June 2010

This publication was prepared by Hong Wang, Tesfaye Dereje, and Benedict Harris from Health Systems 20/20

Project.

Who Benefits from Government Subsidies

to Public Health Facilities in Liberia?

Evidence from Benefit Incidence Analysis

Health financing consultative workshop with county officials May 2010. Photo by Ben Harris.

1

Introduction

The World Health Assembly in 2005 called for all health systems to move toward

universal coverage, defined as “access to adequate health care for all at an affordable

price.” To achieve this goal, a major challenge for many low-income countries is to

develop and implement appropriate health financing mechanisms that move away from

out-of-pocket payments at the point of service. In addition to prepayment approaches, many low-income countries have selected direct public subsidization of health services

in order to ensure that services are accessible and that use is based on need rather than

on ability to pay.

Liberia has been devastated by years of war, which has affected all sectors including

health. Currently, Liberia has among the poorest health indicators in the world. Based

on World Health Organization (WHO) statistics for 2006 (the most recently available

data), the infant mortality rate is about 157 per 1,000 live births, which ranks Liberia

third highest in the world; life expectancy at birth, at about 44 years, is the seventh

lowest; under 5 mortality is about 235 per 1,000 live births, the fourth highest in the

world; and the maternal mortality ratio is about 1,200 maternal deaths per 100,000 live

births, the eighth highest.

To ensure the population’s access to quality health services and improve health status,

the Liberian Ministry of Health and Social Welfare (MOHSW) has developed a National

Health Policy that pledges to “improve the health status of an increasing number of

citizens, on an equal basis, through expanded access to effective basic health care,

backed by adequate referral services and resources.” With strong external commitment

and funding support, a “free healthcare for all” policy was adopted in 2007 to ensure

that the basic package of health services (BPHS) is available to every citizen (MOHSW

2006). The government supports investments in health care delivery institutions,

including infrastructure, human resources, and drugs and supplies to cover the cost of

delivering the BPHS.

Undoubtedly, this government supply-side subsidy has the potential to reduce the

financial burden on patients and increase access to the BPHS. However, this approach

raises a series of health financing policy questions related to affordability, sustainability,

and equity in the distribution of benefits. For instance, a recent costing study found that

only about 50 percent of required primary- and secondary-level health services are

actually provided in health practices at the present time. This indicates that Liberia is still

in the process of expanding provision of the full BPHS, and substantially more resources

will be needed to deliver the full package. In addition, National Health Accounts results

for 2008 indicated that donor contributions still accounted for 55 percent of total health

expenditures, while private out-of-pocket expenditures accounted for 29 percent of

total health expenditures even with the implementation of the free health care policy

(Government of Liberia and Health Systems 20/20 2009). Although the majority of

households in Liberia (76 percent) live below the poverty line, each household spends

about $10 per person per year on health or approximately 3 percent of their income.

2

There is also general concern about who benefits from government investments in the

health care system, or the equity aspects of health care financing policy. There is

substantial evidence to indicate that without careful targeting strategies, government

investments in the health care system may not benefit those most in need, and the

wealthy will tend to benefit more than the poor (Wagstaff 2010).

The objective of this study is to analyze how users of different types of health services

are distributed across income groups and hence are accessing the government subsidy.

It is expected that poor populations will have less ability to access and utilize health care

services due to physical and financial constraints, with the result that Liberia’s current

“free healthcare” financing policy may end up benefiting the rich more than the poor.

Methodology

The Benefit Incidence Analysis (BIA) method was employed for the analysis of the

distribution of public expenditures across different income groups in this study.

Measuring the distribution of benefits of publicly provided goods (such as health) has

long been a concern in the economics literature. BIA combines the cost of providing

public services (usually obtained from government or service provider data) with

information on their use (usually acquired through household sample surveys) to show

how the benefits of government spending are distributed across the population (Demery

2000).

BIA of public subsidies involves three steps.

Step one: estimating unit subsidies. In BIA, a unit subsidy is defined as total government

spending on a particular type of service divided by the total number of users of that

service; for example, government spending on primary health care divided by the

number of users of primary health services gives us the unit government subsidy per

visit for primary health care. However, user fee revenue collected at health facilities that

is returned to the treasury needs to be netted out from government spending before

dividing it by the number of service users. When the total net subsidy is divided by the

total number of users, it gives us the net unit subsidy.

In this study, we first identified all public facilities that received government supply-side

investments using the Health Facility Accreditation Survey conducted by the MOHSW

(2009). There were a total of 349 public facilities.

Second, we estimated the total number of outpatient and inpatient health care services

delivered in 2008 by extrapolating from the facilities in the Health Information System

database compiled by the MOHSW, to all 349 public health facilities that were identified

to be functioning according to the accreditation survey.

3

Third, in order to quantify the total amount of services delivered using one standard

unit of measurement across all health facilities, we applied the concept of outpatient

equivalents1 to convert inpatient days into an equivalent number of outpatient services.

In this case, we estimated that one inpatient visit is equivalent to 4.62 outpatient visits

based on a costing study done in 2009 (David Collins and Zina Jarrah, 2009).

Fourth, we obtained the government’s total budgetary allotment to public facilities for

curative care services from the MOHSW Office of Financial Management. We then

estimated the unit subsidy per outpatient service by dividing this allotment by the total

number of outpatient services utilized. The unit subsidy per inpatient service was equal

to the unit subsidy per outpatient service, times the outpatient equivalent ratio of 4.62

times the average length of stay (ALOS) which was seven days for hospitals and two

days for health centers.

Step two: identifying service users. This involves identifying the service users by level of

health facility and by income group (Table 1). The most common source for such data is

household surveys with service utilization information, as well as some measure of

welfare such as household expenditures, income, or asset ownership. In this study, we

used Liberia Community Survey data (MOHSW 2008) collected in January 2008 to

estimate the number of individuals who sought different types of health services, broken

down by household income deciles based on household expenditures.

1 The outpatient equivalent is a ratio between resources required to provide an inpatient day of

care and an outpatient service.

Unit subsidy Outpatient services = Total spending on curative care in public facilities

Total number of outpatient visits

Unit subsidy Inpatient services = Unit subsidy outpatient services x 4.62 x ALOS

4

Table 1: Distribution of Households per Expenditure Decile

Decile Rural Urban Total

1 43,249 9,536 52,786

2 41,353 11,183 52,536

3 35,718 17,177 52,894

4 34,458 17,753 52,211

5 29,224 23,580 52,804

6 26,197 26,276 52,473

7 21,351 31,213 52,564

8 23,817 28,894 52,711

9 21,930 30,539 52,469

10 22,344 30,154 52,498

Total 299,640 226,306 525,946

Source: Community Survey 2008

Step three, estimate the benefits (government subsidies) received by income groups.

In this step, the unit subsidy calculated in step one for the different types of health care

at different levels of health facilities is multiplied by the total number of users from each

income group of the respective types of health care at the respective level of health

facilities, to arrive at the benefit accruing to the specific socioeconomic group. (Demery

2000; Davoodi et al. 2003; Castro Leal et al. 2000; O'Donnell et al. 2007)).

In this study, the unit subsidy calculated during step one was multiplied by the utilization

of types of health services, at different levels of health facilities and by income group to

derive the distribution of government subsidies by income deciles.

The results then are presented using concentration indices and concentration curves.

The concentration curve and concentration index (Evans et al. 2001) provide means of quantifying the degree of income-related inequality in government subsidies for health

services. The concentration curve plots the cumulative percentage of government

subsidies for health services (y-axis) against the cumulative percentage of the sample,

ranked by income, beginning with the poorest, and ending with the richest (x-axis). If

everyone, irrespective of his/her living standards, has received exactly the same value of

government subsidies, the concentration curve will be a 45o line, running from the

bottom left-hand corner to the top right-hand corner. This is known as the line of

equality. If, by contrast, the government subsidy variable takes higher (lower) values

among poorer people, the concentration curve will lie above (below) the line of

equality. The concentration index is defined as twice the area between the concentration curve, and the line of equality. So, in the case where there is no income-

related inequality, the concentration index is zero. The convention is that the index

takes a negative value when the curve lies above the line of equality, indicating

5

disproportionate concentration of the government subsidy among the poor, and a

positive value when it lies below the line of equality.

Results

1. Unit government subsidy

The estimates of unit government subsidies for health services provided within public

health facilities are presented in Table 2. It was reported that US$9,041,670 of public

funds were allocated to public health facilities for curative services (excluding capital

spending) (Government of Liberia and Health Systems 20/20 2009). Of this allotment, 59

percent was allocated to public hospitals, and 41 percent was allocated to public health centers and clinics.

An estimated 1,148,064 outpatient services were provided by all 349 public health

facilities in 2008. Among these services, 87 percent were provided within public health

centers and clinics, and 13 percent were provided within public hospitals. There were

62,131 inpatient services that were provided by public health facilities during the same

time period. About 70 percent of them were provided within public hospitals, and 30

percent were provided within health centers. Based on the estimate that the outpatient

equivalent per inpatient day was 4.62 and the average lengths of stay in public hospitals

and health centers were seven days and two days respectively (according to the experts’

estimations), the total outpatient equivalent days for all inpatient services was 1,585,650.

Of these, there were 1,416,189 total outpatient equivalent days for public hospital

inpatient services and 169,462 total outpatient equivalent days for public health center

inpatient services.

Table 2. Estimated Government Unit Subsidies per Outpatient Visit and inpatient Service

Government

Facilities

Government

Subsidy

(US$)

Outpatient

Service

Inpatient Service

Unit

Subsidy

per

Outpatient

Visit (US$)

Unit

Subsidy

per

Inpatient

Service

(US$)

Number

of Visits

Number of

Admissions

Number of

Outpatient

Equivalents

Health

centers and

clinics

3,690,007 997,825 18,340 169,462 3.16 29.21

Hospitals 5,351,663 150,240 43,791 1,416,189 3.42 110.49

Total 9,041,670 1,148,064 62,131 1,585,650

Calculation of the government subsidies reveals that the government on average

provided US$3.16 per outpatient service in public health centers/clinics and US$3.42 per

6

service in hospitals. When it comes to inpatient services, the unit subsidy per service in

public hospitals is US$110, while it is US$29 per services in public health centers.

2. Service utilization

Table 3 shows the percentage of total utilization accounted for by patients in each decile

of household expenditure, for each category of health service. The results indicate that

the population in the lowest three deciles has relatively low outpatient utilization rates

compared to other groups with the exception of clinics. In addition, the population in

the highest three deciles is more likely to use outpatient services in hospitals and health

centers than clinics. There is a clear trend that overall higher-income groups have higher

inpatient utilization rates than lower-income groups.2 These utilization patterns drive the

distribution of government subsidy as we will see in the next section.

Table 3. Share of Unit Utilization, by Decile and Service

Outpatient Service Inpatient Service

Decile

Government

Hospitals

Government

Health

Centers

Government

Clinics

Total

Outpatient

Services

Government

Hospitals

Government

Health

Centers

Total

Inpatient

Services

1 9.8% 1.9% 9.3% 8.2% 4.3% 3.0% 3.7%

2 5.4% 4.2% 9.1% 7.0% 3.8% 1.9% 3.0%

3 7.4% 12.9% 7.7% 8.5% 6.1% 12.2% 8.9%

4 9.5% 18.1% 16.4% 14.4% 9.5% 12.6% 11.0%

5 13.0% 7.3% 12.2% 11.6% 7.5% 9.2% 8.3%

6 12.3% 12.0% 12.1% 12.1% 10.0% 11.4% 10.6%

7 9.5% 10.6% 8.3% 9.1% 9.6% 8.7% 9.2%

8 14.2% 12.3% 5.6% 9.7% 17.3% 15.5% 16.5%

9 10.4% 9.1% 5.4% 7.7% 12.9% 4.5% 9.0%

10 8.5% 11.7% 13.8% 11.7% 19.0% 21.0% 19.9%

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

3. The distribution of government subsidies

(1) Estimation of concentration index

Table 4 shows the percentage of total government subsidies by income deciles for each

category of health services, as well as Concentration Indices (CIs). The results show

that the CIs for all categories of health services, except for government clinics, are

positive. This means that government subsidies are generally not pro-poor, with the

exception of subsidies to government clinics. The magnitude of CI getting higher with

2 For the purpose of estimating government subsidies, the percentage of total utilization as measured by

outpatient equivalents at each level of health facility has been calculated (see Annex A). This provides a similar

outlook.

7

the level of facilities indicates that the pro-rich bias in the distribution of government

subsidies is more significant in higher-level health facilities such as hospitals than in

lower-level facilities such as health centers.

Table 4. Share of Government Subsidies by Deciles and Type of Health Service

Government

Hospitals

Government

Health

Centers

Government Clinics Government- All

Levels

1 4.8% 2.8% 9.3% 4.8%

2 4.0% 2.3% 9.1% 4.1%

3 6.2% 12.3% 7.7% 7.5%

4 9.5% 13.6% 16.4% 10.9%

5 8.0% 8.8% 12.2% 8.5%

6 10.2% 11.5% 12.1% 10.6%

7 9.6% 9.0% 8.3% 9.4%

8 17.0% 15.0% 5.6% 15.6%

9 12.6% 5.3% 5.4% 10.7%

10 18.1% 19.4% 13.8% 17.9%

CI 0.173 0.031 -0.002 0.203

SE(CI) 3.986 6.170 6.524 2.747

t-test(CI) 0.043 0.005 0.000 0.074

Note: CI=Concentration Index A closer look into the distribution of the subsidies by type of service indicates that the

pro-rich bias in the distribution of government subsidies is greater in magnitude for

inpatient services than for outpatient services (Annex B).

(2) Concentration curve

Figure 1 provides a graphical presentation of the distribution of government subsidies

among the population by income decile (as measured by level of annual household

spending) by level of health facilities. As with CIs, the concentration curves for the

health centers and hospitals lie below the line of equity, depicting a pro-rich bias in the

distribution of government subsidies. The concentration curve for hospitals being

predominantly further away from the line of equity compared with that of health centers

reveals that the pro-rich bias is greater at the hospital level than at the health center

level. The concentration curve for clinics is less clear-cut; however, the fact that the

curve lies above line of equality for the 40-80 percent richest population seems to

indicate that the subsidies at the clinic level are pro-middle income group.

8

Figures 2a and 2b present the concentration curves of government subsidies for

outpatient and inpatient services, respectively, by level of health facility. With respect to

outpatient services, the concentration curves for the health centers and hospitals mostly

lie below the line of equality. This indicates that government subsidies for outpatient

services at these facilities are more likely to be pro-rich than government subsidies for

outpatient services at the clinic level. For inpatient services, the concentration curves for both health centers and hospitals clearly lie below the line of equality, with the curve

for hospitals being farther below the line. This reiterates that that government subsidy

for all levels of inpatient services is clearly pro-rich. The pro-rich bias is more significant

at public hospitals than at health centers.

9

10

Discussion

Direct subsidization of health services is one of most common instruments that

governments have adopted to improve equity in access to adequate health care at an

affordable price in many developing countries. However, the performance of

government subsidies of health services can be enhanced by addressing other factors that affect health service utilization.

(1) Better population targeting of subsidies. Although direct government subsidies for

services delivered in public health facilities can eliminate fees or reduce the price of

health services, these subsidies normally are not targeted to the poor. If subsidized

services can be used by both rich and poor populations, richer populations will likely

have higher demand for these services and greater ability to access them than the

poor. They will therefore obtain more benefit from subsidy policies than the poor.

The results from this study confirmed that in Liberia, higher-income groups are

more likely to use health services than lower-income groups and thus are more likely to benefit from government subsidies than lower-income groups. If “free care”

policies are not accompanied by effective targeting strategies, they are more likely to

be pro-rich rather than pro-poor.

(2) Covering non-service costs. Although government subsidies might able to cover the

cost of services in subsidized health care facilities, the “real” price of accessing these

services is usually not limited to service costs. In most cases, the poor are more

likely to live in remote areas that are farther away from health facilities and

therefore face higher transportation costs. Some subsidized services require co-

payments, which as a proportion of income is also relatively more expensive for the

poor than for the rich. These costs disproportionately limit access for poor

populations to “free” or subsidized health services. The results from this study also

demonstrate that comparatively, richer groups are more likely to utilize higher-level

facilities and inpatient care than poorer groups, implying that the cost of accessing

clinic-level services might be lower than the cost of accessing health center- and

hospital-level services, making them more affordable and accessible to the poor.

Concurrent strategies to reduce other access barriers, including making health care

providers more available in the areas where the poor reside and vouchers for target

populations that cover transport costs, would potentially improve equity in the

distribution of government subsidies as well.

(3) Better service targeting of subsidies. Potential health effects. It is well established

that the health profiles of rich and poor groups are different, and that therefore

their need for health care is also different. While government subsidies to all levels

of health facilities can make all health services more affordable, health services that

are provided at hospitals might better meet demand among wealthier population

groups, and services provided at the clinic level might better meet demand among

poorer groups. Targeting government subsidies more toward outpatient services at

lower-level health facilities could potentially improve the equity in the distribution of

subsidy benefits.

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Study Limitations

Although the BIA applied in this study provided an estimate of the distribution of

government subsidies among different income groups, the results presented in this paper are a very rough estimate based on several assumptions, constrained by the

availability of information and the limitations of the BIA method itself. One common BIA

assumption made in this analysis is that the unit subsidy per outpatient visit or per

inpatient service is the same across all income groups, which is unlikely to be true. In

fact, the amount of resources consumed per service utilized is likely to be higher among

richer groups than poorer groups due to the higher demand for health services in

higher-income groups. If this is true, our results will underestimate the pro-rich bias of

government subsidies. Due to data availability, we assumed the cost per outpatient visit

was the same in heath centers and clinics. If the cost per outpatient visit is higher in

health centers than in clinics, our results will also underestimate the pro-rich bias of government subsidies. Another limitation is that this analysis reflects the situation in

2008. The absence of data from prior years precludes determining if the observed pro-

rich bias reflects an improving or a worsening trend. But the 2008 BIA does serve as a

baseline for Liberia to evaluate its future progress to improve equity.

Conclusions

The government of Liberia has aimed to ensure equitable access to quality health

services by implementing a “free healthcare for all” policy since 2007, hoping that

utilization of services would thenceforth be based on need, not on individuals’ ability to

pay. The results of this study, however, have demonstrated that implementation of this

policy by providing direct government subsidies to public health facilities is not equally

benefitting all income groups. In fact, greater government subsidies to higher-level

facilities and to hospitalization services have tended to benefit wealthier groups more

than poorer groups. The government subsidy policy is currently a pro-rich, rather than

pro-poor, resource distribution policy. This study also suggests that the pro-rich

tendency can be reduced by better targeting the subsidy policy, by subsidizing the

services and level of facilities that are most likely to be used by poorer population

groups.

12

References

Castro-Leal, F. et al. 2000. Public Spending on Health in Africa: Do the poor benefit?

Bulletin of the World Health Organization 78(1): 66-74.

Collins, David and Zina Jarrah. 2009. Costing study of basic package of health services

(BPHS) for performance-based contracts under USAID’s RBHS project.

Community Survey for Health Seeking Behavior and Health Financing in Liberia 2008.

Demery, Lionel. 2000. Benefit incidence: a practitioner’s guide. Unpublished mimeo,

Poverty and Social Development Group, The World Bank.

Evans, Timothy, Margaret Whitehead, Finn Diderichsen, Abbas Bhuiya, and Meg Wirth.

2001. Challenging Inequities in Health: From Ethics to Action. Oxford University Press.

Government of Liberia and Health Systems 20/20. 2009. Liberia National Health

Accounts 2007/08. Monrovia, Liberia and Bethesda, MD, USA: Health Systems 20/20,

Abt Associates Inc.

Liberia Institute of Statistics and Geo-Information Services. 2008. Millennium

Development Goals Up-Date on Health Related Goals and Targets. Report.

Liberia Ministry of Health and Social Welfare. 2009. Health Facility Accreditation Survey.

O'Donnell, Owen, Eddy van Doorslaer, Adam Wagstaff, and Magnus Lindelow. 2007.

Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their

Implementation. Washington, DC: The World Bank.

Wagstaff, Adam. 2010. Benefit Incidence Analysis, Are Government Health Expenditures

More Pro-rich Than We Think? Policy Research Working Paper 5234, Washington, DC:

The World Bank

Wang, Hong, Darwin Young, and Catherine Connor. 2009. Synthesis of Health Financing

Studies and the Alternatives for Health Financing Policy in Liberia. Bethesda, MD: Health

Systems 20/20, Abt Associates Inc.

13

Annex A. Share of Unit Utilization, by Deciles (Number of outpatient equivalents)

Deciles Government

Hospitals

Government

Health Centers

Government

Clinics

Government

Health Center

and Clinics

Government-

All Levels

1 4.8% 2.8% 9.3% 4.9% 4.8%

2 4.0% 2.3% 9.1% 4.6% 4.1%

3 6.2% 12.3% 7.7% 10.8% 7.5%

4 9.5% 13.6% 16.4% 14.5% 11.0%

5 8.0% 8.8% 12.2% 9.9% 8.6%

6 10.2% 11.5% 12.1% 11.7% 10.6%

7 9.6% 9.0% 8.3% 8.8% 9.4%

8 17.0% 15.0% 5.6% 11.9% 15.5%

9 12.6% 5.3% 5.4% 5.3% 10.5%

10 18.1% 19.4% 13.8% 17.6% 17.9%

Total 100.0% 100.0% 100.0% 100.0% 100.0%

*The outpatient equivalent ratio is 4.62; Average Length of Stay at Health Centers is 2 days and at

Hospitals is 7 days

Annex B. Share of Government Subsidies, by Deciles, with Concentration Index (CI)

values

Outpatient services Inpatient services

Govern-

ment

Hospitals

Government

Health

Centers

Government

Clinics

Government

All Levels

Government

Hospitals

Government

Health

Centers

Government

All Levels

1 9.8% 1.9% 9.3% 8.2% 4.3% 3.0% 4.0%

2 5.4% 4.2% 9.1% 7.0% 3.8% 1.9% 3.5%

3 7.4% 12.9% 7.7% 8.5% 6.1% 12.2% 7.2%

4 9.5% 18.1% 16.4% 14.2% 9.5% 12.6% 10.1%

5 13.0% 7.3% 12.2% 11.7% 7.5% 9.2% 7.8%

6 12.3% 12.0% 12.1% 12.1% 10.0% 11.4% 10.3%

7 9.5% 10.6% 8.3% 9.1% 9.6% 8.7% 9.4%

8 14.2% 12.3% 5.6% 9.8% 17.3% 15.5% 16.9%

9 10.4% 9.1% 5.4% 7.8% 12.9% 4.5% 11.3%

10 8.5% 11.7% 13.8% 11.6% 19.0% 21.0% 19.4%

CI 0.020 0.017 -0.009 0.028 0.208 0.035 0.243

SE(CI) 5.110 6.244 4.173 2.041 3.631 6.145 2.891

t-test(CI) 0.004 0.003 -0.002 0.014 0.057 0.006 0.084