Demand for public health care in Pakistan.
Akbari, Ather H. ; Rankaduwa, Wimal ; Kiani, Adiqa K. 等
A health care demand model is estimated for each province in
Pakistan to explain the outpatient visits to government hospitals over
the period 1989-2006. The explanatory variables include the number of
government hospitals per capita, doctors' fee per visit at a
private clinic, income per capita, the average price of medicine and the
number of outpatient visits per capita in the previous period. All
variables are significant determinants of the demand for health care in
at least one province but their signs, magnitudes and the levels of
significance vary. These variations may be attributed to cultural,
social and religious factors that vary across provinces. Variations in
health care quality offered at public hospitals may also be a factor.
These factors and improved accessibility of health care facilities
should be the focus of public policy aimed at increasing the usage of
public sector health care facilities in Pakistan.
JEL classification: I110, I180, O150
Keywords: Health Care, Hospitals, Human Resources, Policy, Public
Health
I. INTRODUCTION
Governments in many developed and developing countries intervene in
health care markets due to large positive externalities whose presence
renders the provision of health care by the private sector insufficient.
However, private health care provision has been growing in low and
middle income countries [Aljunid (1995); Swan and Zwi (1997)]. Shorter
waiting time, better access, greater confidentiality, and greater
sensitivity to patients' needs by private providers are among the
major reasons cited in literature [Lonnroth, Tran, Thuong, Quy, and
Diwan (2001)].
The growing reliance on private sector for health services in
developing countries has raised concern among some health policy
analysts who view the quality of care offered by many private providers
to be poor [Brugha and Zwi (1998)]. Poor people spend a greater
proportion of their income on health care than do the rich, often using
less qualified or totally untrained private providers. Despite this
concern, and the lack of usage of public health care services, very few
studies in health economics literature have focused on estimating the
effects of economic and non-economic factors on demand for public health
care in developing countries. Such an analysis is necessary for an
appropriate policy response to rising health care usage in the private
sector if the social objectives of health care policy are to be met.
Rising income inequality is another reason for justifying such an
analysis. The present paper attempts to fill in the gap in empirical
demand analysis of health care in developing countries by estimating a
demand function for public health care in Pakistan. The demographic
transition of Pakistani population is expected to result in a growing
percentage of its elderly population [Durr-e-Nayab (2008)]. Hence,
demand for health care is expected to grow in this country in the next
few decades which provides further impetus for studying the demand for
health care in Pakistan.
Pakistan has a population of about 176 million people who comprise
six major ethnic groups including Punjabi 44.68 percent, Pashtun
(Pathan) 15.42 percent, Sindhi 14.1 percent, Sariaki 8.38 percent,
Muhagirs 7.57 percent, and Balochi 3.57 percent [Central Intelligence
Agency (2009)]. Punjabis and Sariakis are mostly found in the Punjab
province, Sindhis and Muhajirs in Sindh, Pashtuns in Khyber Pakhtunkhwa
(formerly NWFP) and Balochis in Balochistan. In addition, Khyber
Pakhtunkhwa has been home to Afghan refugees for the past 30 years and
about 1.7 million Afghan refugees were living there in 2009. This ethnic
distribution of Pakistani population has contributed to a diverse set of
cultural practices which may also reflect in the usage of health care
services. Cultural, as well as religious norms, often determine if and
when health care is sought by patients. (1) It is difficult to quantify
the impact of cultural and religious norms in a health demand
estimation. Hence, we estimate the health demand model separately for
each province. (2)
The paper is organised as follows. Section II presents a brief
overview of the current health care system in Pakistan. Section III
presents the health demand model and discusses the data used in its
estimation. Section IV presents the econometric analysis while Section V
discusses its results. Section VI presents a summary, concludes the
study and provides some directions for future research.
II. A BRIEF REVIEW OF PAKISTANI HEALTH CARE SYSTEM
Health care management in Pakistan is primarily the responsibility
of provincial governments, except in case of federally administered
territories. However, the federal government is responsible for planning
and formulating national health policies. Each provincial government has
established a department of health with the mandate to protect the
health of its citizens by providing preventive and curative services.
The provincial health departments also regulate private health care
providers. Large variations are found in public sector spending on
health care across provinces. Balochistan and Khyber Pakhtunkhwa spend
the least share of their public expenditure on health care and in recent
years, this share has declined rapidly in Balochistan [Akram and Khan
(2007)]. Health care provision in each province is in a three-tiered
system in which public, private and non-governmental sectors
participate. Private sector serves nearly 70 percent of the population.
It is primarily a fee-for-service system and covers a range of health
care provision from trained allopathic physicians to faith healers
operating in the informal private sector [World Bank (1993)]. Neither
private nor the government sector work within a regulatory framework and
very little information is available regarding the extent of human,
physical and financial resources engaged in these sectors.
According to the Pakistan Social and Living Standards Measurement
Survey [PSLM (2004-05)], as many as 67.4 percent households in Pakistan
consult health providers in private sector when they have health
problems (see Table 1). Majority of both rural and urban households
consult private health care providers which mainly include: private
clinics/hospitals, chemist/medical stores, and/or pharmaceutical
industry. A large number also consults homeopaths and tabbibs, (3) the
latter being especially popular in rural areas. The highest private
consultation is in the province of Sindh and the lowest in Balochistan,
especially rural Balochistan.
In contrast with private health care, public health care is offered
at a low cost. Generally, a patient treated at a public hospital's
Out Patient Department (OPD) does not pay any consultation fee but has
to incur own cost when buying medicines.
Health care facilities under public sector comprise more than
10,000 health facilities ranging from Basic Health Units (BHUs) to
tertiary referral centres (Table 2). In the 1990s, a BHU covered around
10,000 people, whereas the larger Rural Health Centres (RHCs) covered
around 30,000-45,000 people. (4) The "tehsil" headquarter
hospitals cover population at sub-district level whereas the district
headquarter hospitals serve an entire district. (5)
The finding that only one out of three patients uses public health
care raises an important question for health policy-makers in Pakistan:
should public funds not be directed towards subsidising the private
sector health care rather than to public sector health care which should
focus on serving the poorest of the poor?
To answer this question, it is important to understand the factors
that affect demand for public sector health care.
III. ECONOMIC MODEL AND DATA USED FOR PRESENT STUDY
Most health care demand studies in developing countries have
analysed the effects of various determinants of health care demand, such
as user fee (or consultation fee), income, quality of health care and
distance to the health care provider, on the demand for public health
care. These studies are based on data obtained through surveys sponsored
by international organisations such as the World Bank. (6)
In Pakistan, the PSLM is a comprehensive annual survey of
households which provides indicators of health in addition to other
social indicators such as education, water supply and sanitation, and
household economic situation and satisfaction. Unfortunately no
information on the price charged for consultation at health care
facilities, which is a major variable of interest to us, is available
through this or any other survey. The Federal Bureau of Statistics
publishes annual data, for major cities in Pakistan, on consultation fee
charged in a private health care facility that is viewed as a substitute
for a public health care facility in health economics literature. Annual
data on other determinants of health care demand are also available at
provincial levels. So we decided to base our analysis on time series
data for each province.
To estimate the demand for public health care in Pakistan, we
resort to the standard demand theory. In health economics, the demand
function for health care is measured at individual level as well as for
the entire market. (7)
Due to data limitations, we focus on market demand for outpatient
services in public hospitals. We specify the following demand function:
[VPC.sub.t] = f ( [HOSPC.sub.t], [DOCFE.sub.t], [INCOME.sub.t],
[PMED.sub.t], [VPC.sub.t-1]) ... ... (1)
This model postulates that the number of outpatient visits per
capita in period (VPC) at government hospitals is a function of the
number of government hospitals per capita (HOSPC), doctors' fee per
visit at private hospitals (DOCFE), income level per capita (INCOME),
price of medicine (PMED), and the number of outpatient visits per capita
in the past period ([VPC.sub.t-1]). The subscript "t" refers
to the time period.
Data on the number of patient visits were obtained from Development
Statistics of each province (Various Issues) covering the period
1989-2006. These were divided by the estimated population in each
province on which data were obtained from Agricultural Statistics of
Pakistan (Various Issues). For the province of Sindh, these data were
available only until 1998 and had to be extrapolated for the remaining
period of our analysis. We based our extrapolation on the average share
of outdoor patient visits in total (indoor and outdoor) hospital visits
in the late 1990s. (8)
We include the number of hospitals per person as an independent
variable as it is an indicator of the accessibility of public health
care service to the patient. If a public hospital is far from the
patient's place of residence, it may be inconvenient to visit that
hospital as the visit may involve incurring extra transportation and
time costs. For women, the inconvenience may be even more serious
because due to cultural and social reasons, many have to depend on a
male member of the household to accompany to the clinic. Hence, if a
private clinic is nearby, a patient may decide to visit the same by
paying the consultation fee, instead of travelling long distance to the
public hospital where consulting a doctor is free. Therefore, we expect
the number of hospitals per person to have a positive sign in our model.
Data on the number of hospitals in each province were obtained from each
province's Development Statistics and were divided by the estimated
population in each province.
Our second independent variable is doctors' fee charged for
consultation in a private clinic. A private clinic may be viewed as a
substitute for the OPD in a public hospital. Hence, we expect the sign
of this variable to be positive. Other studies, mentioned earlier, have
found a growing use of private health care in developing countries
despite the availability of public health care. This raises the issue
regarding the substitutability of public and private health care systems
in those countries. The sign and magnitude of this variable will help
determine the role of prices in patients' choice of a health care
system, keeping other demand determining variables constant. Data on fee
charged at private clinics were obtained from the Statistical Bulletin
published by the Federal Bureau of Statistics (Various Issues) which
provides data for major cities. Average fee reported in each
province's capital city was considered.
Income also plays an important role in determining whether a
patient uses private or public health care system. Due to the public
perception of lower quality of care provided in public sector hospitals,
and expectations of longer waiting times, one would expect public health
care service to be an inferior good. Hence, our dependent variable
should have a negative relationship with the income per capita variable.
Data on this variable were obtained from Household Integrated Economic
Survey (Various Issues) and are for average household income.
Another important independent variable in the model is the price of
medicine. Patients have to purchase medicine in the market regardless of
whether they attend OPD at a public hospital or go to a private clinic
for consultation. Hence, medicine price may be viewed as a rationing
tool in the use of public health care by patients in Pakistan. In fact,
patients' response to changes in this variable may also be viewed
as similar to how they would respond if a user fee were introduced for
using OPD at the public hospital. We expect this variable to have a
negative sign in the model. Data on medicine prices are averages of
various major medicines reported in the Statistical Bulletin of the
Federal Bureau of Statistics (Various Issues) and are for the capital
city in each province.
The model also includes the past level of visits per capita
representing the past behaviour of patients. This variable captures the
effect of habit persistence in the case of patient visits to OPDs in
government hospitals.
The demand model (1) is estimated in the following log-linear form:
Ln [(VPC).sub.t] = [[alpha].sub.0] + [[alpha].sub.1] Ln
[(HOSPC).sub.t] + [[alpha].sub.2] Ln [(DOCFE).sub.t] + [[alpha].sub.3]
Ln [(INCOME).sub.t] + [[alpha].sub.4] Ln [(PMED).sub.t] +
[[alpha].sub.5] Ln [(VPC).sub.t-1] + [e.sub.t] ... ... ... (2)
The coefficients [[alpha].sub.1], [[alpha].sub.2], [[alpha].sub.3],
[[alpha].sub.4] and [[alpha].sub.5] in this formulation are the
elasticities of demand with respect to the respective variables. The
term e is the stochastic error term.
The model is estimated using the annual time series data from 1989
to 2006 separately for each of the four provinces of Pakistan. An
analysis of linear long term trend in each series indicated that VPC had
a positive trend in all provinces, except in Sindh for which the series
displayed a negative trend. The linear trend in the HOSPC series was
negative in all provinces, again with the exception of Sindh, where a
positive linear trend was found in this variable. For all provinces, the
INCOME and PMED series displayed positive trends while DOCFE displayed a
negative trend. The PMED series displayed the strongest trend, among all
variables, in all four provinces.
Mean values of the data series and the corresponding standard
errors are reported for the four provinces during the period of study in
Table 2. Our dependent variable, i.e., patient visits per capita to the
OPD in public hospitals is significantly higher in Punjab than in any
other province. The second highest value of this variable is in Khyber
Pakhtunkhwa, while in the other two provinces this number is roughly the
same. Among the determinants of demand, medicine price is the lowest in
Punjab followed by Khyber Pakhtunkhwa. On per capita basis,
Balochistan--the least populated province in Pakistan--has the highest
number of hospitals. Income per capita and doctor's consultation
fee is the second highest, while medicine price is the highest, in
Balochistan. Doctor's fee and income per capita are positively
related across provinces. Our econometric analysis of the next Section
will allow us to assess the sensitivity of the visits per capita in each
province with respect to each of the demand determinants, while keeping
the effect of others constant.
IV. ECONOMETRIC RESULTS
Recent research in time series analysis emphasises the need for
investigating (or testing for) evidence for stationarity and
co-integration among time series data prior to estimation of econometric
models using them. These tests are necessary to ensure that the model
estimation would not yield spurious results. Accordingly,
Phillips-Perron unit root tests are applied to all of the data series
used in this study. The results are reported in Table 3. Results
reported in Table 3 confirm that all of the data series used for Punjab
are integrated of order zero (i.e., I(0)), and hence the estimation of
the model given by the Equation (2) would not yield spurious results.
Therefore, the model given by the Equation 2 is estimated for Punjab
with the addition of the variable T that accounts for the time trend.
Results of this exercise are reported in Table 4.
Results of the unit root tests indicate that many of the data
series for the other three provinces are not integrated of order zero.
However, the first differences of all of those series are found to be
stationary (i.e., the data series are integrated of order one, I(1)).
The results of the unit root tests that confirm their stationarity are
also reported in Table 3. The Phillips tests of cointegration are then
applied to verify evidence of a long run equilibrium relationship among
these variables in each of the provinces. In the presence of such
evidence, an appropriately specified error correction model, derived
from the model Equation (2), is estimated.
The error correction model specified in this study is given by the
following equation:
[DELTA]Ln [(VPC).sub.t] = [[beta].sub.0] + [[beta].sub.1] [DELTA]Ln
[(HOSPC).sub.t] + [[beta].sub.2] [DELTA]Ln [(DOCFE).sub.t] +
[[beta].sub.3] [DELTA]Ln [(INCOME).sub.t] + [[beta].sub.4] [DELTA]Ln
[(PMED).sub.t] + [[beta].sub.5] [DELTA]Ln [(VPC).sub.t-1] +
[[belta].sub.6] [e.sub.t-1] + [v.sub.t] ... ... ... (3)
The evidence for a long run relationship (i.e., cointegration) in
terms of Phillips tests is present in the case of Sindh. Accordingly,
the error correction formulation is considered the appropriate model to
be estimated for that province. However, we estimated the error
correction model for all of the three provinces for which all the data
series were found to be integrated of order one and have reported those
results in Table 4.
V. DISCUSSION OF RESULTS
Based on the results of Phillips Perron unit root tests and
Phillips tests of cointegration, we estimated the model given by
Equation 2 for Punjab and the model given by Equation 3 for the rest of
the provinces. The results of this exercise are summarised in Table 4.
The estimated coefficients are the demand elasticities with respect to
explanatory variables included in the model. These explanatory variables
include the number of government hospitals per capita, doctors' fee
per visit at private hospitals, income level per capita, price of
medicine, and the number of outpatient visits per capita.
The estimated demand elasticity with respect to the hospitals per
capita (HOSPC) are greater than unity in absolute value for all the four
provinces. The estimates for Punjab, Sindh and Balochistan are positive
and statistically significant at the 5 percent level of significance.
The estimate for Khyber Pakhtunkhwa is negative and significant at 10
percent level of significance. The negative sign for Khyber Pakhtunkhwa
appears puzzling and may be partly due to a strong aversion to treatment
outside of home. Greater availability of health professionals that
accompanies a public hospital may create a higher demand for treatment
at home, especially in case of women in Khyber Pakhtunkhwa. This
interpretation however needs further investigation by a survey of
preferences among patients, women in particular. The lowest elasticity
value in Balochistan is perhaps also reflecting lower quality of public
health care in that province where the share of public health care
expenditure has declined in total public expenditures over the period
covered in this study.
The estimated elasticity with respect to doctors fee (DOCFE) is
statistically significant at 5 percent level of significance for all the
provinces but Balochistan. The estimated elasticity value is positive in
Punjab and Sindh and negative in Khyber Pakhtunkhwa, indicating that
patients in the first two provinces view health services provided at
OPDs in public hospitals as substitutes for services provided at private
clinics, while patients in Khyber Pakhtunkhwa view them as complementary
goods. The Khyber Pakhtunkhwa result could be an indication that
patients in that province view public and private health care as a
collective good and reduce their demand to all health care provided
outside of home when consultation fee at private clinics rises. They may
be seeking home care when fee charged at a private clinic increases.
This explanation can only be confirmed or rejected by further research.
The statistically insignificant effect of DOCFE in Balochistan is
consistent with the lowest availability of hospitals per capita in that
province.
Among the four provinces, Sindh is the only province where the
estimated income elasticity is found to be significant at 5 percent
level of significance. In case of Punjab, the estimated income
elasticity is found to be significant only at 10 percent level of
significance and is positive only in that province. However, the demand
for OPD services is income inelastic, as the income elasticity value is
lower than unity. In Khyber Pakhtunkhwa and Balochistan, income has no
statistically significant effect on the demand for public health care,
i.e., demand is income inelastic in these two provinces as well. The
income inelasticity of public health care in most of the provinces of
Pakistan indicates that health care is generally viewed as a necessity
in that country. (9) The perfect income inelasticity of demand in Khyber
Pakhtunkhwa and Balochistan may be due to lower per capita income levels
in these two provinces which are also home to a large number of refugees
from Afghanistan. Sindh is the only province where public health care is
viewed as inferior good which could be due to greater availability of
private health care in that province but this needs further
investigation.
The estimated elasticities with respect to price of medicine (PMED)
are negative in all of the provinces, as expected, but not statistically
significant at 5 percent level of significance in any of the provinces.
In Khyber Pakhtunkhwa, the estimated elasticity value is the highest in
magnitude and is significant at the 10 percent level of significance.
Hence, patient visits are inelastic with respect to the price of
medicine, particularly in the provinces of Punjab, Sindh and
Balochistan.
The estimated coefficient of the lagged visits per capita which
captures the effect of past behaviour is significant at 5 percent level
of significance in all of the provinces. The estimate is positive for
Punjab, Khyber Pakhtunkhwa and Balochistan. In these provinces, the
higher the demand in the past, the higher the demand in the present. In
contrast, the estimated coefficient for the province of Sindh indicates
that the higher the demand in the past the lower the present demand in
that province. The positive sign is an indication of repeat patient
visits in three provinces, but not in Sindh.
VI. SUMMARY, CONCLUDING REMARKS AND DIRECTIONS FOR FUTURE RESEARCH
The present study provided a model of demand for health services to
analyse the demand for outpatient visits to OPDs at government hospitals
in the four provinces of Pakistan. The model was estimated using the
annual time series data from 1989 to 2006. Based on the results of the
unit root tests (Phillips Perron unit root tests) and cointegration
tests (Phillips tests) performed on the data, an error correction
formulation of the model is estimated for Sindh, Khyber Pakhtunkhwa and
Balochistan. The explanatory variables included the number of government
hospitals per capita, doctors' fee per visit at a private clinic,
income per capita, the price of medicine and the number of outpatient
visits per capita in the previous period.
The estimated coefficients of any explanatory variable do not
display uniformity in terms of the sign or the magnitude across the four
provinces. However, all the explanatory variables can be identified as
empirically significant determinants of the demand for health care at
OPDs in government hospitals as the estimate for any given explanatory
variable is significant at the 10 percent level of significance at least
for one among the four provinces. We believe differences in the
estimated elasticities across the provinces could be due to two main
reasons: First, they could partly reflect the differences in the
dominant culture and customs that influence the choice of patients among
home care, private health care and public health care services. Indeed,
Ali, Bhatti and Kuroiwa (2008) have found that cultural norms dictate
women's utilisation of maternal health care facilities in the
provinces of Khyber Pakhtunkhwa and Punjab. Second, these results could
also be a reflection of differential quality of health care offered at
public hospitals in Pakistan in different provinces. For example, the
declining share of health care spending in total public spending in
Balochistan could have resulted in lower quality of health care in that
province than in others and is probably reflected in lower coefficient
of the hospital per capita variable in that province. Available data do
not allow us to identify the extent to which cultural norms and quality
of health care delivery result in differential utilisation of public
services across the four provinces. However, to gain more insights into
the above results, one can classify the data into urban and rural
regions, because rural population is usually more reliant on traditional
and religious values. To assess the effects of quality differences in
health care, one may construct the appropriate provincial health quality
measures.
In most provinces, the demand for health care is positively related
to the availability of health services, the doctors' fees at
private clinics, and the past level of demand. (1) The demand is
negatively related with the price of medicine in majority of provinces.
The elasticity of demand with respect to the number of hospitals per
capita is the highest of all estimated elasticities. As such, the
availability of services is perhaps the most significant determinant of
the demand for health care in Pakistan. The negative value of this
elasticity in Khyber Pakhtunkhwa is an indicator of general aversion to
out-of-home care which is substituted for home care with greater
availability of health professionals accompanying government hospitals.
The results confirm the fact that services provided at OPDs in public
hospitals and in private clinics are substitutes in most parts of
Pakistan and, based on the test of significance, are viewed as inferior
goods only in the province of Sindh. Income does not have an effect on
public health care demand in the Khyber Pakhtunkhwa and Balochistan
provinces, while it does have a positive effect in Punjab.
There are two main policy implications of this study: First, public
policy should be sensitive to different economic, cultural and religious
practices in each province that play their role in health care demand,
(10) Second, accessibility of health care providers is an important
determinant of health care demand. Hence, the lack of demand for public
health care services should not be viewed as indicating patients'
preference for private health care services rather the lack of
availability of health care services in the public sector. According to
the PSLM (2007-08), about 46 percent of patients do not use a public
sector hospital in Pakistan either because it is too far away from their
home or because there is none available in their region. Health policy
may consider opening smaller and cost-effective health care units on a
larger scale in the country. By doing so, the current regressive nature
of public health care expenditures in Pakistan as indicated by Akram and
Khan (2007) can also be addressed.
The markets for health care in many developing countries undergo
significant transformations as a result of such factors as the increase
in the participation of both the public and private sectors and the
improvements in the level of health education among the general public.
Higher level of understanding of both demand and supply sides of these
markets is essential for effective policy-making in health related
sectors in these countries. The level of interest found in the empirical
research on demand side studies of health markets does not match the
interest in supply side studies of these markets, in developing
countries in particular. The difficulties of finding quality data may
have been partly responsible for the low level of interest in empirical
investigations of health markets in developing countries. Pakistan is.
no exception in this regard. For example, focusing specifically on
United Nation's Emergency Obstetric Care (UN EmOC) indicators of
maternal morbidity and mortality, Ali and Kuroiwa (2007) found poor
record keeping practices in the health care facilities of the Khyber
Pakhtunkhwa and Punjab. Yet, the present study focuses on the demand
side of the health care market using the best available data obtainable
from the domestic data sources in Pakistan. To the best of our
knowledge, this is the first attempt to empirically estimate demand
functions for health care for four provinces of Pakistan. Our economic
model is based on sound economic theory and a strong econometric
analysis. In our view, the quality of data used in this study cannot be
significantly different from those used in any other empirical study in
Pakistan based on time series data. It is also not known to us as to
what extent the quality of our data have differentially affected our
results across the four provinces. A curious researcher may corroborate
our results with future survey data that focus on economic determinants
of health care demand in Pakistan.
REFERENCES
Akin, J., K. Guilkey David, and Denton E. Hazel (1995) Quality of
Services and Demand for Health Care in Nigeria: A Multinomial Probit
Estimation. Social Science and Medicine 1527-1537.
Akram, M. and F. Khan (2007) Health Care Services and Government
Spending in Pakistan. Pakistan Institute of Development Economics,
Islamabad (PIDE Working Paper 2007: 32).
Ali, M. and C. Kuroiwa (2007) Accurate Record Keeping in Referral
Hospitals in Pakistan's North West Frontier Province and Punjab: A
Crucial Step Needed to Improve Maternal Health. Journal of Pakistan
Medical Association 443-446.
Ali, M., M. Bhatti, and C. Kuroiwa (2008) Challenges in Access to
and Utilisation of Reproductive Health Care in Pakistan. Journal of Ayub
Medical College Abbottabad 20:4, 3-7.
Aljunid, S. (1995) The Role of Private Medical Practitioners and
their Interactions with Public Health Services in Asian Countries.
Health Policy and Planning 10, 333-49.
Asfaw, A., Joachim Von Braun, and Stephan Klasen (2004) How Big is
the Crowding-out Effect of User Fees in the Rural Areas of Ethiopia:
Implications for Equity and Resources Mobilisation. World Development
2065-2081.
Brugha, R. and A. Zwi (1998) Improving the Quality of Private
Sector Delivery of Public Health Services: Challenges and Strategies.
Health Policy and Planning 13, 107-120.
Central Intelligence Agency (2009) The World Factbook--Pakistan.
(https://www.cia.gov/library/publications/the-world-factbook/geos/
pk.html#People, Accessed August 28-2009).
Ching, Panfila (1995) User Fees, Demand for Children's Health
Care and Access Across Income Groups: The Phiiappine Case. Social
Science and Medicine, 37-46.
Durr-e-Nayab (2008) Demographic Dividend or Demographic Threat in
Pakistan. The Pakistan Development Review 47:1, 1-26.
Falvo, D. R. (2004) Effective Patient Education: A Guide to
Increased Compliance. Jones and Bartlett Publishers.
Folland, S., A. Goodman, and M. Stano (2009) Economics of Health
and Health Care. (6th Edition). Prentice Hall.
Lonnroth K, TU Tran, LM Thuong, HT Quy, and V. Diwan (2001) Can I
Afford Free Treatment? Perceived Consequences of Health Care Provider
Choices Among People with Tuberculosis in Ho Chi Minh City, Vietnam.
Social Science Medicine 52, 935-48.
Mwabu, Germano, Martha Ainsworth, and Andrew Nyamete (1993) Quality
of Medical Care and Choice of Medical Treatment in Kenya: An Empirical
Analysis. The Journal of Human Resources 838-862.
Pakistan, Government of (1995) Situation Analysis of Health in
Pakistan. Islamabad: Ministry of Health.
Pakistan, Government of (2009) PSLM- 2004-05, 2007-08--Pakistan
Social and Living Standards Management Survey. Islamabad: Federal Bureau
of Statistics.
Swan, M. and A. Zwi (1997) Private Practitioners and Public Health:
Close the Gap or Increase the Distance. London: London School of Hygiene
and Tropical Medicine.
Toor, I. A. (2001) Determinants of Health Expenditure and its
Distributional Impact on Various Income Groups in Pakistan. Applied
Economics Research Centre, University of Karachi. (Master of Philosophy
Thesis.)
Valery, E. (2002) Biomedical Service Delivery for Women in Northern
Pakistan: Ideological Contrasts and Social Resistance. Faculty of
Graduate Studies, The University of British Columbia. (Master of Arts
Thesis.)
World Bank (1993) Health Sector Study Islamic Republic of Pakistan:
Key Concerns and Solutions. Washington, DC. 65-80.
Xu, Ke, D. Evans, P. Kadama, J. Nabyonga, P. Ogwal, P. Nabukhonzo,
and A. Aguilar (2006) Understanding The Impact of Eliminating User Fees:
Utilisation and Catastrophic Health Expenditures in Uganda. Social
Science and Medicine, 866-876.
(1) Falvo (2004) points out that in some cultures seeking medical
help may be considered a sign of weakness. In other cultures, women may
take a medical advise from older women in the family rather than seeing
a health practitioner. Varley (2002) attributes the failure of
biomedical pharmaceuticals and clinical practices in its treatment of
women in Northern Pakistan to social and religious norms.
(2) It is understood that due to ethnic diversity within a
province, cultural practices within that province may not be
homogeneous. For example, as one would expect in the provinces of Punjab
and Sindh. However, in each case, there is a dominant ethnic group whose
practices may largely dominate the data that we describe in a later
section.
(3) Tabbibs or hakims, are traditional health providers who operate
under ancient Greek system of remedy relying mainly on herbal medicine.
In 2006, there were about 55,000 tabbibs in Pakistan with 31 specialised
institutions offering diploma and 3 universities offering degrees
(http://www.pakistan.gov.pk/ministries/planninganddevelopment-ministry/
mtdf/7-Health/7-Health.pdf).
(4) BHUs and RHCs comprise Primary Health Care (PHC) units.
(5) Government of Pakistan, Situation Analysis of Health in
Pakistan, Ministry of Health, Islamabad, 1995.
(6) For example, Mwabu, Ainsworth, and Nyamete (1993) on rural
Kenya; Akin, David, and Hazel (1995) on the Ogun state on Nigeria; Xu,
et al. (2006) on Uganda; Asfaw, Braun, and Klasen (2004) on Ethiopia;
and Ching (1995) on the Phillipines.
(7) In their textbook on health care economics, Folland, Goodman,
and Stano (2009) discuss the application of demand theory in economics
of health care.
(8) During the period 1989-1998, the share of outdoor patients in
total patient visits was fairly stable, varying between 96 and 97
percent.
(9) The PSLM survey of 2007-08 indicated that the percentage of
respondents in Punjab who indicated their reason for not using a public
hospital to be its distance from their place of residence is the
highest, about 26 percent, among all four provinces [Pakistan (2009)].
If public hospitals are not evenly distributed in the province, then the
positive income elasticity can be justified as poor are unable to travel
long distance to a public hospital. This aspect of our result needs
further research.
(10) We suggest a future research should investigate the impacts of
cultural and social factors in determining the demand for health care in
Pakistan more directly.
Ather H. Akbari <
[email protected]> is Professor of
Economics, Saint Mary's University, Halifax, Canada. Wimal
Rankaduwa <
[email protected]> is Professor of Economics,
University of Prince Edward Island, Canada. Adiqa K. Kiani
<
[email protected]> is Assistant Professor of Economics,
Federal Urdu University of Arts, Science and Technology, Pakistan
Authors' Note: Financial support for this study was provided
to the first author by the Higher Education Commission during 2008-09
under its Start-up research grant programme available to foreign
faculty. We thank the anonymous reviewer for providing valuable comments
on an earlier draft.
Table 1
Health Consultations by Type of Health Provider- Consulted (Percentage)
Health Provided/ Consulted
Private/ Public
Region/ Dispensary/ Dispensary/ Hakims/
Province Hospitals Hospitals RHC/BHU Herbalist
Urban Areas 71.5 20.47 0.52 1.76
Punjab 73.5 15.42 0.26 3.09
Sindh 78.93 17.79 0.75 0.75
Khyber
Pakhtunkhwa 55.81 31.31 0.39 0.67
Balochistan 56.47 40.68 1.2 0.99
Rural Areas 64.31 20.68 3.5 2.32
Punjab 71.08 15.27 1.2 4.74
Sindh 76.29 18.71 3.23 0.53
Khyber
Pakhtunkhwa 51.73 21.73 3.6 1.15
Balochistan 47.57 37.51 10.21 2.13
Overall 67.4 20.59 2.22 2.08
Punjab 72.27 15.34 0.74 3.93
Sindh 77.6 18.25 2 0.64
Khyber
Pakhtunkhwa 52.92 24.53 2.66 1.01
Balochistan 50.34 38.5 7.41 1.77
Health Provided/ Consulted
Region/ Chemist/ Sauna/
Province Homoeopathic Pharmacy Saini Other
Urban Areas 1.54 3.1 1.01 0.11
Punjab 2.28 3.91 1.37 0.18
Sindh 0.96 0.14 0.65 0.03
Khyber
Pakhtunkhwa 1.44 9.13 1.09 0.16
Balochistan 0.1 0.13 0.43 0
Rural Areas 0.6 6.89 1.36 0.35
Punjab 1.22 3.85 2.28 0.35
Sindh 0.16 0.28 0.52 0.29
Khyber
Pakhtunkhwa 0.44 19.9 1.13 0.32
Balochistan 0.17 0.69 1.2 0.51
Overall 1 5.26 1.21 0.24
Punjab 1.74 3.88 1.83 0.27
Sindh 0.56 0.21 0.58 0.16
Khyber
Pakhtunkhwa 0.73 16.75 1.12 0.28
Balochistan 0.15 0.52 0.96 0.35
Source: Federal Bureau of Statistics, PSLM (2004).
Table 2
Mean Values and Standard Errors of Variables in Model (1989-2006) *
Punjab Sindh
VPC 0.62998 0.26145
(0.27444) (0.063070)
HOSPC 0.0000582 0.0000363
(0.0000040) (0.0000066)
DOCFE 46.637 110.87
(14.624) (58.050)
INCOME 3051.4 4163.9
(168.98) (299.72)
PMED 83.939 86.593
(32.289) (24.380)
Variable Khyber
Pakhtunkhwa Balochistan
VPC
0.35727 0.27787
HOSPC (0.21442) (0.21463)
0.0000728 0.0002012
DOCFE (0.0000070) (0.0000366)
39.785 100.98
INCOME (6.0771) (19.172)
2945.8 3527.0
PMED (237.15) (336.75)
84.462 87.046
(26.493) (24.507)
Notes: * Numbers in the parentheses are standard errors.
VPC = Visits per capita to Out Patient Department at government
hospitals, HOSPC = Number of hospitals per capita, DOCFE =
Consultation fee charged by a doctor at a private clinic, INCOME
= Income per capita, PMED = Medicine price.
Table 3
Results of Phillips-Perron Unit Root Tests on Variables Used in
the Model
F-test (1) F-test(2)
Province Variable [H.sub.0]: [H.sub.0]:
[[alpha].sub.0] [[alpha].sub.0]
+ +
[[alpha].sub.1] [[alpha].sub.1]
+ +
[[alpha].sub.2] [[alpha].sub.2]
= 0 = 0
Punjab Ln (VPC) 7.4167 6.0146
Ln (HOSPC) 4.5993 6.0348
Ln (DOCFE) 9.1662 4.9177
Ln (INCOME) 4.1485 6.0785
Ln (PMED) 4.3206 3.1681
Sindh ALn (VPC)
[DELTA]Ln (HOSPC)
[DELTA]Ln (DOCFE)
[DELTA]Ln (INCOME)
ALn (PMED)
Khyber [DELTA]Ln (VPC) 8.0116 12.016
Pakhtunkhwa [DELTA]Ln (HOSPC) 9.8209 14.719
[DELTA]Ln (DOCFE) 3.8042 5.7057
[DELTA]Ln (INCOME) 16.922 25.383
[DELTA]Ln (PMED) 5.0248 7.5277
Balochistan [DELTA]Ln (VPC)
[DELTA]Ln (HOSPC)
[DELTA]Ln (DOCFE)
[DELTA]Ln (INCOME)
[DELTA]Ln (PMED) 5.2658 7.8978
F-test(3) t-test(1)
Province Variable [H.sub.0]: [H.sub.0]:
[[alpha].sub.0] [[alpha].sub.0]
+ +
[[alpha].sub.1] [[alpha].sub.1]
+ +
[[alpha].sub.2] [[alpha].sub.2]
= 0 = 0
Punjab Ln (VPC)
Ln (HOSPC)
Ln (DOCFE)
Ln (INCOME)
Ln (PMED)
Sindh ALn (VPC) 5.4312 -3.2553
[DELTA]Ln (HOSPC) 3.7430 -2.7521
[DELTA]Ln (DOCFE) 6.4262 -2.9592
[DELTA]Ln (INCOME) 12.526 -4.8401
ALn (PMED) 4.1253 -2.8062
Khyber [DELTA]Ln (VPC)
Pakhtunkhwa [DELTA]Ln (HOSPC)
[DELTA]Ln (DOCFE)
[DELTA]Ln (INCOME)
[DELTA]Ln (PMED)
Balochistan [DELTA]Ln (VPC) 4.0011 -2.8180
[DELTA]Ln (HOSPC) 4.2927 -2.9236
[DELTA]Ln (DOCFE) 7.4273 -3.8595
[DELTA]Ln (INCOME) 6.2744 -3.5234
[DELTA]Ln (PMED)
Notes: For variable legend, please see notes below Table 2. The
equation estimated for a given variable y for t-test (1) and F-
test (3) is given as:
Table 4
Model Estimates for Punjab, Sindh, Khyber Pakhtunkhwa
and Balochistan
Model Equation 3
Model Error Correction Model
Equation 2
Khyber
Variable (a) Punjab Sindh Pakhtunkhwa Balochistan
Ln (HOSPC) 3.1599 6.6768 ** -7.5582 * 2.7145 **
(5.028) (4.754) (-2.124) (3.147)
Ln (DOCFE) 0.84649 ** 1.2633 ** -6.2606 ** 0.40214
(3.068) (3.978) (-4.292) (0.8543)
Ln(INCOME) 0.58216 * -2.9310 ** -1.3687 -0.68622
(2.076) -(6.069) (-0.4610) (-0.6806)
Ln (PMED) -0.16337 -1.1600 -5.7969 * -1.0185
(-1.035) -(0.9885) (-2.187) (-0.8424)
Ln [(VPC). 0.43679 ** -0.87355 ** 0.61074 ** 1.3711 **
sub.t-1] (2.668) -(3.450) (3.104) (4.498)
[e.sub.t-1] -1.5119 ** -1.6605 ** -1.2341 **
-(4.454) (-5.697) (-3.358)
T 0.13343 **
(5.704)
Constant 21.819 ** -0.01949 0.03551 0.10105
(4.000) (-0.8381) (0.1541) (0.9565)
N 17 13 16 16
Df 10 6 9 9
[R.sup.2] 0.99 0.90 0.83 0.67
Adj.[R.sup.2] 0.98 0.81 0.71 0.45
D.W. 2.1570 2.4707 1.9337 2.2351
D.H -1.4497 -2.6429 2.3446 -1.2402
Notes:
(a) the error correction model includes the error correction
term [e.sub.t-1] and the first differences of the other
variables. Values reported in parentheses are the t-ratios for
coefficients.
* Statistically significant at 10 percent level.
** Statistically significant at 5 percent level.
Variable legend provided under Table 2.
