An Analysis of the Effect of Insurance on American Children · According to Dr. Kenneth Bock, the...

1

Econ 495 - Fall 2009

Panel Study of Income

Dynamics

Professor Frank P. Stafford

Lisa Luo

December 2009

An Analysis of the Effect of Insurance on

American Children

2

Introduction

The issues of uninsured families in America have been an ongoing debate between

Congress, private insurance companies, and the general public in need of healthcare. Due to the

lack of access to clinical care for a growing population of immigrants, unemployed parents, and low-

income families, over 47 million individuals in America were without health insurance during the

entire year of 2008, and that number continues to increase1. Of the 47 million individuals, nearly

20% of the uninsured are children. Using the PSID data from the Child Development Supplement II

and Child Development Supplement III, this paper compares the shifts in lifestyle behaviors of the

uninsured and insured children from 2002 to 2008. The primary goals of this paper are to evaluate

the importance of health insurance coverage for specific age cohorts and to recommend policies

that address the disadvantages of the uninsured children. Public policy surrounding insurance

coverage should focus on the health of the children, since the children will be the future parents,

future workers, and future leaders of the next generations to come.

Insurance Coverage Shifts

The rising medical costs over the last decades are the main cause of uninsurance in America.

Along with increasing copayments and out-of-pocket expenses, health insurance premiums have

outpaced the rate of inflation and wages since the 1980’s2. The majority of health care packages are

employer-related, while some employers do not offer any health insurance. Employers that cannot

afford the high medical expenditures have reduced their health care benefits, leaving their workers

with the option of purchasing expensive private insurance packages.

As a result, the increasing medical costs have affected low-income and unemployed families

the most. Below is a table of the national unemployment rates from 2001 to 2008 from the Bureau

of Labor Statistics3:

Table 1. National Unemployment Rate by Year

Year Unemployment Rate

Year Unemployment Rate

2001 4.7% 2005 5.1%

1 "Facts about Healthcare." National Coalition on Healthcare. 2009. NCHC, Web.

<http://www.nchc.org/facts/coverage.shtml>. 2 "Out of Pocket Spending on Health Care Services." Cover the Uninsured. 2009. Robert Wood Johnson Foundation,

Web. 9 Nov 2009. <http://covertheuninsured.org/content/out-pocket-spending-health-care-services>. 3 "Employment status of the civilian noninstitutional population, 1940 to date." Bureau of Labor Statistics. 2009.

Bureau of Labor Statistics, Web. 9 Nov 2009. <http://www.bls.gov/cps/cpsaat1.pdf>.

3

2002 5.8% 2006 4.6%

2003 6.0% 2007 4.6%

2004 5.5% 2008 5.8%

Table 1 shows the unemployment rate from 2001 to 2008. Although the unemployment

rate decreases from 2004 to 2007, it does not signify that more caregivers purchased health

insurance in those years. The fact that unemployment increased from 2001 to 2003 and 2007 to

2008 shows that the employment rate is volatile and some caregivers may be too risk averse to

purchase insurance if they do not think they will be employed in the next year.

Table 2 uses the variables ER23277 (family unit member with health insurance in the last

two years - 2003), ER40409 (family unit member with health insurance in the last two years - 2007),

and Q32J50 (child health insurance status in 2007) to illustrate the insurance coverage shifts from

2001 to 2007. Constructing Table 2 required the following assumptions:

If a family unit member is covered with health insurance, then it is assumed that the

child is also covered.

ER23277 is used to indicate the insurance status for years 2001 and 2002, while

ER40409 is used to indicate the insurance status for years 2005 and 2006.

The sample decreased slightly when comparing 2005 children to 2007 children,

resulting in slight differences in the proportion of insured and uninsured in 2005 in

the two panels below. To rectify the discrepancies, taking the averages of the two

values was necessary to achieve an approximation. For example, the average

proportion of children not insured in 2005 or 2006 is 4.64% ((4.82+4.46)/2).

Table 2. Insurance Status of children from 2001 to 2007

At first glance, it may seem that more children are becoming insured - 3.32% of the children

who didn’t have health insurance in 2005 became insured in 2007 while only 2.37% who didn’t have

insurance in 2001 received health insurance in 2005. However, the totals for no insurance in all the

years are increasing.

Health Insurance in 2005 or 2006

Health Insurance in 2007

No Yes Total No Yes Total

No 1.06 2.37 3.43 No 1.14 3.32 4.46

Yes 3.76 92.81 96.57 Yes 4.28 91.26 95.54

Total 4.82 95.18 100 Total 5.42 94.58 100

Health

Insurance

in 2001 or

2002

Health

Insurance

in 2005 or

2006

4

In 2001 and 2002, 3.43% of children were uninsured, while approximately 4.64% were uninsured in

2005 and 2006. In 2007, the fraction of uninsured children increases to 5.42%. In 2007, more

children lost health insurance than in 2005 (compare values 4.28% and 3.76%). This is an indication

that there is greater transition to non-coverage between the years 2005 and 2007. Also, the

percentage that has insurance in both years (Yes, Yes) decreases from 2005 to 2007 (compare

values 91.26% and 92.81%). The overall result is that more and more children became uninsured

from years 2001 to 2007.

Along with shifts in insurance coverage, the differing economic climates from 2002 to 2007

contribute to the changes in the type of insurance covering American families.

Figure 1a. Type of Insurance of All Children, Age 6-11.9 in 2002

1b. Type of Insurance of All Children, Age 12-18 in 2002

4.68%

64.90%3.25%

0.48%

19.02%

1.82%4.68% 1.19%

Type of Insurance of All ChildrenAge 6-11.9 in 2002

Uninsured

Employer

Private Directly Purchased

Medicare

Medicaid

Military

State-sponsored

Other Gov

5.74%

69.18%

4.53%0.30% 13.60%

1.51% 3.32% 1.81%

Type of Insurance of All Children Age 12-18 in 2002

Uninsured

Employer


Medicare

Medicaid

Military

State-sponsored

Other Gov

5

1c. Type of Insured Child’s Insurance, Age 12-18 in 2007

Figure 1a shows that larger proportion of younger children enrolled in government

assistance programs such as Medicaid and state-sponsored insurance than older children in 2002

(compare to Figure 1b). A larger percentage of older children are covered by employer-based

healthcare in 2002 than in 2007.In Figure 1c, a smaller portion of the insurance purchased in 2007 is

employer-related (64.1%). In 2007, slightly more children are covered by directly purchased private

insurance, and a larger percentage of the children are covered by government programs such as

Medicaid, and SCHIP (total 27.6%) relative to 2002. This indicates that at least 27.6% of the insured

children come from poorer families, since Medicaid and SCHIP is distributed only to low-income

families who cannot afford to purchase health care coverage.

A pooled regression of government assistance programs on age and illnesses gives a clearer

picture of the type of children who were enrolled in the government programs in 2002 and 2007. In

Table 3, the coefficient of 0.033442 indicates a 3% shift to government programs in 2007. This is

consistent with Figure 1c, and may be due to the fact that SCHIP and Medicaid coverage became

more readily available to more families under the poverty line in 2007. With the negative

coefficients of -.00991 and -.0116, older children and children with allergies were less likely to be

covered by a government assistance program in 2002 or 2007. The positive coefficient of .105815

for obesity indicates that those insured by government programs were more likely to be obese. This

emphasizes the direct cost of obesity to the government, and the indirect cost to taxpayers.

33.70%

23.90%6.50%

4.60%

19.80%

7.80%

1.00%

2.70%Type of Insured Child's Insurance

Age 12-18 in 2007PCG Employer

OCG Employer

Ex-Spouse Employer


Medicaid

SCHIP

Medicare

Other/Military

6

Table 3. Pooled Regression of Government Programs on Age and Illness Conditions

R Square 0.007968

Standard Error 0.423515

Observations 2946

ANOVA

df SS MS F Significance F

Regression 5 4.235551 0.84711 4.722833 0.000267

Residual 2940 527.3327 0.179365

Total 2945 531.5682

Coefficients Standard Error t Stat P-value

Intercept 0.350493 0.036166 9.691316 6.95E-22

Asthma 0.022301 0.02189 1.018756 0.308402

Allergy -0.00991 0.018298 -0.54138 0.588285

Obese 0.105815 0.031796 3.327893 0.000886

Age -0.0116 0.003577 -3.24308 0.001196

2007 0.033442 0.023881 1.400376 0.161506

Figure 1c illustrates that approximately 1/3 of the insurance is healthcare provided by the primary

caregiver’s employer (usually the mother), while only 24% is provided by the other care giver

(usually the father). Table 4 shows that a larger proportion of men were unemployed in 2007 and

2008. Most primary caregivers are mothers and more females are employed in 2007 than men,

resulting in a larger portion of health insurance to be provided by the mothers’ employers.

Table 4. Unemployment Rate by Sex4

Year Men Women

2006 4.6% 4.6%

2007 4.7% 4.5%

2008 6.1% 5.4%

Perhaps government health insurance programs should target their marketing to female

caregivers, since it has been proven that females play a more significant role in their children’s

health, where mothers’ income has 20 times more impact on their children’s survival than fathers’

4 "Employment status of the civilian noninstitutional population 16 years and over by sex, 1973 to date." Bureau of

Labor Statistics. 2009. Bureau of Labor Statistics, Web. 9 Nov 2009. <http://www.bls.gov/cps/cpsaat2.pdf>.

7

income. This is due to the fact that females spend around 51% of their income on household

improvements, food, and healthcare5.

Problems of Uninsurance

The central problem of lack of health care is the rising need for medical attention among children.

In the last four decades, the prevalence of children with chronic health conditions such as asthma,

obesity, ADHD, autism, and allergies has dramatically increased6. Poor dietary habits and lack of

physical exercise contributes to the 16% to 33% of adolescents who are considered obese7. The

exact causes of allergies and asthma are unknown, but some theorize that it is a result of climate

change, air pollutants, or sedentary lifestyles; children are exposed to more indoor allergens such as

dust and pet dander8. According to Dr. Kenneth Bock, the author of Healing the New Childhood

Epidemics: Autism, ADHD, Asthma and Allergies: The Groundbreaking Program for the 4-A

Disorders, allergies and asthma are underestimated because they are such common illnesses9.

However, they can become life-threatening conditions very quickly and should be regarded as

serious risks to a child’s well-being. Furthermore, obesity has been associated with other chronic

illnesses such as Type 2 diabetes, heart disease, cancer, and kidney failure. According to a report

published by The New England Journal of Medicine, if obesity is left untreated, the current

generation of children may be the first in American history to live shorter life spans than their

parents10. The causes of children epidemics are a combination of behavioral and biological factors,

and while obesity can be diminished by a change of behavioral habits, the key point to acknowledge

from the stark statistics is that children need access to medical physicians in order for the chronic

conditions to be diagnosed and treated. Preventative measures such as immunization, medication,

and doctor checkups are vital to the development of a child’s health.

5 "The BoP Community Has to do Much More for Women." NextBillion. 2009. NextBillion, Web. 9 Nov 2009.

<http://www.nextbillion.net/blog/2009/10/29/the-bop-community-has-to-do-much-more-for-women>. 6 Perrin, James. "The Increase of Childhood Chronic Conditions in the United States." TheJournal of the American

Medical Association. 27 07 2007. JAMA, Web. 12 Dec 2009. <http://jama.ama-assn.org/cgi/content/extract/297/24/2755>. 7 "Obesity In Children And Teens." Facts for Families. 05 2008. American Academy of Child & Adolescent

Psychiatry, Web. 12 Dec 2009. <http://www.aacap.org/cs/root/facts_for_families/obesity_in_children_and_teens>. 8 "The Increasing Incidence of Asthma in Children." Increasing Incidence. Children's Asthma, Web. 10 Dec 2009.

<http://www.childrens-asthma.info/articles/increased-asthma-children/index.php>. 9 Bock, Kenneth. Healing the New Childhood Epidemics: Autism, ADHD, Asthma, and Allergies: The Groundbreaking

Program for the 4-A Disorders. Ballantine Books, 2007. 153. Print. 10

Belluck, Pam. "Children's Life Expectancy Being Cut Short by Obesity." New York Times. 17 03 2005. New York Times, Web. 13 Dec 2009. <http://www.nytimes.com/2005/03/17/health/17obese.html?_r=1>.

8

Figure 2 illustrates the phenomena of increasing chronic conditions of asthma, obesity and

allergies among American children across a span of five years (from 2002 to 2007). By comparing

Figure 2b and 2c, the proportion of insured and uninsured children diagnosed with the illnesses in

2007 is strictly greater than the proportion of diagnosed children in 2002. The most dramatic result

is the 14.63% increase of insured children diagnosed with obesity in 2007 compared to 2002. A

possible explanation of the relatively low proportion of uninsured children with obesity in 2007

(1.61%) may be the issue of undiagnosed conditions amongst children not covered by insurance.

This supports the notion that many uninsured children are left untreated or undiagnosed with

chronic illnesses such as obesity.

Figure 2a. Percentage of Children with Illness Ages 6-11.9 in 2002

2b. Percentage of Children with Illness Ages 12-18 in 2002

12.12%

24.24%

0.00%

16.86%

26.18%

0.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

Asthma Allergy Obesity

Percentage of Children with Illness 2002 Age 6-11.9

Insured

Uninsured

2.56%

15.38%

0.00%

16.42%

27.34%

0.42%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%


Percentage of Children with Illness 2002 Age 12-18

Insured

Uninsured

9

19.35%30.65%

1.61%

16.90%

30.93%

15.05%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%


Percentage of Children with Illness2007 Age 12-18

Insured

Uninsured

2c. Percentage of Children with Illness Ages 12-18 in 2007

In order to see the effect of insurance coverage and age on the likelihood of being diagnosed for

asthma, allergies, and obesity in 2007, a regression analysis with the illnesses as the dependent

variables is necessary.

Table 5a. Asthma

Multiple R 0.031112

R Square 0.000968

Adjusted R Square -0.00078


Observations 1145

ANOVA


Regression 2 0.15661 0.078305 0.553252 0.575231

Residual 1142 161.6338 0.141536

Total 1144 161.7904


Intercept 0.26292 0.088898 2.957558 0.003165

Age 2007 -0.00464 0.005017 -0.92537 0.354969

Insured -0.02689 0.049191 -0.54667 0.584709

10

Table 5c. Obesity

Multiple R 0.086994

R Square 0.007568

Adjusted R Square 0.00583


Observations 1145

ANOVA


Regression 2 1.063378 0.531689 4.354275 0.013066

Residual 1142 139.4467 0.122107

Total 1144 140.51


Intercept 0.029386 0.082571 0.355892 0.721987

Age 2007 -0.00089 0.00466 -0.19039 0.849034

Insured 0.133936 0.045691 2.931358 0.003442

The large p-values in Table 5a and 5b for the “insured” independent variable indicate that

insurance coverage does not have an observed impact on the likelihood of asthma and allergies

among children. This subtle result shows that all children are at risk for asthma and allergies,

further supporting the view that uninsured children are at a disadvantage due to their lack of health

Table 5b. Allergy

Multiple R 0.027175

R Square 0.000738



Observations 1145

ANOVA


Regression 2 0.180598 0.090299 0.421983 0.655848

Residual 1142 244.3731 0.213987

Total 1144 244.5537


Intercept 0.221884 0.109308 2.029896 0.042599

Age 2007 0.005659 0.006169 0.917443 0.359104

Insured 0.005701 0.060485 0.094248 0.924929

11

care. However, the p-value of .003442 for the insured variable in Table 5c shows that there is a

statistically significant positive relationship between obesity and insured children. Age does not

have an observed influence on asthma, allergy, and obesity. A closer look at the lifestyle habits of

the insured and uninsured children in the last section of the paper will reveal whether this is a

result of lack of physical exercise or poor dietary habits.

Utilization of Healthcare Services

The main outcome of uninsurance is that uninsured children are less likely to receive medical

attention for illnesses such as asthma, sore throats, and earaches11. If left untreated, these

conditions may result in severe and fatal consequences. The next section will compare the

utilization of medical services of uninsured and insured children.

Figure 3a. Average Number of Doctor Visits in 2002 by Age 6-11.9

11

http://covertheuninsured.org/content/childrens-health-care-coverage

1.57 1.101.91 2.48

1.532.46

10.99

3.43

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Excellent Very Good Good Fair

Ave

rage

# o

f D

oc

Vis

its

in 2

00

2

PCG Rating 2002

Average # of Doctor Visits in 2002 by PCG Rating and Age 6-11.9

Uninsured

Insured

12

3b. Average Number of Doctor Visits in 2002 by Age 12-18

3c. Average Number of Doctor Visits in 2006 by Age 12-18

Figure 3 shows that the average number of doctor visits for insured is always higher than

the average number of doctor visits for the uninsured (the only exception is the “excellent

category” in Figure 3a). In Figure 3c, an insured parent who rates his child’s health as “fair” is more

likely to bring their children to the doctor more frequently than an insured parent who views his

child’s health as “excellent”. In 2002, on average, younger children received more health care. The

0.00

2.00

4.00

6.00

8.00

10.00

Excellent Very Good

Good Fair Poor

1.06 1.452.30

0.00 0.00

4.77 4.96

2.56

4.88

9.00

Ave

rage

# o

f D

oc

Vis

its

in 2

00

2

PCG Rating 2002

Average Number of Doctor Visits in 2002 by Age 12-18

Uninsured

Insured

0.39

1.68

0.91

2

1.19

2.07

2.56

5.05

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

Excellent Very Good Good Fair

Ave

rage

# D

oct

or

Vis

its

in 2

00

6

PCG Rating 2007

Average Number of Doctor Visits in 2006 by Age 12-18

Uninsured

Insured

13

zero values in Figure 3b for “fair” and “poor” is due to the fact that there were no data values for

those categories.

There does not appear to be a relationship between the primary care giver’s rating and

average number of doctor visits for the uninsured in 2002 and 2006. This may be due to the fact

that primary care giver’s rating doesn’t play a large factor in the utilization of healthcare services.

However, uninsured children rated with “excellent” health by their primary caregivers only visited

the doctors 0.39 times during 2006, which is the lowest value amongst the other rating categories.

This is consistent with adverse selection, since parents who believe that their child has excellent

health will be less likely to bring their child to the doctor or pay for medical coverage.

A comparison of scatter plots of the number of doctor visits with family income as the

independent variable reveals that income has a larger effect on uninsured children than insured

children in 2002.

Figure 4a. Number of Doctor Visits of Uninsured Children in 2002

y = 2E-05x + 0.728R² = 0.058

0

2

4

6

8

10

12

14

-50000 0 50000 100000 150000 200000

# o

f D

oct

or

Vis

its

for

an il

lne

ss

Family Income 2002

Number of Doctor Visits of Uninsured Children 2002

14

Figure 4b. Number of Doctor Visits of Insured Children in 2002

The upward sloping trend line in Figure 4a implies that for uninsured families, the higher

the family income, the more times a child visits the doctor for an illness such as a common cold, flu,

or asthma. With only one child who visited a maximum of 12 visits per year (one visit per month),

some children didn’t see the doctor at all: 53% of the uninsured children did not visit their doctor

over a period of a year. Could it be that the children never caught a cold in a given year? It is highly

unlikely. This makes sense since the household doesn’t receive any financial relief in healthcare bills

and must pay for everything out of their own pockets. Along with increasing premiums and co-

payments for insured individuals, medical services for uninsured patients are becoming more costly.

On the other hand, with one child who visited the doctor a maximum of 62 visits per year, on

average, the insured children receive more care from medical professionals. Only 38% did not visit

the doctor at all in the last 12 months of the assessment. As shown by the flat trend line, there is

no direct relationship between family income in 2002 and the number of doctor visits per child,

indicating that children see their doctors if there is a need. This seems logical since they are already

insured, and the only fee they have to pay for doctor visits is a co-payment. Although co-payments

are increasing and is dependent on the amount of health insurance coverage, Figure 5b shows that

family income doesn’t affect the frequency of doctor visits for insured children.

Figure 4a and 4b show that family income does make a difference among the insured and

uninsured children in 2002. Without health insurance, doctor visits are extremely costly and are

rationed throughout the year for severe cases. However, around 98% of the insured children visited

the doctor under or equal to 12 times a year. Thus, for the large part, insured children don’t go to

the doctors more frequently just because they are insured. Doctor visits, especially for children,

aren’t fun. However, when in need, insured children have more access to doctors, and some may go

as often as 5 times a month, without as much financial burden as uninsured children. Furthermore,

y = -5E-07x + 1.928R² = 0.000

-18

2

22

42

62

0 50000 100000 150000 200000

# o

f D

oct

or

Vis

its

for

an il

lne

ss

Family Income 2002

Number of Doctor Visits of Insured Children 2002

15

it turns out that only 3 out of the 112 uninsured families received outside help from relatives to

help them relieve the financial burden of high medical bills in 2002. This indicates that uninsured

families either limit the number of doctor visits or go to public clinics rather than asking for

financial help from others.

Pooling the data from 2002 and 2006 achieves a more holistic analysis of the relationship

between the number of doctor visits, income, and age in Table 6. The 2006 constant dollars were

calculated by multiplying 2002 income by the ratio of 2006 to 2002 Consumer Price Index. This

pooled regression shows that on average, insured children visit the doctors 3.09 more times than

uninsured in 2002 and 2006. Age has a slight positive effect on doctor visits, so older children visit

the doctors .089 more times annually than younger children. Consistent with Figure 4b, the pooled

regression illustrates that there is not much of an effect of income on number of doctor visits in

2002 and 2006 combined. A possible cause is that more insured families were from lower income

brackets, due to the increased proportion of insured families covered by government assistant

programs like Medicaid and SCHIP (as shown in 1c). This results in a slightly more equal income

distribution among insured families, and a decreased effect of income on doctor visits.

Table 6. Pooled Regression of Doctor Visits on Income, Insurance Coverage, and Age

Multiple R 0.020426

R Square 0.000417



Observations 2132

ANOVA


Regression 3 1664.422 554.8072 0.296086 0.828255

Residual 2128 3987460 1873.806

Total 2131 3989124


Intercept 0.257566 5.88051 0.0438 0.965068

Age 0.08904 0.285392 0.311991 0.755078

Insured 3.094109 4.675571 0.661761 0.508196

Income (Constant $2006) -8.3E-06 1.18E-05 -0.70623 0.480122

An potential explanation of the difference of the number of visits among insured and

uninsured children may be the fact that insured families allocate a higher percentage of their family

spending on their child’s medical care. Figure 5 compares the percentage of total family medical

16

expenditure spent on children in 2002 and 2006.

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑜𝑓 𝑚𝑒𝑑𝑖𝑐𝑎𝑙 𝑐𝑜𝑠𝑡𝑠 𝑠𝑝𝑒𝑛𝑡 𝑜𝑛 𝑐𝑕𝑖𝑙𝑑 =𝐴𝑛𝑛𝑢𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑛 𝐶𝑕𝑖𝑙𝑑 ′ 𝑠 𝐷𝑒𝑛𝑡𝑎𝑙 𝑎𝑛𝑑 𝑀𝑒𝑑𝑖𝑐𝑎𝑙 𝐶𝑎𝑟𝑒

𝑇𝑜𝑡𝑎𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑎𝑚𝑖𝑙𝑦 𝑀𝑒𝑑𝑖𝑐𝑎𝑙 𝐶𝑜𝑠𝑡

It’s important to note that this value is a rough approximation. For insured individuals, the annual

cost includes insurance premiums and out-of-pocket expenses such as copayments, prescriptions,

and deductibles. For uninsured individuals, the annual cost includes out-of-pocket expenses only,

which includes fee-per-service payments and prescriptions.

Figure 5a. Percentage of Medical Expenditure Spent on Uninsured Children

5b. Percentage of Medical Expenditure Spent on Uninsured Children 2006

y = -0.000x + 28.84R² = 0.190

-10

0

10

20

30

40

50

60

0 50000 100000 150000

% o

f m

ed

ical

co

sts

on

un

insu

red

ch

ild

Total Family Income 2002

% of Medical Costs on Uninsured Child 2002

Series1

Linear (Series1)

y = 1E-06x + 0.07R² = 0.039

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

0 50000 100000 150000 200000

% M

ed

Co

sts

Sep

nt

on

U

nin

sure

d C

hild

20

06


% Med Costs Spent on Uninsured Child 2006

% Med Costs Spent on Uninsured Child

Linear (% Med Costs Spent on Uninsured Child)

17

Figure 5a and 5b compares the percentage of medical costs spent on uninsured children

in 2002 and 2006. The negative trendline in 2002 reveals that as family incomes increases, the

percentage of medical expenditure spent on the child decreases. A reason is perhaps the families

allocate their spending on other important goods and services such as food and education. On the

other hand, the slight positive coefficient of 1E-06 for income implies that as income increases, the

percentage of medical expenditure spent on children increases in the household in 2006. This may

be due to the increasing health costs over the years or the increase percentage of uninsured

children diagnosed with asthma, obesity, and allergies. (see Figure 2c).

Figure 5c. Percentage of Medical Expenditure Spent on Insured Children in 2002

5d. Percentage of Medical Expenditure Spent on Insured Child in 2006

y = 2E-05x + 11.79R² = 0.004

0

20

40

60

80

100

0 200000 400000 600000 800000

% o

f m

ed

ical

co

sts

spe

nt

on

insu

red

ch

ild


% of Medical Costs Spent on Insured Child 2002

Series1

y = 3E-07x + 0.147R² = 0.018

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0 500000 1000000 1500000

% M

ed

Co

sts

spe

nt

on

Insu

red

Ch

ild 2

00

6


% Med Costs Spent on Insured Child 2006

% Med Costs Spent on Insured Child

Linear (% Med Costs Spent on Insured Child)

18

Figure 5c and 5d compares the percent of medical costs spent on insured children in 2002

and 2006. The higher R-squared in 2006 (0.018) implies that there is a stronger correlation between

percent of medical costs spent on children and the total family income in that year. There is an

upward slope even with high income outliers. Since the data gathered from 2006 is from CDS-III, the

graph for 2006 contains 92.81% of the sample from 2002 of insured children (refer to Table 1).

Thus, a possible reason for the stronger relationship in 2006 may be due to the fact that older

childre visit the doctor more frequently (refer to Table 6). Also, the increase in medical service costs

over the years also attributes to a higher percentage. While some insured families spend the

entirety of their medical costs on their children, the highest percentage that was spent on an

uninsured child was 50% in 2002 and 78% in 2006. In Figure 5b, the majority of uninsured familes

spent less than 40% of their medical costs on their child.

Table 7. Pooled Regression of Total Child Medical Expenditure on Age, Income, and Insurance Status

Multiple R 0.369638

R Square 0.136632



Observations 1584

ANOVA


Regression 4 474228044.6 1.19E+08 62.47102 4.60906E-49

Residual 1579 2996613644 1897792

Total 1583 3470841689


Intercept -308.681 223.248797 -1.38268 0.16696

Age 12.72926 15.93420304 0.798864 0.42449

Insured 373.0053 156.5671747 2.382398 0.017318

Income (Constant $2006) 0.006141 0.000462812 13.26929 3.56E-38

2006 432.4203 104.2493101 4.147944 3.53E-05

Pooling the data from 2002 and 2006, Table 7 tests for the correlation between the total amount of

medical expenditure spent on children and independent variables such as age, income, and

insurance status. Note that the income is in constant 2006 dollars. The results show that there is a

relationship between the amount of family spending on the child’s healthcare and insurance status

(p-value of 0.017). This may be due to the fact that those insured are from higher income brackets

and are likely to spend more on their children’s health. Also, as the family’s income rises, more is

spent on the child. The increase in copayments and premiums as the years increase is reflected in

19

the coefficient for 2006 (432.42), which indicates that families spent more of their income on their

child’s healthcare in 2006 than in 2002. This is consistent with rising medical costs over the last

decade. Age doesn’t have a significant relationship with medical expenditure. In general, uninsured

families spend a smaller proportion of their medical spending on their children than insured

families. Consequently, because the parents are the individuals making the decision to bring their

child to the doctor, uninsured children are less likely to receive health care even when it may be

necessary.

Lifestyle and Food Patterns The following section focuses on the comparison of behavorial patterns between uninsured and

insured families in regards to dietary habits, food sufficiency, and physical exercise by addressing

the following question: Do insured children exhibit moral hazard behavior through poorer diet and

less physical exercise?

A child’s body mass index is an objective tool to measure the child’s obesity level. The pooled

regression in Table 8 shows that variables such as age, days of exercise, and insurance status has an

effect on a child’s body mass index. The coefficient for “age” (.7911) shows that older children have

higher BMI scores, and the pvalue of 8.80804E-13 is the main predictor that there is a strong

positive relationship between body mass index and age. On average, insured children are more

likely to have a higher BMI score. This is consistent with Table 3 and Table 5c, which showed that

those covered by health care were more likely to be obese. The coefficient for “2006” shows that as

the years have increased from 2002 to 2006, children’s BMI increased. This is due to the fact that

2006 children are from CDS-III and there are more older children (ages 12-18) than in CDS-II, where

the ages range from 6 to 18. Income has a slight negative coefficient, implying that as income

increases, the body mass index decreases.

Table 8. Pooled Regression of BMI on Income, Age, Insurance, and Exercise

Multiple R 0.377671

R Square 0.142636



Observations 1202

ANOVA


Regression 5 6398.07 1279.614 39.79457397 6.50966E-38

Residual 1196 38457.97 32.15549

20

Total 1201 44856.04


Intercept 12.19404 1.395994 8.735023 8.07397E-18

Age 0.791181 0.109483 7.226516 8.80804E-13

Days of Exercise -0.12035 0.0711 -1.69267 0.09077896

Insured 0.140846 0.789098 0.17849 0.858368479

Income (Constant $2006) -1.9E-06 1.87E-06 -1.03719 0.299859125

2006 0.303293 0.607046 0.499622 0.617433238

The negative correlation between income and body mass index in 2002 in Figure 6a and

6b is consistent with the negative coefficient for income in Table 8. Thus, the higher the

household’s income, the lower the body mass index. The difference between the Figures 6a and 6b

is the slope of the trendline. The steeper slope in Figure 6a indicates that uninsured child’s body

mass index is more dependent on income. Thus a small change in income would result in a higher

change in uninsured child’s body mass index compared to a insured child’s body mass index. An

explaination may be that families with higher incomes are able to purchase more nutritious fresh

food or have extra income for their children to join sport programs and teams.

Figure 6a. Uninsured Child’s BMI 2002

y = -4E-05x + 25.82R² = 0.014

0

10

20

30

40

50

60

0 50000 100000 150000

Ch

ild's

BM

I 20

02

Family Income 2002

Uninsured Child's BMI 2002

Series1

Linear (Series1)

21

Figure 6b. Insured Child’s BMI 2002

To test the consistency of the result that insured children have higher body mass indexes

on average (see Table 8) , Figure 7 shows that there are similar patterns of exercise behaviors in

2002 and 2007. For example, in both years, a larger percentage of insured children exercised in all

categories of days except for the category of “3 to 5 days/week”. Although a larger share of insured

children exercise six to seven days a week than uninsured children, more insured children also do

not exercise at all in both 2002 and 2007 (zero days a week). The result is ambigious, and it’s

difficult to say whether insured or uninsured children exercise more.

Figure 7a. Share of Children who Exercise X Number of Days per Week in 2002

y = -3E-06x + 21.43R² = 0.000

0

10

20

30

40

50

60

70

0 200000 400000 600000 800000 1000000

Insu

red

Ch

ild's

BM

I

Family Income 2002

Insured Child's BMI 2002

Series1

Linear (Series1)

40.00%

7.27%

41.82%

10.91%

44.79%

9.16%

26.41%

19.64%

0%5%

10%15%20%25%30%35%40%45%50%

0 1 to 2 3 to 5 6 to 7

% o

f U

nin

sure

d o

r In

sure

dP

op

ula

tio

n 2

00

2

Number of Days of Exercise 2002

Share of Children Who Exercise X Number of Days Per Week 2002

Uninsured

Insured

22

Figure 7b. Share of Children who Exercise X Number of Days per Week in 2007

Table 9a and 9b shows the regression of number days of exercise on body mass index and

will give a better estimate of the effect of insurance coverage on a child’s physical activity. In 2002,

insured individuals exercise on average more than uninsured children (positive coefficient for

dummy variable for insured), and there is a slight positive correlation between body mass index and

number of days of exercise. Th p-value of .17 shows that this is not statistically significant. Similarly,

the p-value for 2007 is .24, also portraying no statistically significant correlation. In 2007, as shown

by the negative coefficient of -0.01519, those who exercise more days per week had a lower body

mass index. An important finding is that on average, insured children in 2007 exercised .3 days less

than uninsured children,

Table 9a. Regression of Days of Exercise on BMI and Insurance Coverage in 2002

Multiple R 0.045406

R Square 0.002062



Observations 1701

ANOVA


Regression 2 24.8984 12.4492 1.754009 0.173392

3.51%

10.53%

57.89%

28.07%

7.14%

18.57%

43.67%

30.61%

-10%

0%

10%

20%

30%

40%

50%

60%

0 1 to 2 3 to 5 6 to 7

Shar

e o

f U

nin

sure

d o

r In

sure

dP

op

ula

tio

n 2

00

7

Number of Days of Exercise 2007

Share of Children who Exercise X Number of Days Per Week 2007

Uninsured

Insured

23

Residual 1698 12051.67 7.09757

Total 1700 12076.57

Coefficients Standard Error t Stat P-value Lower 95%

Upper 95%

Lower 99.0%

Upper 99.0%

Intercept 2.01155 0.446865 4.501476 7.21E-06 1.135087 2.888014 0.859208 3.163893

Insured 0.096201 0.369619 0.260272 0.794686 -0.62876 0.821158 -0.85695 1.049348

BMI 2002 0.01982 0.010601 1.869664 0.061702 -0.00097 0.040612 -0.00752 0.047156

Table 9b. Regression of Days of Exercise on BMI and Insurance Coverage in 2007

Multiple R 0.053416

R Square 0.002853



Observations 993

ANOVA


Regression 2 13.35613 6.678066 1.416433 0.243069

Residual 990 4667.558 4.714705

Total 992 4680.914


Upper 95%

Lower 99.0%

Upper 99.0%

Intercept 4.809478 0.393387 12.22583 4.11E-32 4.03751 5.581445 3.794223 5.824732

BMI 2007 -0.01519 0.011383 -1.33429 0.182415 -0.03753 0.007149 -0.04457 0.014189

Insured -0.30231 0.303941 -0.99462 0.320163 -0.89875 0.294136 -1.08672 0.482106

Table 10 shows that age does affect the number of days of exercise, with older kids

exercising more frequently than younger children. The positive coefficient for income and insured

independent variables indicate that the higher income and insured children exercised more than

uninsured in 2002. This is consistent with the results from Table 9a. Although the dummy variable

for “males” has a negative coefficient, the 95% confidence interval for the coefficients have a

positive coefficient (.2766), showing that males exercise more than females. On average, females

aged 14 to 18 years old exercised a day less than males in 2002.

24

Table 10. Regression of Days of Exercise in 2002 with Age, Gender, Income, and Insurance Status

Multiple R 0.238038

R Square 0.056662



Observations 1724

ANOVA


Regression 6 694.7168 115.7861 20.6385 1.88E-23

Residual 1718 11565.99 6.732243

Total 1724 12260.71


Upper 95%

Lower 99.0%

Upper 99.0%

Intercept -0.238 0.460255 -0.51711 0.605145 -1.14072 0.664716 -1.42486 0.948853

Age 0.226084 0.023599 9.580091 3.25E-21 0.179798 0.272371 0.165229 0.28694

Income 2002 7.7E-07 6.44E-07 1.19451 0.232443 -4.9E-07 2.03E-06 -8.9E-07 2.43E-06

Insured 0.015409 0.360145 0.042786 0.965877 -0.69096 0.721779 -0.9133 0.944114

Female 14-18 -1.10956 0.215538 -5.14787 2.94E-07 -1.53231 -0.68682 -1.66537 -0.55376

Male -0.00483 0.143508 -0.03368 0.973136 -0.2863 0.276635 -0.3749 0.365229

Dietary Behaviors

To sum up the previous findings, on average, insured children have higher body mass

indexes relative to uninsured children. Insured children’s exercise habits shifted from 2002 to 2007,

and exercised less than uninsured children in 2007. In terms of body mass indexes, insurance

coverage does have a negative effect on insured children. A closer look at the dietary behaviors and

the parents’ value of nutritional food will decipher whether insurance coverage is linked to what

kids eat.

Table 11 is a t-test of the importance of vegetables, assuming unequal variances. The scores

range from 1 to 4, with 4 indicating that vegetables are very important to the child’s diet. The

means are high, conveying that both insured and uninsured families value nutritious food. With a

confidence level of 95% and the null hypothesis that there is no difference between the means of

the scores between uninsured and insured samples, the two-tail p-value (.9818) show that the

mean difference is not statistically significant. Thus, insurance status doesn’t change the

importance of vegetables in a family unit.

25

Table 11. T-test of Importance of Veggies

Uninsured Insured

Mean 3.431034 3.428705

Variance 0.565336 0.611344

Observations 58 1066

Hypothesized Mean Difference 0

df 64

t Stat 0.022926

P(T<=t) one-tail 0.49089

t Critical one-tail 1.669013

P(T<=t) two-tail 0.981781

t Critical two-tail 1.99773

Using the variables ER21706 (Enough food and kind wanted) and ER24099 (total family

income from 2002), Table 12 analyzes at the availability of food for uninsured and insured families.

An assumption made is that the “kind” of food that is wanted is nutritious food such as vegetables

and fruit, which are generally more expensive than snacks and fast food. Using child weight from

2002, the income is divided into three tiers: low, middle, high. In general, insured families have a

higher range of income than uninsured families.

A larger percentage of insured families have enough food compared to uninsured families

while a larger percentage of uninsured families don’t always have the kind of food desired or

sometimes not enough. Furthermore, income plays a factor in the availability of food, although

there is no strong correlation shown in this data. The only strong relationship between income and

availability of food is shown through the column 2 and 3 for insured families, where the percentage

of those not always having the kind of food wanted or sometimes not enough decreases as income

rises.

Table 12. Availability of Food by Income for Uninsured and Insured in 2002

Insured 2002

Income $ 1-enough food

2-enough but not always kind of

food wanted

3-sometimes

not enough

4-often not enough

Total

0-42920 24.02% 15.31% 1.41% 0.48% 41.22%

42920-83977 26.70% 5.98% 0.04% 0.00% 32.73%

83977-2051900 24.55% 1.41% 0.00% 0.09% 26.04%

Total 75.27% 22.70% 1.45% 0.57% 100%

26

Uninsured 2002

Income $ 1-enough 2-enough but not always kind of

food wanted

3-sometimes

not enough

4-often not enough

Total

0-23850 24.00% 14.67% 1.33% 0.00% 40.00%

23850-47453 20.00% 14.67% 1.33% 0.00% 36.00%

47453-153000 21.33% 2.67% 0.00% 0.00% 24.00%

Total 65.33% 32.00% 2.67% 0.00% 100.00%

Breakfast is considered to be the most important meal of the day, jumpstarting one’s

metabolism and has been shown to be negatively correlated with obesity in many cross sectional

studies. Figure 8a splits the CDS-II children into two age cohorts, ages 6 to 11.9 and ages 12 to 18.

Note that the uninsured children’s sample size is significantly smaller than insured children’s sample

size (there were only ten uninsured children in the age group 6 to 11.9 and 32 uninsured children in

age group 12 to 18, while there were 366 insured children in age group 6 to 11.9 and 867 insured

children in age group 12 to 18). Since there were a total of ten uninsured children in age group 6 to

11.9, there were no children that had nothing for breakfast. This probably would be a larger

percentage if the sample size was larger. Younger insured children ate less breakfast than older

insured children in 2002, while 28.13% of the older uninsured children didn’t eat breakfast. Figure

8b is constructed with all the children from CDS-III, since they are aged 12 to 18. Again, the sample

sizes differ based on insurance status (991 insured children, 34 uninsured children). Athough there

isn’t much of a difference between insured and uninsured children in Figure 8b, a higher proportin

of insured children do not eat breakfast, and this is consistent with the previous findings that

insured children are more likely to be obese.

27

Figure 8a. Share of Children who had Nothing for Breakfast in 2002

8b. Share of Children who had Nothing for Breakfast in 2007

With the incomes separated into quartiles, Figure 9 compares the average number of days

per week that an insured or uninsured child eats dairy, fruit, and fast food. Note that there is no

data on fast food consumption for 2002. Also, the income ranges are much higher for insured

children. Thus, we can compare the average number of days of food consumption for the same

income ranges. For insured children in 2002, there is a distinct positive relationship between

income and the average number of days a child eats fruit and dairy. This is the logical result, since

one would imagine that a family would allocate more of their family spending on fruits and dairy as

their income rises. This is due to the fact that nutritious food is more expensive than snack food or

junk food. The trends of fruit and dairy consumption follow one another in uninsured children in

2002, with an overall result of more dairy consumption than fruit intake for all children. On average,

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

6 to 11.9 12 to 18

27.32%20.88%

0%

28.13%

% o

f C

hild

ren

wh

o h

ad N

oth

ing

for

Bre

akfa

st 2

00

2

Age in 2002

Share of Children Who had Nothing for Breakfast in 2002

Insured

Uninsured

3.37%

3.38%

3.39%

3.40%

3.41%

3.42%

3.43%

Insured Uninsured

3.43%

3.39%

% o

f C

hild

ren

wh

o h

ad N

oth

ing

for

Bre

akfa

st in

20

07

Share of Children who had Nothing for Breakfast in 2007

12 to 18

28

$0-34366 $34367-$62618 $62619-$99500$99501-

$2051900

Fruit 3.55 3.67 3.78 4.45

dairy 3.41 3.81 3.98 4.85

3

3.5

4

4.5

5

Ave

rage

# D

ays/

Wk

20

02

Average # of Days/Wk of Fruit/Dairy Consumed by Insured Kids 2002

insured children seem to be eating more dairy and fruit than uninsured children. In 2007, with an

addition of the average number of days a child eats fast food, the results are humbling – all children

in each income bracket eat more fast food than fruit and dairy products each week. This shows

what the fast food industry has done to the eating habits of children in America. On average,

uninsured children eat more fruit and dairy than insured children. For all children, the average

number of days of fruit and dairy consumption decrease as the average number of days of fast food

consumption rises as income rises. This figure shows that in 2007, insured children eat more fast

food, and less fruit and dairy tan uninsured children and adds more consistency to the result that

insured children are more obese. This is an interesting finding, since in Table 11, insured and

uninsured parents equally value the importance of vegetables. Also, as shown in Table 12, although

insured children have a higher food sufficiency score than uninsured children, insured children are

consuming more fast food. A possible reason to explain why insured children are eating more fast

food than uninsured children may be due to the insured family’s busy lifestyle, as more insured

families are employed, and have a higher income, translating to higher productivity in their

occupations. It is quite ironic that higher income results in poorer eating habits among insured

children across time.

Figure 9a. Average Number of Days per Week of Fruit/Dairy Consumed by Insured Kids 2002

29

$0-$15000$15001-$30000

$30001-$48550

$48551-$129000

Fruit 4.67 3.06 3.86 2.67

Dairy 3.92 3.12 3.93 4.33

2.5

3

3.5

4

4.5

5

Ave

rage

Day

s/w

k

Average # of Days/Wk Fruit/Dairy Consumed by Uninsured Kids 2002

9b. Average Number of Days per Week of Fruit/Dairy Consumed by Uninsured Kids 2002

9c. Average Number of Days per Week of Food Consumption by Insured Children 2007

$0-$39000$39001-$70300

$70301-$116400

$116401-$1067300

Milk 3.57 3.77 3.67 3.47

Fruit 3.92 3.88 3.62 3.49

Fast Food 4.57 4.51 4.84 5.25

33.5

44.5

55.5

Ave

rage

# o

f D

ays

/Wk

Insu

red

Ch

ildre

n 2

00

7

Average # Days/Wk of Food Consumption by Insured Children 2007

30

9d. Average Number of Days per Week of Food Consumption by Uninsured Children 2007

Table 13 consists of multiple regressions of the number of days of food consumption on age,

income and insurance status in years 2002 and 2007. The R-squares of Table 13a and 13b show that

there is a correlation between dairy and fruit intake with the independent variables. As children get

older and as their family’s income increases, they consume more fruit and dairy. The positive

coefficient for the dummy variable “insured” indicates that insured children eat more fruit and dairy

than uninsured children. The negative intercept in both Table 13a and 13b conveys the fact that the

number of average days of fruit and dairy consumption are low (approximately 3 days a week).

Table 13a. Regression of Days of Dairy Intake on Age, Income and Insurance Status 2002

Regression Statistics

Multiple R 0.721095

R Square 0.519978



Observations 641

ANOVA


Regression 3 2725.008 908.336 230.0079 4.4127E-101

Residual 637 2515.61 3.949152

Total 640 5240.618


Intercept -10.9928 0.705254 -15.587 1.22E-46

$0-$24000$24001-$40900

$40901-$62026

$62027-$181962

Milk 3.53 4 3.86 3.8

Fruit 3.95 4 2.57 3.1

Fast Food 4.95 4.07 4.07 5.1

2.53

3.54

4.55

5.5

Ave

rage

# o

f D

ays/

wk

Un

insu

red

Ch

ildre

n 2

00

7

Average # Days/Wk of Food Consumption by Uninsured Children

2007

31

Age 2002 1.295565 0.05026 25.77736 6.5E-101

Income 2002 1.39E-06 1.04E-06 1.335247 0.182273

Insured 0.199789 0.453701 0.440355 0.65983

Table 13b. Regression of Days of Fruit Intake on Age, Income and Insurance Status 2002


Multiple R 0.705042

R Square 0.497084



Observations 641

ANOVA


Regression 3 2604.128 868.0426 209.8708 1.2E-94

Residual 637 2634.684 4.136081

Total 640 5238.811


Intercept -11.1347 0.721752 -15.4273 7.4E-46

Age 2002 1.267959 0.051436 24.6514 9.91E-95

Income 2002 1.19E-06 1.06E-06 1.122412 0.26211

Insured 0.55955 0.464314 1.20511 0.228608

Table 13c. Regression of Days of Dairy Intake on Age, Income, and Insurance Status 2007

Multiple R 0.016883

R Square 0.000285



Observations 1029

ANOVA


Regression 3 1.362586 0.454195 0.097419 0.961459

Residual 1025 4778.826 4.662269

Total 1028 4780.189


Intercept 3.796633 0.542202 7.002251 4.55E-12

Income 2006 6.2E-08 8.56E-07 0.072352 0.942336

Age 2007 -0.00156 0.030951 -0.05026 0.959922

Insured -0.15899 0.294218 -0.54038 0.589051

In 2007, the coefficients for income, age, and insurance status vary among the regressions.

In Table 9c, there isn’t much of a significant relationship between dairy intake and age, income and

insurance status, as denoted by the R-square of 0.000285 and p-value of .9614. Insured children

and older children consume less dairy, which is conveyed by the negative coefficients for age and

insured children. The positive coefficient for income indicates that the higher the income, the more

32

dairy the child consumes. This is consistent with Table 13a and 13b. Note that the children in CDS-III

are 12 to 18 years old.

Table 13d. Regression of Days of Fruit Intake on Age, Income, and Insurance Status 2007


Multiple R 0.075228058

R Square 0.005659261



Observations 1029

ANOVA


Regression 3 26.73607 8.912025 1.944586 0.120701

Residual 1025 4697.569 4.582994

Total 1028 4724.305


Intercept 2.557042488 0.537572 4.756648 2.25E-06

Income 2006 -8.3396E-07 8.49E-07 -0.98232 0.326173

Age 2007 0.063948826 0.030687 2.083904 0.037416

Insured 0.347700352 0.291706 1.191955 0.233555

Table 13d shows that there isn’t a large correlation between fruit intake and age, income,

and insurance status, as shown by the small R-square (0.00565). A strange result is that as income

increases, children eat less fruit on average. Since nutritious food such as fruit is usually more

expensive than snack foods, one would hypothesize that as family’s income increases, the children

would eat more fruit per week. Older children and insured children eat more fruit, but insurance

status seems to have a larger effect than age, due to the larger coefficient.

Table 13e. Regression of Days of Fast Food Intake on Age, Income, and Insurance Status 2007


Multiple R 0.12126

R Square 0.014704



Observations 1029

ANOVA


Regression 3 62.85077 20.95026 5.098806 0.001661

Residual 1025 4211.577 4.108855

Total 1028 4274.428


Intercept 4.718787 0.509006 9.270598 1.06E-19

Income 2006 3.09E-06 8.04E-07 3.839867 0.000131

33

Age 2007 -0.0209 0.029056 -0.71919 0.47219

Insured 0.06825 0.276205 0.247099 0.804881

The intercept of 4.718 in Table 9e is consistent with Figure 9c and 9d, showing that children

eat more fast food than fruit and dairy on average. Insured children and families with higher income

eat more fast food, which is surprising since one would expect that lower income families would

treat their children to more fast food meals. Younger teens also eat more fast food. To sum up

Table 9, the type of food consumption is an age-dependent process, where the older children in

CDS II from 2002 eat more fruit and dairy. The older children in CDS III, who comprise the majority

of the younger children in CDS II, eat less dairy than the relatively younger children in CDS III. Also,

fruit and fast food consumption does not differentiate too much between younger and older

children in CDS III, whereas age had a much larger effect in 2002 since there was a wider range of

ages among the children.

Conclusion

Across a span of six years, the decreasing amount of health coverage has negatively affected the

access of health services for the low-income and unemployed families of America. In times where

medical physicians need to treat and prevent new childhood chronic illnesses such as obesity,

asthma, and allergies, uninsured children receive less medical attention due to the financial burden

of increasing medical costs. Thus, it is important for Congress to invest their budget in a public

option that will provide coverage for all children. If more and more children are unable to receive

medical service, the future generations will suffer from shorter life expectancies, lower standard of

living, and more illnesses.

Insurance coverage also negatively affects the amount of physical exercise and dietary

habits among the insured children. This is a complex problem associated with our society’s

mentality regarding nutrition and fitness. With the busy American lifestyles, the insured families

substitute nutritious meals that take time to prepare for fattening fast food available through a

drive-through. As a result, insured children are more likely to be obese, conveying a monetary and

physical cost to society. Parents need to be positive role models when the children are young, for

unhealthy eating habits stick with children as they age and as they make their own dietary choices.

A recommendation is that the education system should promote healthy habits in the youth

through an increase in breakfast programs, physical education classes, and nutrition classes. A

child’s health is one of the most important factors in their development, and this is an issue that

34

needs to be addressed by parents, Congress, and the education system. A unified effort is necessary

to combat the rising health problems among the children, and it begins with a change in our

lifestyle choices. While parents and the education system should encourage healthier lifestyles, the

government is morally and socially responsible for providing preventative medical care to ensure

the well-being of our future generations. Insurance matters.

Appendix of Variables Variables are taken from the CDS-II and CDS-III Primary caregiver interviews from 2002 and 2008 and PSID Family-level and Individual-level indexes. All analysis was conducted through Excel 2007. ER30001 1968 INTERVIEW NUMBER ER30002 PERSON NUMBER ER32000 SEX OF INDIVIDUAL ER15666 H52A IMPORTANCE OF FRUITS/VEGGIES ER33501 1999 INTERVIEW NUMBER ER33502 SEQUENCE NUMBER ER33503 RELATION TO HEAD ER33601 2001 INTERVIEW NUMBER ER33602 SEQUENCE NUMBER 01 ER33603 RELATION TO HEAD 01 CH02PRWT CHILD LEVEL WEIGHT 02 Q21A4B ASTHMA 02 Q21A4P ALLERGIES 02 Q21A4V OBESITY 02 Q21A5 DR VISIT - ILLNESS 02 Q21A11 PCG RATED CHILD HEALTH 02 Q21A15 # DR VISITS - ASTHMA 02 Q21A17 AMT -DENTAL INSUR 02 Q21A19 AMT -DENTAL CARE 02 Q21A21 AMT -MEDICAL INSUR 02 Q21A23 AMT -MEDICAL CARE 02 Q21IWAGE CHILD AGE AT TIME OF PCG IW - YEARS 02 Q23K13M BREAKFAST: NOTHING 02 Q23K14A # DAYS ATE: DAIRY 02 Q23K14B # DAYS ATE: FRUIT 02 Q23K17 # DAYS EXERCISE OUTSIDE SCH 02 Q24BMI BODY MASS INDEX 02 ER21706 F26 WTR ENOUGH FOOD AND KIND WANTED ER23277 H60 WTR FU MEMBER W/HLTH INS LAST 2 YRS ER23297 H82 TOTAL COST ALL MEDICAL CARE ER23338 K34A PRIMARY ETHNIC GROUP ER23430 L41 PRIMARY ETHNIC GROUP ER24099 TOTAL FAMILY INCOME LAST YEAR ER33701 2003 INTERVIEW NUMBER ER33702 SEQUENCE NUMBER 03 ER33703 RELATION TO HEAD 03

35

ER36016 # IN FU ER36020 # CHILDREN IN FU ER36701 F15 # FU MEMBERS RECEIVED FOOD STAMPS ER40409 H60 WTR FU MEMBER W/HLTH INS LAST 2 YRS

ER40420 H70 TOT DR/OUTPT SURGRY/DENTAL EXPENSES

ER40432 H82 TOTAL COST ALL MEDICAL CARE ER41027 TOTAL FAMILY INCOME-2006 ER33901 2007 INTERVIEW NUMBER ER33902 SEQUENCE NUMBER 07 ER33903 RELATION TO HEAD 07 CH07PRWT PRIMARY CAREGIVER / CHILD WEIGHT 07 Q31A4B ASTHMA 07 Q31A4P ALLERGIES 07 Q31A4P2 OBESITY 07 Q31A5 DR VISIT ILLNESS 07 Q31A11 PCG RATED CHILD HEALTH 07 Q31A15 NUM DR VISITS - ASTHMA 07 Q31IWAGE CHILD AGE AT TIME OF PCG IW - YEARS 07 Q32J50 CHILD HEALTH INS 07 Q32J51A CHLD HLTH INS - PRIVATE THRU PCG EMPL 07 Q32J51B CHLD HLTH INS - PRIVATE THRU OCG EMPL 07 Q32J51C CHLD HLTH INS - PRIV THRU EX-SPS EMPL 07 Q32J51D CHLD HLTH INS - PRIV PURCHASED DIRECT 07 Q32J51E CHLD HLTH INS - MEDICAID 07 Q32J51F CHLD HLTH INS - SCHIP 07 Q32J51G CHLD HLTH INS - MEDICARE 07 Q32J53 CHILD 2 HEALTH INS 07 Q32JA33 CHILDREN HEALTH COV COSTS 07 Q32JA34 CHILDREN HEALTH OUT OF POCKET COSTS 07 Q33K13_M BREAKFAST: NOTHING 07 Q33K13C MILK PAST 7 DAYS 07 Q33K13G FRUIT PAST 7 DAYS 07 Q33K13J FAST FOOD PAST 7 DAYS 07 Q33K17 # DAYS EXERCISE OUTSIDE SCH 07 Q34BMI BODY MASS INDEX 07

An Analysis of the Effect of Insurance on American Children · According to Dr. Kenneth Bock, the...

Documents

Transcript of An Analysis of the Effect of Insurance on American Children · According to Dr. Kenneth Bock, the...