HEALTH INSURANCE COVERAGE FOR FAMILIES WITH CHILDREN:

FINDINGS FROM THE CONSUMER EXPENDITURE SURVEY

Geoffrey D. Paulin, Elizabeth M. Dietz, US Bureau of Labor Statistics

Geoffrey D. Paulin, 2 Massachusetts Avenue, NW, Washington, DC 20212

Key Words: Multinomial logit

Note: This work is substantially abridged. For a

complete version, see Monthly Labor Review, August

1995, pp. 13-23. The views expressed herein are those

of the authors and do not reflect the policies of the

Bureau of Labor Statistics (BLS) or the views of other

BLS staff members.

Health insurance coverage is important in the

maintenance of good health for families with children.

Cunningham and Monheit (1990, p. 78) find children

in families with no coverage are “at a disadvantage

regarding access to, quality of, and continuity of health

care.” Kasper (1987) finds uninsured children are

least likely to have seen a doctor in the past year, even

for immunization or general check-up (tables 4 and 7).

Preventive care is important for children, who are

prone to illness.

However, health care costs have risen substantially

in recent years. From 1989-94, the Consumer Price

Index for medical care increased 41.3 percent,

compared with 18.2 percent for all other items. In

1993, the Nation’s costs rose 7.8 percent from 1992

(HHS News 1994). Paulin and Weber (1995) suggest

that as a result of these increases, direct costs of care

are shifting from business and government to families,

thus affecting expenditures for nonhealth items.

Meanwhile, in 1992, more than 8 million American

children had no health coverage (U.S. Bureau of the

Census 1994). While many of the poorest families

received Medicaid benefits (Cunningham and Monheit,

pp. 77-78), the percentage of children without public or

private coverage grew by more than 40 percent

between 1977 and 1987 (Ibid., pp. 80-81).1

This study identifies families that have full, partial,

and no health insurance coverage. It examines the

characteristics of each insurance group, policies held,

health expenditure patterns, and relationships between

demographics and the probability of being insured.

Background. Many studies examine coverage of

individuals (Bazzoli 1986). They define the medically

indigent as those whose “lack of public or private

health insurance coverage” means they incur the direct

costs of their health care (p. 356).

Miller (1990) uses data from the 1987 Consumer

Expenditure Interview Survey (CE) to study medically

uninsured consumer units2

 rather than individuals.

Reise (1993) examines families with children, dividing

the sample into those with full coverage (all members

covered), partial coverage (at least one, but not all,

Table 1 (Abridged). Demographic characteristics of

families with children by health insurance status,

Consumer Expenditure Survey, 1991-93

Characteristic Insurance Group

 (1) (2) (3)

-----------------------------------------------

Sample size 2,605 347 773

Characteristics of average family

 Age of reference person 37.3 37.1 35.7

 Family size 3.8 3.6 3.7

 Number of earners 1.8 1.7 1.5

 Persons under 18 years old 1.9 1.9 2.0

 Total expenditure outlays (annual, in 000’s)

 Mean $40.8 $32.5 $28.6

 Median $34.7 $28.7 $24.3

Other characteristics (in percent):

 Living in the--

 Northeast 22.8 16.7 14.8

 Midwest 28.6 24.2 19.3

 South 27.3 35.3 34.0

 West 21.5 22.8 30.5

 Urban areas 87.8 87.3 89.0

 Black 7.0 7.8 10.9

 Hispanic 4.7 8.7 15.3

 Occupation of the reference person:

 Wage and salary 88.2 87.6 75.9

 Manager/professional 39.0 30.6 23.3

 Technical/sales 18.4 23.9 15.1

 Service 5.9 11.2 10.1

 Blue collar 24.9 21.9 27.4

 Self-employed 7.0 6.6 10.5

 Retired 0.3 0.3 0.3

 Unemployed 0.4 0.3 1.3

 Out of the labor force 4.1 5.2 12.0

 Education of the reference person:

 Less than high school 7.0 11.4 28.8

 High school graduate/

 some college 58.7 65.4 57.2

 College graduate 34.3 23.9 20.2

 Family composition:

 Single parent 12.6 30.0 22.5

 Husband/wife family 87.4 70.0 77.5

 Earner status:

 No earners 0.8 1.4 6.9

 One earner 30.9 39.2 44.1

 Two earners 60.8 51.6 43.3

 At least three earners 7.5 7.8 5.7

 Housing tenure:

 Homeowner with mortgage 68.9 54.8 41.5

 Homeowner, no mortgage 7.8 6.9 9.3

 Renter 23.3 38.3 49.2

 At least one child:

 Under age 6 50.6 47.3 47.6

 6 to 11 51.3 43.5 53.6

 12 to 17 37.7 42.1 41.3

 Student status of reference person:

 Full time 1.4 3.8 2.2

 Part time 5.5 4.6 4.9

Group 1: Fully insured

Group 2: Partially insured

Group 3: Uninsured

members covered) and no coverage (none covered).

Both studies predict probability of coverage.

The data. The data are from the 1991-93 CE for

families. Families include only parents (single or

married) with all children under 18.3

 Families with

Medicaid or Medicare are excluded from the analysis.

As with Miller and Reise, only second interview data

are used to ensure that all families are unique.

All data presented in this study are unweighted.

Demographic characteristics. There is little

difference in age or family size by level of coverage
(table 1); but income (proxied by total expenditure

outlays4

) seems to be correlated with coverage. The

fully covered have the highest incomes; the uninsured

have the lowest incomes. Also, the uninsured have

lower levels of education and work force participation,

fewer earners, and are more likely to be black or

Hispanic. Only the uninsured are as likely to own as

rent, although they have the highest rate of “outright”

ownership (i.e., owning with no mortgage).

Table 2 (Abridged). Health insurance

policies, by health insurance coverage

status, 1991-93

 Insurance

Group

Type of policy (1)

(2) (3)

--------------------------- ------------

------

Family size 3.8

3.6 3.7

 Members covered 4.3

1.8 0.0

 Percent of members covered 113.1

50.0 0.0

Percent with at least one--

 Blue Cross policy 28.1

28.5 9.2

 Commercial health policy 47.8

44.7 13.7

 HMO policy 24.1

18.4 8.9

 Dental only policy 9.3

8.7 7.0

 Other health policies1 13.6

19.0 4.4

Average number of-

 Policies held 1.39

1.32 .45

 Blue Cross .31

.30 .09

 Commercial health .56

.50 .15

 HMO .26

.21 .09

 Dental only .10

.09 .07

 Other health insurance1 .16 .22

.05

1 Includes policies providing special

limited coverage, Medicare

supplements, and other health

insurance policies.

See table 1 for insurance group names.

Policies held. Insured families have similar types

and numbers of policies. But quality is different. The

fully insured on average cover 113 percent of their

members. The partially insured cover 50 percent.

Children make up a large percentage of individuals

not covered in partially insured families. Although the

CE does not ask which members are covered, assuming

there is no overlap until all members are covered, a

lower and upper bound on the number of children

covered can be estimated. For the lower bound,

families are assumed to follow an “adult first” strategy;

i.e., the first persons covered are adults. To get the

upper bound, families are assumed to follow a “child

first” strategy; i.e., children are covered before adults.

Results are shown in the following tabulation:

Data for Partially Insured Families:

Number of children ....................... 1.9

Number of children covered:

 Adults first ................................ 0.5

 Children first ............................. 1.5

Percent of children covered:

 Adults first ................................ 26.3

 Children first ............................. 78.9

In other words, at least one-fourth of all children in

the partially insured families sampled have no health

insurance coverage. If combined with children in

uninsured families, between one-ninth and one-sixth of

the children in the sample lack coverage.5

Table 3. Health care expenditures by

health insurance coverage status, 1991-

93

 Insurance

Group

Expenditure allocation (1) (2)

(3)

---------------------- ------ -----

-----

Total health care (annual) $1,880

$1,668 $972

 Health insurance 920

663 269

 Medical services 732

811 556

 Prescription drugs/

 medical supplies 229

194 147

Percent of health care

 allocated to-- 100.0

100.0 100.0

 Health insurance 48.9

39.7 27.6

 Medical services 38.9

48.6 57.2

 Prescription drugs/

 medical supplies 12.2

11.6 15.1

Percent of total expenditure

 outlays allocated to--

 Health insurance 2.3

2.0 .9

 Medical services 1.8

2.5 1.9

 Prescription drugs/

 medical supplies .6

.6 .5

Percent reporting expenditures

(quarterly):1

 Health insurance 67.3

58.8 23.0
 Medical services 70.1

62.0 51.2

 Prescription drugs/

 medical supplies 57.7

54.2 40.5

----------------------------------------

-------

1 Does not include reimbursements for

payments made in previous quarters but

received in current quarter.

See table 1 for insurance group names.

Health care expenditures. The fully insured pay

the most for health care (table 3). Although the

partially insured appear to pay more for medical

services, the difference is not statistically significant.6

The fully insured spend the largest share on

insurance, but the smallest on medical services.

However, the fully and partially insured spend about

the same share on prescription drugs. The uninsured

spend the largest shares on medical services and

prescription drugs and medical supplies, and the

smallest share for insurance.

However, insurance premium payments for the

uninsured are for someone outside the immediate

family (older relative, etc.), and so perhaps should not

be counted when comparing expenditures by insurance

status. Furthermore, insurance policies may “favor”

certain types of treatment--they may pay for medical

services, but not prescription drugs. Therefore, it is

interesting to examine expenditures for items other

than insurance premiums. Of these dollars, the fully

insured allocate 76 percent to medical services and 24

percent to prescription drugs and medical supplies,

compared to an 81/19-percent split for the partially

insured, and a 79/21-percent split for the uninsured.

Probability of purchase. The fact that the fully and

partially insured spend more for items other than

premiums does not, by itself, indicate coverage is

related to usage. The uninsured have the lowest

incomes, so they are expected to spend the least. To

estimate the direct effect of coverage, all other factors

must be held constant. Rubin and Koelln (1993) find,

ceteris paribus, coverage is positively correlated with

health expenditures for items other than premiums.

They do not model frequency of usage, probably

because the CE does not measure it directly;

respondents are not asked how many doctor visits

occurred during the past 3 months. But if there is an

expenditure, then someone must have visited.

Table 4 (Abridged). Results of

binomial logit predicting

probability of incurring

expenditures for medical

services with median income

held constant for all insurance

groups ($32,175)

 Parameter

Estimates

 (1) (2)

(3)

 ------- ------

------

Intercept -3.4201

 -2.712

-2.0502

Annual Outlays* 0.2081 0.1342

0.081

Predicted Probability for

Reference Group: 0.732 0.759

0.661

* Box-Cox transformation

1 Significant at the 95 percent

confidence level.

2 Significant at the 90 percent

confidence level.

See table 1 for insurance group names.

Selected results of a binomial logistic regression

(Maddala 1993) modeling the probability of incurring

expenditures for different types of health care are

shown in tables 4 and 5.

The statistical significance of parameter estimates in

the first column (tables 4 and 5) indicates the

importance of the variable in predicting the probability

of an expenditure. The second and third columns show

whether this probability is different for the partially or

uninsured. If either estimate is statistically significant,

there is a difference.

Table 5 (Abridged). Results of

binomial logit predicting probability

of incurring expenditures for

prescription drugs and medical

supplies with median income held

constant for all insurance groups

($32,175)

 Parameter

Estimates

 (1) (2)

(3)

 ------- ------

------

Intercept -3.0601

 -1.605

-1.055

Annual Outlays* 0.1601 0.080

0.028

Predicted Probability for

 Reference Group: 0.584 0.606

0.468

See table 4 for footnotes.

See table 1 for insurance group names.

A reference group facilitates comparisons. In this

case, each reference group consists of families with

median income ($32,175), two earners, and reference

person who is between ages 25 and 44, married with
two children, and neither black nor Hispanic.7

The fully and partially insured have similar

probabilities of incurring medical service expenditures

(table 4); but the difference in probability for the fully

insured (73 percent) and the uninsured (66 percent) is

significant in the statistical and economic sense.

Thus, the data may indicate that uninsured families

are less likely to seek preventative care, as Kasper

finds. By contrast, insured families may be more likely

to visit doctors for minor illness, as Rubin and Koelln

imply. To further investigate usage, expenditures for

prescription drugs and medical supplies are examined.

Insured families may incur expenditures for doctor

visits; but if illnesses are not severe, doctors need not

prescribe medicine. If insured families are more likely

ceteris paribus to have prescription drug expenditures,

then it is assumed that if they are ill, they recover

faster than the uninsured.

The predicted probabilities that the fully or partially

insured incur expenditures for prescription drugs or

medical supplies are similar (table 5). The predicted

probability for the uninsured suggests they are less

likely to incur an expenditure than the insured, even

with all else equal. However, neither the intercept nor

income parameter estimate is statistically significant,

so caution must be used when interpreting this result.

Given the findings of Kasper, of Rubin and Koelln,

and tables 4 and 5, there appears to be a relationship

between level of coverage and receipt of medical care.

Therefore, it is important to understand the

relationship between demographics and coverage.

Probability of coverage. Parameters are estimated

using ordered multinomial logistic regression

(Maddala 1983). From them the probability that a

family is fully, partially, or not insured is predicted.

Several independent variables are chosen. The first

is annual total expenditure outlays (quarterly outlays

multiplied by four), used as a proxy for permanent

income (Friedman 1957). Outlays are subjected to a

Box-Cox transformation (Box and Cox 1964) using a

maximum-likelihood technique (Scott and Rope 1993).

Table 6 (Abridged). Predicted probabilities for

insurance status: Multinomial logit results

 Predicted Probability

 Parameter (by Insurance Group)

 Estimate (1) (2) (3)

 --------- ----- ------------

Sample (size: 3,725) ... 0.699 0.093 0.208

Reference Group: ... 0.757 0.090 0.153

Intercept 1 -3.7431

Intercept 2 -3.1601

Annual Outlays (Box-Cox) 0.2321 0.005 -0.001 -0.003

Reference Person

 Under 25 -0.3812

 -0.074 0.021 0.053

Ref. Person at least 45 0.042 0.007 -0.002 -0.005

One Child 0.129 0.022 -0.007 -0.015

Three or More Children 0.061 0.011 -0.003 -0.007

Family Type (Husband/Wife)*

 Single Parent 0.083 0.014 -0.005 -0.010

Ethnic Origin (White/other)*

 Black 0.121 0.021 -0.007 -0.014

 Hispanic -0.4121 -0.081 0.023 0.058

Occupation (Manager/Professional)*

 Technical/Sales -6.20E-04 -0.000 0.000 0.000

 Blue collar -0.052 -0.009 0.003 0.007

 Service -0.3481 -0.068 0.019 0.048

 Self-employed -0.7171 -0.150 0.038 0.112

 Retired 1.185 0.147 -0.052 -0.095

 Unemployed -0.210 -0.039 0.012 0.028

 Out of labor force -0.238 -0.045 0.013 0.032

Education (High school/some college)*

 Did not graduate HS -0.5251

 -0.106 0.029 0.077

 College graduate 0.012 0.002 -0.001 -0.001

At least one child

 over 12 -0.2581

 -0.049 0.014 0.035

Region (South)*

 Northeast 0.6481

 0.095 -0.032 -0.063

 Midwest 0.6331

 0.094 -0.032 -0.062

 West -0.044 -0.008 0.002 0.005

Degree Urbanization (Urban)*

 Rural 0.188 0.032 -0.010 -0.022

Number of Earners (Two Earners)*

 No earners -1.2661

 -0.285 0.055 0.230

 One earner -0.3681

 -0.072 0.020 0.052

 At least three -0.216 -0.041 0.012 0.029

Housing Tenure (Owner with mortgage)*

 Owner, no mortgage -0.199 -0.037 0.011 0.026

 Renter -0.5311

 -0.107 0.029 0.078

Student Status (Non-student)*

 Full-time -0.260 -0.049 0.014 0.035

 Part-time 0.025 0.004 -0.001 -0.003

* Omitted category in parentheses.

1 Significant at the 95 percent confidence level.

2 Significant at the 90 percent confidence level.

See table 1 for insurance group names.

In addition to normalizing the distribution of

outlays, the estimated value (1/8) of Box-Cox

transformation parameter λ is consistent with the

assumption that the probability of full coverage

increases with income, but at a decreasing rate. This

indicates that a set increase in income (say, $1,000) is

associated with an increase in probability of full

coverage, but the increase in probability is greater for a

low-income family than for a high-income family.8

Also included are several dummy variables

describing characteristics of the reference person or the

family (including housing tenure9

) to control for

differences in tastes, opportunity of coverage, and other

factors. The reference group consists of husband/wife

families with two children (both under age 12), median

outlays ($32,175),10 and two earners, living in their

own (mortgaged) homes in the urban South, whose

reference person is between ages 25 and 44, neither

black nor Hispanic, working for a wage or salary in a

managerial or professional position, a high school (but

not college) graduate, and not a student.

Logistic regression can be sensitive to weighting. If

weighted, parameter estimates are statistically
significant in nearly all cases. On the other hand, the

relatively small sample size (especially for the partially

and uninsured) may lead to large standard errors, thus

understating the number of significant relationships.

Therefore, no weights are applied, but the 90 percent

confidence level defines statistical significance.

Table 6 shows the predicted difference in probability

for each group compared to the reference group. For

example, families whose reference person is under age

25, but who are otherwise identical to the reference

group, are about 7 percent less likely to have full

coverage than the reference group, or -0.074 in the

fully insured column for the younger group.

Income and insurance status. Despite the

statistical strength of income as a predictor of

coverage,11 the probability that a family has full

coverage increases slowly with income. Given a 1-

percent increase in income, the probability of being

fully insured barely rises--76.7 percent to 76.9 percent.

Even with increases up to $3,000 per year the

probability rises only to 78.1 percent.

Nevertheless, three-fourths of the reference group

are predicted to have full coverage, and over four-fifths

are predicted to have at least partial coverage.

Therefore, it is interesting to study families like the

uninsured--those similar to the reference group, except

they have lower income ($24,277, the median value for

the uninsured), rent their homes, and the reference

person, a blue collar worker, is the sole earner.

Uninsured families are also slow to purchase health

insurance given increased income. For example, an

increase of $3,000 dollars is associated with a higher

probability of full coverage; however, the difference is

small--52.6 percent, compared with 50.2 percent.

Other characteristics. Other characteristics are also

associated with coverage. Families with young parents

(reference person under age 25) are significantly less

likely to have full coverage than older families. On the

other hand, families with young children are more

likely to have coverage than families with at least one

child over age 12. Families may choose coverage more

readily when the risks to their children are greater,

during early childhood development. Families with

older children may also be trying to put more savings

into college funds, and may choose not to buy health

insurance as a result.

Educational attainment also raises the probability of

full coverage. Those who did not graduate from high

school are less likely to be fully covered than those who

did graduate, although there is no statistically

significant difference in probability of full coverage for

high school and college graduates.

Occupational status appears to be associated with

different levels of health insurance coverage. Of the

wage or salary occupations, only those families whose

reference person is employed in services have a lower

probability of being fully insured than members of the

reference group. Families whose reference person is

self-employed are even less likely to have full

coverage.12

Number of earners is significantly related to

coverage. Two-earner families are expected to have

more coverage on average than families with fewer

earners but equal income, because two-earner families

have a greater chance that someone is eligible for an

employer-sponsored plan. Some families with three or

more earners need the incomes to cover expenses.

Earners may be in jobs with low wages or few benefits;

therefore, multiple earner families are expected to have

a lower probability of full coverage. The negative

coefficient for these families seems to confirm the

intuition, but it is not statistically significant, so no

firm inference can be drawn.

Cultural differences may make some groups less

averse to the risks of being uninsured. The coefficient

for Hispanics is statistically significant. Its negative

sign indicates Hispanics are less likely to be insured.

Regional differences are significantly related to

differences in health insurance coverage. Compared

with the South, families in the Northeast and Midwest

have a much higher probability of being fully insured.

This may be attributed to any number of differing

factors, including State laws, health care costs, work

force unionization, or other factors. The West,

however, is not significantly different from the South.

In most cases, the probability of being partially

insured does not change much with characteristics.

This may imply that families “vault over” the partially

insured category--i.e., given extra income, they will

move from no insurance to full coverage. But this is

not necessarily true. For example, it is possible that a

two-earner family with full insurance coverage moves

to the partial coverage class if an earner loses a job,

rather than slipping all the way into no coverage.

Some of those with partial coverage may move to the

no coverage category under similar circumstances.

Thus, the probability of partial coverage is similar

across demographic characteristics, even though some

families may be moving in and out of the category.

Conclusions. Health insurance is important to

families. Those with coverage are more likely to

receive some kinds of care than those with no

coverage, even if income and other factors are equal.

Income, age, education, and number of earners are

positively related to a family's level of health insurance

coverage. Reference person characteristics--service

worker, self-employed, or Hispanic--are negatively
related to the probability of full coverage.

Although income is an important predictor of

insurance status, families do not change their level of

coverage much, even when income increases

substantially. This implies that if increased health

insurance coverage is a desired outcome, direct grants

of cash to families will not raise levels of coverage in

any substantial way. Although prices and qualities of

insurance plans are not studied in this article, it would

be useful to find out what influence these factors have

on the probability of receiving coverage. Also, data on

difficulty of obtaining access to health insurance

coverage is useful to understanding why some families

are uninsured. For example, if plans are readily

available through an employer, are families likely to

take advantage of them? (See Wiatrowski 1995.)

Exploration of these issues should provide for

interesting future research.

References

Bazzoli, Gloria J. (1986) “Health Care for the

Indigent: Overview of Critical Issues,” Health

Services Research 21:3 (August), pp. 353-393.

Box, G.E.P. and D.R. Cox (1964) “An Analysis of

Transformations,” Journal of the Royal Statistical

Society, Series B, pp. 211-243.

Branch, E. Raphael (1993) “Short Run Income

Elasticity of Demand for Residential Electricity

Using Consumer Expenditure Survey Data,” The

Energy Journal, pp. 111-21.

Cunningham, Peter J., and Alan C. Monheit (1990)

"Insuring the Children: A Decade of Change,"

Health Affairs, Winter, pp. 76-90.

Friedman, Milton (1957) A Theory of the

Consumption Function (Princeton, NJ, Princeton

University Press for National Bureau of Economic

Research).

HHS News (1994), U.S. Department of Health and

Human Services (1994), November, p.1.

Kasper, Judith D. (1987) "The Importance of Type of

Usual Source of Care for Children's Physician

Access and Expenditures," Medical Care 25: pp.

386-398, esp. tables 4 and 7.

Maddala, G.S. (1983) Limited Dependent and

Qualitative Variables in Econometrics

(Cambridge, England: Cambridge University

Press), chapter 2, esp. pp. 22, 46-47.

Miller, Richard D. (1990) “Another Look at the

Medically Uninsured Using the 1987 Consumer

Expenditure Survey.” BLS Working Paper (206),

October.

Nelson, Julie (1989) “Individual Consumption Within

the Household: A Study of Expenditure on

Clothing,” Journal of Consumer Affairs, Summer,

pp. 21-43.

Paulin, Geoffrey D. (1995) “A Comparison of

Consumer Expenditures by Housing Tenure.”

Journal of Consumer Affairs, Summer, 29(1), pp.

164-198, esp. p. 189.

Paulin, Geoffrey D. and Wolf D. Weber (1995) “The

effects of health insurance on consumer

spending.” Monthly Labor Review, March, pp.

34-54.

Reise, Elizabeth M. (1993) “A Look at Private Health

Insurance Coverage of Families with Children

under 18 Using Data from the Consumer

Expenditure Interview Survey for 1989-91.”

Proceedings of the Section on Social Statistics,

American Statistical Association, pp. 832-837.

Rubin, Rose M. and Kenneth Koelln (1993)

“Determinants of Household Out-of-Pocket Health

Expenditures.” Social Sciences Quarterly, 74(4),

December, pp. 721-735.

Scott, Stuart and Daniel J. Rope (1993) “Distributions

and Transformations for Family Expenditures.”

1993 Proceedings of the Section on Social

Statistics, American Statistical Association, pp.

741-746.

U.S. Bureau of the Census (1994) Statistical Abstract

of the United States: 1994 (114th edition.)

Washington, DC, table no. 165. "Health

Insurance Coverage Status, by

Selected Characteristics: 1987-

92," p. 118.

Wiatrowski, William J. (1995) “Who really has access

to employer-provided health benefits?” Monthly

Labor Review, June, pp. 36-44.

 

1

Based on data from the 1977 and 1987

National Medical Care Expenditure

Surveys. (See exhibit 2.)

2

A person living alone or sharing a

household with others who are all

financially independent; members of a

household related by blood, marriage,

adoption, or other legal arrangement;

or two or more persons living

together who are responsible for at

least two of three major expenses:

food, housing, and other expenses.

3

This includes only children living at
 

home.

4

Includes mortgage principal payments

and actual outlays for vehicles.

5

If all partially insured families

follow the “children first” strategy,

then 11.2 percent of children in the

sample are uninsured. If they all

follow the “adult first” strategy,

then 15.8 percent are uninsured.

6

The standard errors of the mean for

medical services are: 32.49 (fully

insured) and 92.64 (uninsured).

7

These variables are a subset of those

chosen for the multinomial logit

model described later.

8

Evidence of plausibility comes from

Reise and Miller. Both authors find

that increasing income is related to

a lack of coverage at a decreasing

rate. Both authors’ specifications

are also forms of the Box-Cox

transformation. Reise, in effect,

assumes λ is zero: the natural log

is the appropriate transformation.

Miller assumes λ is 2: a squared

term is appropriate. In this study λ

(1/8) is between these estimates.

9

Miller uses a dummy variable for

renters “as a proxy for wealth” (p.

8), and finds them more likely than

owners to lack full coverage (p. 24).

In this study a variable denotes

those who own their homes outright.

Paulin (1995) finds “outright” owners

spend 11 cents of each additional

dollar on health and personal care,

compared to 5 cents for mortgage and

rent payers. This may reflect a

wealth effect, or simply that

“outright” owners have more money

available to spend than mortgage

payers, ceteris paribus. 10Outlays are not normally

distributed. Using the mean would

raise predicted probabilities (table

3).

11The parameter estimate for income is statistically

significant at the 99.9 percent confidence level.

12In fact, ceteris paribus, only those

with no earners have a lower

predicted probability of full

coverage.