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The demand for adult outpatient services in the Bicol region of the Philippines
Sot. SL.I. .Mrd. Vol. 22. No. 3. pp. 321-328. Pnnted ,n Grear Bntun. -\II nghts reserved
1986 Copyright
0X7-9536 56 S3 00 + 0.00 (’ 1986 Pergamon Press Ltd
THE DEMAND FOR ADULT OUTPATIENT SERVICES IN THE BICOL REGION OF THE PHILIPPINES JOHS
S.
AKIS.
CHARLES
and Carolina
Population
Center.
University
C.
GRiFFIN. DAVID K. GUILKEY
BARRY M.
POPKIS
of Sorth Carolina, NC 27514, U.S.A.
University
Square
300A. Chapel
Hill.
.Gstract-The absence of demand analysis for primary health care services has hampered efforts to finance these services and to make them permanent parts of Third World medical systems. This paper introduces a demand model for adult outpatient services, describes the types of data required for estimating it, and presents the results of a preliminary estimation using data from a poor rural region of the Philippines. The results indicate that prices and distance are not nearly as important as determinants of demand in this sample as has usually been assumed by planners. There appears to be considerable room for full or partial financing of outpatient services from user fees.
I. ISTRODUCTION
The principal goal of the primary health care (PHC) movement has been to improve the health of the rural poor. Thus the problem of resource allocation has included a biological aspect (the improvement of health status), a social component (the improved distribution of health resources) and an underlying development objective (investment in human capital). The actual allocation of resources has usually been based on inventories of health needs and on a desire to improve the per capita supply of modem government medical practitioners. This approach has led to an overriding concern with the geographic distribution of medical personnel and facilities. It has generally been assumed that governmental personnel and facilities should be provided free to clients, or for only a nominal charge. This could imply at least four assumptions: (1) that the most important barrier to utilization is the cash price of the service, (2) that the clients attracted by a cash cost of zero are the ones the government desires to reach. (3) that there is little or no current private spending on medical services that could be redirected towards governmental services and (4) that services would be underutilized if there were a charge. In addition medical care may be extended gratis because it is taken as a right or is deemed a desirable use of government moneys. Recently, governments and donor agencies have become more aware of the tremendous financial burden of trying to supply areas with new personnel and facilities. As international agencies have begun to withdraw their seed money from PHC programs, health ministries have had difficulty meeting recurrent program costs, managing dispersed service delivery networks, and maintaining the commitment of voluntary paraprofessionals. In this context, it is surprising that so little attention has been paid to community-based analyses of the demand for PHC services. If health care were purely a clinical phenomenon, then it would be adequate to simply take an epidemiological inventory 321
of health needs and supply the appropriate preventive and curative health services. However, fostering the use of new health services and changing specific practices, such as water collection methods. excreta disposal and traditional methods of childbirth, require that attention be paid to individual behavior patterns. Moreover. because health services are supplied in a world of limited resources, the assumption that people are entitled to free health care often must give way to practical financial considerations. These neglected aspects of PHC planningcommunityand household-level behavior and financial considerations-are exactlv the focus of microeconomic demand analysis. If it were possible to answer at least some of the following questions before PHC programs were put in place, planners would find the job of sustaining the programs much easier. (1) In a descriptive sense, what are existing medical service consumption patterns? What proportion of people use existing traditional, private modem, and public medical practitioners? (2) Why do people follow the patterns described in (1) above? In other words, what are the determinants of demand? How important are acces
using household and community data from a poor region of the Phillipines*. The absence of community-based analyses of the demand for primary care services has hampered efforts to find new ways to finance these services. Demand analysis. in g.eneral, describes the relationship between quantities of the good or service desired to be purchased and the price charged for that service. In order to isolate this price-quantity relationship. it is necessary to control (statistically with control variables) for all nonprice factors, such as tastes. needs. income level. and demographic factors, that also affect the quantity demanded. It is also imperative that supply characteristics be accounted for so that the relationship that is isolated is actually a demand-induced phenomenon and not due simply to availability of the service. Because demand analysis requires the examination of all these nonprice factors. it can be useful as a guide to policy when the policy-maker requires information on the whole array of possible points of intervention. A full-scale demand analvsis. therefore, describes the relationship between desired purchases of consumers and all factors affecting these demands. In this article the demand for public modem, private modern and private traditional health services for adult outpatients will be examined in the manner described above. This paper is organized in the following manner. Part II briefly describes a medical service demand model. Part III discusses both the data required to estimate such a model and the actual surveys used for this paper. In Part IV, a model of the demand for adult outpatient care is estimated using data from the Philippines. II. AN ECONOMIC MODEL FOR PROJECT PLANNING AND EVALUATION
Anthropologists, sociologists and geographers have had a considerable amount of success finding negative correlations between medical service use and so-called ‘barriers to utilization’. These barriers include such items as physical distance from households to facilities, cultural distance between patients and providers, the unavailability of drugs, the length of time spent waiting at facilities and the unavailability of transportation. Building on this work, we have developed a demand model under the assumption that a sick individual faces the choice of self-treatment as one alternative, and professional treatment from a traditional practitioner, a government clinic or a private physician as another. The choice of practitioner depends on household constraints, such as income, the number of residents, the authority structure of the household and its assets. The choice is also determined partially by the barriers to use mentioned earlier, *The authors wish to emphasize the preliminary nature of the analysis done in this paper. We have since completed a much more complete improved analysis, the report of which has been published as a book, Tk Demand for Primary Healrh Care in rhe Third World [I], in the fall of 1984.
which have important economic significance. Distance is translated into the opportunity cost, stated in terms of both time and money, ot getting to a practitioner. Similarly, there are time and pecuniary costs associated with waiting to be seen. purchasing drugs and making return visits to continue treatment. Variables must also be included to account for the effects of education, sex. rural/urban residence and practitioner supply on medical service use. Figure 1 presents these factors. which are reveiwed in more detail elsewhere [I]. 111.DATA Data requirements for such a model are extensive. Community data are required to supply the prices, travel time and waiting time associated with each type of facility or practitioner. To this end, it is necessary to inventory the practitioners used by residents from each community and to visit each practitioner to collect facility-level data. Household data must supply information on assets, household organization, household location, income and time constraints. Individual data on education, illnesses, pregnancies and medical practitioner choices must be collected for all people living in the survey households. Estimating the demand model described in the previous section is accomplished in this paper using two sets of data: a 1978 household survey conducted in one of the poorest regions of the Philippines, the Bicol Multipurpose Survey (BMS78) [2], and a 1982 medical facility survey (BMSSSI) collected during visits to 518 traditional and modern facilities (or lone practitioners) serving the 100 communities included in the 1978 survey [3]. From the 1978 survey, household information was collected on the condition of the dwelling, ownership, assets and a wide variety of other economic and demographic variables. Individual information on about 17,000 household members includes extensive work, income and home time-allocation data; health, child-rearing and pregnancy-related information: and a massive compilation of demographic variables. Respondents were asked whether anyone in the household had been sick in the month previous to the survey and, if so, what was done for the illness, whether it was serious, how much was spent, how long physician visits took, and so on. One problem with this survey was that only cash- and timeexpenditure data reported by people who used medical services were available to use as proxies for medical visit prices. In the medical service demand literature, the use of expenditures instead of prices is a common approach. but it is to be avoided for several reasons. First, expenditures are simply the number of visits multiplied by the average price of each visit. What a demand model explains, however, is the number of visits; so it is a serious mismeasurement of the price variable to include elements of the dependent variable in it. Second, expenditures are likely to include more than just the price of the visit; drugs are the most obvious addition. Third, an individual will not report expenditures for services not used. For example, if someone chooses a private physician, he or she will have missing values for expenditures at traditional
Demand
for adult
outpatient
services
in the Philippines
323
PRICE
cost
rransportar1on
rime
waltlng
money cost co~nwrance OTHER
cash price
-
/soc~aI
securlry
PRICES
subst!rJtes
INCOME level sources fYpes assefs
8
TIME nature
wealth
ALLOCATION of work
felt
FOR
HEALTH
CARE
modern
public
modern
prlvare
trodiflonal
accupaflon
HEALTrl physlologtcal
DEMAND
PRIMARY
prtvote
NEEDS
/real
household
stze
KNDWLEDGE culrural
/
INFORMATION
Issues
education
SEASONALITY healfh effecrs cost
effecrs
Fig. I. Determinants of demand for health services in the Third World.
healers and government clinics. Analysts commonly replace these missing values with average expenditures based on the experiences of other users, but such numbers do not necessarily give a true measure of the whole array of prices facing each household. For these reasons, the 1982 survey of health facilities and practitioners was undertaken. Data were gathered on payment prices, hours of operation, and available medical personnel. Additional questions were asked on the villages served as well as distance, cost of travel and patient’s usual mode of transportation to each facility or practitioner. Data were also gathered on the types of services offered. For adult outpatient care, child outpatient care, clinic deliveries, home deliveries, prenatal care, well-baby care, child immunizations and adult vaccinations, practitioners were asked the usual price for one visit, the usual practitioner and the usual waiting time. They were also asked about services extended for five gastroenteritis, illnesses: specific tuberculosis, influenza, pneumonia and bronchitis. Iv.
E>lPIRICAL
STUDY
In conventional demand analysis, consumers are assumed to allocate their budgets simultaneously over all goods and services they are interested in
purchasing. Because all purchases are related to one an0ther-e.g. if a large medical bill is expected, it may not be a good time to buy a full-length ermine coat-the prices and quantities of all goods ideally must enter a system of demand equations. It is a misspecification of the model to break off one set of services, such as medical care, and estimate the demand for those services in isolation from all other consumption behavior. To get around this problem, the analyst makes use of the concept of separability, which allows one set of goods, such as medical services, to be analyzed separately. To do this requires one of two assumptions: either that the prices of the goods involved maintain a stable relationship with each other (e.g. that prices at private clinics, public clinics, and traditional practitioners stay in the same positions relative to each other) or that consumers allocate their budgets in a well-defined fashion. The behavior implied for consumers is that they make an initial allocation of their budgets to different categories of goods, such as to food. shelter, clothing. transportation and medical care, and thereafter maximize utility within each of these categories without further reference to the others. Only at the time of the initial budget allocations are the branches considered together [4]. These issues are explained because a potential
JOHN S. AKIS
37-l
source of disagreement with the approach used here is the matching of 1978 household data (BMS78) and 1981 barangay-level prices from the facility survey (BMSS8l). It should be clear at the outset. however. that in order to isolate demand equations for medical services from other goods categories. the assumption is already made that the relative prices of the goods are stable. which is the same assumption necessary to validate the matching of 1978 and 1981 surveys. We are not assuming that in 1978 the sample faced the same prices as were collected in 1981, only that they faced the same price structure. If private clinics were twice as expensive as traditional healers in I98 1, they are assumed to have been twice as expensive in 1978. This is not, in fact, an outlandish assumption over such a short time period for services that are close substitutes for each other. The adult outpatient model is a modification of the following general demand system:
where Q,, = whether medical service i is used by the jth individual. where i = (public. private. traditional. or no care) and j = (all sick or pregnant individuals, the sample depending on the model) PU, = public clinic or hospital serving the jth individual PR, = private clinic or hospital serving the jth individual TR, = traditional healer or midwife serving thejth individual P = vector of cash prices paid for each service (including visit cost, drug costs and transport costs) T = vector of time costs associated with each facility and service (waiting time, transport time) Y, = household assets for the jth individual Z, = a vector of social, demographic, and biological control variables for thejth individual.
a,=f,jvPL,. p,,, pm,, Tw,, TIT,, TTR,,y,>Z,, (1) Table
I. List of variables
Dependent
The choice
of practitioner
with sample statistics. used in estimating the demand Philioaines. 1978 (samale size = 399)
variable
for adult
Frequency
Visit: Traditional Public Private
60 73 124 142
NOM
Independent
er al.
variables
*8 pesos were equal
to USSl.00
at the time of the survey.
0.49 pesos* 14.49 pesos 3. IO pesos 13.72 km 9.41 km 0.31 km 63.24 100.32 I I.68 26.39 0.1 I
on the relative
outpatient ?/, 15 IS 31 36
Mean
Opporiunit~ cm0 Cash prices for one adult outpatient visit Public Private Traditional Distance to closest facility of practitioner Public Private Traditional Waiting time Public Private Traditional Amount spent on drugs Whether covered by insurance (0 = no; I = yes) Household msers and income Number of rooms in house Annual household income from all sources Number of individuals in household Sanitary raring of water source Sanitary quality of toilet facilities Demographic Male (0 = female; I = male) Urban (0 = rural; I = urban) Model Type of household 0 = extended family I = nuclear with other residents 3 = nuclear or single Education Highest grade completed Perceprion Perceived quality of life-family health and physical condition: I = dissatisfied 7 = very satisfied Perceived quality of life--availability of health services: I = dissatisfied 7 = very satisfied
depends
SD
2.00 4.05
1.66 13.37 10.45 0.53
min min min pesos
84.32 101.67 36.3 I 70.81 0.3 I
I .43 1955.83 pesos 6.79 1.56 1.82
0.94 10426.23 2.78 1.04 1.33
0.52 0.23
0.50 0.42
2.44
I .08
6.57
3.80
I
I .40
3.09
I.21
3.4
wits,
Demand for adult outpatient services in the Philippines cash and time costs associated with each facility, household income. and a set of control variables. Table 1 contains a list of variables used in the analysis. along with their definitions and simple descriptive statistics. The dependent variable is constructed in the following manner using the BMS78 data: Traditional
Visit = visit to herbolario (healer) hilot (traditional midwife)
or
Public Visit = visit to rural health unit, city health office. puericulture center, or public hospital Private
Visit = visit to private private hospital
No Visit = no professional sought.
clinic or to a consultation
The variables above include housecalls when the type of practitioner can be identified. Sixty-three percent of the traditional visits fall in this category. The cash cost variables come from the closest single private facility, public facility. and traditional practitioner serving each barangay. Sick individuals reported the total amount of time required for each visit; this is transformed into the time-price for each practitioner. The distance variable is calculated as an average for each type of facility by barangay, using the supplemental facility data gathered in 1981. It is clear from the means for the distances reported in Table 1 that all areas are well supplied with traditional practitioners, but that a considerable cost in terms of time, distance and transportation expenses may be incurred in order to use either the public or private modern practitioners. Economic theory requires that the dependent variable in the demand system defined above be continuous; i.e. for each individual the exact quantity of each service consumed should be known. However, only the type of practitioner used is known; so the dependent variable is limited to values of 0 or 1 for each alternative. Consequently, ordinary least squares is an inappropriate technique to use for estimating this model, and the multinominal logit procedure is used. In the logit approach, the system of demand equations takes this form: P (Public log
P (Traditional
log
P (Traditional
P (Private
log P
Visit) Visit)
= JYPublic
Visit) Visit)
= WLXe
P (No Visit) (Traditional
Visit)
= UN0 “,W.
The probability of a traditional visit is then equal, by definition, to 1 minus the sum of the three probabilities listed above, or P (Traditional
Visit) = 1 - [P(Public) + P(Private) + P(No Visit)].
Table 2 contains the expected coefficient the economic variables appearing in the model. The coefficient estimates are reported 3. In reporting the results, the ‘economic’
signs for demand in Table variables
315
described above are grouped by prices, distances and time. Microeconomic theory suggests that the probability of a visit to a particular practitioner will be negatively related to the costs associated with that practitioner and positively related to the costs associated with the substitutes. Table 3 indicates, however, that in no case is the cash price statistically significant at the 10% level (an asymptotic t-statistic of approx. 1.6). The estimated coefficients are extremely small, and the signs are occasionally opposite of what is expected. These results showing little significant effect of cash prices on the choice are not without meaning, however. First. a number of studies have found that individuals are insensitive to the money cost of health care, even in low-income countries [5]. Second, the variation in prices for the three types of practitioners is quite large and in exactly the direction one would expect: the average cost for public visits is 0.5 pesos, traditional visits cost an average of 3.10 pesos, and private visits cost an average of 14.49 pesos. Private clinics and hospitals thus charge over 28 times as much, on the average, as government clinics and public hospitals. The insensitivity to price that is apparent from the model suggests that public services may have a fair degree of latitude for manipulating their charges to help offset operating costs. Third, since this model does not control for the severity of illnesses or quality, it is possible that the apparent insensitivity to price may be at least partially caused by the wtlhngness of individuals to pay more to get higher quality care. Alternatively, private practitioners and traditional healers may informally cut their prices below those reported by using sliding fee scales. In financing public health care services, this possibility may be an important consideration. In our survey of 518 health facilities in the 100 sample Bicol barangays, 288 of the 397 (or 73%) facilities which charged fees indicated that fees varied according to the income of patients. In addition, 329 (or 63%) of the facilities accepted payment in the form of goods or services instead of cash. These different pieces of evidence suggest that the insensitivity to costs may be rational in the face of a recognized need for health care, especially if informal mechanisms exist for ameliorating the apparently high formal costs of private care. Distance should also act as a price variable. The farther a particular facility is from a barangay, the less frequently it should be used by residents of that barangay, other things equal. Conversely, the farther away are alternative facilities, the more likely it is that the closest facility will be used. The distance coefficients reported in Table 3 tend to have the predicted signs, and although the asymptotic tstatistics are generally higher than they are for cash prices, only three of the coefficients, two of which are for the distance from traditional practitioners, are statistically significant (10% level). This is an interesting result, because the mean distance from traditional practitioners is 0.31 km compared to 9.41 km for private and 13.72 km for public facilities. Small increases in the distance to traditional practitioners appear to induce a substantial increase in the proba-
JOHN S. AKIN et al.
326 Table
2.
Expected
signs
far
economic
variables
\s
distance.
waiting
logrt
1s
traditional
Public
L ?
Private
?
_
7
1
+
coberqe
No
No
VIJlt
vs
traditional
>
estrmates
PrrKite
pribate
public
,
VlSli vs
\s
pubhc
time
Traditional
Insurance
outpatient
visit
No
‘rs
traditional Pnces.
in adult
Pn\ste
Pubix
1
1
?
+ _
L
,
?
Income--assets Sumber
of
Income--all
T.ible
rooms
in house
3.
Multiple
logit
results:
demand
for
adult
-
+
outpatient
-
+
i
?
so~~rces
services,
+
Philippines.
1978.
Coefficient
estrmates
(asymptotic
r-value
in
parentheses) Probability
of
‘A‘
visit
Private
Public
vs ‘8‘
No
traditional
traditional
No
Private
VS
“S
-.S
visitt
visit
\&it
traditional
No
visit
“S
“S
VS
private
public
public
Prices Traditional
0.1513
0.0897
(1.136) Public
(0.763)
0.0499
Prirate
(0.625)
0.047s
0.009 (0.090)
-0.036
(-0.137)
(-0.166) -0.0782
- 0.069
(-0.860)
(-0.617)
-0.055
(-0.754)
-0.0166
-0.078 (-0.663)
-0.019
-0.0067
(O.SS5)
-0.062 (-0.524)
(-0.199)
0.0592
(0.4397)
0.073 (0.653)
-0.0295
-0.084’
(-1.18)
(-
(-0.752)
1.75)
DiciUtlCtT
Traditional Public Pri\
1.212’
0.881’
ate
(I ,696) 0.00 I2
(2.496)
(0.0669)
(0.582)
0.0094
-0.0234 (-
0.607
(1.08)
0.0137
(-1.39)
-0.605’
(-0.790)
0.0082
(0.887)
-0.0253
1.061)
-0.275
0.330
(1.27)
-0.0126
(0.587)
-0.01
-0.002
(-0.973)
( - 0.098)
(-2.09) 0.0044
(0.879)
(0.355)
0.0061
0.008
(0.247)
(0.494)
Waiting rime Traditional
-0.0195*
-0.0107* (-
Public
2.10)
(-
-0.008’ -0.0032
Drug
expenditure
(0.032)
0.0181*
0.01879
Insurance
I.22
1.378’
(1.497)
(I ,827)
Income
a.sW,S
Number
of
rooms
in
house
-O.OS-ll
Income--all
sources
0.242
Household
size-people
o.OvOao3 (0.208)
(-0.777)
(-0.3%) Quality
of
water
0.4-l5*
0.4232’
(1.95) Quality
of
toilet
-0.023
I
(-0.0136)
(-
-0.027
(0.345)
(-0.445) (-
0.943
1.39)
(-0.250) 0.0778
0.0506
(1.378)
(0.785)
-3.91’
-0.412’
1.097)
(-
1.94) - 0.000007
(-1.14)
(-2.07)
(-0.197)
(0.649)
-0.366’ (-
-0.CGO3
(-0.021)
(0.172)
-0.219
-0.0402
(-1.04)
0.233
-0.977’ (-1.97)
(-0.0184)
-0.00@02
0.0321
(2.07)
-0.820
0.326*
(-1.02)
-0.0545
-0.0619* (-5.33)
(-1.37)
(1.66)
- 0.00003
(-0.899)
-0.0615*
0. I57
-0.124
0.00014 (0.081)
(-5.28)
(0.280)
(-0.527)
-0.00002
-0.0272
0.00035
0.40 I
(1.04)
(-0.325)
0.00339 (1.47)
(0.192)
(0.517)
0.0038 (1.55)
3.24)
(1.52)
(-3.44)
coverage
-0.0067’ (-
-0.0032
-0.0432.
(2.74)
(2.672)
I*
(-4.14)
O.OOQ? I (0.1033)
-0.00007
(1.57)
(0.321)
-0.01
(0.539)
0.0104
0.0016
(-1.28)
-0.0012
(-0.923)
(-1.25)
-0.0088
(-2.154)
-0.0025
(3.22) Private
-0.0091*
2.923)
(-
2.35)
O.lli
1.307)
0.314’ (2.44)
(0.803)
Demographic
(I = male,
Male
0 = female)
0.463
-0.0149
tirban
(I
Scale
= urban,
of
0 = rural)
wad.-nuclear
HH
0.22
(1.29)
(-0.376) 0.310
0.406
0.370
(0.491)
(0.719)
(0.676)
-0.336’
-0.0812
Education
0.0955
-0.0354
0.0601
0.0659
(-0.0873)
0.0375
-0.0285 (-0.205)
(0.239)
0.023
0.0437
0.607
(0.455)
(1.39)
(-0.833)
(0.120)
(0.427)
0.076
(0.558)
- 0.242
0.371 (1.10)
(0.195)
1.94)
(-
0.0325
0.0092 (0.149)
0.0618’ (1.80)
-0.363’
(-1.720)
(-0.329)
I
(0.657)
(0.942)
(1.33)
Perceptions Satisfaction
with
family
health
-0.00421
-0.0625 (-0.413)
Satisfaction
with
health
services
-0.074
Consmnt lr+alues +The
numbers first
relative
at
reported
entry
in the
or
are first
traditional
visit
abobe the
column
to a traditional
to a traditional the
(-
-0.451
is signiricant
visit goes
practitioner.
0.10
level.
logarithms
of
is positive goes
up.
down-people
the
In contrast. are
more
(-
which
that
‘A’
indicates
1s traditional likely
to
that
relative as the
waiting use
tradrtronal
time
goes rather
probabihty price
up (-0.0107*), than
pubhc
0.0172
(0.800)
(0.125)
3.00
I.41
I .59
to the
(-0.426)
-0.122
(-0.937)
traditional
-0.0486
(-0.213)
-0.140
1.269) 2.55
occurs
-0.0281
(0.162)
-0.1964
1.38) I.144
probability
(O.l513).
(-0.686)
-0.2136
( - 0.429)
0.0204
-0.0907
(-0.310)
that
‘B’
increases. the facilities
occurx
the
probabdit) if they
For
example.
probability
the
of
a public
of a public
relative
must
wait
longer
for
Demand for adult outpatient services in the Philippines bility of using modem facilities. There also appears to be a certain amount of substitutability between traditional and private modern practitioners. However, because some of the coefficients to support this hypothesis are statistically insignificant, this hypothesis requires further testing. The time-price coefficients tell a much more confusing story. While six of these coefficients are statistically significant, the results are generally less strong than expected. All other things constant, the longer it takes for a public visit, the less likely that patient is to choose a public facility over a traditional one. This, however, is not the case for public vs private modern. As time to use public facilities increases, the use of public relative to private modern also increases. There is the obvious possibility that waiting time at public facilities may be positively correlated with higher quality medical care. These results are interesting in light of the usual impression, culled from simple correlations, that waiting time is an urgent problem at public facilities and makes them significantly less desirable than the alternatives [I]. In every case the traditional time variable is significantly, negatively related to the probability of making alternative visits. In other words, the longer a traditional visit takes, the more likely that it will be preferred to the alternatives. This result may be due to the fact that a few of the types of techniques that require longer visits at traditional practitioners, such as massages and certain rites or incantations, are the ones most favored by patients. In such a case the traditional practitioners who take more time would be more likely to be chosen. In fact, these traditional visits may be pleasant experiences which add to the attractiveness of traditional medicine and thus cause the time variable, as we have used it, to be misleading. An alternative explanation is that the time of day when these long visits take place may be off-hours when the opportunity cost of the visit is actually quite low even though the commitment in minutes may be high. Higher pharmaceutical prices have counterintuitive effects: they increase the probability of using both modern alternatives relative to traditional practitioners. As expected, having insurance leads to a preference for private practitioners over traditional ones. However, insurance does not appear to have a major influence in the choice of private vs public modern practitioners as the coefficient is not statistically significant. The income variable is included to show the effects on medical purchases of an enhanced capacity to consume all goods and services while controlling as best we could for the quality and quantity of household assets. The general result for income is that as income rises, both types of modern services tend to be substituted for the other two alternatives (no visit and traditional), and private doctors tend to be substituted for public clinics within the modern sector. Demographic
variables
The demographic
variables indicate a statistically
significant increase in the probability of a private visit relative to a public visit if the sick person is male. This may be indicative of a diversion of resources towards
327
males to improve the quality of their care: alternatively. if males in the sample suffer from more serious problems, it may be simply a need-oriented rather than a behavioral phenomenon. In only one case does the education variable even approach statistical significance. This is in the cell for the probability of no visit relative to a traditional visit. The sign is positive, a finding consistent with many simple correlations done by other authors, which show that educated people are more willing and confident than others to engage in home treatment. The perception variables are included to control for perceptions and acculturation with no expectations about the signs.
V. CONCLUSION
This paper reviews some of the issues involved in an economic analysis of demand, and provides a preliminary analysis of data collected in the Bicol region of the Philippines. This case study, conducted for adult outpatient visits, provides insight into the ways such analysis can clarify various planning issues. The method calls for price, time and other factors to be considered together, and leads to a number of important findings relevant to the financing issue. First, before conducting the survey of facilities in the Bicol region, we assumed that there would be a dearth of private doctors and that existing facilities would be fairly inaccessible. The survey uncovered a large and vigorous network of public, private and traditional practitioners. Second, almost half the sample used private modem or traditional fee-forservice medicine even though the public system of free clinics and hospitals is at least as accessible as the private modern clinic system. Third, prices and distance do not appear to be important determinants of demand; so there appears to be some room to reduce the drive to reduce both the distance to, and the prices of, rural health clinics to zero. Some selffinancing is probably feasibie, and attention to the quality of public services may be more important to their use than a low money price. Considerable work must be done to improve the estimation of this model. In addition, it should be extended to include multiple visits and other services, such as delivery and well-child care. Expenditure and use patterns must also be more carefully analyzed to ascertain which income groups benefit from free government services. Ackno~~(edgemenrs-The basic research for this project has been supported by a grant from PPC,,PDR,‘USAID. Dr Maureen Lewis of PPC,PDR is thanked for her assistance throughout this project. The Bicol River Basin Development Program and the Office of Local and Regiona! Development. AID Philippines are thanked for allowing us to use their BMS78 data. In particular, David Heesen, C. Stuart Callison and Don Wadley have been helpful. The Social Survey Research Unit. Ateneo de Naga University is also thanked for providing access to these BMS78 data. The price and other community data were collected by the Research Service Center. Ateneo de Naga University. They are thanked for working with us on the collection of these
JOHN S. AKIN er al
328
data and with providing us with insights into the problems ofcollecting the price data. The authors contributed equally to this paper.
REFERENCES
I. Akin J. S., Griffin C. C.. Guilkey D. K. and Popkin B. M. The Demand for Primary Healrh Setrices in rhe Third World. Littlefield, Adams. Totowa, N.J., 1984. 2. Popkin B. M. and Roco S. S. Jr with Bragais P. Jr. and Callison C. S. 1979 Bicoi Multipurpose Sutuey. Vol. I.
Survey Design and Implemenration. U.S. Agency for International Development, Manila, 1979. S.. Popkin B. M. and the 3. Griffin C. C.. Hamilton Ateneo de Saga Research and Service Center. The Bicol multipurpose supplemental survey. Carolina Population Center, University of North Carolina, Chapel Hill, 1981. 4. Deaton A. and Muellbauer 1. Economics and Consumer Behavior. Cambridge University Press. Sew York, 1980. 5. Heller P. S. A model of the demand for medical and health services in Peninsular Malaysia. Sot. Sci. Med. 16, 267-254. 198 I.
1986 Copyright
0X7-9536 56 S3 00 + 0.00 (’ 1986 Pergamon Press Ltd
THE DEMAND FOR ADULT OUTPATIENT SERVICES IN THE BICOL REGION OF THE PHILIPPINES JOHS
S.
AKIS.
CHARLES
and Carolina
Population
Center.
University
C.
GRiFFIN. DAVID K. GUILKEY
BARRY M.
POPKIS
of Sorth Carolina, NC 27514, U.S.A.
University
Square
300A. Chapel
Hill.
.Gstract-The absence of demand analysis for primary health care services has hampered efforts to finance these services and to make them permanent parts of Third World medical systems. This paper introduces a demand model for adult outpatient services, describes the types of data required for estimating it, and presents the results of a preliminary estimation using data from a poor rural region of the Philippines. The results indicate that prices and distance are not nearly as important as determinants of demand in this sample as has usually been assumed by planners. There appears to be considerable room for full or partial financing of outpatient services from user fees.
I. ISTRODUCTION
The principal goal of the primary health care (PHC) movement has been to improve the health of the rural poor. Thus the problem of resource allocation has included a biological aspect (the improvement of health status), a social component (the improved distribution of health resources) and an underlying development objective (investment in human capital). The actual allocation of resources has usually been based on inventories of health needs and on a desire to improve the per capita supply of modem government medical practitioners. This approach has led to an overriding concern with the geographic distribution of medical personnel and facilities. It has generally been assumed that governmental personnel and facilities should be provided free to clients, or for only a nominal charge. This could imply at least four assumptions: (1) that the most important barrier to utilization is the cash price of the service, (2) that the clients attracted by a cash cost of zero are the ones the government desires to reach. (3) that there is little or no current private spending on medical services that could be redirected towards governmental services and (4) that services would be underutilized if there were a charge. In addition medical care may be extended gratis because it is taken as a right or is deemed a desirable use of government moneys. Recently, governments and donor agencies have become more aware of the tremendous financial burden of trying to supply areas with new personnel and facilities. As international agencies have begun to withdraw their seed money from PHC programs, health ministries have had difficulty meeting recurrent program costs, managing dispersed service delivery networks, and maintaining the commitment of voluntary paraprofessionals. In this context, it is surprising that so little attention has been paid to community-based analyses of the demand for PHC services. If health care were purely a clinical phenomenon, then it would be adequate to simply take an epidemiological inventory 321
of health needs and supply the appropriate preventive and curative health services. However, fostering the use of new health services and changing specific practices, such as water collection methods. excreta disposal and traditional methods of childbirth, require that attention be paid to individual behavior patterns. Moreover. because health services are supplied in a world of limited resources, the assumption that people are entitled to free health care often must give way to practical financial considerations. These neglected aspects of PHC planningcommunityand household-level behavior and financial considerations-are exactlv the focus of microeconomic demand analysis. If it were possible to answer at least some of the following questions before PHC programs were put in place, planners would find the job of sustaining the programs much easier. (1) In a descriptive sense, what are existing medical service consumption patterns? What proportion of people use existing traditional, private modem, and public medical practitioners? (2) Why do people follow the patterns described in (1) above? In other words, what are the determinants of demand? How important are acces
using household and community data from a poor region of the Phillipines*. The absence of community-based analyses of the demand for primary care services has hampered efforts to find new ways to finance these services. Demand analysis. in g.eneral, describes the relationship between quantities of the good or service desired to be purchased and the price charged for that service. In order to isolate this price-quantity relationship. it is necessary to control (statistically with control variables) for all nonprice factors, such as tastes. needs. income level. and demographic factors, that also affect the quantity demanded. It is also imperative that supply characteristics be accounted for so that the relationship that is isolated is actually a demand-induced phenomenon and not due simply to availability of the service. Because demand analysis requires the examination of all these nonprice factors. it can be useful as a guide to policy when the policy-maker requires information on the whole array of possible points of intervention. A full-scale demand analvsis. therefore, describes the relationship between desired purchases of consumers and all factors affecting these demands. In this article the demand for public modem, private modern and private traditional health services for adult outpatients will be examined in the manner described above. This paper is organized in the following manner. Part II briefly describes a medical service demand model. Part III discusses both the data required to estimate such a model and the actual surveys used for this paper. In Part IV, a model of the demand for adult outpatient care is estimated using data from the Philippines. II. AN ECONOMIC MODEL FOR PROJECT PLANNING AND EVALUATION
Anthropologists, sociologists and geographers have had a considerable amount of success finding negative correlations between medical service use and so-called ‘barriers to utilization’. These barriers include such items as physical distance from households to facilities, cultural distance between patients and providers, the unavailability of drugs, the length of time spent waiting at facilities and the unavailability of transportation. Building on this work, we have developed a demand model under the assumption that a sick individual faces the choice of self-treatment as one alternative, and professional treatment from a traditional practitioner, a government clinic or a private physician as another. The choice of practitioner depends on household constraints, such as income, the number of residents, the authority structure of the household and its assets. The choice is also determined partially by the barriers to use mentioned earlier, *The authors wish to emphasize the preliminary nature of the analysis done in this paper. We have since completed a much more complete improved analysis, the report of which has been published as a book, Tk Demand for Primary Healrh Care in rhe Third World [I], in the fall of 1984.
which have important economic significance. Distance is translated into the opportunity cost, stated in terms of both time and money, ot getting to a practitioner. Similarly, there are time and pecuniary costs associated with waiting to be seen. purchasing drugs and making return visits to continue treatment. Variables must also be included to account for the effects of education, sex. rural/urban residence and practitioner supply on medical service use. Figure 1 presents these factors. which are reveiwed in more detail elsewhere [I]. 111.DATA Data requirements for such a model are extensive. Community data are required to supply the prices, travel time and waiting time associated with each type of facility or practitioner. To this end, it is necessary to inventory the practitioners used by residents from each community and to visit each practitioner to collect facility-level data. Household data must supply information on assets, household organization, household location, income and time constraints. Individual data on education, illnesses, pregnancies and medical practitioner choices must be collected for all people living in the survey households. Estimating the demand model described in the previous section is accomplished in this paper using two sets of data: a 1978 household survey conducted in one of the poorest regions of the Philippines, the Bicol Multipurpose Survey (BMS78) [2], and a 1982 medical facility survey (BMSSSI) collected during visits to 518 traditional and modern facilities (or lone practitioners) serving the 100 communities included in the 1978 survey [3]. From the 1978 survey, household information was collected on the condition of the dwelling, ownership, assets and a wide variety of other economic and demographic variables. Individual information on about 17,000 household members includes extensive work, income and home time-allocation data; health, child-rearing and pregnancy-related information: and a massive compilation of demographic variables. Respondents were asked whether anyone in the household had been sick in the month previous to the survey and, if so, what was done for the illness, whether it was serious, how much was spent, how long physician visits took, and so on. One problem with this survey was that only cash- and timeexpenditure data reported by people who used medical services were available to use as proxies for medical visit prices. In the medical service demand literature, the use of expenditures instead of prices is a common approach. but it is to be avoided for several reasons. First, expenditures are simply the number of visits multiplied by the average price of each visit. What a demand model explains, however, is the number of visits; so it is a serious mismeasurement of the price variable to include elements of the dependent variable in it. Second, expenditures are likely to include more than just the price of the visit; drugs are the most obvious addition. Third, an individual will not report expenditures for services not used. For example, if someone chooses a private physician, he or she will have missing values for expenditures at traditional
Demand
for adult
outpatient
services
in the Philippines
323
PRICE
cost
rransportar1on
rime
waltlng
money cost co~nwrance OTHER
cash price
-
/soc~aI
securlry
PRICES
subst!rJtes
INCOME level sources fYpes assefs
8
TIME nature
wealth
ALLOCATION of work
felt
FOR
HEALTH
CARE
modern
public
modern
prlvare
trodiflonal
accupaflon
HEALTrl physlologtcal
DEMAND
PRIMARY
prtvote
NEEDS
/real
household
stze
KNDWLEDGE culrural
/
INFORMATION
Issues
education
SEASONALITY healfh effecrs cost
effecrs
Fig. I. Determinants of demand for health services in the Third World.
healers and government clinics. Analysts commonly replace these missing values with average expenditures based on the experiences of other users, but such numbers do not necessarily give a true measure of the whole array of prices facing each household. For these reasons, the 1982 survey of health facilities and practitioners was undertaken. Data were gathered on payment prices, hours of operation, and available medical personnel. Additional questions were asked on the villages served as well as distance, cost of travel and patient’s usual mode of transportation to each facility or practitioner. Data were also gathered on the types of services offered. For adult outpatient care, child outpatient care, clinic deliveries, home deliveries, prenatal care, well-baby care, child immunizations and adult vaccinations, practitioners were asked the usual price for one visit, the usual practitioner and the usual waiting time. They were also asked about services extended for five gastroenteritis, illnesses: specific tuberculosis, influenza, pneumonia and bronchitis. Iv.
E>lPIRICAL
STUDY
In conventional demand analysis, consumers are assumed to allocate their budgets simultaneously over all goods and services they are interested in
purchasing. Because all purchases are related to one an0ther-e.g. if a large medical bill is expected, it may not be a good time to buy a full-length ermine coat-the prices and quantities of all goods ideally must enter a system of demand equations. It is a misspecification of the model to break off one set of services, such as medical care, and estimate the demand for those services in isolation from all other consumption behavior. To get around this problem, the analyst makes use of the concept of separability, which allows one set of goods, such as medical services, to be analyzed separately. To do this requires one of two assumptions: either that the prices of the goods involved maintain a stable relationship with each other (e.g. that prices at private clinics, public clinics, and traditional practitioners stay in the same positions relative to each other) or that consumers allocate their budgets in a well-defined fashion. The behavior implied for consumers is that they make an initial allocation of their budgets to different categories of goods, such as to food. shelter, clothing. transportation and medical care, and thereafter maximize utility within each of these categories without further reference to the others. Only at the time of the initial budget allocations are the branches considered together [4]. These issues are explained because a potential
JOHN S. AKIS
37-l
source of disagreement with the approach used here is the matching of 1978 household data (BMS78) and 1981 barangay-level prices from the facility survey (BMSS8l). It should be clear at the outset. however. that in order to isolate demand equations for medical services from other goods categories. the assumption is already made that the relative prices of the goods are stable. which is the same assumption necessary to validate the matching of 1978 and 1981 surveys. We are not assuming that in 1978 the sample faced the same prices as were collected in 1981, only that they faced the same price structure. If private clinics were twice as expensive as traditional healers in I98 1, they are assumed to have been twice as expensive in 1978. This is not, in fact, an outlandish assumption over such a short time period for services that are close substitutes for each other. The adult outpatient model is a modification of the following general demand system:
where Q,, = whether medical service i is used by the jth individual. where i = (public. private. traditional. or no care) and j = (all sick or pregnant individuals, the sample depending on the model) PU, = public clinic or hospital serving the jth individual PR, = private clinic or hospital serving the jth individual TR, = traditional healer or midwife serving thejth individual P = vector of cash prices paid for each service (including visit cost, drug costs and transport costs) T = vector of time costs associated with each facility and service (waiting time, transport time) Y, = household assets for the jth individual Z, = a vector of social, demographic, and biological control variables for thejth individual.
a,=f,jvPL,. p,,, pm,, Tw,, TIT,, TTR,,y,>Z,, (1) Table
I. List of variables
Dependent
The choice
of practitioner
with sample statistics. used in estimating the demand Philioaines. 1978 (samale size = 399)
variable
for adult
Frequency
Visit: Traditional Public Private
60 73 124 142
NOM
Independent
er al.
variables
*8 pesos were equal
to USSl.00
at the time of the survey.
0.49 pesos* 14.49 pesos 3. IO pesos 13.72 km 9.41 km 0.31 km 63.24 100.32 I I.68 26.39 0.1 I
on the relative
outpatient ?/, 15 IS 31 36
Mean
Opporiunit~ cm0 Cash prices for one adult outpatient visit Public Private Traditional Distance to closest facility of practitioner Public Private Traditional Waiting time Public Private Traditional Amount spent on drugs Whether covered by insurance (0 = no; I = yes) Household msers and income Number of rooms in house Annual household income from all sources Number of individuals in household Sanitary raring of water source Sanitary quality of toilet facilities Demographic Male (0 = female; I = male) Urban (0 = rural; I = urban) Model Type of household 0 = extended family I = nuclear with other residents 3 = nuclear or single Education Highest grade completed Perceprion Perceived quality of life-family health and physical condition: I = dissatisfied 7 = very satisfied Perceived quality of life--availability of health services: I = dissatisfied 7 = very satisfied
depends
SD
2.00 4.05
1.66 13.37 10.45 0.53
min min min pesos
84.32 101.67 36.3 I 70.81 0.3 I
I .43 1955.83 pesos 6.79 1.56 1.82
0.94 10426.23 2.78 1.04 1.33
0.52 0.23
0.50 0.42
2.44
I .08
6.57
3.80
I
I .40
3.09
I.21
3.4
wits,
Demand for adult outpatient services in the Philippines cash and time costs associated with each facility, household income. and a set of control variables. Table 1 contains a list of variables used in the analysis. along with their definitions and simple descriptive statistics. The dependent variable is constructed in the following manner using the BMS78 data: Traditional
Visit = visit to herbolario (healer) hilot (traditional midwife)
or
Public Visit = visit to rural health unit, city health office. puericulture center, or public hospital Private
Visit = visit to private private hospital
No Visit = no professional sought.
clinic or to a consultation
The variables above include housecalls when the type of practitioner can be identified. Sixty-three percent of the traditional visits fall in this category. The cash cost variables come from the closest single private facility, public facility. and traditional practitioner serving each barangay. Sick individuals reported the total amount of time required for each visit; this is transformed into the time-price for each practitioner. The distance variable is calculated as an average for each type of facility by barangay, using the supplemental facility data gathered in 1981. It is clear from the means for the distances reported in Table 1 that all areas are well supplied with traditional practitioners, but that a considerable cost in terms of time, distance and transportation expenses may be incurred in order to use either the public or private modern practitioners. Economic theory requires that the dependent variable in the demand system defined above be continuous; i.e. for each individual the exact quantity of each service consumed should be known. However, only the type of practitioner used is known; so the dependent variable is limited to values of 0 or 1 for each alternative. Consequently, ordinary least squares is an inappropriate technique to use for estimating this model, and the multinominal logit procedure is used. In the logit approach, the system of demand equations takes this form: P (Public log
P (Traditional
log
P (Traditional
P (Private
log P
Visit) Visit)
= JYPublic
Visit) Visit)
= WLXe
P (No Visit) (Traditional
Visit)
= UN0 “,W.
The probability of a traditional visit is then equal, by definition, to 1 minus the sum of the three probabilities listed above, or P (Traditional
Visit) = 1 - [P(Public) + P(Private) + P(No Visit)].
Table 2 contains the expected coefficient the economic variables appearing in the model. The coefficient estimates are reported 3. In reporting the results, the ‘economic’
signs for demand in Table variables
315
described above are grouped by prices, distances and time. Microeconomic theory suggests that the probability of a visit to a particular practitioner will be negatively related to the costs associated with that practitioner and positively related to the costs associated with the substitutes. Table 3 indicates, however, that in no case is the cash price statistically significant at the 10% level (an asymptotic t-statistic of approx. 1.6). The estimated coefficients are extremely small, and the signs are occasionally opposite of what is expected. These results showing little significant effect of cash prices on the choice are not without meaning, however. First. a number of studies have found that individuals are insensitive to the money cost of health care, even in low-income countries [5]. Second, the variation in prices for the three types of practitioners is quite large and in exactly the direction one would expect: the average cost for public visits is 0.5 pesos, traditional visits cost an average of 3.10 pesos, and private visits cost an average of 14.49 pesos. Private clinics and hospitals thus charge over 28 times as much, on the average, as government clinics and public hospitals. The insensitivity to price that is apparent from the model suggests that public services may have a fair degree of latitude for manipulating their charges to help offset operating costs. Third, since this model does not control for the severity of illnesses or quality, it is possible that the apparent insensitivity to price may be at least partially caused by the wtlhngness of individuals to pay more to get higher quality care. Alternatively, private practitioners and traditional healers may informally cut their prices below those reported by using sliding fee scales. In financing public health care services, this possibility may be an important consideration. In our survey of 518 health facilities in the 100 sample Bicol barangays, 288 of the 397 (or 73%) facilities which charged fees indicated that fees varied according to the income of patients. In addition, 329 (or 63%) of the facilities accepted payment in the form of goods or services instead of cash. These different pieces of evidence suggest that the insensitivity to costs may be rational in the face of a recognized need for health care, especially if informal mechanisms exist for ameliorating the apparently high formal costs of private care. Distance should also act as a price variable. The farther a particular facility is from a barangay, the less frequently it should be used by residents of that barangay, other things equal. Conversely, the farther away are alternative facilities, the more likely it is that the closest facility will be used. The distance coefficients reported in Table 3 tend to have the predicted signs, and although the asymptotic tstatistics are generally higher than they are for cash prices, only three of the coefficients, two of which are for the distance from traditional practitioners, are statistically significant (10% level). This is an interesting result, because the mean distance from traditional practitioners is 0.31 km compared to 9.41 km for private and 13.72 km for public facilities. Small increases in the distance to traditional practitioners appear to induce a substantial increase in the proba-
JOHN S. AKIN et al.
326 Table
2.
Expected
signs
far
economic
variables
\s
distance.
waiting
logrt
1s
traditional
Public
L ?
Private
?
_
7
1
+
coberqe
No
No
VIJlt
vs
traditional
>
estrmates
PrrKite
pribate
public
,
VlSli vs
\s
pubhc
time
Traditional
Insurance
outpatient
visit
No
‘rs
traditional Pnces.
in adult
Pn\ste
Pubix
1
1
?
+ _
L
,
?
Income--assets Sumber
of
Income--all
T.ible
rooms
in house
3.
Multiple
logit
results:
demand
for
adult
-
+
outpatient
-
+
i
?
so~~rces
services,
+
Philippines.
1978.
Coefficient
estrmates
(asymptotic
r-value
in
parentheses) Probability
of
‘A‘
visit
Private
Public
vs ‘8‘
No
traditional
traditional
No
Private
VS
“S
-.S
visitt
visit
\&it
traditional
No
visit
“S
“S
VS
private
public
public
Prices Traditional
0.1513
0.0897
(1.136) Public
(0.763)
0.0499
Prirate
(0.625)
0.047s
0.009 (0.090)
-0.036
(-0.137)
(-0.166) -0.0782
- 0.069
(-0.860)
(-0.617)
-0.055
(-0.754)
-0.0166
-0.078 (-0.663)
-0.019
-0.0067
(O.SS5)
-0.062 (-0.524)
(-0.199)
0.0592
(0.4397)
0.073 (0.653)
-0.0295
-0.084’
(-1.18)
(-
(-0.752)
1.75)
DiciUtlCtT
Traditional Public Pri\
1.212’
0.881’
ate
(I ,696) 0.00 I2
(2.496)
(0.0669)
(0.582)
0.0094
-0.0234 (-
0.607
(1.08)
0.0137
(-1.39)
-0.605’
(-0.790)
0.0082
(0.887)
-0.0253
1.061)
-0.275
0.330
(1.27)
-0.0126
(0.587)
-0.01
-0.002
(-0.973)
( - 0.098)
(-2.09) 0.0044
(0.879)
(0.355)
0.0061
0.008
(0.247)
(0.494)
Waiting rime Traditional
-0.0195*
-0.0107* (-
Public
2.10)
(-
-0.008’ -0.0032
Drug
expenditure
(0.032)
0.0181*
0.01879
Insurance
I.22
1.378’
(1.497)
(I ,827)
Income
a.sW,S
Number
of
rooms
in
house
-O.OS-ll
Income--all
sources
0.242
Household
size-people
o.OvOao3 (0.208)
(-0.777)
(-0.3%) Quality
of
water
0.4-l5*
0.4232’
(1.95) Quality
of
toilet
-0.023
I
(-0.0136)
(-
-0.027
(0.345)
(-0.445) (-
0.943
1.39)
(-0.250) 0.0778
0.0506
(1.378)
(0.785)
-3.91’
-0.412’
1.097)
(-
1.94) - 0.000007
(-1.14)
(-2.07)
(-0.197)
(0.649)
-0.366’ (-
-0.CGO3
(-0.021)
(0.172)
-0.219
-0.0402
(-1.04)
0.233
-0.977’ (-1.97)
(-0.0184)
-0.00@02
0.0321
(2.07)
-0.820
0.326*
(-1.02)
-0.0545
-0.0619* (-5.33)
(-1.37)
(1.66)
- 0.00003
(-0.899)
-0.0615*
0. I57
-0.124
0.00014 (0.081)
(-5.28)
(0.280)
(-0.527)
-0.00002
-0.0272
0.00035
0.40 I
(1.04)
(-0.325)
0.00339 (1.47)
(0.192)
(0.517)
0.0038 (1.55)
3.24)
(1.52)
(-3.44)
coverage
-0.0067’ (-
-0.0032
-0.0432.
(2.74)
(2.672)
I*
(-4.14)
O.OOQ? I (0.1033)
-0.00007
(1.57)
(0.321)
-0.01
(0.539)
0.0104
0.0016
(-1.28)
-0.0012
(-0.923)
(-1.25)
-0.0088
(-2.154)
-0.0025
(3.22) Private
-0.0091*
2.923)
(-
2.35)
O.lli
1.307)
0.314’ (2.44)
(0.803)
Demographic
(I = male,
Male
0 = female)
0.463
-0.0149
tirban
(I
Scale
= urban,
of
0 = rural)
wad.-nuclear
HH
0.22
(1.29)
(-0.376) 0.310
0.406
0.370
(0.491)
(0.719)
(0.676)
-0.336’
-0.0812
Education
0.0955
-0.0354
0.0601
0.0659
(-0.0873)
0.0375
-0.0285 (-0.205)
(0.239)
0.023
0.0437
0.607
(0.455)
(1.39)
(-0.833)
(0.120)
(0.427)
0.076
(0.558)
- 0.242
0.371 (1.10)
(0.195)
1.94)
(-
0.0325
0.0092 (0.149)
0.0618’ (1.80)
-0.363’
(-1.720)
(-0.329)
I
(0.657)
(0.942)
(1.33)
Perceptions Satisfaction
with
family
health
-0.00421
-0.0625 (-0.413)
Satisfaction
with
health
services
-0.074
Consmnt lr+alues +The
numbers first
relative
at
reported
entry
in the
or
are first
traditional
visit
abobe the
column
to a traditional
to a traditional the
(-
-0.451
is signiricant
visit goes
practitioner.
0.10
level.
logarithms
of
is positive goes
up.
down-people
the
In contrast. are
more
(-
which
that
‘A’
indicates
1s traditional likely
to
that
relative as the
waiting use
tradrtronal
time
goes rather
probabihty price
up (-0.0107*), than
pubhc
0.0172
(0.800)
(0.125)
3.00
I.41
I .59
to the
(-0.426)
-0.122
(-0.937)
traditional
-0.0486
(-0.213)
-0.140
1.269) 2.55
occurs
-0.0281
(0.162)
-0.1964
1.38) I.144
probability
(O.l513).
(-0.686)
-0.2136
( - 0.429)
0.0204
-0.0907
(-0.310)
that
‘B’
increases. the facilities
occurx
the
probabdit) if they
For
example.
probability
the
of
a public
of a public
relative
must
wait
longer
for
Demand for adult outpatient services in the Philippines bility of using modem facilities. There also appears to be a certain amount of substitutability between traditional and private modern practitioners. However, because some of the coefficients to support this hypothesis are statistically insignificant, this hypothesis requires further testing. The time-price coefficients tell a much more confusing story. While six of these coefficients are statistically significant, the results are generally less strong than expected. All other things constant, the longer it takes for a public visit, the less likely that patient is to choose a public facility over a traditional one. This, however, is not the case for public vs private modern. As time to use public facilities increases, the use of public relative to private modern also increases. There is the obvious possibility that waiting time at public facilities may be positively correlated with higher quality medical care. These results are interesting in light of the usual impression, culled from simple correlations, that waiting time is an urgent problem at public facilities and makes them significantly less desirable than the alternatives [I]. In every case the traditional time variable is significantly, negatively related to the probability of making alternative visits. In other words, the longer a traditional visit takes, the more likely that it will be preferred to the alternatives. This result may be due to the fact that a few of the types of techniques that require longer visits at traditional practitioners, such as massages and certain rites or incantations, are the ones most favored by patients. In such a case the traditional practitioners who take more time would be more likely to be chosen. In fact, these traditional visits may be pleasant experiences which add to the attractiveness of traditional medicine and thus cause the time variable, as we have used it, to be misleading. An alternative explanation is that the time of day when these long visits take place may be off-hours when the opportunity cost of the visit is actually quite low even though the commitment in minutes may be high. Higher pharmaceutical prices have counterintuitive effects: they increase the probability of using both modern alternatives relative to traditional practitioners. As expected, having insurance leads to a preference for private practitioners over traditional ones. However, insurance does not appear to have a major influence in the choice of private vs public modern practitioners as the coefficient is not statistically significant. The income variable is included to show the effects on medical purchases of an enhanced capacity to consume all goods and services while controlling as best we could for the quality and quantity of household assets. The general result for income is that as income rises, both types of modern services tend to be substituted for the other two alternatives (no visit and traditional), and private doctors tend to be substituted for public clinics within the modern sector. Demographic
variables
The demographic
variables indicate a statistically
significant increase in the probability of a private visit relative to a public visit if the sick person is male. This may be indicative of a diversion of resources towards
327
males to improve the quality of their care: alternatively. if males in the sample suffer from more serious problems, it may be simply a need-oriented rather than a behavioral phenomenon. In only one case does the education variable even approach statistical significance. This is in the cell for the probability of no visit relative to a traditional visit. The sign is positive, a finding consistent with many simple correlations done by other authors, which show that educated people are more willing and confident than others to engage in home treatment. The perception variables are included to control for perceptions and acculturation with no expectations about the signs.
V. CONCLUSION
This paper reviews some of the issues involved in an economic analysis of demand, and provides a preliminary analysis of data collected in the Bicol region of the Philippines. This case study, conducted for adult outpatient visits, provides insight into the ways such analysis can clarify various planning issues. The method calls for price, time and other factors to be considered together, and leads to a number of important findings relevant to the financing issue. First, before conducting the survey of facilities in the Bicol region, we assumed that there would be a dearth of private doctors and that existing facilities would be fairly inaccessible. The survey uncovered a large and vigorous network of public, private and traditional practitioners. Second, almost half the sample used private modem or traditional fee-forservice medicine even though the public system of free clinics and hospitals is at least as accessible as the private modern clinic system. Third, prices and distance do not appear to be important determinants of demand; so there appears to be some room to reduce the drive to reduce both the distance to, and the prices of, rural health clinics to zero. Some selffinancing is probably feasibie, and attention to the quality of public services may be more important to their use than a low money price. Considerable work must be done to improve the estimation of this model. In addition, it should be extended to include multiple visits and other services, such as delivery and well-child care. Expenditure and use patterns must also be more carefully analyzed to ascertain which income groups benefit from free government services. Ackno~~(edgemenrs-The basic research for this project has been supported by a grant from PPC,,PDR,‘USAID. Dr Maureen Lewis of PPC,PDR is thanked for her assistance throughout this project. The Bicol River Basin Development Program and the Office of Local and Regiona! Development. AID Philippines are thanked for allowing us to use their BMS78 data. In particular, David Heesen, C. Stuart Callison and Don Wadley have been helpful. The Social Survey Research Unit. Ateneo de Naga University is also thanked for providing access to these BMS78 data. The price and other community data were collected by the Research Service Center. Ateneo de Naga University. They are thanked for working with us on the collection of these
JOHN S. AKIN er al
328
data and with providing us with insights into the problems ofcollecting the price data. The authors contributed equally to this paper.
REFERENCES
I. Akin J. S., Griffin C. C.. Guilkey D. K. and Popkin B. M. The Demand for Primary Healrh Setrices in rhe Third World. Littlefield, Adams. Totowa, N.J., 1984. 2. Popkin B. M. and Roco S. S. Jr with Bragais P. Jr. and Callison C. S. 1979 Bicoi Multipurpose Sutuey. Vol. I.
Survey Design and Implemenration. U.S. Agency for International Development, Manila, 1979. S.. Popkin B. M. and the 3. Griffin C. C.. Hamilton Ateneo de Saga Research and Service Center. The Bicol multipurpose supplemental survey. Carolina Population Center, University of North Carolina, Chapel Hill, 1981. 4. Deaton A. and Muellbauer 1. Economics and Consumer Behavior. Cambridge University Press. Sew York, 1980. 5. Heller P. S. A model of the demand for medical and health services in Peninsular Malaysia. Sot. Sci. Med. 16, 267-254. 198 I.