, a high school diploma,
some post-secondary education, or a university or college degree. Table 2 shows almost
70% of the sample has received a university or college degree, which suggests the sample
may have a high education bias.
3.4 Independent Variables – Health Status
The health status variable is collected from the self-reported health status of the respondents.
The respondents are asked to evaluate their current health status between five categories: excellent,
very good, good, fair, or poor. Table 3 also includes a variable for chronic conditions, where
respondents report if they have a chronic health condition. The variable does not specify the health
condition, so the severity of the respondent’s condition has a wide range. For example, the
respondent may have asthma or diabetes.
Table 3 - Summary Statistics: Health Status Variables
Excellent HS 28882 0.225 0.418 0 1
Very Good HS 28882 0.393 0.488 0 1
Good HS 28882 0.272 0.445 0 1
Fair HS 28882 0.080 0.272 0 1
Poor HS 28882 0.029 0.168 0 1
Health Status not stated 28882 0.000 0.022 0 1
Has chronic health condition 28882 0.717 0.450 0 1
Table 3 shows almost 90% of respondents report health status as ‘good’ or higher. Specifically, the
majority of respondents report health status as ‘very good’ with approximately 39% of the sample
in this category. Also, Table 3 shows over 70% of the sample report having some form of chronic

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health condition. It is possible to infer these chronic conditions are less severe, as the majority of
the sample reports higher health status while simultaneously reporting a chronic condition.
3.5 Independent Variable – Insurance Coverage
The insurance variables are divided into three types: prescription drug, dental, and eye care
insurance. Respondents report whether they hold a form of insurance in each category. Each
insurance category is further divided into the provider of that insurance. Cycle 3.1 specifies the
insurance providers as federal government, employers, or private firms. Each descriptive table
shows the percentage of sample that holds some form of insurance in the respective category and
the percentage of sample for each specific provider.
Table 4 – Summary Statistics: Drug Insurance Variables
Has drug insurance 28882 0.733 0.442 0 1
No drug insurance 28882 0.267 0.442 0 1
Has gov't drug insurance 28882 0.083 0.276 0 1
Has employer drug insurance 28882 0.606 0.489 0 1
Has private drug insurance 28882 0.043 0.202 0 1
Table 4 shows approximately 73% of the sample holds some form of prescription drug insurance.
After decomposing the provider of the insurance, Table 4 shows an overwhelming majority of
respondents, over 60%, receive employer provided drug insurance. This is compared to 8.3% of the
sample that has government insurance and 4.3% of the sample that has private insurance.
Table 5 – Summary Statistics: DentalInsurance Variables
Has dental insurance 28882 0.688 0.463 0 1
No dental insurance 28882 0.312 0.463 0 1
Has gov't dental insurance 28882 0.064 0.245 0 1
Has employer dental insurance 28882 0.587 0.492 0 1
Has private dental insurance 28882 0.035 0.183 0 1
Table 5 shows approximately 68% of the sample holds some form of dental insurance. Similar to
table 4, decomposing the provider of the insurance in table 5 shows approximately 58% of

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respondents receive employer provided dental insurance. This is compared to 6.4% of the sample
that has government insurance and 3.5% of the sample that has private insurance.
Table 6 – Summary Statistics: Eye Care Insurance Variables
Has eye care insurance 28882 0.622 0.485 0 1
No eye care insurance 28882 0.378 0.485 0 1
Has gov't eye care insurance 28882 0.060 0.238 0 1
Has employer eye care insurance 28882 0.530 0.499 0 1
Has private eye care insurance 28882 0.031 0.172 0 1
Table 6 shows approximately 62% of the sample holds some form of eye care insurance. Again,
decomposing the provider of the insurance in table 6 shows the majority of respondents receive
employer provided eye care insurance, approximately 53%. This is compared to 6.0% of the sample
that has government insurance and 3.1% of the sample that has private insurance.
4. Methodology
This paper will be using the ordinary least square (OLS) method to estimate the unknown
parameters in a regression model. The regression models will follow:
𝑦̂ = 𝛽0 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + ⋯ 𝛽 𝑛 𝑋 𝑛 + 𝜀
This method will find estimate values for 𝛽 coefficients of the independent variables, which will be
used to estimate the linear relationship between health care utilization and income and supplemental
insurance. This estimate will represent the slope of the estimated OLS line between the dependent
variable and the independent control variables. A positive coefficient estimate suggests as the
independent variable increases, the dependent variable increases. A negative coefficient estimate
suggests as the independent variable increases, the dependent variable decreases. Hypothesis testing
will be used to determine the statistical significance of each 𝛽 coefficient. The hypothesis test will
be:
H0: β1 = 0
HA: β1 ≠ 0

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This means if a coefficient estimate is not equal to 0 it will demonstrate there is some linear
relationship between the dependent and independent variable. The statistical significance of this
estimate will be evaluated with 95% confidence. This confidence interval was selected because the
CCHS sample was significantly reduced after making the sample restrictions. The smaller sample
reduces the accuracy of estimate on the true population parameter, so a slightly lower confidence
interval more effectively represents statistical significance of the coefficient estimates.
5. RegressionAnalysis
In order to provide evidence for the research hypotheses and to address the larger research
questions, the regression analysis has been divided into two sections. The first section provides
evidence for the separate effects of household income and supplemental insurance on health care
utilization. Health care utilization is represented differently in each model as GP visits, dentist
visits, and eye exams to provide extensive evidence of the effect of income and insurance. The
second section explores the relationship between household income and supplemental insurance and
how income could effect utilization via supplemental insurance and contribute to the double
disadvantage faced by low-income residents.
5.1 Household Income and Supplemental Insurance on Utilization
The first regression models focused on how income and prescription drug insurance affected
the number of GP visits made by respondents as a form of health care utilization. Separate models
were used to determine the effects before and after controlling for other variables that affect health
care utilization. Column 2 shows the income coefficient estimates without control variables and
column 3 shows the drug insurance coefficient estimate without control variables. Column 4 shows
income and drug insurance estimates with control variables. Column 5 shows estimates for specific
insurance types with control variables.

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Table 7 – Estimates of income levels and holding prescription drug insurance on decision to visit GP
Independent Variable # of GP visits in last
12m
# of GP visits in last
12m
12m
12m
Income Less than $15k 1.870*
(0.135)
0.864*
(0.130)
0.681*
(0.134)
Income $15k - $30k 1.3148
(0.102)
0.590*
(0.100)
0.489*
(0.101)
Income $30k-$50k 0.591*
(0.076)
0.276*
(0.073)
0.243*
(0.073)
Income $50k-$80k 0.333*
(0.064)
0.095*
(0.060)
0.089*
(0.060)
Has drug insurance 0.412*
(0.055)
0.433*
(0.055)
Has gov't drug insurance 0.918*
(0.103)
Has employer drug insurance 0.333*
(0.057)
Has private drug insurance 0.258*
(0.119)
𝛽0 Estimate 2.677*
(0.039)
2.729*
(0.047)
0.093*
(0.156)
0.161*
(0.155)
n 28882 28882 28882 28882
R2 0.012 0.002 0.151 0.152
Note: Parentheses representsthe standard error
* significant at 5% confidence level
Income category $80k or higher was omitted from the regression model to serve as the
reference category for the coefficient estimates. The 𝛽0 estimate in column 1 represents the effect
of having income greater than $80 000, where having this level of income is associated with having
made approximately 2.7 visits to the GP in the last 12 months. The estimates of the other income
categories demonstrate there is a clear income gradient to making GP visits. Specifically,
respondents with lower income make more visits to the GP. Table 7 shows a household with
income less than $15 000 has made 1.87 more visits to the GP in the last 12 months, than a
household with income greater than $80 000. A household with income between $50 000 and $80
000 only makes 0.33 visits more than a household with income greater than $80 000. Therefore, as
the household income increases, respondents make fewer visits to the GP.
Column 4 shows, after controlling for age, sex, health status, and highest attained education
level, this income gradient persists, but magnitude of the estimates decrease. A household with
income greater than $80 000, represented by the 𝛽0 estimate, had made approximately 0.1 visits to
the GP. Households with income less than $15 000 had made 0.86 more visits, and a household
with income $50 000 to $80 000 only made 0.1 more visits. This still provides evidence for the

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income gradient, but the smaller estimate magnitudes imply the estimates in column 2 were
overstated because of omitted variable bias. Estimates in column 4 demonstrate income only has a
partial effect on the decision to visit the GP. Though the magnitudes suggest income has virtually
no effect on GP visits, in relative terms, a low-income household makes almost one full visit more
than households in the highest income category. This further provides evidence for the income
gradient.
Table 7 also shows the estimated effects of holding prescription drug insurance on having
visited the GP in the last 12 months. The 𝛽0 estimate in column 3 represents the effect of holding no
form of drug insurance, where not holding drug insurance is associated with having made
approximately 2.7 visits to the GP in the last 12 months. Table 7 shows a respondent who holds a
form of drug insurance is estimated to have made 0.4 more visits to the GP than a respondent
without drug insurance. After controlling for age, sex, health status, and highest attained education
level, column 4 shows the positive effect of holding some form of drug insurance holds true. The
effect of holding drug insurance actually increases as respondents with drug insurance made 0.43
more visits to the GP than respondents without drug insurance.
Column 5 shows the estimated effect of different prescription drug insurance types on GP
visits. After controlling for age, sex, health status, and highest attained education level, Table 7
shows each type of drug insurance has a positive effect on GP visits. The 𝛽0 estimate in column 5
still represents the effect of holding no form of drug insurance, which is associated with having
made approximately 0.16 visits to the GP in the last 12 months. A respondent with private drug
insurance made 0.25 more visits and a respondent with employer drug insurance made 0.33 more
visits. The largest effect comes from having government drug insurance where respondents made
0.92 more visits to the GP. Therefore, the positive effect of holding prescription drug insurance on
GP visits holds true regardless of the insurance provider.

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Table 8 – Estimates of self-reported health status on decision to visit GP
Independent Variable # of GP visits in last
12m
Very Good HS 0.458*
(0.059)
Good HS 1.156*
(0.065)
Fair HS 3.149*
(0.102)
Poor HS 6.989*
(0.158)
Has chronic health condition 1.246*
(0.051)
(0.156)
n 28882
R2 0.151
There is also a noticeable gradient in terms of self-reported health status. The regression
model that controlled for income, holding drug insurance, age, sex, and highest attained education
level, showed those with lower health status made more visits to the GP. This aligns with the basic
intuition that when an individual is sicker, the individual will demand more health care services. In
this model, health status category ‘excellent’ was omitted and the 𝛽0 estimate represents the effect
‘excellent’ health status has on GP visits. Specifically, a respondent with ‘excellent’ health status is
associated with having made approximately 0.1 GP visits in the last 12 months. Table 8 also shows
respondents with ‘very good’ health status only made 0.46 more visits to the GP than those in
‘excellent’ health. However, respondents with ‘poor’ health status made 7 more visits to the GP in
12 months than respondents in ‘excellent’ health. This is a substantial change in the effect on
visiting the GP. Additionally, table 8 shows respondents who report a chronic health condition
made 1.25 more visits to the GP than respondents without a chronic condition. This aligns with the
health status gradient, where a respondent with a chronic condition is likely to demand more health
services to combat the condition.
Similar regression models from table 7 were used in table 9 to provide evidence for the
effect of income and insurance on health care utilization represented by dentist visits and eye care
exams. These models were intended to provide further evidence to the coefficient estimate patterns
demonstrated in table 7. Column 2 shows the estimated effects of income and holding dental

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insurance with control variables, and column 3 shows the estimated effects of income and specific
dental insurance type with control variables. Column 4 shows the estimated effects of income and
holding eye care insurance with control variables, and column 5 shows the estimated effects of
income and specific eye care insurance type with control variables.
Table 9 – Estimates of income levels and holding dental or eye care drug insurance on decision to visit
dentist or receive eye exam
Independent Variable Dentist visit in last
12m
Dentist visit in last
12m
Eyeexam in last
12m
Eyeexam in last
12m
Income Less than $15k -0.194*
(0.015)
-0.052*
(0.016)
Income $15k - $30k -0.208*
(0.011)
-0.081*
(0.012)
Income $30k-$50k -0.146*
(0.008)
-0.047*
(0.009)
Income $50k-$80k -0.087*
(0.007)
-0.050*
(0.007)
Has dental(eye care)
insurance
0.228*
(0.006)
0.077*
(0.006)
Has gov't dental(eye care)
insurance
0.161*
(0.013)
0.092*
(0.014)
Has employer dental(eye
care) insurance
0.231*
(0.006)
0.074*
(0.006)
Has private dental(eye care)
insurance
0.239*
(0.014)
0.079*
(0.017)
(0.017)
0.590*
(0.017)
0.282*
(0.018)
0.284*
(0.018)
n 28882 28882 28882 28882
R2 0.126 0.126 0.046 0.046
Income category $80k or higher was omitted from the regression model to serve as the
of having income greater than $80 000 after controlling for age, sex, health status, and highest
attained education level. Household income greater than $80 000 is associated with a 0.59
probability of having visited the dentist in the last 12 months. Compared to the results from table 7,
the income gradient in this regression model is the opposite. The probability of having visited the
dentist increases as household income increases. Specifically, the probability of a household with
income between $50 000 and $80 000 having visited the dentist is only 9.0 percentage points lower
than a household with income greater than $80 000. However, the probability of a household with

18
income less than $15 000 having visited the dentist is 19.0 percentage points lower. Therefore, in
this model, health care utilization increases as household income increases.
Column 2 also shows the effect of holding some form of supplemental dental insurance. The
estimate demonstrates the probability a respondent with some form of dental insurance having
visited the dentist is 22.8 percentage points higher than the probability of a respondent with no
dental insurance. However, column 3 shows the estimated effect of different dental insurance types
on dentist visits. After controlling for income, age, sex, health status, and highest attained education
level, column 3 shows each type of dental insurance still has a positive effect on dentist visits. The
𝛽0 estimate in column 3 represents the effect of holding no form of dental insurance, which is
associated with having visited the dentist with a probability of 0.59. The probability of a respondent
with employer dental insurance and private dental insurance having visited the dentist is 0.23 and
0.24 percentage points higher, respectively, than a respondent with no dental insurance.
Government dental insurance shows the smallest effect, as the probability of a respondent with
government dental insurance having visited the dentist is 0.16 percentage points higher than
respondents with no dental insurance.
Similar results are demonstrated when health care utilization is represented as receiving an
eye exam. The 𝛽0 estimate in column 4 represents the effect of having income greater than $80 000,
after controlling for age, sex, health status, and highest attained education level. Household income
greater than $80 000 is associated with a 0.28 probability of having received an eye exam in the last
12 months. The income category estimates demonstrate households with higher income have a
higher probability of having received an eye exam. The probability of a household with income
between $50 000 and $80 000 having received an eye exam is only 5.0 percentage points lower than
a household with income greater than $80 000. However, the probability of a household with
income between $15 000 and $30 000 having received an eye exam is 8.0 percentage points lower.
The smaller difference in magnitude of the estimates suggests a weaker income gradient than
estimates in column 2 with dentist visits. However, the direction of the gradient holds true, where
health care utilization increases as household income increases.
Column 4 also shows the effect of holding some form of supplemental eye care insurance.
The estimate demonstrates the probability a respondent with some form of eye care insurance
having received an eye exam is 7.7 percentage points higher than the probability of a respondent
with no eye care insurance. The positive effect of each insurance type is also demonstrated with eye

19
care insurance, after controlling for income, age, sex, health status, and highest attained education
level. The table 9 estimates show a more even distribution, where government, employer, and
private eye care insurance have a relatively even effect on the probability of having received an eye
exam. The 𝛽0 estimate in column 5 represents the effect of holding no form of eye care insurance,
which is associated with having received an eye exam with probability 0.28. The probability of a
respondent with government, employer, or private eye care insurance having received an eye exam
is 9.0, 7.0, and 7.0 percentage points higher, respectively.
In conclusion, section 5.1 of the regression analysis provides evidence for the effect of
household income and supplemental insurance on health care utilization in the form of GP visits,
dentist visits, and eye exams. Results in table 7 show respondents visited the GP more when income
was lower, whereas the results in table 9 show respondents were more likely to have visited the
dentist and to have received an eye exam when income was higher. Results in table 7 and table 9
demonstrate that holding any form supplemental insurance increases visits to the GP and the
probability of having visited the dentist or receiving an eye exam in the last 12 months.
5.2 Household Income on Supplemental Insurance
This section of the regression analysis is intended to provide evidence for the relationship
between household income and supplemental insurance. Evidence for this relationship could
contribute to the double disadvantage concept by demonstrating the overlapping effects income and
insurance have on health care utilization. The three types of health care utilization divide the
regression models in this section and the results tables show how income level affects the
probability of having insurance for each utilization type.

20
Table 10 – Estimates of income levels on probability of holding prescription drug insurance
Independent Variable Drug Insurance
(no controls)
Drug Insurance
(with controls)
Employer Drug
Insurance
(0.014)
-0.302*
(0.014)
-0.577*
(0.015)
Income $15k - $30k -0.356*
(0.010)
-0.365*
(0.010)
-0.491*
(0.011)
Income $30k-$50k -0.228*
(0.008)
-0.233*
(0.008)
-0.251*
(0.008)
Income $50k-$80k -0.079*
(0.006)
-0.080*
(0.006)
-0.085*
(0.007)
(0.004)
0.829*
(0.016)
0.748*
(0.017)
n 28882 28882 28882
R2 0.106 0.133 0.169
Table 10 shows the effect of household income level on the probability of holding
prescription drug insurance. Column 2 shows there is an income gradient in the probability of
holding drug insurance, where households with higher income are more likely to hold drug
insurance. Income category $80k or higher was omitted from the regression model to serve as the
of having income greater than $80 000, which is associated with having a 0.87 probability of
holding prescription drug insurance. Compared to household income greater than $80 000, the
probability of a household with an income between $15 000 and $30 000 holding drug insurance
decreases by 35.6 percentage points. However, the probability of households with income between
$50 000 and $80 000 holding drug insurance only decreases by 7.9 percentage points. Therefore, as
the household income increases, the probability of holding drug insurance also increases.
Column 3 shows estimated effects of household income on the probability of holding drug
insurance, after controlling for age, sex, health status, and highest attained education level. The
estimates show the same pattern as column 2, as the same income gradient is present. The 𝛽0
estimate in column 2 shows having income greater than $80 000 is associated with having a
probability of 0.83 for holding prescription drug insurance. Compared to household income greater
than $80 000, the probability of a household with an income between $15 000 and $30 000 holding
drug insurance decreases by 36.5 percentage points. However, the probability of households with
income between $50 000 and $80 000 holding drug insurance only decreases by 8.0 percentage
points. In this model, the magnitude of the estimates increase, which suggests household income

21
has a stronger effect on the probability of holding prescription drug insurance after controlling for
other health related variables.
The summary statistics showed majority of respondents with drug insurance held employer-
provided drug insurance. Table 10 shows the effect of household income on the probability of
specifically holding employer drug insurance. Column 4 shows the estimated effect of each income
level, after controlling for age, sex, health status, and highest attained education level. The same
income gradient persists for the estimates. A household with income greater than $80 000,
represented by the 𝛽0 estimate, holds employer-provided drug insurance with probability of 0.748.
The probability of a household with an income between $15 000 and $30 000 holding employer
drug insurance decreases by 57.7 percentage points and the probability of household with income
between $50 000 and $80 000 holding employer drug insurance only decreases by 8.5 percentage
points.
Table 11 – Estimates of income levels on probability of holding dental insurance
Independent Variable Dental Insurance
(no controls)
Dental Insurance
(with controls)
Employer Dental
Insurance
(0.014)
-0.390*
(0.014)
-0.580*
(0.015)
Income $15k - $30k -0.435*
(0.010)
-0.432*
(0.011)
-0.512*
(0.011)
Income $30k-$50k -0.274*
(0.008)
-0.272*
(0.008)
-0.274*
(0.008)
Income $50k-$80k -0.105*
(0.007)
-0.103*
(0.007)
-0.101*
(0.007)
(0.004)
0.884*
(0.016)
0.773*
(0.017)
n 28882 28882 28882
R2 0.129 0.147 0.174
The same regression models were used to estimate the effect of household income on the
probability of holding dental insurance. Similar to the previous results, table 11 demonstrates an
income gradient in the probability of holding dental insurance. Column 3 demonstrates, after
controlling for age, sex, health status, and highest attained education level, the probability of a
household with an income greater than $80 000 holding dental insurance, represented by the 𝛽0
estimate, is 0.86. Compared to household income of greater than $80 000, the probability of a
household with an income between $15 000 and $30 000 holding dental insurance decreases by

22
43.2 percentage points. However, the probability of households with income between $50 000 and
$80 000 holding dental insurance only decreases by 10.3 percentage points. Therefore, as household
income increases, the probability of holding dental insurance increases.
Summary statistics also shows majority of respondents with dental insurance are provided
the insurance through an employer. Column 4 shows the effect of household income on the
probability of specifically holding employer dental insurance, after controlling for age, sex, health
status, and highest attained education level. The same income gradient from table 10 persists for the
estimates. A household with income greater than $80 000, represented by the 𝛽0 estimate, holds
employer dental insurance with probability 0.773. Compared to household income greater than $80
000, the probability of a household with an income less than $15 000 holding employer dental
insurance decreases by 58.0 percentage points. The probability of household with income between
$50 000 and $80 000 holding drug insurance only decreases by 10.1 percentage points. Therefore,
the probability of holding employer dental insurance decreases by less as income increases,
suggesting higher household income leads to a higher probability.
Table 12 – Estimates of income levels on probability of holding eye care insurance
Independent Variable EyeCare Insurance
(no controls)
EyeCare Insurance
(with controls)
Employer EyeCare
Insurance
(0.015)
-0.364*
(0.015)
-0.539*
(0.015)
Income $15k - $30k -0.413*
(0.011)
-0.417*
(0.011)
-0.485*
(0.012)
Income $30k-$50k -0.277*
(0.008)
-0.279*
(0.008)
-0.271*
(0.009)
Income $50k-$80k -0.106*
(0.007)
-0.106*
(0.007)
-0.101*
(0.007)
(0.004)
0.760*
(0.017)
0.688*
(0.018)
n 28882 28882 28882
R2 0.1101 0.1321 0.1503
The same regression models were used to estimate the effect of household income on the
probability of holding eye care insurance. Similar to the results in table 10 and 11, table 12
demonstrates the same income gradient in the probability of holding eye care insurance. Column 3
shows, after controlling for age, sex, health status, and highest attained education level, the
probability of a household with an income greater than $80 000 holding eye care insurance,

23
represented by the 𝛽0 estimate, is 0.76. Compared to household income greater than $80 000, the
probability of a household with an income between $15 000 and I03 holding eye care insurance
decreases by 41.7 percentage points. However, the probability of households with incomes between
$50 000 and $80 000 holding eye care insurance only decreases by 10.6 percentage points.
Therefore, as household income increases, the probability of holding eye care insurance decreases
by less compared to a household with income greater than $80 000. This suggests higher household
income is associated with higher probability of holding eye care insurance.
Table 12 also shows the effect of household income on the probability of specifically
holding employer drug insurance, as majority of eye care insurance holders have employer provided
insurance. Column 4 shows the estimated effect of each income level, after controlling for age, sex,
health status, and highest attained education level. The same income gradient persists for the
estimates as the results of table 10 and 11. A household with income greater than $80 000,
represented by the 𝛽0 estimate, holds employer eye care insurance with probability 0.688. The
probability of a household with an income less than $15 000 holding employer eye care insurance
decreases by 53.9 percentage points and the probability of household with income between $50 000
and $80 000 holding employer eye care insurance only decreases by 10.1 percentage points. The
results in table 12 demonstrate that the probability of holding employer eye care insurance increases
as household income increases.
6. Discussion
The first section of the regression analysis addressed hypothesis 1 stating Canadian
households with higher income utilize more health services. Unfortunately, the evidence did not
clearly demonstrate that higher household income results in higher utilization of health services.
When health utilization was measured as the probability of having visited the dentist or the
probability of having received an eye exam in the last 12 months, evidence for the first hypothesis
was provided. A respondent was more likely to have visited a dentist or received an eye exam as
household income increased. However, when health care utilization was measured as visits made to
the GP in the last 12 months, income level had the opposite effect. As household income increased,
the number of visits made to the GP decreased. In this context, higher income did not lead to higher
utilization of health services.

24
It can be argued the strong effect of health status, reported in table 8, on visits made to the GP
is what contributes to this opposite effect. The measure of self-reported health status is a general
measure that does not specify the health conditions that are used to evaluate one’s health status.
Dental visits and eye exams are very specific forms of health care utilization. A respondent will
utilize these services if a specific oral health or eye care condition arises. Therefore, general health
status has a minimal effect on the probability of having visited the dentist or receiving an eye exam
because a specific oral health or eye care condition may not be included in a respondent’s report
(refer to appendix tables 5a and 7a). However, a visit to the GP can be motivated by any form of
health condition based on the broader services provided by a GP. Thus, self reported health status
has a larger effect on the utilization of GP visits because any health condition considered in the
respondent’s health self-evaluation might have induced a GP visit.
Table 13 - Estimate of income level on probability of having poor health status
Independent Variable Poor HS
Income Less than $15k 0.123*
(0.004)
Income $15k - $30k 0.061*
(0.004)
Income $30k-$50k 0.019*
(0.003)
Income $50k-$80k 0.008*
(0.003)
𝛽0 Estimate -0.010*
(0.005)
n 28882
R2 0.043
This distinction between the effects of health status on the different measures of health service
utilization is important to the research hypotheses because there is a prominent relationship between
health status and income. Table 13 shows there is an income gradient in the probability of having
‘poor’ self reported health. Specifically, as household income decreases, the probability of having
‘poor’ health status increases. After controlling for age, sex, and highest attained education level,
the probability of a household with an income greater than $80 000 reporting ‘poor’ health status,
represented by the 𝛽0 estimate, is - 0.01. Compared to household income greater than $80 000, the
probability of a household with an income less than $15 000 reporting ‘poor’ health status increases

25
by 12.3 percentage points. However, the probability of a household with income between $50 000
and $80 000 reporting ‘poor’ health status only increases by 0.08 percentage points.
Table 13 demonstrates having lower income increases the probability of having ‘poor’ health
status. This could be contributing to the evidence that low-income households make more GP visits,
because underlying this pattern is the fact that low-income households also have lower health status.
This income and health status relationship has less effect in dental and eye care utilization because
of the specificity of the services. Therefore, when health care utilization is measured as dentist visits
or eye exams, high-income households utilize more health services. When health care utilization is
measured as GP visits, high-income households utilize fewer health services, likely due to the effect
of self reported health status.
The first section of the regression analysis also demonstrates respondents with supplemental
insurance visited the GP more and were more likely to have visited a dentist and to have received an
eye exam. This finding from the first section is necessary for addressing hypothesis 2 because it
first needed to be found that supplemental insurance has a direct effect on health service utilization
in order to argue that income indirectly affects health service utilization via insurance. The second
section of the regression analysis considered the effects of household income on supplemental
insurance. These regression results provide evidence that the financial burden of supplemental
insurance also reduces health care utilization, as a lower household income decreases the
probability of holding supplemental insurance. High-income households were more likely to hold
some form of prescription drug, dental, and eye care insurance compared to low-income
households. These results contribute to the double disadvantage of low-income residents because
holding supplemental insurance increases health service utilization, but a higher income increases
the probability of holding supplemental insurance. Therefore, a respondent with lower household
income reduces health service utilization because the respondent would be less likely to have
supplemental insurance. Thus, the double disadvantage holds true. However, the first section of the
regression analysis demonstrated having a lower income directly results in more GP visits, which
contradicts the double disadvantage.
This contradiction appears when utilization is measured in GP visits, and not in dentist visits
or eye exams, because the health status and income relationship has a stronger effect on making GP
visits compared to the effect of holding prescription drug insurance. Holding prescription drug
insurance will induce more visits to the GP if the individual is demanding prescription drugs for

26
their health condition. However, GPs provide broad health services that extend further than writing
drug prescription. This means an individual may be induced to visit a GP for other health conditions
that are unrelated to prescription drugs, making the effect of holding prescription drug insurance
insignificant. Thus, prescription drug insurance has less effect on inducing GP visits. The effect of
being low-income and having ‘poor’ health status becomes a larger determinant for utilizing the
broad GP services, which ultimately contradicts the double disadvantage. However, it is possible
the double disadvantage holds true for dental visits and eye exams because supplemental insurance
has a greater effect on inducing utilization for dental and eye care services based on the specificity
of the services. In terms of dental visits and eye exams, having low household income decreases the
probability of holding supplemental insurance, which decreases utilization of these services and
provides evidence for the double disadvantage.
7. Conclusion
This paper demonstrates there is evidence to support the double disadvantage low-income
Canadian residents face when utilizing health services. The research shows having a low household
income decreases the probability of having made a dentist visit or the probability of having received
an eye exam. Low household income does not decrease the number of GP visits made. This
different result may occur based on the noticeable relationship between health status and income
level, where low income increases the probability of having ‘poor’ health status. The research also
clearly demonstrates that having any form of supplemental health insurance increases health service
utilization. Respondents who held government, employer, or private provided insurance for
prescription drugs, dental services, or eye care services were more likely to utilize the health
services. After exploring the separate effects of income level and supplemental insurance, it was
demonstrated that high household income also increases the probability of holding supplemental
insurance. This suggests low-income households indirectly utilize fewer health services because
these residents are less likely to have supplemental insurance, the owning of which, increases health
service utilization. In conclusion, it can be argued low-income Canadian residents are unable to pay
for health services directly and are unable to pay for supplemental health insurance, the owning of
which would reduce health service cost. With the caveat discussed with GP visits, the research does
provide evidence that Canadian residents face a financial barrier based on household income that
limits their use of health services and suggests they face a financial double disadvantage.

27
References
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Health Services Research, 7: 41
Curtis, LJ. & MacMinn, WJ. (2008) Health care utilization in Canada: Twenty-five years of
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Devlin, R., Sarma, S., & Zhang, Q (2011). The role of supplemental coverage in a universal health
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Kapur, V. & Basu, K. (2005) Drug coverage in Canada: Who is at risk? Health Policy, 71: pp. 181-
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29
RegressionTables – Income, Drug Insurance, and GP Visits
Table 1a – GP visits vs. Income (no control variables)
# of GP visitsin last12m Coef. Std. Err. t P>t [95%Conf. Interval
]
Income Less than $15k 1.870 0.135 13.85 0.00 1.606 2.135
Income $15k - $30k 1.314 0.102 12.90 0.00 1.114 1.514
Income $30k-$50k 0.591 0.076 7.83 0.00 0.443 0.739
Income $50k-$80k 0.333 0.064 5.21 0.00 0.208 0.458
Income not stated 0.278 0.079 3.54 0.00 0.124 0.432
_cons 2.677 0.039 68.52 0.00 2.600 2.753
Table 2a – GP visits vs. Drug Insurance (no control variables)
# of GP visitsin last12m Coef. Std. Err. t P>t [95%Conf. Interval]
Has drug insurance 0.412 0.055 7.43 0.00 0.303 0.521
_cons 2.729 0.047 57.50 0.00 2.636 2.822

30
Table 3a – GP visits vs. Income vs. Drug Insurance (with control variables)
# of GP visitsin last12m Coef. Std. Err. t P>t [95%
Conf.
Interval]
Income $15k - $30k 0.590 0.100 5.92 0.00 0.395 0.786
Income $30k-$50k 0.276 0.073 3.78 0.00 0.133 0.419
Income $50k-$80k 0.095 0.060 1.58 0.11 -0.023 0.212
Has drug insurance 0.433 0.055 7.88 0.00 0.326 0.541
Age: 20-24 0.241 0.132 1.82 0.07 -0.018 0.499
Age: 25-29 0.345 0.135 2.56 0.01 0.080 0.609
Age: 30-34 0.475 0.134 3.54 0.00 0.212 0.739
Age: 35-39 0.214 0.131 1.63 0.10 -0.044 0.471
Age: 40-44 0.074 0.129 0.57 0.57 -0.178 0.326
Age: 45-49 0.268 0.131 2.04 0.04 0.011 0.524
Age: 50-54 0.097 0.135 0.72 0.47 -0.167 0.362
Age: 55-59 0.023 0.137 0.17 0.87 -0.246 0.291
Age: 60-64 0.135 0.142 0.95 0.34 -0.143 0.413
female 0.997 0.046 21.73 0.00 0.907 1.086
No high schooldiploma -0.115 0.145 -0.79 0.43 -0.400 0.170
Some PostSec.
Education
0.062 0.127 0.49 0.63 -0.187 0.312
Uni/College Degree 0.060 0.082 0.73 0.47 -0.101 0.221
Education levelnot
stated
-0.128 0.109 -1.18 0.24 -0.342 0.085
Very Good HS 0.458 0.059 7.70 0.00 0.341 0.574
Good HS 1.156 0.065 17.68 0.00 1.027 1.284
Fair HS 3.149 0.102 30.73 0.00 2.948 3.350
PoorHS 6.989 0.158 44.26 0.00 6.680 7.299
Health Status not stated -0.036 1.178 -0.03 0.98 -2.345 2.273
Has chronic health
condition
1.246 0.051 24.22 0.00 1.145 1.347
_cons 0.093 0.156 0.60 0.55 -0.212 0.398

31
Table 4a - GP visits vs. Income vs. Drug Insurance type (with control variables)
# of GP visitsin last12m Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k 0.489 0.101 4.85 0.00 0.291 0.687
Income $30k-$50k 0.243 0.073 3.33 0.00 0.100 0.387
Income $50k-$80k 0.089 0.060 1.48 0.14 -0.029 0.206
Has gov'tdrug insurance 0.918 0.103 8.91 0.00 0.716 1.120
Has employer drug
insurance
0.333 0.057 5.87 0.00 0.222 0.445
Has private drug
insurance
0.258 0.119 2.17 0.03 0.025 0.491
Age: 20-24 0.253 0.132 1.92 0.06 -0.006 0.511
Age: 25-29 0.356 0.135 2.64 0.01 0.092 0.620
Age: 30-34 0.490 0.134 3.64 0.00 0.226 0.753
Age: 35-39 0.232 0.131 1.77 0.08 -0.025 0.490
Age: 40-44 0.090 0.129 0.70 0.49 -0.163 0.342
Age: 45-49 0.279 0.131 2.13 0.03 0.023 0.536
Age: 50-54 0.108 0.135 0.80 0.42 -0.157 0.373
Age: 55-59 0.036 0.137 0.27 0.79 -0.232 0.305
Age: 60-64 0.143 0.142 1.01 0.31 -0.135 0.421
female 0.991 0.046 21.61 0.00 0.901 1.081
Some PostSec. Education 0.047 0.127 0.37 0.71 -0.203 0.296
Education levelnot
stated
-0.145 0.109 -1.33 0.18 -0.359 0.068
Very Good HS 0.460 0.059 7.75 0.00 0.344 0.576
Good HS 1.151 0.065 17.61 0.00 1.023 1.279
Fair HS 3.114 0.103 30.36 0.00 2.913 3.315
PoorHS 6.890 0.159 43.40 0.00 6.579 7.201
Has chronic health
condition
1.242 0.051 24.14 0.00 1.141 1.343

32
RegressionTables – Income, Dental Insurance, and Dentist Visits
Table 5a - Dentist visits vs. Income vs. Dental Insurance (with control variables)
Dentistvisit in last12m Coef. Std. Err. t P>t [95%Conf. Interval]
Income Less than $15k -0.194 0.015 -13.22 0.00 -0.223 -0.165
Income $15k - $30k -0.208 0.011 -18.45 0.00 -0.230 -0.186
Income $30k-$50k -0.146 0.008 -17.68 0.00 -0.162 -0.130
Income $50k-$80k -0.087 0.007 -12.84 0.00 -0.100 -0.073
Income not stated -0.095 0.009 -10.45 0.00 -0.113 -0.077
Has dental insurance 0.228 0.006 37.92 0.00 0.216 0.240
Age: 20-24 -0.053 0.015 -3.61 0.00 -0.082 -0.024
Age: 25-29 -0.093 0.015 -6.16 0.00 -0.123 -0.064
Age: 30-34 -0.075 0.015 -4.98 0.00 -0.105 -0.046
Age: 35-39 -0.059 0.015 -3.98 0.00 -0.087 -0.030
Age: 40-44 -0.015 0.014 -1.01 0.31 -0.043 0.014
Age: 45-49 -0.002 0.015 -0.14 0.89 -0.031 0.027
Age: 50-54 0.017 0.015 1.10 0.27 -0.013 0.046
Age: 55-59 0.010 0.015 0.67 0.50 -0.020 0.040
Age: 60-64 0.017 0.016 1.08 0.28 -0.014 0.048
female 0.071 0.005 13.87 0.00 0.061 0.081
No high schooldiploma -0.067 0.016 -4.10 0.00 -0.099 -0.035
Some PostSec.
Education
0.021 0.014 1.47 0.14 -0.007 0.049
Uni/College Degree 0.059 0.009 6.41 0.00 0.041 0.077
Educ.level not stated 0.038 0.012 3.09 0.00 0.014 0.062
Very Good HS -0.021 0.007 -3.11 0.00 -0.034 -0.008
Good HS -0.052 0.007 -7.22 0.00 -0.066 -0.038
Fair HS -0.114 0.011 -10.05 0.00 -0.136 -0.092
PoorHS -0.154 0.017 -8.81 0.00 -0.188 -0.120
Health Status not stated 0.078 0.131 0.60 0.55 -0.179 0.334
_cons 0.585 0.017 33.60 0.00 0.551 0.619

33
Table 6a - Dentist visits vs. Income vs. Dental Insurance type (with control variables)
Dentistvisit in last12m Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k -0.201 0.011 -17.66 0.00 -0.223 -0.178
Income $30k-$50k -0.145 0.008 -17.52 0.00 -0.161 -0.128
Income $50k-$80k -0.087 0.007 -12.86 0.00 -0.100 -0.074
Has gov'tdental insurance 0.161 0.013 12.76 0.00 0.136 0.185
Has employer dental
insurance
0.231 0.006 37.20 0.00 0.219 0.243
Has private dental
insurance
0.239 0.014 16.77 0.00 0.211 0.266
Age: 20-24 -0.057 0.015 -3.83 0.00 -0.086 -0.028
Age: 25-29 -0.098 0.015 -6.45 0.00 -0.127 -0.068
Age: 30-34 -0.081 0.015 -5.36 0.00 -0.110 -0.051
Age: 35-39 -0.066 0.015 -4.45 0.00 -0.094 -0.037
Age: 40-44 -0.021 0.014 -1.45 0.15 -0.049 0.007
Age: 45-49 -0.008 0.015 -0.54 0.59 -0.037 0.021
Age: 50-54 0.011 0.015 0.71 0.48 -0.019 0.040
Age: 55-59 0.004 0.015 0.28 0.78 -0.026 0.034
Age: 60-64 0.010 0.016 0.65 0.51 -0.021 0.041
female 0.072 0.005 14.01 0.00 0.062 0.082
Education levelnot stated 0.038 0.012 3.11 0.00 0.014 0.062
Very Good HS -0.021 0.007 -3.18 0.00 -0.034 -0.008
Good HS -0.053 0.007 -7.27 0.00 -0.067 -0.038
Fair HS -0.111 0.011 -9.83 0.00 -0.134 -0.089
PoorHS -0.146 0.018 -8.34 0.00 -0.181 -0.112
_cons 0.590 0.017 33.90 0.00 0.556 0.624

34
RegressionTables – Income, Eye Care Insurance, and Eye Exams
Table 7a - Eye Exams vs. Income vs. Eye Care Insurance (with control variables)
Eyeexamin last 12m Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k -0.081 0.012 -6.70 0.00 -0.104 -0.057
Income $30k-$50k -0.047 0.009 -5.26 0.00 -0.064 -0.029
Income $50k-$80k -0.050 0.007 -6.97 0.00 -0.065 -0.036
Has eye care insurance 0.077 0.006 12.67 0.00 0.065 0.089
Age: 20-24 -0.068 0.016 -4.28 0.00 -0.099 -0.037
Age: 25-29 -0.120 0.016 -7.38 0.00 -0.152 -0.088
Age: 30-34 -0.142 0.016 -8.74 0.00 -0.173 -0.110
Age: 35-39 -0.142 0.016 -9.01 0.00 -0.173 -0.111
Age: 40-44 -0.063 0.016 -4.04 0.00 -0.093 -0.032
Age: 45-49 0.001 0.016 0.04 0.97 -0.030 0.032
Age: 50-54 0.026 0.016 1.61 0.11 -0.006 0.058
Age: 55-59 0.043 0.016 2.62 0.01 0.011 0.075
Age: 60-64 0.062 0.017 3.63 0.00 0.028 0.095
female 0.080 0.006 14.48 0.00 0.069 0.091
No high schooldiploma 0.000 0.018 -0.02 0.99 -0.035 0.034
Some PostSec.
Education
0.044 0.015 2.87 0.00 0.014 0.074
Educ.level not stated -0.021 0.013 -1.62 0.11 -0.047 0.004
Very Good HS 0.012 0.007 1.66 0.10 -0.002 0.026
Good HS 0.020 0.008 2.58 0.01 0.005 0.035
Fair HS 0.016 0.012 1.30 0.19 -0.008 0.040
PoorHS 0.051 0.019 2.73 0.01 0.014 0.088
_cons 0.282 0.018 15.32 0.00 0.246 0.318

35
Table 8a - Eye Exams vs. Income vs. Eye Care Insurance type (with control variables)
Eyeexamin last 12m Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k -0.083 0.012 -6.84 0.00 -0.107 -0.059
Income $30k-$50k -0.047 0.009 -5.33 0.00 -0.064 -0.030
Income $50k-$80k -0.051 0.007 -6.99 0.00 -0.065 -0.036
Has gov'teye care
insurance
0.092 0.014 6.69 0.00 0.065 0.120
Has employer eye care
insurance
0.074 0.006 11.80 0.00 0.062 0.086
Has private eye care
insurance
0.079 0.017 4.76 0.00 0.046 0.111
Age: 20-24 -0.069 0.016 -4.33 0.00 -0.100 -0.038
Age: 25-29 -0.120 0.016 -7.42 0.00 -0.152 -0.089
Age: 30-34 -0.142 0.016 -8.77 0.00 -0.174 -0.110
Age: 35-39 -0.143 0.016 -9.03 0.00 -0.174 -0.112
Age: 40-44 -0.063 0.016 -4.08 0.00 -0.094 -0.033
Age: 45-49 0.000 0.016 0.00 1.00 -0.031 0.031
Age: 50-54 0.025 0.016 1.56 0.12 -0.006 0.057
Age: 55-59 0.042 0.016 2.58 0.01 0.010 0.074
Age: 60-64 0.061 0.017 3.56 0.00 0.027 0.094
female 0.080 0.006 14.47 0.00 0.069 0.091
Some PostSec. Education 0.044 0.015 2.86 0.00 0.014 0.074
Education levelnot stated -0.021 0.013 -1.60 0.11 -0.047 0.005
Very Good HS 0.012 0.007 1.69 0.09 -0.002 0.026
Good HS 0.020 0.008 2.58 0.01 0.005 0.035
Fair HS 0.015 0.012 1.24 0.22 -0.009 0.039
PoorHS 0.049 0.019 2.59 0.01 0.012 0.086
_cons 0.284 0.018 15.43 0.00 0.248 0.320

36
RegressionTables – Income vs. Drug Insurance
Table 9a – Drug Insurance vs. Income (no control variables)
Hasdrug insurance Coef. Std. Err. t P>t [95%Conf. Interval]
Income Less than
$15k
-0.290 0.014 -21.26 0.00 -0.316 -0.263
Income $15k - $30k -0.356 0.010 -34.65 0.00 -0.376 -0.336
Income $30k-$50k -0.228 0.008 -29.95 0.00 -0.243 -0.213
Income $50k-$80k -0.079 0.006 -12.23 0.00 -0.092 -0.066
_cons 0.866 0.004 219.60 0.00 0.858 0.874
Table 10a – Drug Insurance vs. Income (with control variables)
Hasdrug insurance Coef. Std. Err. t P>t [95%
Conf.
Interval]
Income $15k - $30k -0.365 0.010 -34.88 0.00 -0.385 -0.344
Income $30k-$50k -0.233 0.008 -30.22 0.00 -0.248 -0.218
Income $50k-$80k -0.080 0.006 -12.49 0.00 -0.093 -0.068
Age: 20-24 -0.061 0.014 -4.30 0.00 -0.088 -0.033
Age: 25-29 -0.057 0.014 -3.98 0.00 -0.086 -0.029
Age: 30-34 0.007 0.014 0.51 0.61 -0.021 0.036
Age: 35-39 -0.018 0.014 -1.29 0.20 -0.046 0.009
Age: 40-44 0.002 0.014 0.13 0.90 -0.025 0.029
Age: 45-49 0.026 0.014 1.84 0.07 -0.002 0.053
Age: 50-54 0.015 0.014 1.02 0.31 -0.014 0.043
Age: 55-59 0.043 0.015 2.91 0.00 0.014 0.071
Age: 60-64 0.024 0.015 1.58 0.11 -0.006 0.054
female 0.020 0.005 4.16 0.00 0.011 0.030
No high schooldiploma 0.011 0.016 0.71 0.48 -0.019 0.042
Some PostSec.
Education
0.056 0.014 4.06 0.00 0.029 0.082
Education levelnot
stated
-0.168 0.012 -14.44 0.00 -0.191 -0.145
Very Good HS -0.007 0.006 -1.03 0.31 -0.019 0.006
Good HS -0.006 0.007 -0.81 0.42 -0.019 0.008
Fair HS 0.017 0.011 1.54 0.12 -0.005 0.038
PoorHS 0.072 0.017 4.31 0.00 0.040 0.105

37
Has chronic health cond. 0.045 0.006 8.13 0.00 0.034 0.056
_cons 0.829 0.016 51.98 0.00 0.797 0.860
Table 11a – Employer Drug Insurance vs. Income (with control variables)
Hasemployerdrug
insurance
Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k -0.491 0.011 -43.77 0.00 -0.513 -0.469
Income $30k-$50k -0.251 0.008 -30.38 0.00 -0.267 -0.235
Income $50k-$80k -0.085 0.007 -12.42 0.00 -0.099 -0.072
Age: 20-24 -0.045 0.015 -2.98 0.00 -0.075 -0.015
Age: 25-29 -0.010 0.015 -0.63 0.53 -0.040 0.021
Age: 30-34 0.078 0.015 5.04 0.00 0.048 0.108
Age: 35-39 0.052 0.015 3.47 0.00 0.023 0.082
Age: 40-44 0.059 0.015 3.98 0.00 0.030 0.088
Age: 45-49 0.069 0.015 4.57 0.00 0.039 0.098
Age: 50-54 0.064 0.016 4.13 0.00 0.034 0.094
Age: 55-59 0.059 0.016 3.73 0.00 0.028 0.089
Age: 60-64 0.005 0.016 0.30 0.77 -0.027 0.037
female 0.012 0.005 2.32 0.02 0.002 0.023
Some PostSec.
Education
-0.008 0.015 -0.54 0.59 -0.037 0.021
Uni/College Degree -0.017 0.009 -1.76 0.08 -0.035 0.002
Education levelnot
stated
-0.148 0.012 -11.90 0.00 -0.173 -0.124
Very Good HS 0.010 0.007 1.51 0.13 -0.003 0.024
Good HS 0.011 0.008 1.48 0.14 -0.004 0.026
Fair HS -0.020 0.012 -1.69 0.09 -0.043 0.003
PoorHS -0.061 0.018 -3.39 0.00 -0.096 -0.026
Has chronic health cond. 0.017 0.006 2.92 0.00 0.006 0.029
_cons 0.748 0.017 43.78 0.00 0.715 0.781

38
RegressionTables – Income vs. Dental Insurance
Table 12a – Dental Insurance vs. Income (no control variables)
Hasdental insurance Coef. Std. Err. t P>t [95%Conf. Interval]
Income Less than
$15k
-0.388 0.014 -27.96 0.00 -0.416 -0.361
Income $15k - $30k -0.435 0.010 -41.51 0.00 -0.455 -0.414
Income $30k-$50k -0.274 0.008 -35.30 0.00 -0.290 -0.259
Income $50k-$80k -0.105 0.007 -15.93 0.00 -0.118 -0.092
_cons 0.860 0.004 213.90 0.00 0.852 0.868
Table 13a – Dental Insurance vs. Income (with control variables)
Hasdental insurance Coef. Std.
Err.
t P>t [95%Conf. Interval]
Income $15k - $30k -0.432 0.011 -40.33 0.00 -0.453 -0.411
Income $30k-$50k -0.272 0.008 -34.46 0.00 -0.288 -0.257
Income $50k-$80k -0.103 0.007 -15.67 0.00 -0.116 -0.090
Age: 20-24 -0.065 0.014 -4.47 0.00 -0.093 -0.036
Age: 25-29 -0.085 0.015 -5.77 0.00 -0.114 -0.056
Age: 30-34 -0.007 0.015 -0.50 0.61 -0.036 0.021
Age: 35-39 -0.045 0.014 -3.12 0.00 -0.073 -0.017
Age: 40-44 -0.018 0.014 -1.29 0.20 -0.046 0.009
Age: 45-49 0.001 0.014 0.10 0.92 -0.027 0.030
Age: 50-54 -0.013 0.015 -0.85 0.40 -0.042 0.016
Age: 55-59 -0.011 0.015 -0.76 0.45 -0.041 0.018
Age: 60-64 -0.082 0.015 -5.31 0.00 -0.112 -0.052
female 0.010 0.005 2.01 0.04 0.000 0.020
Educ.level not stated -0.163 0.012 -13.71 0.00 -0.187 -0.140
Very Good HS 0.005 0.006 0.73 0.47 -0.008 0.017
Good HS -0.002 0.007 -0.24 0.81 -0.016 0.012
Fair HS 0.018 0.011 1.66 0.10 -0.003 0.040
PoorHS 0.065 0.017 3.80 0.00 0.031 0.098
_cons 0.884 0.016 54.47 0.00 0.852 0.916

39
Table 14a – Employer Dental Insurance vs. Income (with control variables)
Hasemployerdental
insurance
Income $15k - $30k -0.512 0.011 -45.55 0.00 -0.534 -0.490
Income $30k-$50k -0.274 0.008 -33.09 0.00 -0.290 -0.258
Income $50k-$80k -0.101 0.007 -14.60 0.00 -0.114 -0.087
Age: 20-24 -0.039 0.015 -2.58 0.01 -0.069 -0.009
Age: 25-29 -0.033 0.015 -2.15 0.03 -0.064 -0.003
Age: 30-34 0.063 0.015 4.10 0.00 0.033 0.094
Age: 35-39 0.032 0.015 2.11 0.04 0.002 0.061
Age: 40-44 0.047 0.015 3.15 0.00 0.018 0.076
Age: 45-49 0.052 0.015 3.46 0.00 0.023 0.081
Age: 50-54 0.047 0.015 3.03 0.00 0.017 0.077
Age: 55-59 0.025 0.016 1.60 0.11 -0.006 0.056
Age: 60-64 -0.054 0.016 -3.33 0.00 -0.086 -0.022
female 0.009 0.005 1.72 0.09 -0.001 0.019
Some PostSec.
Education
-0.001 0.015 -0.05 0.96 -0.029 0.028
Education levelnot
stated
-0.143 0.012 -11.44 0.00 -0.167 -0.118
Very Good HS 0.012 0.007 1.75 0.08 -0.001 0.025
Good HS 0.012 0.007 1.64 0.10 -0.002 0.027
Fair HS -0.013 0.012 -1.12 0.26 -0.036 0.010
PoorHS -0.044 0.018 -2.49 0.01 -0.080 -0.009
_cons 0.773 0.017 45.45 0.00 0.740 0.806

40
RegressionTables – Income vs. Eye Care Insurance
Table 15a – Eye Care Insurance vs. Income (no control variables)
Haseye care insurance Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k -0.413 0.011 -36.82 0.00 -0.435 -0.391
Income $30k-$50k -0.277 0.008 -33.21 0.00 -0.293 -0.260
Income $50k-$80k -0.106 0.007 -15.08 0.00 -0.120 -0.092
_cons 0.778 0.004 180.52 0.00 0.769 0.786
Table 16a – Eye Care Insurance vs. Income (with control variables)
Haseye care insurance Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k -0.417 0.011 -36.35 0.00 -0.439 -0.394
Income $30k-$50k -0.279 0.008 -33.05 0.00 -0.296 -0.263
Income $50k-$80k -0.106 0.007 -15.04 0.00 -0.119 -0.092
Age: 20-24 -0.083 0.015 -5.36 0.00 -0.113 -0.053
Age: 25-29 -0.068 0.016 -4.31 0.00 -0.099 -0.037
Age: 30-34 0.017 0.016 1.07 0.29 -0.014 0.048
Age: 35-39 -0.002 0.015 -0.16 0.87 -0.033 0.028
Age: 40-44 0.029 0.015 1.93 0.05 0.000 0.059
Age: 45-49 0.049 0.015 3.20 0.00 0.019 0.079
Age: 50-54 0.027 0.016 1.70 0.09 -0.004 0.058
Age: 55-59 0.060 0.016 3.74 0.00 0.028 0.091
Age: 60-64 0.008 0.017 0.46 0.64 -0.025 0.040
female 0.017 0.005 3.22 0.00 0.007 0.028
No high schooldiploma 0.023 0.017 1.32 0.19 -0.011 0.056
Education levelnot
stated
-0.164 0.013 -12.87 0.00 -0.189 -0.139
Very Good HS 0.011 0.007 1.64 0.10 -0.002 0.025
Good HS 0.001 0.008 0.13 0.90 -0.014 0.016
Fair HS 0.029 0.012 2.48 0.01 0.006 0.053
PoorHS 0.079 0.018 4.35 0.00 0.044 0.115
_cons 0.760 0.017 43.79 0.00 0.726 0.794

41
Table 17a – Employer Eye Care Insurance vs. Income (with control variables)
Hasemployereyecare
insurance
Income $15k - $30k -0.485 0.012 -41.61 0.00 -0.507 -0.462
Income $30k-$50k -0.271 0.009 -31.55 0.00 -0.287 -0.254
Income $50k-$80k -0.101 0.007 -14.11 0.00 -0.115 -0.087
Age: 20-24 -0.066 0.016 -4.20 0.00 -0.097 -0.035
Age: 25-29 -0.034 0.016 -2.12 0.03 -0.065 -0.003
Age: 30-34 0.050 0.016 3.12 0.00 0.018 0.081
Age: 35-39 0.033 0.016 2.09 0.04 0.002 0.063
Age: 40-44 0.052 0.015 3.36 0.00 0.022 0.082
Age: 45-49 0.061 0.016 3.92 0.00 0.030 0.091
Age: 50-54 0.040 0.016 2.51 0.01 0.009 0.072
Age: 55-59 0.047 0.016 2.90 0.00 0.015 0.079
Age: 60-64 -0.020 0.017 -1.22 0.22 -0.053 0.012
female 0.013 0.005 2.30 0.02 0.002 0.023
Education levelnot
stated
-0.152 0.013 -11.72 0.00 -0.177 -0.126
Very Good HS 0.022 0.007 3.10 0.00 0.008 0.036
Good HS 0.017 0.008 2.20 0.03 0.002 0.032
Fair HS -0.001 0.012 -0.07 0.94 -0.024 0.023
PoorHS -0.040 0.019 -2.15 0.03 -0.076 -0.004
_cons 0.688 0.018 39.02 0.00 0.653 0.722

42
RegressionTable – Health Status vs. Income
Table 18a – Poor Health Status vs. Income
PoorHS Coef. Std. Err. t P>t [95%Conf. Interval]
Income $15k - $30k 0.061 0.004 16.79 0.00 0.054 0.069
Income $30k-$50k 0.019 0.003 6.61 0.00 0.014 0.025
Income $50k-$80k 0.008 0.003 3.03 0.00 0.003 0.013
Age: 20-24 -0.006 0.006 -0.95 0.34 -0.017 0.006
Age: 25-29 0.002 0.006 0.38 0.70 -0.009 0.013
Age: 30-34 0.010 0.006 1.85 0.06 -0.001 0.021
Age: 35-39 0.014 0.005 2.57 0.01 0.003 0.025
Age: 40-44 0.021 0.005 3.79 0.00 0.010 0.031
Age: 45-49 0.030 0.006 5.30 0.00 0.019 0.041
Age: 50-54 0.039 0.006 6.97 0.00 0.028 0.050
Age: 55-59 0.045 0.006 8.13 0.00 0.034 0.056
Age: 60-64 0.039 0.006 6.94 0.00 0.028 0.050
female -0.005 0.002 -2.65 0.01 -0.009 -0.001
_cons -0.010 0.005 -1.95 0.05 -0.020 0.000

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