THE EFFECTS OF HEALTH INSURANCE STATUS ON CONSUMER SPENDING

Geoffrey D. Paulin, Wolf D. Weber, Bureau of Labor Statistics

Geoffrey D. Paulin, Rm. 3985, Bureau of Labor Statistics, 2 Massachusetts Ave., Washington, D.C. 20212

Key Words: Health insurance, Consumer spending

Note: This work is substantially abridged. For a

complete version, see Monthly Labor Review, March

1995, pp. 34-54. 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.

In 1993, the nation’s health care costs rose to $884.2

billion, up 7.8 percent from 1992. As a share of Gross

Domestic Product (GDP), this accounted for 13.9

percent (HHS 1994, p.1) compared to 5.9 percent of

GDP in 1965 (NCHS 1993, p. 161), the year in which

the federal government initiated Medicare and

Medicaid.

As health care costs and usage rose, much of the

burden of funding health care shifted to business and

government. The business share of health services and

supplies grew from 16 percent in 1965 to 28 percent in

1981, and remained fairly stable thereafter (Cowan and

McDonnell 1993, p. 229). The federal government’s

share, however, continued to grow (12.2 percent

average yearly increase for 1989-1993), and in 1993

comprised 31.7 percent of the national health care bill

(HHS 1994, table 1).

Consumer expenditures for health insurance

premiums also have increased recently, probably due,

in part, to the shifting of premium costs from employer

to employee. The Consumer Expenditure Interview

Survey (CE) shows that the percentage of families

reporting health insurance expenditures has risen

steadily from 55 percent in 1988 to 61 percent in 1993.

The CE also shows that premiums have increased from

39 percent of the average family’s health care spending

in 1988 to 45 percent in 1993. In actual dollars,

average premium spending rose 69 percent over this

period.

Jacobs and Shipp (1993) show that as a share of

current consumption,1

 out-of-pocket health care

expenditures peaked at 6.7 percent in 1960-61,

declined to 5.4 percent in 1972-73,2

 and rose to 5.7

percent in 1988-89. In 1993, it was 6.9 percent.

Rising health care prices, increased usage, changing

demographics, and perennial concerns about

government fiscal austerity continually spark debates

over health care funding. Households, which have

avoided much of the burden of the increase in health

care expenditures, are likely to pay a larger share in the

future. It is therefore important to examine household

expenditure patterns to assess the effect that a transfer

of health care costs to consumers may have.

Table I. Selected Characteristics by Insurance Status, 1993 CE Data

Fully Partially Not

Item Covered Covered Medicaid Covered

Sample size 13,394 2,399 1,793 3,291

Number of CUs (in 000's) 63,280 11,260 9,057 16,184

Age of reference person 51.4 45.0 45.0 37.0

Annual income1 $33,603 $34,770 $13,041 $21,294

Average number in CU:

 Persons 2.2 3.5 3.2 2.5

 Earners 1.3 2.0 0.8 1.4

 Children under 18 0.5 1.0 1.4 0.8

 Persons over 64 0.4 0.2 0.3 0.0

Percent distribution:

Age of reference person

 Under 25 4 7 14 19

 25-34 18 20 25 28

 35-64 49 62 42 51

 65-74 16 8 10 1

 75 and above 14 4 9 1

Income distribution

 1st quintile 14 10 55 31

 2nd quintile 18 16 26 25

 3rd quintile 21 23 11 20

 4th quintile 23 27 5 13

 5th quintile 24 23 3 11

Ethnicity-reference person

 Black 7 12 26 13

 Hispanic 4 10 17 13

 White and other 90 78 57 74

Educ. of reference person

 Less than H. S. diploma 18 22 50 24

 H. S grad/some college 54 60 46 58

 College graduate 28 18 4 18

Composition of CU

 Husband/Wife only 28 10 6 10

 Husband/Wife/Children 26 36 18 28

 Single parent 4 7 26 9

 Single person 33 0 20 37

 Other 8 47 30 16

Region of residence

 Northeast 22 17 21 15

 Midwest 26 25 23 23

 South 32 36 35 39

 West 19 22 21 23

Occup.-reference person

 Wage and Salary 63 77 37 76

 Prof., mgr., supervisor 27 25 4 18

 Tech., sales, clerical 15 19 8 15

 Service 5 8 8 15

 Blue collar and other 16 25 17 28

 Self employed 7 6 3 8

 Retired 24 10 14 3

 Out of labor force 5 7 47 13

1 Complete income reporters only
Several recent studies have used CE data to analyze

different aspects of health care. Miller (1990) and

Reise (1993) examine the probability of purchasing

health insurance. Rubin and Koelln (1993) test for

moral hazard and adverse selection. Rasell, Bernstein,

and Tang (1993) combine CE data with National

Medical Expenditure Survey data and find regressivity

in the distribution of health care expenditures by

income level. In this study, CE data are examined to

ascertain the relationship of detailed expenditures,

including out-of-pocket health care expenditures, to

demographic characteristics of families with distinctly

different health insurance status.

The Data. The data are for all families interviewed

between January and December 1993. The sample size

is 20,877 observations3

 which, when weighted,

represent about 100 million families.

Definitions of Health Insurance Status. Following

Miller and Reise, the fully insured include families

whose sum of members covered by each insurance

policy4

 is equal to or greater than the number of family

members. The partially insured include those families

whose number of members covered is less than the

total number of members. Medicaid recipients are

those families with at least one member receiving

Medicaid, regardless of what other policies they may

have. Families that report no policies, policies that

covered only someone outside the consumer unit (such

as a child at school), or limited coverage policies5

 are

uninsured.

Demographic Characteristics. The CE also gathers

information on demographic characteristics (BLS

1995). Table I presents selected characteristics by

health insurance status. Characteristics either refer to

the family as a whole, e.g., income before taxes,6

 or to

the reference person,7

 e.g., age.

Expenditure shares. One way to examine the data is

to search for relationships described by Ernst Engel.

In 1857, Engel made his famous proposal that as

income increases, the share of income spent on food

decreases (Graham et al., 1972). The principle still

holds true when shares of total expenditures, rather

than income, are examined. In table II, total

expenditures are used as a proxy for income because

expenditures depend not only on current income, but

also on past as well as expected future income. This

relates to the “permanent income hypothesis”

(Friedman 1957). Furthermore, because all families

report total expenditures, but not all report income, we

do not need to restrict the sample to include only

complete income reporters.

Table II shows that in 1993, total expenditures of

both the partially and the fully insured groups are

about $8,000 higher than those of the uninsured group,

and about $16,000 higher than those in the Medicaid

group. Among the four groups, the fully insured

allocate the smallest expenditure share (about onetenth of total expenditures) to food at home, while

Medicaid families allocate twice that share (about onefifth of total expenditures). This example of an Engel

relationship holds for a number of expenditure

categories when a comparison between the higher and

lower income groups is made.

Table II: Selected Average Annual Expenditures and Budget

Shares

 Fully Partially Not

Expenditure Covered Covered Medicaid Covered

Total Expenditures $30,372 $31,008 $14,967 $22,492

 Share of total 100% 100% 100% 100%

Food at Home ($’s) 3,192 3,908 3,080 2,904

 Share of total 10.5% 12.6% 20.6% 12.9%

Housing ($’s) 9,120 9,276 5,532 7,264

 Share of total 30.0% 29.9% 36.9% 32.3%

Apparel/Services ($’s) 1,396 1,460 728 1,048

 Share of total 4.6% 4.7% 4.9% 4.7%

Transportation ($’s) 5,480 6,220 2,456 4,160

 Share of total 18.0% 20.1% 16.4% 18.5%

Health Care ($’s) 2,064 1,628 544 664

 Share of total 6.8% 5.3% 3.6% 2.9%

Recreation ($’s) 3,908 3,308 1,104 2,624

 Share of total 12.9% 10.7% 7.4% 11.7%

For fully insured families housing comprises the

largest expenditure, accounting for three-tenths of total

expenditures. Expenditure shares for the next largest

major expenditures--transportation (18 percent), other

expenditures (15 percent), and recreational goods and

services (13 percent)--account for substantially smaller

portions of total expenditures. Apparel and services

accounts for about 5 percent of the budget, a share that

is similar regardless of insurance status.

The partially insured group allocates its budget in a

similar fashion to those that have full insurance

coverage. Food at home and transportation shares for

this group are, however, slightly larger than those of

the fully insured group. This is probably the case

because families that are partially insured have, on

average, more family members and income earners

than do families that are fully insured. Also, all subcomponent shares in the recreation category, which

account for about one-tenth of total expenditures for

the partially insured, are slightly lower than those of

the fully insured.

An analysis of the Medicaid group’s expenditure

shares shows a definite Engel relationship for several

categories. This group devotes larger shares than any

other group to housing (37 percent) and food at home8
(21 percent). As noted earlier, their food at home

share is about twice that of fully insured families.

Expenditure shares for recreation and for all other

expenditures are substantially lower for Medicaid

recipients.

The expenditure shares of the uninsured group have a

similar pattern to those families that are fully and

partially insured. The share for housing is larger for

the uninsured than it is for the fully and partially

insured, but smaller than it is for the Medicaid group.

They allocate larger shares for tobacco and alcohol,

food away from home, entertainment, and education

than most or all of the other groups. This may reflect

the greater proportion of single and younger persons in

the uninsured group.

Table III: Average Annual Health Care Expenditures and Budget

Shares

 Fully Partially Not

Expenditure Covered Covered Medicaid Covered

Total Health Care $2,064 $1,628 $544 $664

 Share of health care 100% 100% 100% 100%

Health Insurance ($’s) 1,044 700 280 168

 Share of total 50.6% 43.0% 51.5% 25.4%

Medical Services ($’s) 676 668 152 360

 Share of total 32.7% 41.1% 27.8% 54.6%

Prescription Drugs ($’s) 344 260 112 132

 Share of total 16.7% 15.9% 20.7% 20.0%

Health care spending. Health care expenditures

(table III) are composed of health insurance, medical

services, and prescription drugs and medical supplies.

Fully insured families allocate the largest share to

health care (7 percent), with insurance accounting for

about one-half of health care expenditures for this

group. The health care expenditure share for the

partially insured is lower than that of the fully insured

group due mainly to a smaller share of total

expenditures allocated to health insurance. Health care

consumes a relatively small portion of the Medicaid

group’s budget, due to government subsidization. The

uninsured group allocates the smallest share for health

care (3 percent). The typically younger members of

this group may, on average, be in better health than

members of other groups. Also, because of their age,

they may be less risk averse and may hold entry level

jobs that limit access to employer sponsored health

insurance.

Regressions. Although shares analysis provides

some insight into spending patterns, by itself it is not

conclusive; it makes some attempt to control for

income, but does not control for other characteristics.

For example, table II shows that partially insured

families spend a larger share of income on food at

home than fully insured families, even though they

have slightly (though not statistically significantly)

more income. This apparent violation of Engel’s

proposition may be due to the larger average family

size of those with partial insurance.

Regressions allow comparisons of expenditures

across insurance groups given that characteristics are

held constant. This way differences observed in

expenditure patterns are more likely related to

insurance status than to differences in other

characteristics. Furthermore, they provide insight into

the potential change in expenditure patterns if health

care costs are increasingly borne by the consumer.

Dependent variables. The dependent variables are

food at home, housing (less other lodging), apparel and

services, transportation (less trips), and recreation and

related expenditures. Health care expenditures are

omitted because of the difficulty in adequately

modeling health care expenditures using CE data.

Model Specification. Each model is specified as

follows:

Y = af + ajDj + bifXif + bijDjXij + ej

where

Y is the expenditure to be predicted;

ak is a parameter estimate for insurance group k (fully

insured, partially insured, Medicaid, uninsured);

Dj is a dummy variable describing insurance group j;

bik is a vector of parameter estimates;

Xik is a vector of demographic characteristics.

Regressions are run using Weighted Least Squares

(WLS) to correct for heteroskedasticity.

Income and Expenditures. Including an income

variable is extremely important for two reasons. First,

levels of detailed expenditures in general are expected

to increase as incomes increase. Second, if health care

costs are shifted onto the consumer in the future, as is

possible, then each dollar of total expenditures that the

consumer spends on health care diminishes the amount

of total expenditures available to spend on other items,

if all else is equal. Including an income variable

allows the researcher to estimate both marginal

propesity to consume (i.e., the portion of each

additional dollar of income that the consumer will

allocate to a selected expenditure), and the percent

change in each expenditure given a one percent change

in income (i.e., income elasticity). It is important to

note that a dollar-for-dollar shift of health care costs

onto the consumer does not necessarily imply that

consumers will automatically increase their health care

expenditures by the same level. For example, a family

may have a policy with a $200 deductible for doctor’s

visits, which it reaches or exceeds every year. If the

deductible is raised to $300, the family may still choose

to pay only $200 in out-of-pocket expenditures by not

visiting the doctor for minor ailments. Nevertheless, if
for any reason the family now does use more than $200

in services, its members have less money to allocate to

food, housing, and other expenditures. Since changes

in health care costs are expected to affect total

expenditures for families with different levels of

insurance in different ways, it is important to analyze

the relationship of expenditures to income by insurance

group.9

 For the reasons described earlier, total

expenditures are used in the regressions as a proxy for

permanent income.10

Results. Table IV shows the income parameter

estimates from each regression. For convenience,

“raw” parameter estimates are summed before

presenting in the table.

Table IV: Parameter Estimates: Total Expenditures by Insurance

Status

Fully Partially Not

Regression Covered Covered Medicaid Covered

Food at Home 0.061* 0.074** 0.123** 0.084**

Housing (Owners) 0.285* 0.278 0.287 0.315**

 Renters 0.320* 0.268** 0.429** 0.301

 No Mortgage 0.236* 0.184** 0.242 0.247

Apparel/Services 0.049* 0.056** 0.056** 0.050

Transportation 0.185* 0.228** 0.166** 0.168**

Recreation 0.145* 0.123** 0.089** 0.142

* Parameter estimate is significantly different from zero.

** Parameter estimate is significantly different from fully covered

group’s.

Food at home. Each insurance group has a positive,

statistically significant coefficient for permanent

income in the food at home regression. This means

that given an extra dollar, all families are predicted to

increase their food at home expenditures, but that fully

insured families would increase them the least (six

cents), followed by the partially insured (seven cents),

the uninsured (eight cents) and Medicaid families (12

cents). The income elasticities implied by these figures

are discussed later.

Housing (less other lodging). The relationship

between housing expenditures and permanent income

differs little across insurance groups, at least for

homeowners with mortgages. All are predicted to

spend about 28 cents out of every additional dollar on

housing, except for uninsured families. These families

are predicted to spend three cents more (or 31 cents)

out of every additional dollar on housing.

When the mortgage is paid off, the marginal

propensity to consume housing declines. For the fully

insured, the decrease is nearly five cents. For the

partially insured the decrease is nearly double--9 cents,

while for uninsured families it appears to decrease by

about 2 cents, though the parameter estimate for the

interaction of owning without a mortgage and total

expenditures is not statistically significant for them.

Renters exhibit very different patterns. Fully insured

renters have a predicted marginal propensity to

consume housing: about 32 cents. Partially insured

renters are more similar to homeowners, with a

predicted expenditure of 27 cents per dollar. Medicaid

renters have the largest marginal propensity to

consume housing--42 cents. Uninsured renters are not

significantly different from fully insured renters.

Apparel and services. The marginal propensity to

consume apparel and services is estimated to be

between 5 and 6 cents regardless of insurance status.

Transportation (less trips). Transportation is

strongly related to income regardless of insurance

group. Partially insured families are predicted to spend

the largest share of an additional dollar--nearly 23

cents--on transportation, followed by the fully insured

(18 cents), the uninsured and Medicaid families (17

cents each).

Recreation and related expenditures. Recreation and

related expenditures consume about one-seventh of

every additional dollar (14.5 cents) for the fully

insured. The coefficients for the fully insured and the

uninsured are not statistically significantly different.

Partially insured families are predicted to dedicate a

slightly smaller fraction (12 cents) of their additional

dollars to recreation and related expenditures, with

Medicaid families spending the least (9 cents) of every

additional dollar on these items.

Income Elasticities. Although the regression results

show how expenditures are predicted to change given

an increase of one dollar to permanent income, how

are expenditures predicted to change given an increase

of one percent in permanent income? To answer this

question, income elasticities are estimated using

regression and other results.

An elasticity can be described as the percent change

in one factor given a one percent increase in another

factor. For example, table V shows the income

elasticity of food at home for the fully insured is 0.58.

This means that given a one percent increase in

income, the average fully insured family is predicted to

increase its expenditures on food at home by 0.58

percent. If the income elasticity of a good or service is

less than one, it is called “inelastic.” If it is exactly

one, it is called “unitary elastic.” If it is greater than

one, it is called “elastic.” Expenditures with an

income elasticity that is positive but less than one are

often called “necessities,” while those with elasticities

greater than one are often called “luxuries.”

The calculation of elasticities is straightforward. In

general, the formula for an elasticity (ηY,I) is:

ηY,I = ∂Y/∂I*(I/Y)

where

Y is an expenditure (such as food at home)

I is permanent income.
Elasticities are presented in two tables. In table V

elasticities are shown for average families in each

insurance group. That is, values are computed using

the income parameter estimate for the fully insured

multiplied by the inverse of the expenditure share for

the fully insured. Table VI shows what the elasticity is

predicted to be if income and expenditures are held

constant across groups. That is, the parameter

estimates for income are allowed to vary across groups,

but the inverse share is calculated from the “all

consumer units” column in table II. Because the

elasticities in table VI are standardized for income and

expenditures, it is possible to test differences across

insurance groups for statistical significance.

Table V: Income Elasticities by Insurance Status, Part I

 Fully Partially Not

Expenditure Covered Covered Medicaid Covered

Food at Home 0.58 0.59 0.60 0.65

Housing 0.95 0.93 0.78 0.98

Apparel/Services 1.07 1.19 1.14 1.06

Transportation 1.03 1.13 1.01 0.91

Recreation 1.12 1.15 1.20 1.21

η = ∂Y/∂I*I/Y, Where I/Y = Average for Each Insurance Group

NOTE: Differences in elasticities across insurance groups are not

tested for statistical significance.

Income elasticities do not vary greatly across groups

for most items. In fact, if a good is inelastic for one

insurance group, it is inelastic for all groups, and what

is elastic for one is elastic for all (except for

transportation for the uninsured). Also, housing is

notably more inelastic for Medicaid recipients than the

other groups, which have nearly identical elasticities.

But each of these differences is fairly easy to explain.

Because the average Medicaid recipient has less

income than the average member of any other group, it

is not surprising that housing is more a “necessity” for

this group than the others. At any rate, the order of

elasticities is the same for each group, regardless of

insurance status. That is, food at home is the least

elastic good, followed by housing, transportation,

apparel, and recreation and related expenditures. The

general interpretation of the results in table V is that

given a certain income, most families, regardless of

insurance group, will “settle” at the point where the

average family in one insurance group is about as

“sensitive” to a one percent increase in income as the

average family in any other group for any particular

expenditure item.

More intriguing are the results shown in table VI.

Each family is treated as if it had the same level of

expenditures and permanent income as the average

member of the population. Therefore, any differences

in elasticity must be due to differences in the marginal

propensity to consume each item. Therefore,

differences in table VI more likely reflect differences in

tastes or other less quantifiable factors that differ by

insurance group.

Table VI: Income Elasticities by Insurance Status, Part II

 Fully Partially Not

Expenditure Covered Covered Medicaid Covered

Food at Home 0.52* 0.63** 1.04** 0.71**

Housing 0.93* 0.91 0.94 1.03**

Apparel/Services 1.07* 1.22** 1.22** 1.09

Transportation 1.01* 1.25** 0.91* 0.92**

Recreation 1.19* 1.01** 0.73** 1.17

η = ∂Y/∂I*I/Y, Where I/Y is the Average for All Consumer Units

* Parameter estimate is significantly different from zero.

** Parameter estimate is significantly different from fully covered

group’s.

When all families are given average income and

expenditures, some of the results are noteworthy. For

example, for Medicaid families, food at home has an

income elasticity exceeding one, but for recreation and

related goods the income elasticity is less than one.

This may be because Medicaid families have low

incomes, and are used to “doing without,” even to the

point of cutting back as much as possible on the most

basic necessities, such as food. Given extra income,

therefore, they are more likely to purchase more (or

better quality) food than to spend more for recreation.

Also of interest is that the income elasticities of

housing and apparel do not change much by insurance

group even when everyone is given the same income

and expenditure level, although the elasticity for

housing for Medicaid families moves more in line with

the other groups.

Conclusions. Many recent developments related to

health care have made it a subject of much discussion.

Prices have risen substantially in recent years, and the

share of total expenditures devoted to health care is

high by historical standards. Rising prices have

evidently caused changes in insurance availability, as

employers have either reduced their contribution to

health insurance or offered programs with higher

deductibles as a way to cut costs. Also, as evidence of

rising health care prices and reduced employer

contributions to employee insurance premiums, out-ofpocket expenditures for health care have risen recently,

in conjunction with increased reporting of expenditures

on health insurance premiums.

While other recent studies using CE data have

examined probability of insurance coverage, tested for

moral hazard and adverse selection in insurance

markets, and analyzed the distribution of family health

care expenditures by income level, this study

investigates the relationship of health care

expenditures to other items in the consumer budget.

Four distinct groups are studied: the fully insured,

partially insured, Medicaid recipients, and the

uninsured. First, demographic characteristics of these
groups are compared and expenditure shares for each

of the groups are derived. Next, Engel relationships

and the components of health care expenditures are

discussed. Finally, several expenditure categories are

regressed on demographic characteristics by insurance

group, to ascertain whether relationships between

expenditures and demographics differ across insurance

group. This analysis is extended to examine

differences in income elasticities for expenditures by

insurance groups. The results indicate that there are

clear differences in consumer spending patterns across

insurance groups, and that these are not limited to

differences in health care expenditures alone.

Selected References

Bannock, Graham, Ron Baxter, and R. Rees (1972), A

Dictionary of Economics, Harmondsworth,

Middlesex, England: Penguin Books Ltd., p. 140.

Cowan, Cathy A. and Patricia A. McDonnell (1993),

“Business, Households, and Governments: Health

Spending 1991.” Health Care Financing Review,

14(3) Spring, p. 229.

Friedman, Milton (1957), A Theory of the

Consumption Function. (Princeton, Princeton

University Press for National Bureau of Economic

Research.), p 221.

Jacobs, Eva and Stephanie Shipp (1993), “A History of

the U.S. Consumer Expenditure Survey: 1935-36 to

1988-89.” Journal of Economic and Social

Measurement, 19, pp. 59-96.

Kennedy, Peter (1992), A Guide to Econometrics,

Third Edition, Cambridge, MA: The MIT Press:

157-158.

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

Medically Uninsured Using the 1987 Consumer

Expenditure Survey.” Bureau of Labor Statistics

Working Paper (206), October.

National Center for Health Statistics (NCHS 1993).

Health, United States 1992, Hyattsville, MD: Public

Health Service, 1993, p.161.

Paulin, Geoffrey D. and David L. Ferraro (1994),

“Imputing Income in the Consumer Expenditure

Survey,” Monthly Labor Review, December.

Rasell, Edith, Jared Bernstein, and Kainan Tang

(1993), “The Impact of Health Care Financing on

Family Budgets.” Challenge, November-December,

pp. 12-20.

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.

U.S. Department of Health and Human Services (HHS

1994), HHS News, November.

U.S. Department of Labor, Bureau of Labor Statistics

(BLS 1995), Consumer Expenditure Survey, 1992-

1993: Bulletin 2462, September.

 

1

Total expenditures less gifts outside the family, cash

contributions, and personal insurance and pensions.

2

Some of the decline is undoubtedly due to the advent

of Medicare and Medicaid.

3

Because of the rotating sample, not all observations

represent unique families.

4

In each case Medicare is counted as a policy.

5

E.g., dental only, or policies that only cover children

injured during school-related athletic activities.

6

Data are for complete reporters only--generally

defined as consumer units that provide values for at

least one major source of income. However, they do

not necessarily provide a full accounting of income.

The Division of Consumer Expenditure Surveys has

plans to impute values for missing income. For more

information, see Paulin and Ferraro (1994).

7

The first person mentioned when the respondent is

asked to “Start with the name of the person or one of

the persons who owns or rents this home.”

8

The value of food stamps is included in both the

income and the expenditure tabulations in the CE.

9

Another important measure is the cross-expenditure

elasticity of substitution of health care and other

expenditures. However, it is not immediately clear

how such an analysis could be conducted; no

information on deductibles is collected in the CE.

Deductibles result in kinked budget constraint that

cannot be controlled for in the regressions. Therefore,

no attempt to calculate cross-elasticity is made.

10Although simultaneous equations bias may exist

when the detailed expenditure is a large share of total

expenditures, Kennedy (1992) provides reasons why

the problem may not be serious when OLS is used.