It is a uni-versal, tax-® nanced public health insurance system which provides a basic standard of care in public hospitals for all Australians and subsidizes the cost of medical care by
Trang 1Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=raec20
Applied Economics
ISSN: 0003-6846 (Print) 1466-4283 (Online) Journal homepage: http://www.tandfonline.com/loi/raec20
The determinants of the demand for private
health insurance under Medicare
Sandra Hopkins & Michael P Kidd
To cite this article: Sandra Hopkins & Michael P Kidd (1996) The determinants of the demand for private health insurance under Medicare, Applied Economics, 28:12, 1623-1632, DOI: 10.1080/000368496327598
To link to this article: https://doi.org/10.1080/000368496327598
Published online: 01 Oct 2010
Submit your article to this journal
Article views: 146
View related articles
Citing articles: 38 View citing articles
Trang 21The hospital and medical bene® ts of the public health insurance system represent the main features of Medicare There are a number of additional bene® ts which are not mentioned here for example, optometry examinations
2`Basic’ or `supplementary’ private insurance supplements the hospital component of Medicare It allows patients to have choice of their own doctor in public hospitals and subsidizes the cost of private hospital care `Ancillary’ private health insurance covers the basic requirements of private hospital care in addition to subsidizing ancillary medical care sought outside the public hospital system such as physiotherapy, and dental care
3Medibank was introduced by the Labour Government in 1975 and provided universal cover for free public hospital treatment and medical bene® ts were covered at 85% of the government recommended schedule fee with a maximum $5 gap Medibank was progressively
dismantled when the LiberalÐ National Party Government came to power in 1975 From September 1981 until the introduction of
Medicare in 1984, Commonwealth government support for medical and hospital costs was con® ned to tax rebates for basic health insurance premiums and a Commonwealth subsidy towards medical costs covered by private health insurance Pensioners and the disadvantaged were covered by a government health insurance scheme
Applied Economics , 1996, 28 , 1623Ð 1632
health insurance under Medicare
*School of Economics and Finance , Curtin University of Technology, GPO Box U 1987,
Perth W A 6001 and §Department of Economics , University of Tasmania, GPO Box
Aberdeen , Dunbar Street, Old Aberdeen, AB 24 3 QY
Since the introduction of Medicare in 1984, the proportion of the Australian
popula-tion with private health insurance has declined considerably Insurance for health care
consumption is compulsory for the public health sector but optional for the private
health sector In this paper, we explore a number of important issues in the demand
for private health insurance in Australia The socio-economic variables which in¯
u-ence demand are examined using a binary logit model A number of simulations are
performed to highlight the in¯ uence and relative importance of various characteristics
such as age, income, health status and geographical location on demand A number of
important policy issues in the private health insurance market are highlighted First,
evidence is provided of adverse selection in the private health insurance pool, second,
the notion of the wealthy uninsured is refuted, and ® nally it is con® rmed that there are
signi® cant interstate di erences in the demand for private health insurance
I IN TROD UCTI ON
Medicare was established in Australia in 1984 It is a
uni-versal, tax-® nanced public health insurance system which
provides a basic standard of care in public hospitals for all
Australians and subsidizes the cost of medical care by
suggests that a private health insurance policy is an
imper-fect substitute for Medicare which enables a policyholder to
have access to alternative suppliers for the same type of
treatment or care which is available under Medicare and
case, private health insurers compete with the public health insurer as well as each other; in the second case, the private health insurers compete with one another only The pur-chase of private insurance by an individual does not change the tax liability to the public health insurance system and therefore the private insurance policyholder has access to both the public and private health care sectors
Prior to 1984, all health insurance was private and vol-untary and consequently, 64% of the Australian population
Medicare in 1984, this percentage declined to 50 By 1990, the percentage had declined even further to 43.1 (Willcox,
Trang 34Approximately, 10% of the British population has private health insurance The considerably smaller proportion of the British population with private health insurance re¯ ects a long-established and stable public health insurance system, the National Health Service, and therefore a small private sector and the use of risk-rating rather than community-rating in premium setting by British private insurance companies
5Hospital output is generally considered to be a Lancastrian good (Rice, 1966) One of the attributes is the `hotel’ services or amenities The amenities quality tends to vary between the public and private sector in terms of the general physical environment The second attribute is the medical treatment received This characteristic is assumed to be constant between the private and public hospitals as the medical sta work in both sectors and the medical, nursing and paramedical sta are trained in the public sector
1991) The decline in the demand for private health
insur-ance raises important questions as to the reason why people
purchase private health insurance when public health
insur-ance is compulsory Willcox (1991) suggests that the three
main considerations in the decision-making process about
private health insurance are health status, cost and
inad-equacy of the public health insurance coverage
Eco-nometric analyses of the determinants of private health
insurance in both Australia (Ngui et al., 1990; and Cameron
and Trivedi, 1991) and the United Kingdom (Propper, 1989)
as well as descriptive analyses of the Australian market
(Australian Bureau of Statistics, 1990; Willcox, 1991) have
nominated a consistent set of socio-economic and
demo-graphic factors as the major in¯ uences on demand A similar
set of determinants in Australia and the United Kingdom is
important as there are a number of similarities between the
two countries in the public funding and provision of health
care The United Kingdom like Australia, has a universal,
tax-® nanced public health insurance system where excess
demand is rationed in the public system by queuing,
In the paper, we explore a number of important issues in
the demand for private health insurance in Australia The
® rst contribution of this paper is an examination in an
econometric framework of the relationship between the
demand for private health insurance and the determinants
of the demand The econometric analysis enables the
explo-ration of not only what is important in determining demand
but also the relative importance of competing determinants
In so doing, we are able to address the important
socio-economic di erences in the demand for health insurance,
including the in¯ uence of spousal characteristics on the
insurance decision
We also consider two key issues in the health insurance
policy debate The ® rst, the relationship between private health
insurance and income, is important in managing the queue to
public sector health facilities The second key policy issue, the
relationship between private insurance and health status, is
important in terms of the long-term viability of the private
health insurance market Government regulation prohibits
private health insurance companies from discriminating
stat-istically between high and low-risk groups Low-risk groups
tend over time to drop out of the market as they perceive that
the premium is in excess of their probability of loss
The third contribution is a consideration of the regional
di erences in the demand for private health insurance The
total Australian ® gures hide the considerable interstate vari-ation in the demand for private health insurance For example, in 1990, approximately 31.6% of the Queensland population had private health insurance compared with 50.4% in Victoria
Our analysis updates and extends the papers by Ngui
et al (1990) and Cameron and Trivedi (1991) Ngui et al.
used 1983 Australian Health Survey data and socio-eco-nomic and health status variables to explain the insurance
demand decision Cameron and Trivedi used the 1977Ð 78
Australian Health Survey and the 1983 Australian Health Insurance Survey Their data thus, covered two separate regimes of health insurance management Both papers con-sidered the determinants of health insurance choice pre-Medicare Our paper uses the 1989 Health Survey data and also extends their analyses in a number of important ways First, we include geographical location variables as impor-tant determinants of the health insurance decision in addi-tion to individual health status and socio-economic vari-ables We also incorporate spousal variables in additional to individual health status characteristics to emphasize the importance of family rather than just individual character-istics in the insurance decision Finally, in addition to the logit analysis, we undertake a number of simulations which provide important and interesting additional information
on the magnitude of the impact of a change in one of the explanatory variables on the insurance decision
II EX PECTED U TI LITY G AIN FR OM
PR IVAT E H EALT H IN SU RAN CE The theory of insurance has been applied extensively to the health insurance decision (Arrow, 1963; Feldstein, 1973) Under conditions of consumer rationality and risk aversity, the decision to purchase insurance is made on the basis of expected utility gain Individuals or family groups weigh up both the direct and indirect costs of the insurance premium against the expected bene® ts from a private health insurance state Both the direct and indirect costs and bene® ts are discussed below One bene® t of using the private sector for treatment rather than the public sector, which is di cult to quantify and therefore model, is the quality di erences
The purchase of private insurance confers access to both the private and public health sector, whereas non-purchase
Trang 46Of course, it is possible that some individuals or family groups may self-insure In this case, they consume private sector facilities but do not have private health insurance The data set does not allow us to identify cases of self-insurance
7Private health insurance in Australia covers the use of private health facilities as well as the admission by doctor of one’s own choice to
a public hospital
8Ben-Akiva and Lerman (1985) suggest that coe cient estimates from a restricted choice set are uniformly greater in absolute value than those from the full choice set The interpretation of the results from the empirical analysis in Table 1 focuses on the sign and signi® cance of the coe cient values only and therefore, this issue is not considered to be a problem here
other types of insurance, the expected loss for which the
insurance is purchased is only incurred if one chooses to
consume private sector resources The consumption and
insurance decisions are inextricably bound together Private
health insurance, thus, removes the uncertainty of a ®
nan-cial loss in the event that both ill-health occurs and the
Di erent individuals or families put di erent weight on
the costs and bene® ts of private insurance purchase and
therefore of consuming private sector services For some,
the choice set is restricted not because of income but
be-cause they would not contemplate the consumption of the
services which are made available under private insurance
and would possibly derive disutility rather than utility from
their consumption The motivation for their restricted
choice is possibly ideological Another group of people who
may not actually make a choice between private and public
health insurance are employees for whom private health
insurance membership is part of their employment
con-tract.8
Expected utility gains from the purchase of private health
insurance are in the ® rst instance related to expected
medi-cal need Some individuals face greater risk vulnerability
than others due to their age, sex, pre-existing health status
and marital status The probable distribution of future
health states is based on present and past health states
Medical need, generally, increases with age and is also
higher for female gender Additionally, for many people, the
purchase of private health insurance is a family rather than
an individual decision, therefore, the demographic
charac-teristics of the family unit are important An example is the
impact that a dependent child has on the private health
insurance decision
A second group of factors which a ects the health
insur-ance decision is what van de Ven and van Praag (1981) call
material well-being The direct cost of private health
insur-ance is the insurinsur-ance premium itself An additional cost is
the often considerable copayments which apply for the use
of private sector facilities or doctor of your own choice in
a public hospital There are two sources of utility gain
These are the direct bene® t associated with the
insurance-subsidized access to private sector treatment and the
in-direct bene® t of bypassing the public sector queue, which is
largely for elective surgery, by entering the private sector
The rationing of services in the public sector is done by
either actual waiting time, for example, to consult with
a doctor in an outpatients clinic or by queuing for service via a waiting list Most of the waiting time in the public sector is of the second type Both forms of time rationing, however, impose a cost on the individual and his/her family due to the likelihood that the medical problem and/or the associated discomfort may worsen during the waiting period and also due to the uncertainty of the timing of the
medical intervention Ceteris paribus, the expected utility
gain from bypassing the public sector queue would be greatest for people who place the highest value on the time
of the individual as well as that of the family unit The value
of time is most probably higher for those who are employed rather than unemployed or not in the labour force, and those on higher incomes rather than lower incomes The third group of factors, unlike expected medical need and material wellbeing, is speci® cally Australian and relates
to the interstate di erences in the mix of private and public services The federal system in Australia means that the States and Territories have some autonomy over the organ-ization and delivery of health services, but the overall struc-ture is determined by the Commonwealth Government The autonomy of the States and Territories, however, has varied with changes in health ® nancing arrangements and the preferences of the political party in Government at the Federal level The autonomy has certainly diminished with the increasing focus on a national insurer, however the geographical di erences in quality and services available in the public sector have persisted Historical di erences in the interstate public provision of health services has led to di erences in the size and mix of services o ered by the private sector For example, the private bed to population ratio varies from 0.5 beds per 1000 in the Australian Capital Territory to 1.7 beds per 1000 in South Australia (National Health Strategy, 1991) The implication of the interstate di erences is that the queuing problem is more acute in some locations than others Therefore, by implication the expected utility gain from holding private health insurance
is greater in some locations than others
II I D ESCRIPTI ON OF THE DATA AN D SAM PLE DER IVATI ON
The focus of the current analysis is the decision to purchase
private health insurance The data set utilized is the 1989Ð 90
Demand for private insurance under Medicare 1625
Trang 59This statistic of 51.7% contrasts with the statistic of 43% reported by Willcox (1991) which represents the percentage of the Australian population who have private health insurance
1 0The data set follows the standard Australian Bureau of Statistics practice of treating the male as the head of the household
1 1The proportion of income units in which either the husband or the wife has individual private cover is very small Thus, it is unlikely that the econometric results are sensitive to the current de® nition of the dependent variable Some preliminary speci® cation checks con® rmed this result
1 2There were 25 619 heads and 12 726 spouses of whom 47% and 60.5%, respectively, had private health insurance Since spousal information was merged with head of household information, the initial sample size was 25 619
1 3The two main types of private health insurance Ð basic or supplementary Ð are discussed in footnote 2.
National Health Survey This is a representative sample of
the Australian population which provides detailed
informa-tion on a series of personal characteristics including age,
education, state of residency, income, health status,
con-sumption of health services and private health insurance
status
The raw data comprised 54 241 individual records In the
initial sample, 37.7% have private health insurance and
35.2% do not The remainder of the initial sample consist of
individuals under the age of 15 who are not classi® ed with
respect to insurance status After deletion of these
indi-viduals from the sample, 51.7% of eligible insurance holders
under the age of 15 would be covered by the insurance
policy of their parents and thus the higher percentage would
appear to be appropriate
Although the survey record data are at the individual
level, individuals can be matched in terms of whether they
belong to the same income unit and whether they are related
by marriage (either de facto or de jure) An income unit is
de® ned as consisting of a head of household plus his spouse
and persons in the same family who are assumed to be
dependent on the head including children under the age of
link individual records is important as the decision to
pur-chase private insurance is likely to be a joint decision This is
con® rmed by the preponderance of family insurance policies
rather than individual insurance policies held by
house-holds In the empirical analysis reported below, the income
unit is the unit of observation and the head of the household
is the representative decision maker The con® guration of
the dependent variable recognizes that insurance purchase
is a joint decision by classifying an income unit as having
insurance if either the head or the spouse indicated that they
The ® rst step in de® ning the sample was to select heads of
imposed upon this sample The ® rst and most important
restriction was to delete those households in which either
the head or the spouse have a health care card This
restric-tion led to the delerestric-tion of 7306 households Other delerestric-tions
from the sample were for cases where the head of the
household was aged 18 or less, the head of household was
still at school and if any information relating to one of the
key variables was missing
The empirical analysis focuses on the private health insur-ance decision We were unable, however, to distinguish
pos-sible that the determinants of the decision to purchase private health insurance varies across type of policy In the public release sample of the National Health Survey, how-ever, approximately 50% of records are uncoded with respect to the type of insurance policy purchased Further-more, the type of policy is generally considered to be a secondary rather than a primary choice
IV EC ON OM ETRIC ESTIM ATES Estimates of the model of the probability of the purchase of private health insurance are presented in Table 1 The model is estimated using a maximum likelihood logit es-timator The speci® cation is based on the assumption that
an individual’s or household’s decision to purchase insur-ance is determined by the expected utility gain In the analysis reported in Table 1, the variables attempt to cap-ture the three major determinants of the insurance purchase decision of health status, material wellbeing and geographi-cal location The table contains parameter estimates for two separate speci® cations of the econometric model The ® rst
speci® cation is similar to that of Ngui et al in that the focus
is on the characteristics of the head of the income unit only The second speci® cation, in which the ® rst is nested, cludes variables which capture spousal income, and in-formation on spousal smoking, hospitalization and doctor visits These results are reported in the last two columns of Table 1
The pattern of coe cient signs and the signi® cance of the variables does not vary signi® cantly across the two
speci-® cations A likelihood ratio test of the null hypothesis underlying the nested speci® cation in column one gives a chi-squared statistic of 380 Thus, the null hypothesis that the coe cients on spousal characteristics are jointly zero is rejected This indicates that the second model speci® cation which includes the role of the spousal variables is the pre-ferred model Details of the variables and the de® ned default group in the model are reported in the Appendix
The usual partial derivative interpretation is not appro-priate in the discussion of the coe cient estimates In the binary model, however, the marginal e ect of a change in
Trang 6Table 1 L ogit estimates for demand for private health insurance
Coe cient Asymptotic Coe cient Asymptotic Variable estimate standard error estimate standard error
dr v isit1 0.349** 0.059 0.327** 0.059
dr v isit2 0.336** 0.049 0.323** 0.049
dr v isit3 0.323** 0.055 0.309** 0.055
dr v isit4 0.251** 0.057 0.248** 0.057
** indicates p< 0.001
* indicates p< 0.005
Summary statistics
Number of observations=16 472
- 2[L (0)- L ( b )]= 5402 (50.9) 5782 (50.9)
- 2[L (b a)- L ( b b)]= 380 (18.5)
where L (0) and L ( b ) are the log likelihood value for a model with an intercept only and the
intercept and all covariates respectively L ( b a) and L ( b b) are the log likelihood values for the
® rst set of regressors which has some restrictions and the second set of regressors, respectively
Demand for private insurance under Medicare 1627
Trang 71 4Marginal e ects are not reported since almost all variables are qualitative (refer to Greene, 1990).
1 5It is possible that whether the individual has been hospitalized or not may be a function of the private insurance decision The problems associated with possible endogeneity are ignored here
a speci® c variable is simply a positive constant times the
relevant coe cient Thus, the sign and relative magnitude of
The determinants of the insurance decision are grouped
into three as outlined above The ® rst group capture the
in¯ uence of expected medical need The inclusion of the age
variables is based on the hypothesis that medical need
increases with age Van de Ven and van Praag (1981) note,
however, that age is both an indicator of perceived medical
need and the stock of wealth Young individuals or families
are generally relatively less well-o but healthier So too are
young individuals or couples The probability of insurance
associated with the four age variables beyond the age of 35
con® rms the hypothesis that older people are less healthy
and therefore, more likely to purchase private health
insur-ance than younger people
The doctor consultation and hospitalization variables are
proxies for expected medical consumption The four doctor
visit variables reported represent di erent time periods since
the last doctor consultation, ranging in time from less than
two weeks to 12 months The results in Table 1 show that
the more recent the last doctor visit, the higher the
probabil-ity of purchase of private health insurance Similarly, if the
individual had been admitted to hospital in the last 12
months, the likelihood of the individual being insured
private health insurance under the present regulation does
not include doctor consultations outside of hospitals, the
relationship between doctor visits and private health
insur-ance status is not subject to moral hazard It is reasonable,
therefore, to treat doctor visits as exogenously determined
The inclusion of a smoking variable may be seen as
a proxy for expected health consumption Alternatively, it
may be viewed as a proxy, in the absence of better
indi-cators, of risk aversion in the insurance decision The results
indicate that the probability of the purchase of private
insurance is lower for smokers, ceteris paribus Our result is
similar to that of Propper (1987) who interprets the negative
sign on the coe cient as evidence that less risk averse
individuals are less likely to purchase health insurance
Gender also plays an important role in the insurance
decision through its in¯ uence on expected medical
con-sumption Sindelar (1982), for example notes that most of
the higher demand for medical services by women may be
explained by increased need during the reproductive years
The results presented in Table 1 indicate that, ceteris
paribus, the probability of insurance is signi® cantly higher
for women
Family characteristics have an impact on expected
medi-cal need, but also a ect the insurance decision by changing
expected utility gain directly For example, the presence of
a spouse and/or dependent children may increase the risk
aversity of the decision-makers in the family unit Ngui et al.
(1990) suggest that the composition of the family unit is important in the demand for private health insurance deci-sion due to the impact that illness of one family member has
on the utility of other family members The marital status variable con® rms the importance of the family character-istics in the insurance decision The dependent child vari-able, however, is negative and signi® cant in the ® rst regres-sion only, indicating that dependent children decrease the probability of insurance There are two factors, in addition
to the interdependent utility mentioned above, that may be important in this result First, young children are, in most cases, members of young and healthy families and these factors reduce the probability of family insurance Further-more, Propper (1989) notes that it is probable that public sector treatment for children is viewed as no better or worse than private sector treatment Public hospitals in Australia, for example, have excellent specialized facilities and accom-modation for paediatric services
The second group of regressors are the material wellbeing variables of education and income Education is likely to have both a direct and indirect e ect on the private health insurance decision The direct e ect is related to the produc-tion funcproduc-tion attributes of educaproduc-tion in terms of the accumu-lation of health-related information and the appropriate combination of health inputs This view of the role of education in health decision-making has been well docu-mented by Grossman (1972) and Muurinen (1982) The implication is that not only is a better educated person likely to be healthier which would lower the probability of insurance, but also he/she is likely to be better informed about both the services available in the public hospital system and the bene® ts of joining a private health insurance fund The indirect e ect of education is its impact on in-come Education and income are generally positively corre-lated (van der Ven and Van Praag, 1981) Higher income generally decreases the opportunity cost associated with the purchase of private health insurance Overall, increases in both income and education would be expected to lead to an increase in the probability of insurance These results are borne out in the results where the probability of insurance increases signi® cantly where an individual has either a uni-versity degree and diploma relative to the default of no post-secondary education
The probability of insurance rises with both individual and spousal income The income regressor reported in Table 1 is a continuous variable Trials with income in discrete groups revealed that the relationship between
Trang 81 6The result for the Victorian metropolitan area is statistically signi® cant in the ® rst regression only.
income and the probability of insurance was monotonic
This is in contrast to the view expressed by Feldstein (1973)
who notes that higher income tends to make families more
willing to assume risk, which reduces the demand for
insur-ance Importantly, our results indicate that the view that
high income families do not purchase private insurance at,
at least, the same rate as lower income families is not
supported
The ® nal group of variables relate to the local mix of
public and private services The inclusion of the geographic
location by state or territory and the capital city of each of
the states recognizes that the mix of public and private
facilities di ers between both states/territories and
metro-politan and non-metrometro-politan areas and the di erence is
re¯ ected in interstate variation in the demand for private
insurance The results reported in Table 1 show that
indi-viduals or family groups who live in the Australian Capital
in Queensland metropolitan or non-metropolitan areas are
statistically less likely to have private health insurance than
those residing in the default region of metropolitan New
South Wales This result may re¯ ect interstate variation in
the provision of services The ACT has the lowest private
bed/population ratio of all states and territories The private
sector provides only 11% of total hospital services in the
ACT compared to 19% in NSW and 29% in Victoria
(National Health Strategy, Issues Paper no 2, 1991) In
Queensland, 31.6% of the population has private health
insurance which represents the smallest state percentage
The Victorian result is more di cult to explain in terms of
interstate comparisons as Victoria has both the highest
percentage of privately insured at 50.4% and the highest
number of private bed-days per 1000 population in Australia
The role of geographical location as a determinant of the
demand for private health insurance di ers between the UK
and Australia Propper (1989) ® nds that the demand for
private health insurance is greater in the south-west of
England and that the regional di erences can be largely
explained by income, and as more private facilities are
located in this region, by travel costs The ® rst of these
explanations does not hold in the Australian case The ACT
has the highest per capita income in Australia but has the
lowest population of private insurance membership
The overall results are largely consistent with those of
Propper (1989), and Ngui et al (1990) and Cameron and
Trivedi (1991) Propper ® nds that the probability of
insur-ance is higher with income, employment and higher
socio-economic groupings but lower with dependent children
Ngui et al who used 1983 Australia data ® nd that the
probability of insurance is higher with income, employment,
children, marital status and health status Cameron and
Trivedi report that income, age and gender are important in
explaining the private insurance choice decision but other health risk factors of the number of doctor consultations and hospitalization are not Unlike our results, they ® nd that the relationship between income and insurance pur-chase is concave They report that the number of dependent children has a small positive but frequently insigni® cant e ect on insurance purchase
V SI MU LATIO N RES ULTS: TH E
IM PO RTAN CE OF ILL- HEALTH, I NC OM E
AN D GEO GRA PHI CAL LOCATIO N I N
TH E I NSU RANC E D ECIS ION Tables 2 to 4 present results of simulations of the impact that changes in the important determinants of private health insurance have on the probability of purchase For the purpose of these simulations, a representative married
man is de® ned as being in the age group 25Ð 34, with neither
children nor post-secondary education, on an average in-come of $27 508 and living in South Australia His wife is on the average spousal income of $7361 and neither of them smoke, have seen a doctor nor been hospitalized in the last
12 months
The simulations presented in Table 2 focus on the issue of adverse selection in private health insurance markets Asymmetry of information is a health care market feature which makes discrimination between risk groups di cult and expensive In Australia, regulation of private health insurance markets institutionalizes adverse selection by pro-hibiting statistical discrimination between risk groups The results in Table 2 provide evidence of the outcome of that regulatory structure Both age and frequency of hospitaliz-ation and doctor visits increase the probability of insurance purchase The probability of purchase for the representative
man is 65.1% If the man’s age increase to 70 plus, ceteris
paribus, the probability of insurance increases to 92.9% Similarly, if the health status of the representative man alters
so that he has seen a doctor in the last two weeks and been hospitalized in the last 12 months, the probability of purchase rises to 78.9% The results provide substantial evidence that the privately insured are sicker and that the composition of individuals in the private health insurance pool is adverse for the insurer The results in Table 2 also con® rm the earlier results that the presence of dependent children lowers rather than raises the probability of insurance This result holds across all three representative individuals
The second set of simulations presented in Table 3 con-siders the impact of a change in income on the probability of insurance The relationship between income and private health insurance has been a key feature in the policy debate
on the relationship between the private and public sectors in
Demand for private insurance under Medicare 1629
Trang 9Table 2 The impact of ill-health on the probability of insurance
Probability of purchase (%)
Representative man with depkid 63.4
Rep man seen Dr in last 2 weeks and
hospitalized in last 12 months 78.9
Representative man is now aged 70+ 92.9
Single representative man with depkid 45.0
Single rep man seen Dr in last 2 weeks
and hospitalized in last 12 months 63.8
Single representative man now aged 70+ 86.1
Single representative woman with all other
characteristic as for rep man 59.7
Single representative woman with depkid 57.9
Single rep woman seen Dr in last 2 weeks &
hospitalized in last 12 months 74.8
Single representative woman is now 70+ 91.3
Mean income for the head of household and spouse is 27 508 and
7361 with a standard deviation of 14 852 and 11 359 respectively
Representative man is married, aged 25Ð 34, has no dependent
children, lives in SA, no post-secondary education, self and spouse
earn average income, not seen Dr or hospitalized for at least 12
months, non-smoker
1 7The Federal Coalition Parties’ Fightback policy package, for example, suggested the use of a tax incentives to encourage those on high incomes to purchase private health insurance
1 8Whether the ¯ at rate Medicare levy (presently set at 1.25%) is actually proportional or not is complicated by two important considerations First, the Medicate levy does not fully ® nance health care expenditure The di erence between the Medicare levy and the Commonwealth government’s contribution to total health care expenditure is ® nanced from general taxation revenue (McClelland, 1991) The second consideration is the relationship between gross income and taxable income on the grounds of both horizontal and vertical equity Individuals with the same gross income may have di erent taxable income due to unequal access to tax deductions which reduce taxable income And individuals with di erent levels of gross income may also have unequal access to tax deductions
Table 3 The impact of a change in income on the probability of insurance
Probability of purchase (%)
Representative man’s income increases by 1 s.d 73.3 Representative man’s spouse’s income increases
Single representative man’s income increases by
Single representative woman 59.7 Single representative woman’s income increases
Mean income for the head of household and spouse is 27 508 and
7361 with a standard deviation of 14 852 and 11 359 respectively
Representative man is married, aged 25Ð 34, has no dependent
children, lives in SA, no post-secondary education, self and spouse earn average income, not seen Dr or hospitalized for at least 12 months, non-smoker
Table 4 The impact of a change in geographical location on the probability of insurance
Geographic Representative Representative Representative location married man single man single woman
market segment of the two sectors, in spite of the fact that
Medicare is compulsory The Medicare levy is set at a ¯ at
rate of taxable income and is therefore proportional This
assumed proportionality of the Medicare levy when the
income taxation system is progressive provides the justi®
ca-tion for encouraging those on high incomes to purchase
this way, the capacity constrained public health sector is
available for those who cannot a ord private insurance and
private sector facilities
In the results presented in Table 3, the probability of
insurance increases with an increase in income for all
repre-sentative individuals Notably, an increase in spousal
in-come is not as important in the probability of purchase
decision Spousal income, however, is considerably lower on
average than head of household income The results provide
no support for the view that the wealthy are uninsured The ® nal set of simulations presented in Table 4 consider the impact of a change in geographical location on the
Trang 10probability of purchase Across the three representative
individuals, the Hobart (Tasmanian metropolitan)
prob-abilities are the highest, followed very closely by the
Ad-elaide (South Australian metropolitan) probabilities, and
the Queensland probabilities of purchase are the lowest
The signi® cance of the results in Table 4 is that for a given
representative individual, the di erent probabilities of
pur-chase are a result of the di erent location only, not the
di erent socio-economic composition of population in that
location This immediately raises the issue of what factors
explain such a wide discrepancy in propensities to privately
insure between States For example, a representative
mar-ried man has a probability of purchase of 72% if he lives in
Hobart, but of 36.5% if he lives in Brisbane There are three
possible explanations: interstate di erences in the price of
private health insurance policies, interstate di erences in the
copayments in the private sector and ® nally, interstate
dif-ferences in access to the public sector which, ceteris paribus,
should be re¯ ected in public hospital waiting lists
The costs of insurance policies in respective States does
accord with the di erent propensities to purchase For
example, in October 1990, the average cost of basic hospital
insurance was $10.02 per week for a family in Tasmania and
$15.30 per week for a family in Queensland These ® gures
represent the minimum and maximum values for all
Austra-lian states (Willcox, 1991) Copayments for using the private
sector also vary between states The ® gures on the average
patient copayment for ward accommodation in private
hos-pitals indicate that residents of New South Wales face
almost twice the contribution of residents of Victoria,
Queensland and South Australia (National Health
Strategy, 1991) Figures are not available publicly for the
other States
A further complicating factor is that a low level of private
health insurance does not imply a small private sector The
Queensland population has the lowest level of private
health insurance coverage of 31.6% in 1990, yet Queensland
has the second highest private bed to population ratio
(1989/90 ® gures) (National Health Strategy, 1991) Overall
interpretation of the results, however becomes confused in
the peculiarities of the mix of the role of the private and
public sector in Australia Not only are the private bed-days
in private hospitals important, but account must also be
taken of the private bed-days in public hospitals as well as
the overall hospitalization rate for the State In Queensland,
for example, there is limited use of public hospitals by
private patients, but Queensland has a large private sector,
as mentioned earlier
The interstate di erences in supply of public hospital beds
and interstate variations in the demand for those beds
should, ceteris paribus be re¯ ected in public hospital waiting
lists Waiting list data in Australia are notoriously
unreli-able and even where they are availunreli-able, comparisons
be-tween States are di cult because of the lack of consistency
in the collection and presentation of the data
A pattern in the relationship between the propensity to purchase private insurance and the cost of private health insurance in terms of both the premia and copayments and size of the public and private sectors is di cult to discern The negative relationship between the propensity to pur-chase private insurance and the insurance policy costs is indicative of a downward sloping demand curve An indica-tion of the true relaindica-tionship between the cost of insurance policies, the supply of health services and the demand for private health insurance, however, requires an analysis of time series data and is beyond the scope of this present study
Time series data on the percentages of the population with private health insurance, however, indicate that the Queensland population has traditionally had the lowest national percentage It was 51.1% in 1977 (Hart, 1990) This was followed closely, however, by the Tasmanian percentage of 54.9 Data on premia over the same time span are unavailable The time series data, however, con® rm the common perception of why Queens-land has a lower propensity to purchase private health insurance It is due to an historical tendency towards public sector care rather than private sector care For example, before Medicare, the Queensland population had access to free shared-ward accommodation with treatment
by hospital doctors
VI CO N CL US ION Analysis of the determinants of demand for private health care in Australia indicates that three sets of variables are important These are health status, material wellbeing and the relative importance of the private and the public health insurance coverage Our results con® rm that privately in-sured individuals are on the whole sicker But we refute the view that there is a tendency for high income families not to privately insure Geographical location is an important de-terminant of the probability of insurance The explanation
of why it is so important can in part be explained by reference to the interstate di erences in the relative supply of private versus public facilities and in the price of private insurance The complete explanation, however, relates to both the supply and price factors, and to interstate di erences in the historical tendencies to demand private insurance
ACKN O WL ED G EM EN TS This research was assisted by ® nancial support from the Curtin University Research Grants Scheme We thank
Dr Darrel Doessel, and Dr Thorsten Stromback and other workshop participants from the School of Economics and Finance, Curtin University
Demand for private insurance under Medicare 1631