MADAGASCAR P E R 2014 H S B P - The World Bank€¦ · xxx . Madagascar PER – Health |Section A 6...

MADAGASCAR PUBLIC EXPENDITURE REVIEW 2014 HEALTH SECTOR BACKGROUND PAPER

Version: September 17, 2015

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

i

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ................................................................................................................................................. V

SECTION A. PRESENTATION OF THE HEALTH SYSTEM AND HEALTH NEEDS ................................................................. 6

1. Health Sector Objectives and Organization.................................................................................................. 6 Figure 1. . Health Facility and Hospital Pyramid Referral System ................................................................. 7 2. Recent trends in health outcomes ................................................................................................................ 8 3. The State of Health Service Delivery: Indicators and Equity ......................................................................... 9 1.. Figure 2: Routine Data, Immunization Coverage for DPT, Polio and Measles for children under one, 2008-2012 10

SECTION B. TOTAL PUBLIC EXPENDITURES: SIZE AND OVERALL PERFORMANCE ....................................................... 15

B.1. HEALTH FINANCING ............................................................................................................................................... 15 1. Size and composition of total health financing .......................................................................................... 15 Figure 3. Trends in Total Health Expenditures in GDP 1995-2012, International Comparisons ..................... 15 2. Total public financing sources and their evolution since 2009 ................................................................... 16 Figure 5. Comparing Public Education and Health Financing over 2009-2013 .............................................. 17

B.2. GOVERNMENT HEALTH EXPENDITURES ...................................................................................................................... 18 1. General government expenditures allocated to health: international comparisons.................................. 18 Figure 6. Trends in Total Government Health Expenditures (Public Financing), 1995-2012 .......................... 18 2. Executed expenditures by the Ministry of Health, recent trends ............................................................... 19 Figure 7. Executed Expenditures of the Ministry of Health, 2006-2013 ............................................................ 19 Figure 8. Share of MoH in Government-Executed Expenditures, 2006-2013 ................................................... 20

B.3. BUDGET EXECUTION .............................................................................................................................................. 20 1. Execution rates ........................................................................................................................................... 20 Execution of the health budget ........................................................................................................................... 23 2. Deconcentration of MoH expenditure ........................................................................................................ 24 Figure 9. Deconcentration of MoH Current Non-wage Expenditures, 2006-2013 ............................................ 25

SECTION C. ELEMENTS TO ASSESS THE EFFICIENCY OF MOH EXPENDITURES ........................................................... 30

C1. ASSESSING TECHNICAL EFFICIENCY THROUGH AN ANALYSIS OF INPUT SHARES .................................................................... 30 Figure 11. Decomposition of MoH Expenditures into Broad Input Categories, 2006-2013 ............................. 33

C2. ELEMENTS TO ASSESS ALLOCATIVE EFFICIENCY: EXPENDITURE ANALYSIS BY FUNCTION ............................ 33 1. Data and level of analysis .......................................................................................................................... 34 2. Functional allocation of Total Health Expenditures (NHA) ......................................................................... 35 3. Functional allocation of public health expenditures (NHA 2010) ............................................................... 37 4. Functional allocation of Ministry of Health expenditures: wage expenditures ......................................... 38 Figure 12. Distribution of Salaries by Facilities and Administrative Levels, 2013 ........................................... 39 Figure 14. Allocation of Salaries by Level of Care and Support Activities by Type of Facility, 2013............... 41 Figure 15. Allocation of Salaries by Level of Care, 2013 .................................................................................. 41 5. Allocation of salaries by type of personnel ................................................................................................. 41 Figure 17. Estimated Change in Total Wage Expenditures by Category of Personnel, 2006-2013 .................. 42 6. Functional Allocation of Ministry of Health Expenditures: non-wage expenditures .................................. 43 Figure 18. Allocation of Non-Wage Recurrent Health Expenditures by Budget Program, 2010-2013 ............. 44 Figure 19. Allocation of Health Expenditures by Program—Investment Expenditures, 2010-2013 ................. 44 Figure 20. Classification of Non-Wage Expenditures by Program (recurrent+investment), 2010 and 2013 ... 45

ii

Figure 21. Allocation of Non-Wage Recurrent Health Expenditures by Level of Care, 2006-2013 .................. 46 Figure 22. Distribution of Non-Wage Expenditures by Program, 2008 and 2013 ............................................ 47 Figure 23. Allocation of Non-Wage Recurrent Health Expenditures by Type of Activity, 2006-2013 .............. 48 Figure 24. Allocation of MoH Non-Wage Expenditures (incl. Investment Expenditure) by Type of Activity,

2006-2013 ........................................................................................................................................................... 48 Figure 25. Evolution of MoH Expenditures Targeted to Specific Diseases, 2006-2013 ..................................... 49

C3. ELEMENTS OF A PRODUCTIVITY ANALYSIS ................................................................................................................... 51 Figure 26. Unit Costs by Type of Facility, 2013 ................................................................................................ 51 Figure 27. Expenditure Shares vs Utilization Shares by Type of Facility, 2013 ............................................... 52

SECTION D. DISTRIBUTIONAL ANALYSIS OF PUBLIC HEALTH EXPENDITURE ............................................. 55

D1. DISTRIBUTION OF MOH EXPENDITURES BY REGION AND TYPE OF RESIDENCE ........................................................... 55 1. Regional allocations vs population shares ............................................................................................... 55 Figure 28. Current MOH Health Expenditure Per Capita by Province in Relation to Population .................. 55 Figure 30. MoH Distribution of Wage Expenditure across Regions, 2013 ....................................................... 57 2. Relationship between current expenditures and poverty by region ........................................................ 57 Figure 31. MoH Recurrent Expenditure Per Capita by Region and Poverty Rate, 2006-2010 ......................... 58 Figure 32. MoH Recurrent Expenditure Per Capita on Primary Care by Region, 2013................................... 60 3. Rural/urban differences ............................................................................................................................. 61

D2. OUT-OF-POCKET HOUSEHOLD EXPENDITURES: DO THEY IMPACT REGIONAL AND INCOME INEQUALITIES? .................. 62 1. Distribution of OOP expenditure by income level and region ................................................................... 62 Figure 34. Household Out-of-Pocket Expenditure, 2005, 2010 and 2012 ......................................................... 63 2. Relationship between OOP spending and MoH expenditures using regional data.................................... 65 3. Analysis of “catastrophic” OOP expenditure ............................................................................................ 66

D3. DISTRIBUTION OF MOH EXPENDITURES BY SOCIOECONOMIC CATEGORY OF USER: BENEFIT INCIDENT ANALYSIS ......... 68 1. Marginal benefit of government expenditure by type of facility ................................................................. 69 Figure 40. Lorenz Curves by Quintile for Utilization of Public Health Facilities, 2005-2010 .......................... 69 Figure 41. Lorenz Curves by quintile for Utilization of Public Health Facilities, 2005-2012 .......................... 70 2. Benefit incidence of MoH expenditure ....................................................................................................... 70 Figure 43. Estimated Distribution of Benefits from MoH Non-Wage Expenditures .......................................... 72 Figure 44. Estimated Distribution of Benefits from MoH Expenditures ............................................................ 73 Figure 45. Estimated Distribution of Benefits: Simulations of Four Scenarios ................................................. 74

D4. ASSESSMENT OF GENDER EQUALITY ...................................................................................................................... 74 Figure 46. Utilization of Health Facilities by Gender, 2005 and 2010 ............................................................. 75

REFERENCES ......................................................................................................................................................... 78

ANNEX 1. TABLE SUPPLEMENT ............................................................................................................................ 79

ANNEX 2. FIGURE SUPPLEMENT ........................................................................................................................... 83

LIST OF TABLES

TABLE 3. PUBLIC HEALTH EXPENDITURES: EXTERNAL AND INTERNAL FINANCING, 2009-2013 ................................. 16 TABLE 4. PUBLIC HEALTH EXPENDITURES: EXTERNAL AND INTERNAL FINANCING, 2009-2013 ................................. 17 FINAL AND INITIAL BUDGET APPROPRIATIONS IN HEALTH AND OVERALL, 2006-2013 ............................................... 22 TABLE 4. EXECUTION OF THE MOH BUDGET VERSUS EXECUTION OF THE OVERALL BUDGET, 2006-2013 ................. 23 TABLE 5. MOH EXECUTION RATES FOR NON-WAGE RECURRENT EXPENDITURES, BY PROGRAM, 2010-2013 ........... 24 TABLE 6. MOH EXPENDITURES BY BROAD INPUT CATEGORIES (BUDGET CLASSIFICATIONS) , 2006-2013 .................. 31 TABLE 7. INPUT SHARES ACROSS BUDGET CATEGORIES, SHARE OF INTERNALLY FINANCED MOH EXPENDITURES,

2006-2013 ........................................................................................................................................................... 32 TABLE 8. ALLOCATION OF TOTAL HEALTH EXPENDITURES ACROSS FUNCTIONS (ALL SOURCES) ............................... 36

iii

TABLE 9. SHARE OF HEALTH EXPENDITURE ON INPATIENT CARE, INTERNATIONAL COMPARISON ............................. 36 TABLE 10. ALLOCATION OF CURRENT EXPENDITURES OF THE PUBLIC ADMINISTRATION SYSTEM ............................. 38 TABLE 10. SHARES OF THE RECURRENT NON-WAGE BUDGET BY LEVEL OF CARE, 2006-2013 ................................... 46 TABLE 11. ALLOCATION OF NON-WAGE MOH EXPENDITURES (INCLUDING PIP) BY LEVEL OF CARE, 2006-2013 ..... 46

Annex Tables

TABLE A1. INTERNATIONAL COMPARISONS OF PUBLIC HEALTH EXPENDITURES (INCLUDING EXTERNAL FUNDS) ... 79 TABLE A2. EVOLUTION OF BUDGET ALLOCATIONS BY ADMINISTRATIVE LEVELS REPORTED BY MOH .................. 80 TABLE A3. LIST OF CATEGORIES USED IN THE ALTERNATIVE CLASSIFICATION OF MOH EXPENDITURES ................ 80 TABLE A4. TRACKING OF SPECIFIC ALLOCATION OF INTEREST IN NON-WAGE RECURRENT EXPENDITURE ............ 81 TABLE A5. PIP EXPENDITURES IN SPECIFIC CATEGORIES, 2008-2013 ..................................................................... 81 TABLE A6. ANNUAL MOH SALARIES BY PROVINCE AS A RATIO TO SIGFP REMUNERATIONS BY PROVINCE ........... 82

LIST OF FIGURES

FIGURE 1. HEALTH FACILITY AND HOSPITAL PYRAMID REFERRAL SYSTEM ................................................................ 7

FIGURE 3. TRENDS IN TOTAL HEALTH EXPENDITURES IN GDP 1995-2012, INTERNATIONAL COMPARISONS ............. 15

FIGURE 5. COMPARING PUBLIC EDUCATION AND HEALTH FINANCING OVER 2009-2013 ............................................ 17

FIGURE 6. TRENDS IN TOTAL GOVERNMENT HEALTH EXPENDITURES (PUBLIC FINANCING), 1995-2012 .................... 18

FIGURE 7. EXECUTED EXPENDITURES OF THE MINISTRY OF HEALTH, 2006-2013 ....................................................... 19

FIGURE 8. SHARE OF MOH IN GOVERNMENT-EXECUTED EXPENDITURES, 2006-2013 ................................................ 20

FIGURE 9. DECONCENTRATION OF MOH CURRENT NON-WAGE EXPENDITURES, 2006-2013 ...................................... 25

FIGURE 11. DECOMPOSITION OF MOH EXPENDITURES INTO BROAD INPUT CATEGORIES, 2006-2013 ......................... 33

FIGURE 12. DISTRIBUTION OF SALARIES BY FACILITIES AND ADMINISTRATIVE LEVELS, 2013 ................................... 39

FIGURE 14. ALLOCATION OF SALARIES BY LEVEL OF CARE AND SUPPORT ACTIVITIES BY TYPE OF FACILITY, 2013 .. 41

FIGURE 15. ALLOCATION OF SALARIES BY LEVEL OF CARE, 2013............................................................................... 41

FIGURE 17. ESTIMATED CHANGE IN TOTAL WAGE EXPENDITURES BY CATEGORY OF PERSONNEL, 2006-2013 .......... 42

FIGURE 18. ALLOCATION OF NON-WAGE RECURRENT HEALTH EXPENDITURES BY BUDGET PROGRAM, 2010-2013.. 44

FIGURE 19. ALLOCATION OF HEALTH EXPENDITURES BY PROGRAM—INVESTMENT EXPENDITURES, 2010-2013 ...... 44

FIGURE 20. CLASSIFICATION OF NON-WAGE EXPENDITURES BY PROGRAM (RECURRENT+INVESTMENT), 2010 AND

2013 .................................................................................................................................................................... 45

FIGURE 21. ALLOCATION OF NON-WAGE RECURRENT HEALTH EXPENDITURES BY LEVEL OF CARE, 2006-2013 ....... 46

FIGURE 22. DISTRIBUTION OF NON-WAGE EXPENDITURES BY PROGRAM, 2008 AND 2013 ......................................... 47

FIGURE 23. ALLOCATION OF NON-WAGE RECURRENT HEALTH EXPENDITURES BY TYPE OF ACTIVITY, 2006-2013 .. 48

FIGURE 24. ALLOCATION OF MOH NON-WAGE EXPENDITURES (INCL. INVESTMENT EXPENDITURE) BY TYPE OF

ACTIVITY, 2006-2013 .......................................................................................................................................... 48

FIGURE 25. EVOLUTION OF MOH EXPENDITURES TARGETED TO SPECIFIC DISEASES, 2006-2013 ............................... 49

FIGURE 26. UNIT COSTS BY TYPE OF FACILITY, 2013 .................................................................................................. 51

FIGURE 27. EXPENDITURE SHARES VS UTILIZATION SHARES BY TYPE OF FACILITY, 2013.......................................... 52

Annex Figures

FIGURE A.1. MOH RECURRENT EXPENDITURE PER CAPITA BY REGION VS PER-CAPITA CONSUMPTION, 2006-2010 ......................... 83

iv

LIST OF BOXES

BOX 1. METHODOLOGICAL ISSUES: ACCOUNTING FOR EXTERNAL FUNDING .......................................................................... 16 BOX 2. BUDGET RECTIFICATION ................................................................................................................................... 21 BOX 3. MEASURING THE DECONCENTRATION OF HEALTH EXPENDITURE USING SIGFP ........................................................... 24 BOX 4. SUBNATIONAL FINANCING BOTTLENECKS ON VACCINATION ..................................................................................... 25 BOX 5. ANALYZING INPUT SHARES IN HEALTH ................................................................................................................. 30 BOX 6. ALLOCATIVE EFFICIENCY ANALYSIS USING FUNCTIONAL ALLOCATIONS OF HEALTH EXPENDITURES ................................... 33 BOX 7. CONTRIBUTION OF DIFFERENT DATA SOURCES TO UNDERSTANDING HEALTH EXPENDITURE BY FUNCTION IN MADAGASCAR 34 BOX 8. FINANCING VACCINATION IN MADAGASCAR ......................................................................................................... 50

v

ACKNOWLEDGEMENTS

xxx

Madagascar PER – Health |Section A

6

SECTION A. PRESENTATION OF THE HEALTH SYSTEM AND HEALTH NEEDS

1. Health Sector Objectives and Organization

1. Since the crisis, the health sector has suffered from a lack of strategic leadership. From 2009 to

2014, there were four Ministers of Health appointed, The objectives of the National Health Strategy, which

ended in 2011, was informally extended with no interim strategy put in place. This resulted in a general

loss of direction in the sector and fragmentation of coordination and funding among partners. In the first

year after the elections and the placement of the new Government, the Ministry of Health went through a

period of transition. In March 2014, the Prime Minister was also appointed the Minister of Health. In that

past year, some key developments have taken place including the launch of the development of the new

health sector strategy and the revitalization of the International Health Partnership1 in Madagascar.

2. As of March 2015, a new Minster of Health and Secretary General of the MOH have been appointed

with the key objectives of re-instilling strategic direction in the sector. The new Health Sector Strategy

(2015-2019) is awaiting final Government validation. There are six strategic axes2 and the estimated budget

needed when prioritizing maternal and child health interventions is estimated to be US$1.4 billion over the

next five years. The Ministry has also explicitly committed to a the development of a Universal Coverage

Strategy by the end calendar year 2015 with the first mission already having taken place. There are

important challenges in the overall budget envelope and the coordination of financing (much of it being

external financing) which should be addressed as they are key drivers to the success of the implementation

these strategies.

3. The public health sector is organized in a pyramid structure, with four tiers of access to health

services (Figure 1). The system is organized around 112 health districts, which correspond to administrative

units referred to as Fivondronana, each representing approximately 100,000 inhabitants. Health services

can be accessed at four different levels: basic health centers (Centre de Santé de Base: CSB) I and II; district

referral hospitals (Centre Hospitalier de référence de District: CHRD) without surgery and with surgery;

regional referral hospitals (Centre Hospitalier de référence Régionale: CHRR); and university hospitals

(Centres Hospitaliers Universitaires: CHU) including specialized centers. Each health district typically

contains 10 to 25 primary care centers and a hospital. The districts are divided into service areas for

1 Prior to the crisis, in December 2008, the IHP+ Compact had been signed by the Government and 22 development partners agreeing to overarching principles of coordination as a first step to a pooled financing approach. This was unable to move forward due to the start of the political crisis in January 2009. 2 a) improving the geographical and financial access of the population to high-quality health interventions; b) stimulating demand and use of services; c) equitable coverage and quality of health infrastructure; c) participation, coordinated and efficient government, TFP, Civil Society, the Regional and Local Authorities (CTD) and the community in the implementation and financing of health interventions; d) decentralization / devolution of the health system; e) Improving the Health Information System, which requires the acquisition of reliable health data, prompt and available to all stakeholders, reverse information, analysis of indicators for monitoring and evaluation of results for the purpose of appropriate decision-making; f) the implementation of high impact interventions to accelerate the reduction of maternal and infant mortality, reduce the prevalence of major communicable diseases (HIV / AIDS, malaria and tuberculosis), those of neglected diseases and non-communicable diseases;


7

community health centers (CSB 1 and CSB 2). CSB1s are managed only by paramedical staff whereas

CSB2s are managed by a doctor and paramedical staff. In 2012, there were 3,074 functional CSBs and 150

CHRDs, including approximately 90 with surgical capacity (categorized as CHRD with surgery).

Figure 1. Health Facility and Hospital Pyramid Referral System

Source: Annuaire des Statistiques du Secteur Santé, 2012.

4. The bottom 40% of the population access services at the CSB1 and CSBII levels (the population at

the 4th quintile and under in Madagascar are considered poor given the high poverty rates in the country).

That said, almost 70 percent do not seek care when ill due to low quality of services and out-of pocket costs

on medical consumables and services, long geographic distances to health facilities, and drug stock-outs.

Higher level (tertiary care) facilities provide more primary care interventions than any other interventions

and they are only frequented by the richest quintile of the population. Interestingly, most interventions

delivered at that level are primary care interventions which points to an imbalance in the type of facility

and the types of interventions delivered there.

5. Over the last decade, there has been increasing reliance on the private sector for health

service delivery (Table 1). Private health facilities fall into two categories: not-for-profit, managed by

faith-based groups or NGOs; and the for-profit health clinics, managed by private individuals. All not-for-

profit primary health centers are required to adhere to Ministry of Health (MoH) norms and regulations,

and must integrate their work programs into the district health planning.3 Between 2001 and 2010, the

number of CSB2’s in the private sector doubled and the number of private hospitals (CHD-2) almost tripled.

3 Sharp, Maryanne; Kruse, Ioana. 2011. Health, Nutrition, and Population in Madagascar 2000-09. World Bank.

• Specialized medical or surgical cases

CHU

6 Provincial level hospitals

• Complicated surgery casesCHR

16 Regional level hospitals

• Complicated medical cases and surgery

• Only 90 of the 150 district level health facilities have surgical equipment.

150 Central District Hospitals

60 CHD1 and 90 CHD2

• Simple medical cases and preventionCSB I and CSB II

3074 Commune level health facilities


8

The private sector has been slowly gaining ground particularly in urban areas - with a consultation rates

among private physicians in these areas increasing from 16 % in 2005 to almost 20% in 2010.4

6. In 2003, the National Health Accounts (NHA) estimated that public health providers accounted for

67 percent of all providers in terms of total health expenditures, versus 28 percent for private providers (5

percent were unidentified). The 2007 and 2010 NHAs do not separate providers into public and private, but

the number of financing agents indicates that after a relative increase in utilization of the public sector

between 2003 and 2007, the trend reversed in 2010. In addition, according to the 2010 NHA, nearly 58

percent of health expenditures can be attributed to the private financing5. With regards to out-of-pocket

expenditure (OOP) for health, 2013 NHA-lite data indicate that OOP is an 80% share of private financing

and 30% of total health expenditure.

Table 1. Share of Private and Public Sector in Health, 2003-2010

2003 2007 2010

Public sector 55 62 40

Private sector 40 36 58

Rest of the World 5 2 2 Note: 2010 NHA figures are based on Current Health Expenditures (HE) instead of Total Health

Expenditures (THE); Capital expenditures represented 7 percent of THE in 2010 and can be mostly

attributed to the public sector.

Source : Adapted from Madagascar NHA reports 2003, 2007 and 2010.

7. Risk-pooling mechanisms remain largely underdeveloped with the cost of most medical

consumables borne by patients through cost recovery. Though general revenue financing provides an

opportunity for implicit risk pooling and redistribution of resources, government resources are inadequate

to meet all the needs. And while risk pooling mechanisms provide an opportunity for financial protection

and more equitable distribution of resources, these mechanisms remain scarce in Madagascar.

Furthermore, private/voluntary health insurance is limited, as only a minority of the population is willing

and able to afford unsubsidized voluntary insurance given the small formal employment base. However,

international experience with community-based health insurance, which has existed in Madagascar for more

than ten years, suggests that such schemes can form part of a transition to a more universal health care

coverage system. But there are also shortcomings with community-based health insurance due to limited

incomes of community members and voluntary membership, which reduces the size of the risk pool.

2. Recent trends in health outcomes

8. Since the start of the political crisis in 2009, key social indicators declined dramatically and

Madagascar is now unlikely to meet any of the health MDGs. Between 2002 to 2008, Madagascar made

considerable progress on the social MDGs, and it seemed likely that the fourth MDG, on under-five

4 UNICEF 2013 Sitan. 5 However, it should be noted that it is not clear whether this increase is directly related to provision of health services

through the private sector or whether households are financing more out-of-pocket expenses in the public sector.


9

mortality, would be achieved—child health improved, and under-five mortality rates declined from 163 per

1,000 live births in 1997 to 72 per 1,000 live birth in 2008/096. Madagascar had also started to tackle some

persistent challenges, such as improving maternal health and reducing stunting among children caused by

chronic malnutrition. Since 2009, however, some key health outcomes have declined sharply; and since

2012, the rate of acute malnutrition in increases in some of the most food insecure regions of country has

risen by more than 50 percent. The prevalence of chronic malnutrition among children under five is one of

the highest in the world—53 percent are stunted7 (short for their age) and 5.8 percent are wasted (too thin

for their height).8 Maternal mortality ratios also have remained relatively high and stagnant over the last

ten years: from 469 per 100,000 live births in 2003 to 478 per 100,000 live births in 2012. In 2010, pregnant

women and children under five bore almost 40 percent of the total disease burden in the country.

9. Madagascar’s epidemiological profile remains comparable to many low-income countries

with a high communicable disease burden, including neglected tropical diseases (NTDs), with the burden

of disease falling disproportionately on the poor. About 0.49 percent of all TB cases are Multidrug Resistant

TB (MDR-TB). Malaria is not as widespread as in most Sub-Saharan countries and its incidence has

declined over the past few years as a result of prevention activities, but there have been spikes in the number

of cases in recent years. Over the past decade, non-communicable diseases are increasing in the population,

resulting in a dual burden of disease which will tax an already fragile health system.

10. There are persistently high total fertility (TFR) and population growth rates (4.6 births per

woman and 2.8 percent growth respectively) with significant variations by location and income

quintiles. For example, the TFR for rural areas is almost double that of the capital (5.2 versus 2.7 while

that for the poorest quintile is 2.5 times that of the richest. The percentage of adolescents having given

birth is almost 4 times higher for the poorest quintile than for the richest. Not surprisingly, the contraceptive

prevalence rate (modern methods) for the richest quintile is double that of the poorest and the unmet need

for contraception is 41 percent higher for the poorest quintile than the richest. While there has been a

significant increase in the utilization of family planning (from 18 percent in 2003/04 to 47 percent in 2012),

the significant unmet need and the inequitable availability of family planning services – as well as the non-

health related implications of continued high population growth highlighted – makes this a concerning

issue.

3. The State of Health Service Delivery: Indicators and Equity

11. Coverage of essential health services, especially key maternal and child health services, is very

low and has further deteriorated since the crisis. Access to quality prenatal and antenatal care is a

persistent and increasingly serious challenge, with only 38 percent of births taking place in a health center,

and of those, only 44 percent attended by skilled personnel.9 This is lower than the average in the

6 The 2012 MDG Survey showed the child mortality rate to be 67 per 1000 live births. With the confidence intervals noted in the survey, this is about the same rate as 2008/2009. 7 Long-Term Anthropometric Study (2012). This is particularly concerning in that stunting compromises human

capital and has long-term negative impacts on productivity and resilience due to irreparable cognitive and physical

deficits. 8 UNICEF 2012 9 MDG Survey 2012/2013.


10

developing world, where about 58 percent of all deliveries are attended by skilled health providers and more

than 50 percent of births taken place in health centers. Population policies aimed at reducing fertility by

encouraging family planning brought about a 15 percent increase in the use of modern methods of

contraception prior to 2009, but the contraceptive prevalence rate is still very low at 33 percent.

12. Immunization coverage has also substantially decreased since the crisis. Immunization

coverage is one of the main predictors of the infant mortality rate and also it can be used as a proxy indicator

for the availability of primary health care in a country. According to the Demographic Household Survey

(DHS) 2008/2009, complete immunization coverage for children 12 to 23 months old in 2008 was 62

percent. Comparable data from the MDG survey in 2012 indicates a decrease in complete immunization

coverage to 51.1 percent in just four years. Furthermore, this downward trend is also confirmed by other

country-specific data sources. The regional MICS (2012)10 indicates a decrease in total immunization

coverage to about 33.4 percent in some of the poorest areas of the country. Routine data collected from

health facilities also confirms the drop with one of the greatest decreases seen in coverage of Bacillus

Calmette–Guérin (BCG vaccine for tuberculosis) over the last five years (Figure 2).

1. Figure 2: Routine Data, Immunization Coverage for DPT, Polio and Measles for children under one,

2008-2012

Source: Health statistical yearbooks, 2008-2012.

13. Health Service Delivery is highly inequitable in Madagascar. In 2010, pregnant women and

children under five bore almost 40 percent of the total disease burden in the country. Looking at specific

outcomes such as child mortality, the bottom 40% of the population, bears most of the burden (Figure 3).

Similarly, key service delivery indicators such as skilled birth attendance are 40% lower among the poorest

two quintiles as compared to the richest quintile further highlighting the equity issues in accessing and

utilizing care (Figure 4).

Figure 3&4: Infant/Child Mortality and Percentage of Live Births Attended by Skilled Health Personnel by

Quintile

10 The Madagascar MICS4, funded by the World Bank and UNICEF, was carried in 2012 by the National Institute of Statistics in the southern part

of the country. The survey focused on four southern regions in Madagascar Androy, Anosy, Atsimo Andrefana, and Atsimo Atsinanana with a

representative sample of 2,897 households. The MICS collected data on household members, housing characteristics, information on women between the age of 15-49 years and children under five.

61

64.4

64.8

63.3

83.8

73.9

72.5

75.4

50 55 60 65 70 75 80 85 90

BCG

Polio3

DTC3

Measles

Percent2008 2009 2010 2011 2012


11

14. These trends are further confirmed by an equity analysis using results from the regional 2012 MICS

survey which covered five of the poorest regions in Madagascar shows that that maternal and child health

interventions such as antenatal care and immunization are reaching mostly higher quintiles of the

population.

Table 2. Coverage of select maternal and child health interventions according to income quintile.

Maternal and Child Health Interventions q1

(Poorest) q2 q3 q4

q5

(Richest) Total

Full immunization 23.2 24.7 33.8 37.9 50.9 33.4

Treatment of Diarrhea 31.2 28 27 43.1 39.7 33.2

Medical treatment of Acute Respiratory Infection 63.8 53.1 56.8 63.6 78.5 61.6

Mosquito net use by children 61.4 56.2 62.2 66.4 68.5 62.4

Skilled antenatal care 63.3 68.4 76.7 81.1 91.2 74.6

Skilled antenatal care (4+ visits) 34.5 37.9 40.6 48.8 63.7 43.3

Skilled birth attendance 10.6 15.2 25.7 39 71 28.5

Mosquito net use by pregnant women 55 68.5 68.3 64.2 68.5 64.9

15. Inequitable health service delivery has two critical dimensions in the context of Madagascar: a)

affordability and b) accessibility.

a)Affordability: Poor and unequal health outcomes find their root in disparities in terms of

household income. Out-of pocket costs for service delivery have risen as greater numbers of

households are falling deeper into poverty resulting in a population that is more vulnerable and a

greater risk of falling and staying in poverty by paying for health services. The 2012 MDG Survey

found that financial barriers and distance are key constraints in accessing quality health care. With

the exception of the cost of prescriptions, primary care services are delivered free-of-charge at

facility level, yet in practice, the use of health services also entails high out-of-pocket costs such as

for supplies, medical consumables, and transportation.


12

In an effort to address some of the issues around financial barriers in accessing health services, a

Health Equity Fund was created by the Government to provide drugs free-of charge to the poorest.

Under the cost-recovery mechanism, called “FANOME”, at the health center level, a small

percentage of funds (from the sale of drugs) is placed in an equity fund designed to provide free

access to medicines for the most vulnerable population without adding burden to the health budget.

By design, its solvency is directly tied to the population’s utilization rate of health centers. There

are large variations in the financial sustainability of equity funds, even within a district, and the

overall coverage is very low. However, financial sustainability is not the biggest challenge to the

effectiveness of the equity fund; in fact, funds are underutilized because targeting of eligible

individuals is very difficult and linked to cultural nuances around being targeted as a poor person.

b) Accessibility: Geographic barriers exist and are likely getting worse. Numerous communities

are seasonally isolated for months at a time, leaving entire populations – not only the poor – with

little access to health centers. Even those isolated communities with a health center suffer during

the rainy season, since referrals to hospitals are impossible, replenishment of drugs is slower, and

supervisory visits are virtually non-existent. Prior to the crisis, the WHO reported that only 60% of

the Malagasy population had access to health facilities. By 2013, approximately 856 primary health

care facilities (CSB1s), most accessed by the poor, had closed down due to the impacts of the crisis.

In addition, nearly 78 percent do not have the ability to transport patients to hospitals for further

treatment. The issue challenge geographic access is particularly concerning given the links to

maternal mortality and morbidity outcomes.

16. Human Resource Allocation and Organization is also not equitable. According to the NHA

(2010), there are approximately 2 doctors per 10,000 people in Madagascar. There are three times more

doctors in urban areas than in rural areas. In the public sector, the highest concentration of doctors is in

urban centers in Antananarivo province. On average, a public health center in urban area has twelve

employees out of which three are doctors and an additional five are medically trained staff. On the other

hand, public rural centers are staffed on average with only two people of which there may or may not be a

doctor. Efforts to recruit general practitioners and nurses to work in rural areas has had limited success and

there has been a reduction of 50 percent in the number of midwives between 2007 and 20011. In addition,

the ratio of nurses/midwives compared with physicians is very low (range between 1.9-2.0 between 2007

and 2011). Data from the MOH indicate approximately 640 rural health facilities have closed since 2007

due to a lack of personnel. Of the CSBs that are functional, over 30% are not compliant with staff

requirements as set by the Ministry. An added issue is that nearly 50 percent of public health sector staff

is over 50 years old and will retire in less than ten years. Current health sector human resource policies do

not address this future constraint to service delivery.

17. The quality of health service delivery is low, especially in rural parts of the country. While

there are many services and system components that suffer from poor quality, health service delivery at

primary care levels can be quantified around a few key indicators and these reflect major health system

issues in Madagascar:

a) Provider performance in compliance with diagnostic procedures is low: Observations of

consultations found very few medical professionals, 15 percent, followed the correct protocol of

consultations and recorded all the basic information relating to the children under five. This was

also true for antenatal care consultations where only 18 percent of medical professionals followed

consultation protocol.

b) Supervision and monitoring functions, especially at lower levels, are weak: there is large

variation in supervision across urban/rural and geographic regions with urban centers better


13

supervised than rural centers. Level 2 district hospitals are supervised regularly (96 percent), while

basic health facilities are the least supervised (63 percent).

c) Limited availability of key supply side inputs: More than 66 percent of health facilities reported

at least one essential medicine was not available at the time of the health facility survey. The

duration for essential medical stock-out is as high as ninety days.

Key Findings

Since the start of the political and economic crisis in 2009, progress made on key health indicators has stagnated

or is being reversed with Madagascar falling off track to achieve the MDGs. The prevalence of chronic

malnutrition among children under five is one of the highest in the world. Maternal mortality ratios also have

remained relatively high and stagnant over the last ten years and the country.

Contextual weaknesses

Madagascar’s epidemiological profile remains comparable to many low-income countries with a high

communicable disease burden. Almost 30 percent of all deaths in Madagascar are still attributable to

preventable and infectious and parasitic diseases

The system is plagued by inequitable health service delivery. The two critical dimensions are:

Affordability: As greater numbers of people have fallen into poverty, there have been two likely

implications on the population: i) the poor are more vulnerable and have a greater risk of falling and

staying in poverty by paying for health services and ii) less of the population is seeking health services

due to an inability to pay.

Accessibility: Numerous communities are seasonally isolated for months at a time, leaving entire

populations – not only the poor – with little access to health centers. In addition there are major inequities

in HRH distribution with the greatest negative impact on the poor who access first level primary care

facilities. An added issue is that nearly 50 percent of public health sector staff is over 50 years old and will

retire in less than ten years. Current health sector human resource policies do not address this future

constraint to service delivery.

Structural weaknesses

The quality of health service delivery is low, especially in rural parts of the country. Critical challenges

include: (a) weak provider compliance with diagnostic procedures; (b) weak supervision and monitoring

functions; and (c) lack of availability of key supply-side inputs.

High-out of pocket costs and scarcity of risk pooling mechanisms make it difficult for the poor to access

care

Policy implications

There are several short and medium recommendations that Madagascar should consider implementing as a matter

of priority:

I. Promote equitable access to health services with a focus on delivering an essential package of high,

impact maternal and child health and nutrition interventions in rural areas through tailored strategies

that remove key barriers to access and stimulate demand and utilization of services.

Address financial barriers to access

- Remove out-of pocket costs for services at facility level

- Strengthen risk-pooling and safety-net mechanisms such as the Health Equity Fund, fee exemption

schemes for services and medicines and community health insurance.


14

Address geographic barriers to access

- Finance existing outreach activities and approaches especially in rural areas. This includes training

and deploying community health workers and expanding initiatives like Stratagie Avancee which

bring services into communities. Focusing at this level can promote resilience in service delivery.

- Prioritize and invest in functionality of first level rural health facilities.

II. Improve the quality of health services

Ensure availability of essential commodities and drugs at the primary level, as well as investment in

upkeep and maintenance of health facilities, especially in rural areas.

Invest in supervision and monitoring at lower levels. This includes training and capacity building for

better management.

Strengthen relevant plans to inform priority actions for improving quality to complement the National

Health Sector Strategy:

- Update and implement the National Human Resources Development Plan (short, medium and longer

term actions)

- Develop and implement standardized “Norms and Standards” for all types of health facilities at all

levels

- Develop and implement a National Quality Plan.

Madagascar PER – Health |Section B

15

Section B. Total Public Expenditures: Size and Overall PerformanceB.1. Health Financing

1. Size and composition of total health financing11

18. Madagascar spends less on health than three quarters of the SSA LIC countries. Since 1995,

the percentage of Total Health Expenditure (THE) in GDP has remained around 4-5 percent with a slight

downward trend in recent years. On average, Madagascar spent, 4.3 percent of its GDP on health between

2009 and 2012, compared to 4.8 percent in the four years preceding the crisis (2005-2008).12 This was the

reverse of the trend observed in other SSA countries. (Figure 3). In real terms THE per capita expenditure

has not changed since 1995. Looking at the period between 1995 and 1999 THE per capita was US$21. In

the period between 2010 and 2014, Madagascar’s THE per capita was US$20. This is comparable with

other LICs in SSA but far below the regional average of US$8313.

Figure 3. Trends in Total Health Expenditures in GDP 1995-2012, International Comparisons

Source: Data from WHO/GHED.

11 This section rests on data from the WHOs Global Health Expenditure Data (GHED). Although WHO’s GHED is

based on NHA exercises, there are some notable discrepancies between the two sets of data that could not be explained

but are likely due to differences in the WHO methodology to create internationally comparable data. The GHED

numbers have the advantage of being comparable across countries and require government clearance before

publication. 12 The national NHA reports present a different picture with THE at 5.6 % of GDP in 2010, up from 4.2% in 2007 and

from 3.2% in 2003. The raw NHA data that was used for the different NHA exercises could not be obtained so the

source of the discrepancy with the WHO data could not be identified. 13 Source:

0

2

4

6

8

10

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Madagascar Average SSA Average LICs Average HIC


16

2. Total public financing sources and their evolution since 2009

Box 1. Methodological Issues: Accounting for External Funding

Since 2009, the AMP, managed by the GoM (Primature) records actual disbursements from all bilateral and

multilateral agencies, decentralized cooperation entities from France, and the bulk of international NGOs, including

direct funding by foundations.

Although data prior to 2009 are available for government accounts (MoF/SIGFP), they could not be used to identify

the shares of internal and external financing of public expenditure because significant differences were found

between on-budget disbursements of foreign aid directed to health and externally financed expenditures in the

SIGFP for 2009-2013. In fact, hardly any on-budget foreign aid was included in executed expenditure, as revealed

by the following ratios of disbursed foreign aid marked “on-budget” in the AMP to externally funded expenditures

in SIGFP:

2009 2010 2011 2012 2013(a)

Relative to budget appropriations(b) 0.72 0.16 0.33 0.20 0.14

Relative to executed expenditures 0.13 0.01 0.09 0.03 0.02 (a) The 2013 data from the AMP include disbursements entered on the platform prior to April 30, 2014. (b) Final and initial budget appropriations were equal for the PIP in the SIGFP for health.

19. Between 2009 and 2013, total public expenditures in health increased due to large off-budget

external support. There are significant constraints in assessing the total amounts of public financing to

health, owning to the poor accounting of external funding in the Integrated Public Finance Management

System (SIGFP) (Box 1). For the purpose of this PER, the total funding directed to health, including

domestic, on-budget and off-budget foreign aid, was reconstituted using alternate government sources.14

The analysis shows that health funding continued to increase after the crisis, by about 16 percent a year

between 2009 and 2013. The sector received exceptionally high amounts of public funding in 2010, owing

to large off-budget external support. Directly resulting in public financing to health going from 2.8 to 3.7

percent of GDP between 2009 and 2013, with a peak at 4.6 percent in 2010 (Table 2).

Table 3. Public Health Expenditures: External and Internal Financing, 2009-2013

In billion of constant 2013 Ar. 2009 2010 2011 2012 2013(a)

Total Public Expenditures 585 1,007 734 807 876

Percent of GDP 2.8 4.6 3.3 3.5 3.7 (a) The 2013 data from the Aid Management Platform (AMP) includes disbursements entered prior to April 30, 2014. (b) Expenditures of the MoH marked as RPI in the SIGFP. They are mostly financed by general revenue, but budget

support from foreign sources is typically included in RPI and cannot be identified separately. (c) Based on actual disbursements entered in the Aid Management Platform (AMP) from international multilateral and

bilateral partners as well as major NGOs.

14 Internal resources (RPI) were evaluated using data from SIGFP, while external institutional resources were assessed

using the AMP. In 2006, the MFB introduced SIGFP, a modern computerized integrated financial management system

to process budget execution and accounting operations across all institutions of the government.


17

20. In fact, between 2009 and 2013, 80 percent of public funding to the health sector was financed

through external funds (Table 3). On the basis of the data available on the Aid Management Platform

(AMP), it is estimated that the share of external funding in public financing to health varied from 88 percent

in 2010 to 83 percent in 2013. During this period, external funds amounted to US$164 million on average

(Ar 410 billion).

Table 4. Public Health Expenditures: External and Internal Financing, 2009-2013

(a) The 2013 data from the AMP includes disbursements entered on the platform prior to April 30, 2014. (b) MoH expenditures marked as RPI (Ressources Propres Internes)in the SIGFP are mostly financed by general

revenue, but budget support from foreign sources is typically included in RPI and cannot be identified separately. (c) Based on actual disbursements entered in the Aid Management Platform (www.amp-madagascar.gov.mg) from

international multilateral and bilateral partners as well as major NGOs.

21. The extremely low share of domestic funding to the sector poses serious concerns in terms of

sustainability, ownership and efficiency of existing resources. Over the last five years, domestic funding

of health increased in amount, but its share in total public funds to the sector remained at around 20 percent.

This is low compared with other countries, and certainly very low compared with other sectors in

Madagascar. For instance, the share of domestic funding in education varied between 75 and 80 percent

over the same period (Figure 5). The over-reliance on external funding is a serious concern for the

sustainability of funding to the sector, especially given the volatility of aid in fragile contexts. It also

potentially raises issues in terms of alignment, harmonization and overall efficiency, given the high volume

of external aid provided off-budget and the absence of alignment around a new health strategy that would

facilitate the alignment and harmonization of funding.

Figure 5. Comparing Public Education and Health Financing over 2009-2013

Source: Calculated from AMP disbursements and MFB/SIGFP data.

0

200

400

600

800

1,000

1,200

2009 2010 2011 2012 2013

Total Amounts of Pubilc Funding

Education

Health

0%

20%

40%

60%

80%

100%

2009 2010 2011 2012 2013

Share of Domestic Funding in Total Public Funds

Education

Health

In billion of constant 2013 Ar. 2009 2010 2011 2012 2013(a)

MoH. Exp: Internal funding(b) 21% 12% 20% 17% 17%

Foreign aid: On-Budget(c) 45% 49% 56% 44% 49%

Foreign aid: Off-Budget(c) 35% 39% 24% 39% 34%

Total Public Expenditures 100% 100% 100% 100% 100%

Percent of GDP 2.8 4.6 3.3 3.5 3.7

Percent funded by aid 79 88 80 83 83

http://www.amp-madagascar.gov.mg/


18

B.2. Government Health Expenditures

1. General government expenditures allocated to health: international comparisons15

22. Total Government Health Expenditure to the health sector as a share of GDP decreased

sharply after 2007, but the share of government budget allocated to health remained broadly in line

with regional averages. GoM spent 2.5 percent of GDP and 12.8 percent of its executed budget on health

in 2012, which is roughly on par with averages of SSA and low-income countries (LICs). From 1995 to

2007, the country consistently devoted a larger than average share of its budget to health, and stayed close

to the SSA average in terms of share of GDP. The period after 2007, however, has been marked by a sharp

decrease in health funding as a percentage of GDP, reflecting lower overall government expenditures, and

resulting in health expenditures relative to GDP falling to below the SSA (Figure 6).

Figure 6. Trends in Total Government Health Expenditures (Public Financing), 1995-2012

Source: Data from WHO/GHED.

15 Internationally comparable data produced by the WHO give the relative magnitudes of general government

expenditures on health both in percentage of GDP and in percentage of the budget for most countries since 1995. The

figures cover all health expenditures that go through the public administration, including expenditures managed

outside of the MoH. This is the only source of data on general government health expenditure. Indeed, data obtained

from the MoF for health did not include other health expenditures than those in the Health “mission”. However, it is

unclear if externally funded expenditures that end up not going through the SIGFP are included in WHO’s GHED

numbers and from the reconstitution performed for this PER, it would appear that it is not the case, leading to

underestimating total funding to health in the WHO data.


19

2. Executed expenditures by the Ministry of Health, recent trends16

23. Public expenditures for health decreased by 31 percent over 2007-2013, due mainly to a drop

in on-budget external funding channeled to MoH. Total expenditure by the MoH (externally and

internally funded) decreased from 229 billion Ar in 2006 to 158 billion Ar in 2013.17,18 This decrease was

entirely due to the contraction of investment financing from external aid. Even though internally financed

health expenditures, in real terms, nearly doubled over the period, this was not enough to compensate for

the 95 percent drop in externally financed health expenditure (Figure 7). Although, according to

Government accounting, more than half of MoH expenditures had been financed by external grants and

loans in 2006 (before SIGFP was in place), externally financed expenditures dropped to less than 5 percent

in 2013.19

Figure 7. Executed Expenditures of the Ministry of Health, 2006-2013

Note: Financing from internal resources (RPI) includes budget support. All current expenditures are financed by RPI.

All externally financed expenditures are in the investment budget.

Source: Data from MoF/SIGFP.

24. Overall, when looking only at domestic spending, the share of MoH spending in total

government spending has remained the same since 2006. The GoM devoted 6.25 percent of internally

financed expenditures to health in 2013, and this allocation had remained relatively stable, in the 6-8 percent

16 The raw data was provided by the MFB; it is extracted from the SIGFP for 2009-2013 and reconstituted in the same

format for 2006 and 2008 (when the SIGFP was not operational). Throughout the document, executed expenditures

are taken at the mandatement level, which corresponds to the formal clearance before going for payment to the

treasury. Although they cannot be compared to other countries, these figures have the advantage of relying on primary

data and provide a time consistent series up to and including 2013. 17 Given the changes in responsibilities of the ministry over time, the figures only include expenses in the programs

related to health (i.e., the social protection and population components of the ministry in 2008 and 2009 are excluded).

This will be the case throughout the report when looking at MoH expenditures. 18 The figures calculated from government accounts excluding external funding are based on expenditures financed

by RPI. They exclude external grants and loans (most of investment expenditure) but includes budget support (budget

support can be identified in 2006 but was added to RPI for comparability with later years). 19 The contraction of external funding depicted here probably reflects the overall increase in off-budget external

support, but it may also be the result in inaccuracies in the way externally financed expenditures go (or do not go)

through the SIGFP. Therefore, caution is required when interpreted the apparent drop in MoH- executed expenditures.

229

202 195

153

204

156 158

87

122 121 119

146137

151

0

50

100

150

200

250

2006 2008 2009 2010 2011 2012 2013

Bill

ion

s o

f 2

01

3 A

r.

MoH expenditure

MoH exp. financed by RPI


20

range, since 2006 (Figure 8). This result is robust across the different budget categories, except for the share

of MoH in civil servant wages, which increased from 8.5 to 10 percent of the civil servant wage bill between

2006 and 2010. The stagnating share of health in Government expenditures could be a signal of sluggish

political commitment to health, but since we have been considering executed expenditures rather than

budget appropriations, it could also be the result of weak execution of the budget.

Figure 8. Share of MoH in Government-Executed Expenditures, 2006-2013

Note: All current expenditures are financed by internal resources (RPI), which may include budget support. All

externally financed expenditures are in the investment budget.

Source: Data from MoF/SIGFP.

B.3. Budget Execution

1. Execution rates

25. Some factors need to be taken into consideration when interpreting execution rates. Normally,

comparing budget appropriations to executed expenditures can be a way to assess planning and governance

capacity at the line ministry level, as well as the general quality of governance in budget management. For

Madagascar, several issues need to be considered before interpreting execution rates. First, executed

expenditures recorded in the SIGFP may not fully account for all realized expenditures. This could lead to

the over- or underestimation of execution rates, depending on the performance of expenditures not recorded

(or recorded differently) in the SIGFP. Second, various blockages in the budget exist that may prevent

ministries from using budgeted funds that would otherwise be committed. These blockages should not

necessarily be attributed to a lack of capacity in terms of budget execution, but rather to the need to improve

overall management of the budget, in particular cash flows.

26. There is a lack of clarity regarding on the inclusion/exclusion of foreign aid managed by the

Government. Differences between budget appropriations and expenditures could, in large part, be

attributed to the way externally financed investment expenditures are recorded in the government budget.

Disbursements of foreign aid marked on-budget in the Aid Management Platform are much higher than of

externally financed health expenditures in SIGFP (Table 3). This can lead to an underestimation of

execution rates, as some expenditures related to externally funded projects may not be included in the

0.00

2.00

4.00

6.00

8.00

10.00

12.00

2006 2008 2009 2010 2011 2012 2013

%

MoH expenditures % of the general executed budgetMOH expenditure % of the general executed budget, excluding external grants and loansMOH non-wage recurrent exp % of non-wage recurrent gov. exp.MOH expenditure on regular salaries % of executed wage budget


21

government accounts at the stage of execution but are still included in budget appropriations.20

Documentation setting out the rules regarding inclusion/exclusion of foreign aid managed by the

Government would be necessary to allow for better tracking of investments and to analyze execution rates

more precisely. This is particularly true of investment expenditures, which, for the most part, are financed

by foreign aid and therefore not fully included in the SIGFP. Therefore, it is important to separate the

current and investment budgets in the analysis of budget execution.

Table 5. Under-Accounting of Foreign Aid in Government Accounts, 2009-2013

2009 2010 2011 2012 2013

AMP Foreign aid disbursements marked on-budget

in billion 2013 Ar. 98.5 147.0 146.0 60.8 27.5

Of which, share included in SIGFP 0.3 6.1 8.3 20.4 10.2 Data sources: Primature (AMP), MFB (SIGFP).

27. Rules related to the execution of the budget may prevent full execution, especially for non-

wage expenditure; most notably the case for regulation rates imposed by MoF. Operational budgets

financed by internal resources are subject to quarterly regulation rates—a maximum percentage of

appropriations that can be committed by the end of each quarter—the fourth quarter rate normally being

100 percent. Civil servant salaries are subject to a linear regulation, i.e., 25, 50, 75 and 100 percent, but the

quarterly percentages for other expenses in the operational budget, including indemnities, are subject to a

non-linear schedule that can be changed during the year. For 2013 these rates were: 8.8, 52.1, 67.2, and

100 percent. However, at the end of September 2013, the fourth quarter regulation rate was changed to 53

percent (Arrêté 29109/2013). The fluctuations around regulation rates are largely out of the control of the

MOH. This can be due to issues like other government priorities taking precedence or a reduction in the

liquidity of the state’s revenue.

20 Another issues identified in the course of this PER is related to the payment of Value Added Taxes (VAT) which

are paid by some externally funded projects and subsequently reimbursed by the Government. It is not clear exactly

where these provisions appear in the budget, but they seem to be included in the SIGFP procedure and provisions, and

need to be made as part of the budget process to pay for these liabilities. Due to lack of predictability regarding the

total amount of these expenditures, there would appear to be some blockages. Unfortunately, the data obtained for

this PER is not sufficient to determine the magnitude of the problem.

Box 2. Budget Rectification Budget allocations within broad categories are generally modified mid-year to accommodate requested transfers of

credits. A new Finance Law is not necessary if these changes follow certain criteria. Beginning every May, program

organizers and coordinators can request modifications to the SIGFP to move credits. These requests need to be

approved by the Ministry of Finance and Budget and published by decree before they are effective in the SIGFP.

Rules differ depending on whether the transfer is (a) across programs in the same ministry; (b) across programs of

different ministries; or (c) within programs. The procedure is simplified when changes are within programs,

although the changes still needs to be published by decree. It is from the Law that, for operational expenses,

whatever the nature of the modification requested, the total amount in each economic category (indemnities, goods

and services, transfers) cannot be changed. Regulations and procedures concerning modifications are delineated in


22

28. On the other hand, budget reallocation and rectification may lead to overestimating the

capacity of the Ministry to execute according to plans (Box 2). The observed effective fungibility

between allocations and expenses across different sub-administrations and across programs is likely to

increase execution rates for total expenditures, but significantly reduce the ability to track expenditures by

function or at the program level. While it may be difficult to appreciate the magnitude of the problem and

its impact on allocation and execution, these issues need to be taken into account when evaluating resource

allocations by function at the executed level.

MFB circulaires. Any change across categories requires a rectification of the Finance Law. (Restrictions, however,

do not apply to externally financed credits, in particular for the investment budget.)

In years when the Finance Law is not rectified (has happened in all years except 2008 and 2010), there may be

differences between total appropriations initially granted to a line ministry and final appropriations, but there should

be no difference by broad category (transfers, goods and services, indemnities) for operational expenditures.

Based on past budget data—especially the data from 2010—it is clear that broad allocation changes are often made

to initial budget appropriations. The table below gives the ratio of modified to initial budget appropriations for

health and for the non-financial general budget. The differences could be due to the reduction in budgetary aid,

especially since such financing appears as internal financing in the budget, as mentioned above, and cannot be

identified separately. Because of this issue, caution needs to be used in interpreting execution rates. If international

budget support is included as domestic financing and may be increased or reduced after the rectification of the

budget, final appropriations can no longer be used as benchmarks to calculate execution rates, and the health sector

cannot be identified as a net loser or a net gainer in the budget rectification process overall. Taking the above into

consideration, the health sector was a net beneficiary in 2006 (but only due to additional external funding), 2009

and 2013 and a net loser in 2011 and 2012. The magnitude of these gains/losses was small overall, except in 2013,

when it reached seven percent of the internally financed budget.

Final and Initial Budget Appropriations in Health and Overall, 2006-2013

2006 2008 2009 2010 2011 2012 2013

Ministry of Health 1.12 0.98 1.01 0.87 0.99 0.98 1.05

Current operations: regular wages 1.09 0.96 1.01 1.01 0.98 0.97 1.07

Other current operations 0.84 1.01 1.01 0.71 1.00 1.00 1.06

Internally financed investment program 1.16 0.92 1.00 0.30 1.01 1.00 1.00

Externally financed investment program 1.27 1.00 1.00 1.00 1.00 1.00 1.00

Internally financed health budget 1.00 0.97 1.01 0.82 0.99 0.98 1.07

General non-financial budget 1.03 1.06 1.00 0.80 1.00 1.00 1.00



Internally financed investment program 0.84 1.17 1.00 0.99 0.97 0.94 0.95

Externally financed investment program 1.19 1.00 1.00 0.61 1.00 1.00 1.00

Source: Data from MFB/SIGFP.


23

Execution of the health budget

29. The budget execution rates of the MoH remain low for non-wage expenditures (Table 4).21

Execution for the internally financed part of the MoH budget was lower than for the overall internally

funded government budget in the last two years (2011-2013). In particular, non-wage current operations

performed poorly. This was likely due to the reduction in the final regulation rate, which limited spending

to half of what was allocated (Box 3), indicating that the restriction was binding for the MoH. In fact, the

MoH requested more in September 2013 but could only obtain a final rate of 53.54 percent. Given the

caveats raised above about the accounting of foreign aid in government accounts, it is not possible with the

information obtained to interpret execution rates for the externally financed investment program.

Table 4. Execution of the MoH Budget versus Execution of the Overall Budget, 2006-2013

Executed expenditures/Final appropriations 2006 2008 2009 2010 2011 2012 2013

Ministry of Health 0.71 0.56 0.48 0.65 0.63 0.69 0.68



Internally financed Investment program 0.90 1.00 0.51 0.92 1.00 1.03 0.89

Externally financed Investment program 0.49 0.13 0.17 0.06 0.27 0.13 0.12

Total Internally financed 0.91 0.96 0.70 0.94 0.90 0.93 0.87

General non-financial budget 0.61 0.60 0.49 0.74 0.67 0.80 0.81



Internally financed Investment program 0.74 0.93 0.53 0.97 0.59 1.34 1.48

Externally financed Investment program 0.24 0.26 0.12 0.12 0.15 0.10 0.04

Total Internally financed 0.89 0.76 0.68 0.94 0.84 0.98 0.98

Note: Executed expenditures = dépenses mandatées; Final appropriations = credits modifiés. Source: Data from MFB/SIGFP.

30. Execution rates for non-wage recurrent expenditure have not been equal across MoH

programs, especially in 2011 and 2013 (Table 6). Execution for medical supplies and medicines and for

the maternal and child health program has been consistently above 90 percent. There is no clear pattern

across years for the other programs, which had execution rates as low as 53 and 45 percent in primary health

services and specific disease programs, likely indicating that these two areas suffered most from the final

cut in the regulation rate.

21 Budget execution at MoH is marked by the fact that the health sector is heavily financed by external funds.


24

Table 5. MoH Execution Rates for Non-Wage Recurrent Expenditures, by Program, 2010-2013

Program 2010 2011 2012 2013

Health, administration/coordination 0.95 0.67 0.95 0.62

Primary health services 0.99 0.85 0.85 0.53

Hospital services 0.98 0.78 0.89 0.76

Medical supplies and medicines 1.00 0.95 1.00 0.95

Specific diseases – public health 0.96 0.84 1.00 0.45

Maternal and child health 0.96 0.99 0.95 0.91 Note: Execution rates are calculated relative to final budget appropriations. Prior years are not included in the

comparison because of administrative changes in the program structure.


2. Deconcentration of MoH expenditure 31. The level at which expenditures are administered can impact the overall performance of budget

execution. Importantly, deconcentration of expenditures can improve the efficiency of budget execution,

allowing a closer connection between administrative and operational units. But, increased deconcentration

requires additional capacity at the lower levels, as well as strengthened communication between various

actors along the expenditure chain. Box 3 provides the background necessary to understand how

deconcentration can be measured in government accounts.

Box 3. Measuring the Deconcentration of Health Expenditure Using SIGFP

After independence in 1960, Madagascar’s national health system was highly centralized, following the French

administrative model. The Constitution laid the groundwork for decentralization by outlining a local service delivery

structure, after which the MoH and other sector ministries began to shift limited decision-making power towards

lower levels of government. This was not a true decentralization, but rather a deconcentration of spending, given

that these lower-level structures had little discretion in the allocation and management of resources. Nonetheless, in

an effort to improve public resource management and strengthen public service delivery, the Government introduced

22 regions (doing away with 6 provinces) and progressively integrated the administration’s deconcentrated technical

services under the authority of the regional Chiefs, with the objective of harmonizing all sector activities in each

region through integrated regional development plans. In 2008, for the first time, resources were allocated to the

regions, making them responsible for the implementation of a small part of the investment budget.

The health delivery system is aligned with the country’s administrative structure and includes central, regional and

district levels. Each level has defined roles and responsibilities. The central level provides strategic direction, defines

policies in the sector and oversees national coordination of sector activities. The Regional Departments of Health

coordinate implementation of national health policy in the region, and provide technical assistance to the districts.

The District Health Authorities provide health services through the district hospitals and health centers.

Some information can be inferred about deconcentration of public expenditures by looking at the level at which they

are mandated in government accounts (SIGFP). The following levels of administration relevant to the sector are

coded in SIGFP: the central level (Ministry), the 6 ex-provinces, the 22 regions, the 113 districts, and the public

health facilities when they directly manage funds. Regular salaries are paid at the level of the 6 provincial general

treasuries (plus the central level for MoH personnel), and investment expenditures are 96-99 percent centralized.

This leaves non-wages expenditures to examine, representing only 20 percent of total MoH expenditures.

Source: Sharp, Maryanne; Kruse, Ioana. 2011. Health, Nutrition, and Population in Madagascar 2000-09. World Bank.

https://openknowledge.worldbank.org/handle/10986/5957.


25

32. The central level managed between 50 and 70 percent of current non-wage expenditures

during the 2006-2013 period, with no clear trend toward de-concentration except at the level of some

operational units. Figure 9 represents the relative weight of different levels of administration in mandating

expenses.22 The figure clearly shows an increase in the share of expenses managed at the operational level.

If we exclude the 2006 data (the lower volume of transfers to public entities in 2006 indicate that transfers

to hospitals may have been included in a different account), the figure does not show a clear trend toward

decentralization, with the share of central level expenditure decreasing between 2008 and 2011 and then

increasing again to reach 57 percent in 2013, a level slightly higher than in 2011. The last two years also

show a reduction in the share of non-wage current expenditures managed at the district level. In addition,

the share of expenses that could potentially be deconcentrated has gone down sharply, from over 20 percent

to 13 percent in 2013.

Figure 9. Deconcentration of MoH Current Non-wage Expenditures, 2006-2013


33. But, increased deconcentration requires additional capacity at the lower levels, as well as

strengthened communication between various actors along the expenditure chain as illustrated by the results

of a recent rapid assessment on the flow of immunization resources to districts health centers (Box 4).

Box 4. Subnational Financing Bottlenecks on Vaccination In an effort to better understand the financial flows and utilization of funds for immunization from central to

decentralized levels, the Ministry of Health led a rapid assessment exercise in 2014. The aim of the study,

implemented by UNICEF and the Institut Pasteur de Madagascar, was to diagnose the barriers and bottlenecks to

the timely flow, appropriate use, and reliable tracking of financial resources for immunization from the national to

all subnational levels. The main findings and recommendations are summarized in this text box.

Study objectives and coverage

This rapid assessment focuses on four main areas of potential bottlenecks for financial flows:

22 Operational Units (SOAs) labeled as CH2, CHRR and CHU are considered as management at the health facility

level and assigned to the operational unit level. SOAs labeled CH1 and CSB are included in the district level.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2006 2008 2009 2010 2011 2012 2013

Operational Units (including blocktransfers to hospitals)

District (includes management of CSBand CHD1)

Intermediary (province/region)

Central excluding transfers to hospitals

share of total MOH expendituresincluded (secondary axis)


26

(i) the approach seeks to generate information on delays in receipt of Routine Immunization (RI) and health funds at

each sub-national administrative level, relative to the start of the fiscal year

(ii) the assessment of fund diversion by the transferring administrative level is examined, as well as other factors

that reduce the actual amount of funds received by the subnational level compared to the amount of funds expected

by that level, according to its approved health and immunization budgets

(iii) each administrative level is examined to assess the scope and causes for any reallocations of funds planned for

RI spending,

(iv) problems with systems and capacities for accurate tracking of the receipt and use of immunization and health

funds from donors and domestic sources are assessed.

This study selected 10 out of 22 regions in Madagascar. In each region, one ‘strong’ EPI performer district and one

‘weak’ EPI performer district were selected, and within each of the districts four health centers (two ‘strong’ and

two ‘weak’ performers) were selected using DTP3 coverage rates. Given that numerous health centers lack copies

of reports and files, and subsequently, are missing data on the funds allocated to them, only 42 health centers out

of 80 planned were considered for the assessment of financial flows.

Findings

Main results show that the regional and district health teams manage most of the public funding for health that is

allocated to their respective levels. The district is responsible for distributing funding equitably among health

centers. The district considers the type of health center (HC1 or HC2), total catchment population, geographic

accessibility and health center needs in their allocation process.

The regional health team receives funds for operational costs directly into its bank account. The district health team

receives funds for the districts operational costs and the health center operations in their jurisdiction. Health centers

do not have financial management responsibilities. Most disbursements to CSBs (and even to some districts) are

made in cash, as there are no government-approved banks nearby that are authorized to accept and disburse funds

from government accounts. This creates substantial problems with tracking use of funds, and hinders the ability

of managers to ensure compliance with approved health plans and budgets.

The study was able to identify the major sources of immunization funding at the national, regional and district

level. However, financing details at the health center level were difficult to assess due to lack of data, archives or

copies of reports to cite sources. Overall, Madagascar is heavily dependent on partners and donors for immunization

funds with 92% of Expanded Program of Immunization (EPI) coming from technical and financial partners. At the

district level, a little more than half (54.73%) of the total funds received by the district are used for immunization

activities of which 24% comes from the state and 61.9% from technical and financial partners. Additionally, for a

good number of districts, the amount received is greater than the amount planned (budgeted) and requested from

partners outside of the activities of the Annual work plan (AWP) (46.8%).

Delays in allocation of funds compared to the schedules of activities were identified and are mostly due to the

multiplicity and cumbersome procedures of donors and complexity of banking procedures. Delays had an impact on

utilization rates, which were lower due to delays in credit allocation. The utilization rate is about 35.1% at the level

of the regional directorate of health and 41.9% at the level of the district. The second and third quarters are the only

periods during which public funds can be used and vaccination activities funded by the government are implemented

during these quarters. Thus, a large part of the public funds are prevented from being utilized in full and on time.

Furthermore, delays caused some implementation bottlenecks at the district and health center level. At the district

level, the date on which information for reports is sent can cause delays in the date on which funds are received. For

health centers, the date funds are received overlapped with the planned date for implementation of activities

causing a delay in activities.

The results also indicated that the number of monitoring and evaluation supervisory visits and financial

management checks vary largely with regional health directorates, districts or health centers, and in some cases are


27

not sufficient to ensure good management of funds. These oversight activities are generally integrated with other

objectives of a supervisor’s visit, and may benefit from more explicit and systematic attention.

Recommendations

The findings were by the MoH, development partners and health managers from all administrative levels in late

2014. Main results of the review were:

1. Investment in maintaining health system records and archives is important for improving the availability and

quality of data on financial flows for immunization. This would include making the health centre manager

accountable for record-keeping. Community surveys can also contribute to validating data on immunization

financing.

2. Budgetary reviews at every level are necessary for contrasting rates of expenditures between cost centres. In

some instances, budgetary controls (spending limits) e.g. in the first two trimesters may be warranted.

3. Investment in bottom-up planning would strengthen the approximation of budgets with planned activities.

Methods for ensuring compliance to activities and timelines of the annual work plans would also limit the

number of off-budget activities.

4. Coordination between the central government and donors and development partners is essential. The aim is

to better guarantee timely funding for the implementation of activities contained in the annual work plan of

districts and CSBs.

This analysis is being used by the government and its partners to identify practical approaches to help prioritize and

overcome major financial bottlenecks that constrain achieving equitable and universal immunization coverage.

Source: Case study on immunization expenditures in Madagascar, Thomas O’Connell, UNICEF, 2015


28

Key Findings

Madagascar’s heath sector is not adequately funded and has a very constrained budget envelope, The public

health sector is also largely externally financed with domestic financing very low and unstable.

Level of spending for health

Madagascar spends now less on health than three quarters of the SSA countries. Since, the percentage of

Total Health Expenditure (THE) in GDP has been around 4-5 percent with a downward trend in the period

2009-2012 compared to 2005-2008.

In real terms THE per capita expenditure has not changed since 1995. Looking at the period between

1995 and 1999 THE per capita was US$21. In the period between 2010 and 2014, Madagascar’s THE

per capita was US$20.

Between 2009 and 2013, 80 percent of public funding to the health sector was financed through external

funds. However, except for a large off-budget external investment in 2010, overall external financing

drastically decreased between 2009 and 2012.

The extremely low share of domestic funding to the sector (20%) is low compared with other countries,

and certainly very low compared with other sectors in Madagascar. This poses serious concerns for

sustainability, ownership and efficiency of existing resources.

Budget Execution

There is lack of clarity between budget appropriations and expenditures due to the different ways in which

externally financed investment expenditures are recorded in the government budget under the SIGFP and

the Management Aid Platform.

Expenditure by the Ministry of Health decreased by 31 percent over 2007-2013, due the contraction of

investment financing from external resources, channeled through MoH.

Rules related to the execution of the budget may prevent full execution, especially for non-wage

expenditure; this is notably the case for regulation rates imposed by MoF. The final yearly cuts in

regulation rates have negatively impacted the execution rates of some programs more than others, namely

primary health services and specific disease programs

Despite some inconsistent improvement over the last four years, the budget execution rates of the MoH

remain low for non-wage expenditures.

When looking only at internally financed spending, the share of MoH spending in total government

spending has stagnated at about 6%-8% since 2006. This result is robust across the different budget

categories, except for the share of MoH in civil servant wages, which increased from 8.5 to 10 percent

of the civil servant wage bill between 2006 and 2010.

Lack of de-concentration of resources

Budget execution is highly centralized. Between 2006-2013, the central level managed between 50 and

70 percent of current non-wage expenditures with no clear trend toward de-concentration despite a tiered

management and service delivery system down to primary care level.

Since 2011, there has been a reduction in the share of non-wage current expenditures managed at the

district level. In addition, the share of expenses that could potentially be de-concentrated has gone down

sharply, from over 20 percent to 13 percent in 2013.

Overall efficiency of public spending


29

The extremely low share of domestic funding to the sector poses serious concerns in terms of

sustainability, ownership and efficiency especially given the volatility of aid in fragile contexts. It also

raises issues in terms of alignment, harmonization and overall efficiency, given the high volume of

external aid provided off-budget in the absence of current overarching National Health Strategy.

Policy implications

In a context of stagnation and reversal of progress across several key health outcome indicators, additional

public spending on expanding access and utilization of quality health interventions are needed.

Spending more

The Government needs to better prioritize the health sector in its overall Government budget by increasing

public financing to the overall sector. These additional resources can come from a combination of

increased resources in the total budget (given the current low share of health) and increased external

financing from public and/or private sources.

Executing better

Existing budgeting tools needs to be strengthened particularly the SIGFP especially with regards to

including more comprehensive data on external aid, better tracking of investments and precise analysis of

trends in execution rates across different programs.

De-concentration of resources to lower levels of management and service delivery should be considered;

with more autonomy on execution of at least some of the non-wage budget at district and primary care

levels.

Execution of regulation rates should be revisited with a specific focus on having a more equal impact

across programs consistent with a prioritized budget execution strategy by the MOH.

The impending validation of the New Health Sector Strategy should be seen as a critical opportunity by

Government to better harmonize financing to the sector under one national plan.

More harmonized and dynamic budgeting mechanisms should be put in place including participatory

budgeting with all stakeholders and alignment of budget planning processes with calendar of the Ministry

of Finance and Budget.

Madagascar PER – Health |Section C

30

SECTION C. ELEMENTS TO ASSESS THE EFFICIENCY OF MOH EXPENDITURES

C1. Assessing Technical Efficiency through an Analysis of Input Shares

Box 5. Analyzing Input Shares in Health

The production of health requires a combination of inputs (labor, capital, materials and supplies) which are characterized by a

high degree of interdependence. That is, medical staff need medicines, supplies, equipment, health facilities, and training to

“produce” good health; medicines cannot be administered properly without supervision, and so on. The degree of

substitutability is small in the health sector relative to most other sectors. When too much is spent on one input (particularly

wages), other inputs are crowded out. This imbalance impedes efficiency, reduces the quality of care, and threatens the

sustainability of the system. This is why changes in the relative shares of public expenditures going to each input should be

regularly assessed.

Although the perfect input mix likely varies by country because of differing input prices, geographical conditions, and even

cultural traditions, implicit normative benchmarks have emerged from a systematic review of PERs published between 2002

and 2012 (Gaudin and Yazbeck, 2013). The main findings show that than 50-60 percent spent on labor was considered too

much; less than 5 percent on capital and maintenance was considered too little; and less than 30 or 40 percent on materials

(including pharmaceuticals) was considered inadequate to ensure the normal productivity of the other inputs.

34. Expenditures on wages have been increasing, while other operational expenditures and internally

financed investments have decreased (Box 5). The evolution of expenditures, in constant monetary terms,

shows a clear decreasing trend for expenditures on all types of inputs except labor, which has been increasing

in both in real terms and as a share of total expenditures.. This is also true in terms of shares, whether or not

externally financed expenditures are included (Table 6). The share of regular wages went from 33 to 78 percent

(between 2006 and 2013) of the overall budget (including both internal and external financing), and from 50 to

over 80 percent during the same period excluding external financing. Other recurrent expenditures, which

already received a relatively small share of the budget in 2006, at 22 percent, fell to 15 percent or less of the

budget in 2013.23 This indicates that Madagascar has clearly moved to an unbalanced situation that is critical in

terms of both efficiency and sustainability, especially considering the fact that the MoH wage bill has increased

in terms of shares and in real terms.

23 Externally financed investment expenditures are included in the table but not highlighted, considering the caveats mentioned

in section B1.


31

Table 6. MoH Expenditures by Broad Input Categories (budget classifications), 2006-2013

2006 2008 2009 2010 2011 2012 2013

Amounts in billion constant Ar. 2013

Regular salaries 75.8 89.1 86.7 96.3 111.9 112.3 123.3

Other recurrent expenditure 50.7 71.2 55.8 45.3 40.2 28.7 23.5

Capital expenditure - internal financing 25.6 24.9 37.9 6.6 14.4 6.0 4.5

Capital expenditure - external financing 76.5 23.2 33.1 4.6 37.1 9.0 7.0

In percent of MoH executed expenditure



Capital expenditure - internal financing 11.2 12.0 17.7 4.3 7.1 3.8 2.8

Capital expenditure - external financing 33.4 11.1 15.5 3.0 18.2 5.7 4.4

In percent of MoH internally financed expenditure



Internally financed capital expenditures 16.8 13.5 21.0 4.5 8.6 4.1 3.0

Regular salaries/share of recurrent budget 59.9 55.6 60.8 68.0 73.6 79.6 84.0

Data source: MFB/ SIGFP.

35. Regular salary expenditures in Madagascar have reached levels that are much higher than those

generally observed in less-developed countries. While internationally comparable data are not readily available,

a few points of comparison could be obtained using secondary data sources. Only two other SSA countries could

be identified in past PERs with labor shares above 60 percent: Zanzibar (2003 PER) with 80-85 percent, and Ghana

with about 70 percent (2009 PER)24; most other SSA countries had labor shares around 50 percent or less. Using a

larger and more systematic set of countries, other studies have provided some point of comparison with labor shares

in other income groups and regions. Vujicic et al. (2009) found the average share of wage in government health

spending around 40 percent in Africa for the 2000-2004 period, and the average for high-income countries around

45 percent. These comparisons, however, are based on data prior to 2006. In a more recent PER, a similar trend to

Madagascar was identified in a current study of Zimbabwe, where the share of wages in MoH expenditure reached

80 percent in 2013.25

36. As a point of comparison, in contrast to the Education sector, non-regular salaries are not a

significant part of the non-wage recurrent budget. While it is not possible to identify all labor costs in the non-

wage recurrent budget, a breakdown of expenditures by account code in SIGFP can help to gauge the magnitude of

these costs. Such a breakdown can also provide important information on the structure of MoH expenditures in

more detailed categories of inputs (Table7). Using further disaggregation of the budget to identify expenditures by

24 Ghana was also cited as a case of labor costs crowding out other inputs in Working in Health, World Bank, 2009. Including

additional duty hour allowance, the authors calculated that the wage bill added 90 percent to expenditures as donor funding

contributed solely to non-wage expenditure. 25 The Zimbabwe study, however, included all labor expenditures, including in recurrent expenditures and transfers.


32

input across broad categories increases the share of labor by only 3-4 percent. Even if some portion of transfers to

hospitals goes to pay temporary staff, this would not make a large difference to assessing total labor costs, given

that transfers to hospitals do not exceed 5 percent of the budget.26

Table 7. Input Shares across Budget Categories, Share of Internally Financed MoH Expenditures, 2006-

2013

2006 2008 2009 2010 2011 2012 2013

Labor and assimilated costs 53.4 50.5 50.6 68.5 70.6 80.2 85.2

Regular wages 44.6 42.5 42.9 57.6 60.4 71.7 73.5

Other wages 0.5 0.3 0.3 0.2 0.1 0.0 0.1

Social charges (regular staff) 5.3 5.7 5.4 7.8 7.1 5.1 8.4

Medical student grants and provisional. Salaries 3.0 1.9 2.1 2.9 2.9 3.4 3.2

Goods and services 17.8 20.6 18.7 18.7 14.7 10.5 6.7

Specific medical G&S 6.1 5.3 5.2 5.0 3.6 2.6 2.2

Fuel and transportation costs 4.3 5.1 5.0 5.4 4.4 3.4 2.3

General running costs 2.0 3.6 3.2 2.9 2.2 2.0 0.9

Maintenance and utility costs 2.6 5.5 4.4 4.1 3.5 1.7 0.7

Other G&S 2.9 1.1 0.9 1.3 0.9 0.9 0.5

Transfers and subsidies 2.4 6.3 5.3 4.4 3.9 4.0 5.0

Transfers to public entities (hospitals) 2.1 5.0 4.3 3.9 3.1 3.2 2.7

Transfers to pay staff medical costs 0.2 0.5 0.4 0.4 0.6 0.5 0.3

Other transfers 0.1 0.8 0.6 0.2 0.2 0.3 2.0

Capital costs 26.3 22.6 25.4 8.4 10.9 5.3 3.2

tangible assets 23.9 19.4 19.5 7.4 6.2 3.5 1.7

intangible assets and other 2.4 3.1 5.9 1.0 4.7 1.8 1.5

Internally financed MoH expenditures 100.0 100.0 100.0 100.0 100.0 100.0 100.0


37. Expenditures on goods and services related to the provision of health care make up a very small share of

the budget, and over the years this share has decreased considerably, with the cost of most medical

consumables borne by the patient through cost recovery. Trend data also indicate that maintenance and utility

costs decreased from 5.5 percent of the budget in 2008 to less than 1 percent in 2013. Fuel and transportation

costs also declined, from more than 5 percent of the budget in 2008 to 2.3 percent in 2013. The raw data also

indicate reductions in absolute amounts (by 50 percent in 2013 for maintenance and utility costs, and by 25

percent for transportation costs), indicating a true reduction in the level of activity. Altogether, combining input

types across the health budget, it is clear that since 2010, labor expenditures have crowded out expenditures on

goods and services and investments managed by the MoH (Figure 11). This is apparent looking at the decline

in key service delivery indicators during the same time period. For example, the prescription satisfaction rate,

a key indicator for drug availability, declined from 69 percent in 2008 to 58 percent in 2010/11 at the facility

level and the utilization of basic health centers and prenatal consultations decreased by 20 percent from 2008

to 2011.

26 It is not possible to identify input shares for expenditures paid out of the health facilities own revenues, if any.


33

Figure 11. Decomposition of MoH Expenditures into Broad Input Categories, 2006-2013

Note: Based on executed expenditures (dépenses mandates). Labor and capital include expenditures from the recurrent non-

wage budget (category 3 expenditures). Data source: MFB/SIGFP (data reconstituted for 2006 and 2008).

38. The large bias towards salaries in domestic funding is somewhat compensated by substantial inflows

of external aid targeting other aspects of the system, in particular goods and services. However, investment

financed by external aid has dropped considerably, making it difficult to sustain improvement in the quality and

quantity of health services delivered.

39. In conclusion, although Madagascar was found to do well overall in term of outcomes relative to

expenditures before 2008, the current imbalance in the use of inputs shows that the country is not on track to keep

its advantage, given that the trend since 2009 is not efficient and is unsustainable.

C2. Elements to Assess Allocative Efficiency: Expenditure Analysis by Function

Box 6. Allocative Efficiency Analysis Using Functional Allocations of Health Expenditures

Allocative efficiency analysis generally examines the types and combinations of goods and services produced in relation to

demand. The term is loosely interpreted here in the sense that consumer preferences and demand are not directly measured.

Instead, the analysis rests on a general appreciation of needs and on well-known characteristics of the different types of health

provision (functions) in terms of their public good (public health activities) and best value-for-money (primary care and

prevention) characteristics.

By affecting parameters such as accessibility and quality, the functional distribution of health expenditures is not just a response

to demand or to need; it also influences the types of services that will be effectively used by individuals. There is some degree

of substitutability between the different kinds of care dispensed at the primary, secondary, and tertiary levels or in different

types of facilities, in terms of reaching desired outcomes (i.e., lower mortality and morbidity). Expenditures on prevention, for

example, complement current expenditures on curative care contemporaneously but substitute for future expenditures on

curative care, and are therefore recommended from a sustainability perspective. Finally, while efficiency and equity often

involve a trade-off in other sectors, the two goals tend to be complementary in the health sector. In particular, directing more

resources to primary health care and prevention is usually recommended as both efficiency and equity enhancing. In addition

to looking at functions in terms of health services and levels, analysis of the distribution of expenditures by health priority is

useful to assess allocative efficiency in terms of responding to needs.

0%

20%

40%

60%

80%

100%

2006 2008 2009 2010 2011 2012 2013

Labor

Non allocated transfers

Goods and services

capital


34

The question is whether efforts are placed where they are most needed and where they will have the most effect. The

need to prioritize some health programs against others in the name of allocative efficiency depends on specific conditions in

the country, based on an evaluation of the situation and evolution of major health outcomes, utilization indicators, and

demographic changes.

1. Data and level of analysis

40. The ability to determine whether resources are allocated efficiently (Box 6) depends greatly on the ability

to classify expenditures into categories that can be matched to levels of care, health priorities, and other specifically

identified needs such as those of vulnerable populations. The expenditure analysis presented below is guided by

the feasibility of sorting expenditures by these types of functions using the data available. First, a breakdown of

Total Health Expenditures is presented based on National Health Account (NHA) data, to give a picture of the

overall situation. There is some degree of comparability over time and with other countries. This also gives an idea

of the evolution of costs that are borne mostly by households.27

41. Then, the bulk of the analysis is devoted to government health expenditures measured using: (a) public

expenditures from NHA 2010—-noting that these are 2.3 times larger than total MoH expenditures in SIGFP

(explained below); (b) wage expenditures of MoH based on a compilation of various government data sources and

author’ calculations; and (c) non-wage recurrent and investment expenditures of the MoH, based on SIGFP data.

Specificities about the three types of data sources and the type of analysis they allow are outlined in Box 7.

Box 7. Contribution of Different Data Sources to Understanding Health Expenditure by Function in Madagascar

Different sources of data provide different opportunities to classify expenditures by function in Madagascar. This box lays out

the type of analysis that can be done with each source and for different types of expenditures. Depending on the definition of

health expenditure used, the analysis yields different elements of information that complement each other, to provide an overall

picture of expenditure allocation. The sources presented below are ordered from most comprehensive to most narrow in terms

of the range of expenditures included.

National Health Account: This source encompasses the broadest range of health expenditures. It relies on expenditure

information using external survey questionnaires (and may therefore differ from information obtained from government

accounts). NHA methodology organizes the data into financing schemes and financing agents; total health expenditures include

all financing schemes, public and private. The presentation of the data in matrix form allows identification of the subset of

expenditures using central public administration as a financing scheme (HF). Expenditures under the public financing scheme

exclude social insurance but include all public funds, including off-budget externally financed expenditure. For Madagascar,

this total is 494 billion Ar, compared to on-budget expenditure of 217 billion Ar in SIGFP (see below). In the NHA central

administration HF category: 46 percent of funds go through local governments, 29 percent through NGOs, 21 percent through

the central administration, and 4 percent through international organizations.

MoH expenditures. A direct analysis of MoH expenditure has the potential to reveal trends that cannot be assessed using NHA

data. In particular, one can separate wage and non-wage expenditures and identify the significance of health care provision by

levels of care. Looking at wage and non-wage recurrent expenditures is important for several reasons: (a) the data on non-wage

27 In particular, it is important to look at total expenditures instead of MoH expenditures when analyzing the evolution of

pharmaceutical costs; these costs have been mostly borne by household in Madagascar since the system of cost recovery was

implemented.


35

expenditure are from a single source (SIGFP) and present a much higher degree of reliability than do wage allocations based

on approximations from multiple non-official sources; (b) different types of functional classifications are possible with wage

and non-wage expenditures; (c) non-wage recurrent expenditures have a higher degree of flexibility, as they allow a better

appreciation of short-term changes in priorities/political commitment, and can capture short-term variations in level of activity;

and (d) expenditures on salaries largely dominate in Madagascar (close to 80 percent of expenditures in 2013); therefore,

analysing recurrent expenditures as a whole overshadows important information that can be obtained by looking solely at non-

wage recurrent expenditure.

MoH wage expenditures. SIGFP can be used to evaluate the magnitude and evolution of total salaries and identify the share

of salaries going to the central administration. No further analysis by function can be done using SIGFP, given that regular

salaries and social charges (contributions paid by employers) are all included in the general administration program of the

MoH. A reconstruction of salary shares by type of facility and type of personnel was done for 2013, using MoH data on average

salaries by category of personnel combined with number and category of employees by type of facility. Approximations of

salaries for 2006-2013 were obtained using a combination of MoH and MFB information on salary increases and grade changes

over time. No information was available on changes in number of personnel over time. Instead, the salary estimates by function

were corrected using differences with SIGFP totals each year—a method that does not take into account the relative changes

between functions over time. A full description of the methodology and challenges encountered in gathering and reconciling

the data is given in Annex x 3. Given the size of the effort needed to perform such estimations and the limited availability and

reliability of non-SIGFP data, it is important to stress the need to change government accounting practices so that salaries can

be identified by function (program, type of facility) in government accounts.

Non-wage expenditures from SIGFP. SIGFP data on non-wage expenditures can be used to classify expenditures by type of

health services (administrative, curative, preventive) and by levels of care (primary, secondary, tertiary). The exercise requires

an analysis beyond classifications by program provided in the budget, to allow for some comparability with the pre-crisis years

and to widen the range of possible categories. Only very partial information could be obtained in terms of internally financed

expenditures going to different vertical health programs. Maternal and Child Health (MCH) expenses can be identified through

a specific budget program (Suivie et Developpement de la Mere et de l’Enfant -SDME). The data were further analyzed using

the names of administrative units to identify expenditures directed to other health priorities, but the data were not sufficient to

provide a clear picture of these expenditures, especially given the importance of external funding for vertical programs and the

lack of accounting of realized expenditures for externally funded expenses. A separate analysis of the immunization program

was done by UNICEF this type of analysis would need to be done for other programs (immunization, malaria, MCH) to see

whether the prioritization is consistent with the evolution of a broader range of indicators.

2. Functional allocation of Total Health Expenditures (NHA)

42. When considering the totality of health expenditures, both public and private, the results of the 2003,

2007 and 2010 NHA exercises reveal that Madagascar does not exhibit the common SSA pattern of over-

spending on in-patient care and under-spending on preventive and public care. In fact, the share of hospital care

decreased while the share of spending on prevention and public health programs increased over the period 2003-

2010 (Table 8). In Madagascar, however, these low levels of spending on curative care are likely a signal of system

failure, in the sense that the majority of the population may just not be seeking care. This is verified by the 2010

EPM findings results, which showed that close to 70 percent of people in Madagascar did not seek care when ill.


36

Table 8. Allocation of Total Health Expenditures across Functions (all sources)

2003 2007 2010

Hospital care 7 9.2 6.1

Ambulatory care 28 7.7 25.9

Pharmaceuticals 20 19.1 16.1

Prevention and public health programs 28 24.6 32.9

Health administration 10 13.9 5.3

Note: Shares are based on total expenditures, including for 2010. The residual share is for investment,

which is not separated by function.

Source: Adapted from NHA reports 2003, 2007 and 2010.

43. In comparison to SSA and other comparable low-income countries, Madagascar devotes a much

lower share of total health expenditures to inpatient care. Although international comparisons are difficult at

this level of disaggregation, some data are available, based on country NHAs in the WHO GHED, regarding the

share of inpatient care in total health expenditure (Table 9). Expenditure on inpatient care is the most expensive

type of care and has the potential to crowd out preventive care, resulting in higher future curative costs. This does

not appear to be happening in Madagascar, where the share of expenditures going to inpatient care is less than 7

percent. This is less than a third of SSA and LIC averages, and well below any individual countries in the

comparison group (based on data availability and proximity). Again, this is likely a reflection of low health-seeking

behavior across all quintiles.

Table 9. Share of Health Expenditure on Inpatient Care, International Comparison

Average Years available GDP/c in USD

Madagascar 6.6 2003, 2007 447

Mozambique 18.2 2004-2006 565

Zambia 25.9 1995-2000, 2005 1469

Mauritius 29.5 2002 8119

Kenya 34.6 1995-2001 943

DRC 41.5 2008 262

Tanzania 50.8 1995-2000, 2006, 2010 609

Sub-Saharan Africa 23.9 1995-2008 (unbalanced panel)

Low Income Countries 24.4 1995-2008 ‘’

Low and Middle Income 30.4 1995-2012 ‘’

High Income Countries 34.2 1995-2012 ‘’

Source: Data from WHO/GHED, based on NHAs.

44. Pharmaceutical costs have also remained stable, at less than 20 percent of total expenditures, and

these costs even decreased in 2013. From a general sustainability perspective, given that pharmaceutical costs

have had a tendency to increase in the rest of the world, this result could be seen as encouraging. But in the case of

Madagascar, given that the recent health facility survey indicates a decrease in utilization as a result of a reduction

in health seeking behavior, this could be concerning. The country’s FANOME system includes a cost recovery

component for essential drugs but the system is currently decapitalized in many parts of the country. In areas where

financing is available through donor funding, the FANOME system has been recapitalized and a fee exemption


37

scheme for a set of targeted services and medicines MCH services has been put in place, utilization rates have gone

up drastically.

3. Functional allocation of public health expenditures (NHA 2010)

45. Knowledge of the functional allocation of total health expenditures (THE) is helpful in identifying whether

there are specific gaps in the system as a whole. However, optimal prioritization varies between the public and

private sectors. Higher expenditures on curative care are expected from the private sector, while the public sector

needs to focus on preventive health services, which create the largest positive externalities for the population as a

whole.

46. The 2010 NHA28 results indicate a distribution of public expenditures that strongly prioritizes

activities with high public good characteristics, as would be expected of the public system. NHA is the only

source of data that can provide an overview of all public health expenditures, including externally financed off-

budget expenditures. According to these data, two thirds of public health expenditures are devoted to preventive

activities, most of which are targeted to specific diseases (Table 10).29 There are some limits to these NHA results;

for example, it is not clear from the report what were included as preventive health services.30 Further, since

expenditure on curative care cannot be broken down by level of care, it is likely that a good number of activities

carried out by primary health care facilities and some administrative costs were included as part of preventative

activities.

28 Using the matrices created for the NHA 2010, current public health expenditures can be broken down by specific health

services /health programs and to some extent by type of health facility (following the System of Health Accounts OECD - SHA

2011). 29 Unfortunately, it is not possible to recreate this breakdown for previous years, so trends cannot be analyzed. 30 The raw data for NHA were not available due to personnel changes combined with accidental losses of electronic files, so

the data are limited to tables and matrices published in the NHA reports.


38

Table 10. Allocation of Current Expenditures of the Public Administration System

Percent of public HE

By health program/ service

Prevention and fight against disease (public health) 46.60

Information, education and counseling programs 13.51

Curative services in hospitals (inpatient) 12.34

Curative outpatient services 8.13

Governance and administration 7.55

Vaccination programs 5.31

Pharmaceuticals 4.25

health monitoring programs 1.54

Early disease detection program 0.77

disaster preparedness and response 0.00

By type of facility/main activity

Preventive health services 64.37

Outpatient facilities 14.80

General hospitals 13.15

Health administration 7.44

Pharmacies 0.23

Source: Calculated from NHA 2010 matrices (appendix to 2010 NHA report).

4. Functional allocation of Ministry of Health expenditures: wage expenditures

47. As noted above, given current accounting practices in Madagascar, classifications of salaries into functions

rely on rough estimates. However, with salaries making up 78 percent of executed expenditures of the MoH (85

percent of domestically financed expenditures), salaries need to be considered in order to get a good idea of the

level of effort by type of health service and, in particular, by level of care. Details of the methodology used to

construct salary shares and raw results are presented in Annex 3.


39

a) Allocation of salaries by health facilities

Figure 12. Distribution of Salaries by Facilities and Administrative Levels, 2013

Data sources: MoH, MFB.

48. Even though 50 percent of the population seeking care go to primary health facilities (CSB1 and

CSB2), these facilities only absorb only 27 percent of wages (Figure 12).31 At the second level of primary health

care facilities (CSB2), staff absorb 23 percent of salaries while they are visited by 40-50 percent of the population

seeking care. There are 1610 functional CSB2s (2013) in the country and they are typically staffed by one doctor,

one midwife and a small number of support personnel. In contrast, basic primary health care centers (CSB1) are

normally staffed by one nurse or midwife and one support staff. There are 875 functional CSB1s (2013), which

represent less than 4 percent of the wage bill and are visited by about 10 percent of people who are sick. Personnel

data by region and type of facility (2013) show that CSB1s are typically understaffed. Only 5 regions had on average

more than 1 staff per functional CSB1, with the highest average in Haute-Matsiatra at 1.43 staff per CSB1.

49. Total salaries at service delivery levels have remained low since 2006, while central and regional

administrative salaries increased exponentially in the same period. Despite some data limitations, the graph of

salaries by type of facility reveal some important trends of shares in the MoH wage bill for CHUs, and the central

administration vs. other levels of care. (Figure 12).32 33

31 Enquêtes Permanentes/Périodiques auprès des Ménages (EPM) Household Surveys 2005 and 2010. 32 Changes in staffing per facility type over time could not be obtained, and the evolution of salaries presented here cannot

capture the full variation over time. The only category for which the data on evolution of salaries is not limited is the central

administration. Salaries going to other facilities can only evolve independently because of differences in salary increases over

time and by category of personnel. An estimate of salaries, taking account of changes in the number of facilities at the primary

care level, was tested but did not improve results when compared to totals in SIGFP. The graph in Figure 13 showing the

evolution of salaries since 2006 is virtually unchanged. 33 The decrease in Central Administration shown for 2009 could be due to a decrease in total salaries recorded in SIGFP.

However, there were some issues in the accounting of salaries for 2009, so the decrease could be due to low data quality in

2009.

Central Administration

29%

DRSP5%

SDSP8%CSB2

23%

CSB14%

CHRD7%

CHRR4%

CHU: 20%

Central Administration

DRSP: Regional Administration

SDSP: District Services

CSB2: Health centers (include 1 doctor)

CSB1: Basic Health center (no doctor)

CHRD: Primary hospitals (District)

CHRR: Secondary hospitals (Region)

CHU: Secondary/tertiary hospitals (ex-Province/Capital)


40

Figure 13. Estimated Trends in Salaries by Type of Health Facility/Administrative Unit, 2006-2013

Source: Based on data from MFB/SIGFP and from Ministry of Health/Human Resources Department.

b) Allocation of salaries by level of care

50. In Madagascar, similar to other low-income countries, a great deal of primary health care is provided

outside of primary health care facilities, especially in tertiary hospitals. However, this care is not measured

because of lack of data, so expenses incurred at hospital end up being counted as secondary or tertiary care.34 Our

estimates indicate that 60 percent of activities in university hospitals (CHUs) are at the primary care level, in the

sense that they are dispensed by primary care practitioners.35 In administrative functions, about 40 percent of salaries

(50 percent at the district level) go to PHC/PH personnel.36 The result is also interesting for district hospitals, which

were included in the graph above as primary care hospitals37; in fact, 66 percent of salaries in district hospitals go

to PHC/PH, which is not much different from the share found at the CHU level (Figure 14).

34 Glick and Razakamanantsoa (2002) report that a substantial amount of primary care is dispensed at the hospital level for

individuals who have access to hospitals, based on evidence in EPM, although they do not quantify the size of primary care

provision. 35 Each type of occupation was assigned to a level. General practitioners, dentists, midwifes, public health specialists, nutrition

agents and health workers were automatically assigned to the primary care/public health category. Specialized doctors and

specialized paramedical staff were assigned to secondary/tertiary care. Non-specialized paramedical (nurses) and radiologists

were distributed according to the share of public health care staff relative to specialized staff in the facility. 36 Doctors working in administrative functions who do not dispense or supervise health care are recorded as administrative

personnel. 37 The personnel data do not distinguish between category 1 and category 2 district hospitals. Category 2 district hospital offer

surgery and may be closer in type of health care provision to regional hospitals that provide primarily secondary care.

0

5

10

15

20

25

30

35

2006 2008 2009 2010 2011 2012 2013

Bill

ion

s o

f co

nst

ant

20

13

Ar.

CENTRAL MINISTRY (fromSIGFP)

Regional Admin

District Services

CSB1

CSB2

CHRD

CHRR

CHU


41

Figure 14. Allocation of Salaries by Level of Care and Support Activities by Type of Facility, 2013

Note: Type of health facility described in Figure xx above. Primary health care and Public Health (PHC&PH) includes all

general practitioners, dentists, midwives, nutrition agents, and health workers. General nursing staff are assigned according to

the proportion of other primary care personnel in the facility where they are employed.

Data source: Authors’ calculations from data on occupation and quantity of personnel by facility type, MoH/Human Resources

Department.

51. Administration costs are relatively larger in regional hospitals (24 percent) than in other health care

units, including other hospitals (less than 20 percent in CHU and CHD). In CSB1 and CSB2, it is clear that the

medical/paramedical staff take care of administrative tasks.

52. Overall, based on a combination of information on type of personnel and type of facility, about 62

percent of salaries go to primary and public health care, a figure that resembles what was found in NHA looking

at the totality of public expenditures. Secondary/tertiary care activities absorb less than 6 percent of the MoH wage

bill while administrative staff absorb about one third (Figure 15).

Figure 15. Allocation of Salaries by Level of Care, 2013

Source: Data from MFB data/SIGFP and from MoH/Human Resources Department.

5. Allocation of salaries by type of personnel

53. Basic health centers (CSB1) are almost exclusively run by paramedical staff (nurses, midwives and lab

technicians) with the highest concentration of medical staff salaries in tertiary care facilities (CHUs).

Classification by type of personnel (medical, paramedical, administrative) allowed an analysis over time based

0%

20%

40%

60%

80%

100%

CSB 1 CSB 2 CHRD CHRR CHU SDSP DRSP CENTRAL

ADMIN/SUPPORT

PHARM/LAB

SECONDARY/TERTIARY

PHC&PH

Primary and

Public

Health

61.7%Secondary/

Tertiary care

5.6%

Pharmacy/

Labs

3.8%

Admin and

support

28.9%


42

on actual changes in number of staff.38 The ratio of paramedical salaries to medical staff salaries is significantly

lower in CHUs than in other health facilities. Salaries of paramedical staff, make up about 40 percent of the

wage bill in all other health facilities except the basic health centers (Figure 16).

Figure 16. Share of Salaries by Type of Personnel and Facility, 2013

Source: Data from MFB (average salary increases) and from MoH/Human Resources Department.

54. Looking at personnel, the highest increase in total wage expenditure is from medical staff (doctors).

Figure 17 traces the evolution of salaries by type of personnel, taking account of overall changes in the number of

personnel by type as well as the evolution of average salaries.39

Figure 17. Estimated Change in Total Wage Expenditures by Category of Personnel, 2006-2013

Source: Data from MFB (average salary increases) and from MoH/Human Resources Department.

38 The number of personnel by type (medical, paramedical, and administrative) was available from 2000 to 2012. Some

adjustments were done based on facility level data for 2012 and 2013 to match the total numbers (the numbers broken down

by facility type and region underestimated the total number of staff). Average compensation estimates by category were used

to reconstruct total salaries. See Annex 3 for details. 39 The data on number of personnel cover 2000-2012. The number of personnel recorded by DRH in 2012 and 2013 was used

to estimate the percentage change for 2013.

0%

20%

40%

60%

80%

100%

Paramedical

Medical

Admin

0%

20%

40%

60%

80%

100%

0

10

20

30

40

50

2006 2007 2008 2009 2010 2011 2012 2013

Nb

illio

ns

of

con

stan

t 2

01

3

Ar.

Admin/Support

Medical

Paramedical

% of the total in SIGFP(secondary axis)


43

6. Functional Allocation of Ministry of Health Expenditures: non-wage expenditures

a) Functional classification by budget program, 2010-2013

55. Program budgeting has the great advantage of facilitating the analysis of expenditures by function, when

programs are chosen to reflect the principal health function by level of care and/or type of services. Expenditures

are separated into: a) Administration and Coordination; b) Basic Health Services; Hospital Health Services; c)

Supply of Medicine and Other Medical Products; d) Disease Prevention and Control; and e) Maternal and Child

Health.40,41 Basic health services are fully assimilated into primary health care, while hospital services include all

in-patient services and are mostly directed to secondary/tertiary health care.42 Data for 2009 are not included to

ensure full comparability, with regard to program structure43 and considering that total non-wage expenditure was

cut in half between 2009 and 2010.

Recurrent non-wage expenditure

56. Administration and coordination takes the largest share of recurrent non-wage expenditures, more

than PHC, MCH, and public health combined (Figure 18). Using the information available in the SIGFP shows

that administrative expenses constitute about 40 percent of non-wage recurrent expenditure, (with a decrease in 43

in 2010 to 36 percent in 2013). The share going to hospital services stayed relatively constant at around 25 percent.

The share going specifically to primary health care services was significantly reduced from 19 to 11 percent,

although maternal and child health expenditures compensated for the decrease. Grouped together, non-wage

expenditure on primary health care, maternal and child health and public health (which is labelled fight against

specific diseases and includes some primary care), increased from 28 to 32 percent of recurrent expenditure between

2010 and 2013.

40 Note pour la traduction: Administration & coordination; Services de santé de base; Services de santé hospitaliers;

Fourniture de médicaments, consommables et autres produits; Lutte contre les maladies; and Survie et développement de la

mère et de l'enfant (SDME). 41 The MCH and Family Planning programs were new programs in 2010. Family planning was merged with the MCH program

in 2012 and 2013. 42 If the analysis presented above regarding share of salaries can be extended to non-wage expenditures, about half of hospital

services would be directed to primary care services. 43 Budget programs in 2009 did not include either the MCH program or family planning.


44

Figure 18. Allocation of Non-Wage Recurrent Health Expenditures by Budget Program, 2010-2013

Note: MCH and FP expenditures were not identified separately in the 2009 budget.


Investment expenditure

57. Virtually all investment expenditures go to public health (fight against specific diseases) and primary

care/MCH activities (Figure 19). This is based on the expenditures included in the SIGFP only. A large part of

investment expenditures end up being spent off budget or outside of the MoH accounts and are not accounted for

in this analysis.

Figure 19. Allocation of Health Expenditures by Program—Investment Expenditures, 2010-2013


58. Despite large yearly fluctuations, the overall picture on functional allocation of non-wage

expenditures looks no different in 2010 than it did in 2013 (Figure 20). Combining non-wage recurrent and

investment expenditure, PHC/MCH/PH expenditures went from a low of 40 percent in 2010 to 53 percent in 2013,

with a peak of 68 percent in 2011. The same pattern is revealed when looking only at internally financed

expenditures: from 35 percent in 2010 to 41 percent in 2013, with a peak of 47 percent in 2011.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013

Administration &coordination

Hospital services

Medical supplies andmedicines

Public health

Maternal and ChildHealth/FP

Primary health careservices

0%

20%

40%

60%

80%

100%

2010 2011 2012 2013

Administration & coordination

Hospital services

Medical supplies and medicines

Public health

Maternal and Child Health/FP

Primary health care services


45

Figure 20. Classification of Non-Wage Expenditures by Program (recurrent+investment), 2010 and 2013


b) Additional information using classifications by administrative unit

59. It is not possible to compare the results presented above to the pre-crisis period because of changes in the

administrative structure and the delineation of programs, as well as changes in the way that some expenditures were

classified in the budget after 2009. Program delineations also limit the type of classifications possible. To

circumvent these issues, expenditures in the SIGFP were classified using the name of the Operational Activity Unit

(SOA), the account code (type of expense), and current program assignments to identify the type of service, level

of care, and other specific areas of focus.44 Table A3 in Annex 1 lists the classifications that were created. This

alternative classification offers the advantage of being completely independent of other differences in budget

accounting. Further, it is stable over time, so it can be used to confirm data quality and to compare pre- and post-

crisis periods. In addition, this alternative classification provides further disaggregation by type of activity, which

is useful for tracking specific efforts, including in terms of governance, given the weight of administration in current

expenditure.

60. Non-wage recurrent health expenditure by health service/level of care are close to those reported by

budget program for the combined PHC, MCH and Public Health functions. Results differ somewhat for the other

three functions, with hospital care (secondary and tertiary care) clearly going up since 2010.

61. Relative to 2006, the weight of administration decreased in non-wage expenditures decreased, while

the shares going to hospital services and to high-impact activities increased. Hospital care gained 10 percentage

points, high-impact activities (PHC MCH, and Public Health) together gained 8 points, while the share of

44 About 1,945 different SOAs were identified by combining the data for 2006 and 2008-2013, about half of which were similar

SOAs for different districts or regions. SOAs were manually assigned to the different categories of table x.x3 in view of its

denomination, and when possible matching where expenditure of similar SOAs were classified in the latest budget programs.

Additional identification was performed within some administrative SOAs using PCOP account information. In particular,

hospital transfers were re-coded as secondary and tertiary health services/curative, and pharmacy and medical consumables

were removed from administration when applicable.


46

administration decreased by 20 points. Undifferentiated services, which consist mostly of pharmacy and laboratory

costs, remained stable (Figure 21 and Table 10).

Figure 21. Allocation of Non-Wage Recurrent Health Expenditures by Level of Care, 2006-2013


Table 10. Shares of the Recurrent Non-wage Budget by Level of Care, 2006-2013

2006 2008 2009 2010 2011 2012 2013

Primary care, MCH, and public health 24.1 28.0 24.3 28.2 31.8 26.7 32.3

Secondary and tertiary care 14.1 19.6 23.4 20.2 21.4 22.0 24.0

Undifferentiated by level (mostly pharm/lab) 15.8 13.2 16.1 15.9 12.1 15.4 14.5

General administration 46.0 39.2 31.1 31.3 30.0 32.0 26.3 Source: Data from MFB/SIGFP.

62. Including investment, the evolution of non-wage expenditures is much less stable if we look at yearly

changes since 2008.45 Maternal and child health activities received a boost in 2011, when they made up 28 percent

of non-wage expenditures at the MoH. Excluding wages, the combination of PH, PHC, and MCH activities reached

70 percent in 2011 and since then have remained above 50 percent of the total. (Table 11).

Table 11. Allocation of Non-Wage MoH Expenditures (including PIP) by Level of Care, 2006-2013

2006 2008 2009 2010 2011 2012 2013

Primary care, MCH, and Public Health 17.3 46.5 62.5 44.4 70.3 51.2 55.0

Secondary and tertiary care 4.8 11.9 15.0 17.7 10.8 16.8 17.4

Undifferentiated by level 65.4 8.1 8.2 12.9 5.4 10.2 9.9

General Administration 12.5 33.4 14.3 25.1 13.5 21.8 17.7 Source: Data from MFB/SIGFP.

45 Expenditures on building of facilities and pre-operating costs (65 percent of non-wage expenditures in 2006) could not be

identified by type of facilities and therefore could not be assigned by level so little can be said about functional allocation for

2006.

0%

20%

40%

60%

80%

100%

2006 2008 2009 2010 2011 2012 2013

Secondary and Tertiary Health care

Health, undifferentiated by level (mostly pharm/lab)

Primary and Secondary Health Care

Maternal & Child Health (inc. reproduction/FP)

Primary Health Care (PHC)

Public Health

General Administration


47

63. A comparison of MoH non-wage expenditures (including foreign-financed investment expenditure) in 2008

and 2013 is shown in Figure 22. It is important to note that that the size of the financing envelope was greatly

reduced from 115 billion Ar in 2008 to only 35 billion Ar. in 2013. This new picture reveals a larger share, albeit

of a much smaller pie, given to primary health care and public health, while expenditures of administrative units

went down and the share going to hospital services went up.

Figure 22. Distribution of Non-Wage Expenditures by Program, 2008 and 2013


64. The classification by function/type of activity confirms the steady reduction in administration costs,

mostly to the benefit of curative activities until 2009 and to the benefit of preventive health after 2009 (figures

23 and 24).46 The weight of preventive activities increased significantly (from 7 to 21 percent) from 2009 to 2013,

while the weight of curative activities increased until 2009 (from 16 to 41 percent) and remained stable or decreased

slightly thereafter, to reach 34 percent in 2013.

46 The definition of administration is different in this classification compared to above, explaining the higher weight. For

example, expenditures of District health services were classified as primary health by levels but as administrative(as opposed

to curative or preventive) by function.


48

Figure 23. Allocation of Non-Wage Recurrent Health Expenditures by Type of Activity, 2006-2013

Source: Data from MFB/SIGFP.Data source: MFB/SIGFP.

65. As expected, more variability is found when including investment expenditures. When investment is

included, preventive activities take a larger share, reaching above 40 percent in 2011 and back to a more

representative 27 percent in 2013. Curative activities make up 17-25 percent depending on the year.

Figure 24. Allocation of MoH Non-Wage Expenditures (incl. Investment Expenditure) by Type of Activity,

2006-2013


c) A partial look at specific allocations (health priorities, target groups, governance)47

66. Communicable diseases (CDs) and MCH dominate targeted expenditures identifiable by program.

Excluding the investment budget, expenditure on CDs clearly dominate every year, followed by MCH (Figure 25).

Looking at investment, CDs and MCH still make up the bulk of targeted expenditure, although there is much

variation over the years between both programs. There has been some increase in non-communicable diseases

(NCD) program funding, indicating that are starting to gain importance in the investment budget.

47 The classification by SOA can be used to partially identify expenditures targeted to specific priorities. Amounts targeted to specific priorities in the recurrent budget (all internally financed) and the investment budget (most externally financed) are reported in Annex 1.

0%

20%

40%

60%

80%

100%

2006 2008 2009 2010 2011 2012 2013

Mantenance, logistics

Training

Pharmacy/laboratories

Preventive

Curative

Administration

0%

20%

40%

60%

80%

100%

2006 2008 2009 2010 2011 2012 2013

Training

Pharmacy/laboratories

Preventive

Curative

Buildings, equipment, maintenance, etc.

Administration


49

Figure 25. Evolution of MoH Expenditures Targeted to Specific Diseases, 2006-2013

(a) Recurrent expenditure are entirely financed with internal resources (RPI) which include budget support.

(b) Investments included here are limited to those included in SIGFP. No financing could be identified in the Investment category using this

classification by type of disease in 2006. This could be due to changes in accounting by SOA.


d) An attempt to disaggregate external aid by functional category

67. As highlighted above, external aid is an important component of total public spending on health. While the

lack of systematic recording of external support to the sector makes it difficult to analyze the composition of external

aid, there is substantial evidence pointing to large and continuous support from external partners in providing health

inputs, such as vaccines and other health materials, over recent years. In particular, support from GAVI and the

malaria funds have ensured the provision of essential inputs to the system. However, this support, while largely in

alignment with specific disease control strategies, is generally channeled outside of the MoH. Box 7 summarizes

the findings of a recent case study by UNICEF on financing immunization. As noted above, this type of analysis

needs to be carried out across external financing for health given its large share of the total budget for health.

0

2

4

6

8

2006 2008 2009 2010 2011 2012 2013

Bill

ion

20

13

Ar.

Targeted recurrent expenditures (a)

Communicable Diseases

MCH/Reproductive health

Neglected Tropical Diseases

Non Communicable diseases

0

5

10

15

20

25

30

2006 2008 2009 2010 2011 2012 2013

Bill

ion

s 2

01

3 A

r.

Targeted Investment expenditure (b)

Communicable diseases

Non Communicable diseases

Neglected Tropical Diseases

MCH/Reproductive Health


50

Box 8. Financing Vaccination in Madagascar

The effective financing of the immunization program relies mainly on predicable and timely funding from external development partners such as GAVI Alliance grants, UNICEF, WHO, World Bank, with additional support (technical and resources) provided by other partners such as the development agencies of the UK, USA, French and EU.

The political crisis in 2009 and subsequent macroeconomic constriction. Since the 2009 crisis, authorities have implemented tight budgetary policies to preserve macroeconomic stability which has severely constrained the available fiscal space of the government; and in turn, significantly impacted social sector spending, including on immunization services and . This has been accompanied by a steep fall in vaccination rates: complete immunization coverage for children 12 to 23 months old dropped from 62 percent in 2008 (DHS 2008/2009) to 51.1 percent in 2012 (ENSOMD 2012/2013).Routine vaccination expenditure represents 0.11% of GDP. According to data in the annual progress reports, in 2010 there was a total health expenditure on vaccinations of about US$11.5 million. Out this amount, the Government was responsible for 17%, about $2.2 million. Between 2010 and 2013, the Government share of financing dropped from 17% to 7%, representing a drop of 10 percentage points in 4 years.

Figure 1: Proportional breakdown of vaccination funding

Over the last four years, the Government share of costs for the vaccination program is low and is declining with most expenditures being used for the purchase of vaccines, fuel, and payment of salaries.

Currently, over 93% of program resources are provided by technical and financial vaccination partners. Since 1997, the country’s National Expanded Program on Immunization (EPI) has had a budget line for the purchase of routine vaccines. But with the introduction of new vaccines, the funds allocated fall short of the full costs of procuring routine and new vaccines.

In the context of slow economic recovery current projections do not predict sufficient domestic revenues in the short term for the Government to meet co-financing obligations of vaccine procurement or to finance expansion of immunization coverage to achieve objectives of universal and equitable access to save lives and minimize illness from vaccine preventable diseases. With some traditional partners indicating a progressive reduction in financial support to immunization, the financing gap for immunization can only be filled over the next few years if increasing domestic resources are combined with additional financing from existing and new partners. The Government also noted that a draft law on the sustainable financing of vaccination is under preparation, which would permit the Government to earmark resources for vaccination, thereby securing funding for specific requirements of vaccine procurement and supply chain management at all levels.

The main recommendations from the immunization financing review include: - Capacity building of teams responsible for vaccination at all levels - Improved monitoring of EPI expenditures at all levels - Strengthened reporting, management and feedback on expenditures at all levels - Finalization of the draft law on the sustainable funding of vaccination - Strengthened ministry leadership in coordinating interventions - Strengthened advocacy around partnership and financing for immunization with all partners

Source: Case study on immunization expenditures in Madagascar, UNICEF, 2015

17% 12% 11% 7%

52% 72% 61% 76%

30% 16% 28% 17%

2010 2011 2012 2013

Breakdown of vaccination fundingGovernment GAVI Other Technical and Financial Partners


51

C3. Elements of a Productivity Analysis

68. The impact of MoH expenditures within the country can be assessed in terms of the number of direct

beneficiaries. The classifications constructed for the functional analysis can be used to shed some light on whether

government expenditures go to services that are most used. The unit costs presented below are based on 2013 salary

expenditures.48 Annual utilization (number of people who visit a given facility over the year) is calculated based

on the 2010 EPM.49

69. As expected, unit costs increase exponentially as we move up the levels of care. Relative to level 2

health centers (CSB2) that are the most frequently visited, CSB1s (level 1 health centers) cost 30 percent less per

visit, primary hospitals (CHRDs) cost 30 percent more, secondary regional hospitals (CHRR) cost almost three

times more, and tertiary hospitals (CHU), including main hospitals at the province level, cost about 30 times more

(Figure 26).

Figure 26. Unit Costs by Type of Facility, 2013

Methodology note: Salaries for 2013 based on category and number of staff per facility, and average salary per category (MoH).

Utilization numbers population weighted and annualized from 2010 EPM.

Source: Data from MoH 2013, MFB, EPM 2010.

70. Expenditures roughly follow utilization patterns up to the CHRR level (Figure 27). Unit costs do not

reveal whether differences are due mostly due to low utilization or high costs, so it is useful to present the

components separately. It is clear that low utilization drives the high unit cost at the CHU level. This result need to

be put in perspective, considering what was found in terms of the provision of primary care in CHU hospitals. It

may make sense to deploy some of the primary care staff in CHUs to facilities that cater to the largest numbers.

48 Including other expenditures would not change the interpretation of these results. Similarly, looking at non-wage expenditure

separately using SIGFP data would not be informative, given that expenditures cannot be attributed to the different facilities. 49 The calculation was done using the survey weights and multiplying the results for the last 2 weeks by 24 to get annual

estimates.

2.55 3.45 4.61 9.46

95.69

CSB1 CSB2 CHRD CHRR CHU

Unit costs in thousand Ar.


52

Figure 27. Expenditure Shares vs Utilization Shares by Type of Facility, 2013

Source: Data from MoH 2013, MoF, EPM 2010.

Key Findings

Expenditures on labor have been increasing both in real term and in share of total expenditures, while other

operational expenditures and internally financed investments have decreased. This indicates that Madagascar has

clearly moved to an unbalanced situation that is concerning in terms of both efficiency and sustainability in delivering

a sufficient amount of quality health services to the population.

Economic analysis of public spending

Regular salary expenditures in Madagascar have reached levels (84% of domestic financing in 2013) that are

much higher than those generally observed in other low-income countries.

Expenditures on goods and services related to the provision of health care make up a very small share of the

budget, with the cost of most medical consumables borne by the patient through cost recovery.

Since 2010, labor expenditures have crowded out expenditures on goods/services and investments managed by

the MoH.

The large bias towards salaries in domestic funding, which is already very restricted (approx. 20% of public

financing for health) is somewhat compensated by substantial inflows of external aid targeting other aspects of

the system, in particular goods and services. However, investment financed by external aid has dropped

considerably, making it very difficult to sustain improvement in the quality and quantity of health services

delivered.

Although Madagascar was found to do well overall in term of outcomes relative to expenditures before 2008, the

current imbalance in the use of inputs shows that the country will likely not keep this advantage, given that the

trend since 2009 is not technically efficient and is unsustainable.

Functional Allocation of Total Health Expenditures (Public and Private)

Madagascar does not exhibit the common SSA pattern of over-spending on in-patient care and under-spending on

preventive and public care:

7%

40%

12%7%

33%

15%

64%

15%

4% 2%

CSB1 CSB2 CHRD CHRR CHU

Expenditure % Utilization %


53

o The share of hospital care decreased while the share of spending on prevention and public health programs

increased over the period 2003 -2010.

o The share of expenditures going to inpatient care is less than 7 percent, which is less than a third of LIC

averages.

o Pharmaceutical costs have also remained stable, at less than 20 percent of total expenditures, and these

costs even decreased in 2013.

These indicators could be a signal of system failure, in the sense that the majority of the population may just not be

seeking care. The 2010 Household Survey indicates that close to 70 percent of people in Madagascar did not seek care

when ill.

Functional allocation of public health expenditures

The 2010 NHA results indicate a distribution of public expenditures that strongly prioritizes activities with high

public good characteristics, as would be expected of the public system.

Total salaries at service delivery levels have remained low and constant since 2006, while central and regional

administrative salaries increased exponentially in the same period.

Primary health care facilities absorb only 27 percent of wages, while 50 percent of the population seeking care go

to these facilities.

In Madagascar, similar to other low-income countries, a great deal of primary health care is provided outside of

primary health care facilities, especially in tertiary hospitals.

Recurrent non-wage expenditure

Administration and coordination takes the largest share of recurrent non-wage expenditures, more than PHC, MCH,

and public health combined.

External aid analysis by functional category

Lack of systematic recording of external support to the sector makes it difficult to analyze the composition of

external aid but there is substantial evidence pointing to large and continuous support from external partners in

providing health inputs, such as vaccines and other health materials, over recent years. However, this support is

generally channeled outside of the MoH.

A recent case study on financing vaccination found that routine vaccination expenditure represents only 0.11% of

GDP. According to data in the annual progress reports, in 2010 there was a total health expenditure on

vaccinations of about US$11.5 million. Out this amount, the Government was responsible for 17%, about $2.2

million. Between 2010 and 2013, the Government share of financing dropped from 17% to 7%, representing a

drop of 10 percentage points in 4 years. There was also a fall in nominal terms. With the dramatic fall in

immunization rates over the same period, this downward trend is concerning.

Given the high share of external aid in the total financing of the health sector, the absence of an updated national

strategy and of as well as fully functioning coordination and alignment mechanisms could be impeding the

realization of important synergies across sources of financing.

Policy implications

Over time, there is a need to redirect spending on activities and interventions that improve the delivery of quality

health services

Better use of resources to improve service delivery of quality HNP interventions

In the current budget envelope, there is an urgent need for the Government to address the wage vs. non-wage

expenditures to improve the efficiency and strengthen the sustainability health service delivery over time. Any

incremental increases on available budgets should be directed to operational budget and investments


54

One of the key areas that should be addressed is the exponential expansion of central and regional administrative

salaries in the last decade when service delivery salaries have not changed in the same time period.

The Government aim to improve capacity and service delivery at the primary which will require a redistribution

of the wage bill to ensure that it is in line with the level at which services are being utilized.

The distribution of non-wage expenditures should be re-balanced to support the delivery of critical public health

programs.

Consider the use of output-based approaches compared to the current largely input-based funding methodologies

More informed budgeting

The Government could consider institutionalizing NHA exercises every two years. . The tool should be adapted

to Madagascar’s specific system and needs.

More robust analysis of external aid financing is urgently needed to have a more exact analysis of the budget.

Madagascar PER – Health | Section D

55

SECTION D. DISTRIBUTIONAL ANALYSIS OF PUBLIC HEALTH EXPENDITURE

D1. Distribution of MoH Expenditures by Region and Type of Residence

71. While it is not surprising that private expenditures per capita are higher in richer regions, public

expenditures should be expected compensate the difference in spending. Looking at the totality of health

expenditure, however, per capita total current health expenditures are found to be negatively, though

weakly, correlated with poverty rates (corr=-0.39, sig=0.07).50 This section shows that MoH expenditures

are in fact more regressively distributed across regions than total expenditures (excluding investment). It

also shows that national figures hide significant heterogeneity among regions. The analysis first looks at

the per capita distribution of MoH expenditures by province and region, then analyzes the relationship

between per capita expenditures and regional poverty levels. Expenditures directed to primary health care

are examined separately. The last subsection briefly assesses rural/urban differences.

1. Regional allocations vs population shares

72. Looking at changes in the distribution over time, Antananarivo alone has been driving the

increase in current MOH health expenditures. The allocation of these expenditures by province appears

to roughly follow population ratios. This can be seen in the significant differences between some provinces,

with Antananarivo benefiting from twice the amount of per capita government spending relative to Toliara

and Fianrantsoa in the south (Figure 28).51

Figure 28. Current MOH Health Expenditure Per Capita by Province in Relation to Population

Source: Expenditure data from MFB, population data from INSTAT Madagascar.

50 Regional disaggregation of Current Health Expenditures (CHE) based on the NHA 2010 report and poverty rates

based on EPM 2010. 51 About one third of the MoH current expenditures are allocated to the central level (up to a peak at 42 percent in

2008), the rest being allocated across provinces. Unfortunately, the analysis cannot include investment because only

a very small part is identified outside of the central level in SIGFP.

0

1

2

3

4

5

6

7

0

5

10

15

20

25

30

35

Mill

ion

s

Bill

ion

co

nst

ant

20

13

Ar. Average 2009-2013

Population

54

3

5

3

6

per capita, 2013 (1000 of Ar.)


56

73. By contrast, at the regional level, salary shares (Table 12) show inequality with respect to

population shares, (Figure 30). There is a clear disadvantage to the region of Vatovavy-Vatovivany.

Analamanga accounts for about 15 percent of the population but for 20 percent of all salary expenditure,

not including central ministry personnel. All other regions receive less than their estimated population

shares, as depicted in Figure 30. Regions that receive significantly less than their population shares are

Androy, Atsimo Atsinana, Sofia, Vatovavy Fitovinany, and Vakinankaratra. An important caveat to this

analysis relative to population sizes is that Madagascar has not had a census for many years, so estimated

per capita distributions may not accurately reflect the situation.

Table 12. Share of Salary by Region, excluding Central Administration Personnel, 2006-2013

Expenditure shares, salary only % of total est.

Region 2006 2007 2008 2009 2010 2011 2012 2013 pop., 2013

ANALAMANGA 20.8 18.3 19.6 19.1 19.9 19.6 19.8 15.3

VAKINANKARATRA 4.3 4.7 4.6 4.7 4.8 4.8 4.4 8.3

VATOVAVY-FITOVINANY 3.2 3.2 2.9 2.9 2.7 2.6 2.4 6.5

ATSIMO-ANDREFANA 4.6 5.4 5.3 5.0 4.6 4.5 4.3 6.0

ATSINANANA 5.7 5.5 5.2 5.3 5.1 5.2 5.4 5.8

SOFIA 2.7 3.3 3.3 2.8 3.0 2.6 2.5 5.7 HAUTE MATSIATRA 5.3 5.2 4.4 4.4 4.2 4.2 4.0 5.5

ANALANJIROFO 3.3 2.6 3.0 2.7 2.6 2.7 2.9 4.7

ALAOTRA-MANGORO 3.4 3.8 3.7 3.7 3.7 3.6 3.5 4.7

SAVA 3.0 2.8 3.0 2.8 2.8 2.9 2.7 4.5

ATSIMO-ATSINANANA 1.5 2.6 1.8 1.9 1.8 1.7 1.5 4.1

BOENI 3.7 4.8 4.3 3.8 3.7 3.4 3.5 3.7

ANDROY 1.6 1.4 1.5 1.5 1.4 1.4 1.3 3.4

ITASY 2.0 2.0 2.0 2.0 2.3 2.2 2.0 3.4

AMORON'I MANIA 2.4 2.8 2.2 2.2 2.3 2.2 2.1 3.3

DIANA 4.4 4.0 4.1 3.2 3.1 3.1 2.8 3.2

ANOSY 2.1 2.1 2.0 1.7 1.6 1.6 1.5 3.1

MENABE 2.7 2.4 2.4 2.1 2.0 1.7 1.7 2.7

BONGOLAVA 1.0 1.1 1.1 1.2 1.3 1.3 1.2 2.1

IHOROMBE 0.8 0.9 0.7 0.9 0.8 0.9 0.9 1.4

BETSIBOKA 0.9 0.9 1.0 0.8 0.9 0.8 0.7 1.3

MELAKY 0.9 1.0 1.1 1.1 1.1 1.0 0.8 1.3

Source: MFB, Direction de la Solde.


57

Figure 30. MoH Distribution of Wage Expenditure across Regions, 2013

Note: 45-degree line represents that the share spent on salary expenditure is proportional to the share of population

Source: Data from MFB, Direction de la Solde.

2. Relationship between current expenditures and poverty by region

74. The following considers all non-investment expenditures, combining SIGFP and salary data.52

Poverty levels are calculated using the periodic Household Surveys for 2005 and 2010. To increase data

quality, and in particular to smooth issues related to changes in personnel, averages of 2006-2008 current

expenditures are compared to averages for 2009-2013.53

75. Regions with higher poverty rates received less funding on average. 54,55 The total recurrent per

capita expenditure in health, ordering regions by their 2005 poverty rate in shown in Figure 31. There are

two important caveats to this interpretation: a) per capita expenditures do not include transfers, in particular

the transfers to tertiary hospitals, which represent about 15 percent of total non-wage recurrent expenditure

and are not distributed by region in the government accounts. Given the location of these hospitals,

52 Salaries make up between 69 and 93 percent of the total, depending on the region and the period considered (the

relative share of salaries is higher in the second period). 53 Non-wage current expenditures from SIGFP are averaged over 2006 and 2008 for the first period and 2009 to 2013

for the second period. Salaries are averaged over 2006 to 2008 for the first period and 2010-2013 for the second. 54 The per capita consumption figures do not show the same smoothing out of regions as do poverty rates: per capita

consumption dropped more or less evenly across regions. 55 Ordering countries by decreasing per capita consumption (in PPP) instead of by poverty rates also shows the

strongest differences when comparing the richest and the poorest regions (Figure A in Annex 1).


58

including these transfers in the analysis would likely make the unequal distribution more apparent, and b)

these data do not include foreign aid. Given that foreign aid has been specifically directed to the poorer

regions, it could be the case that some domestic funding in these regions has been displaced.

76. With the exception of Analamanga, the regions that received the highest share of MoH

expenditure also experienced the highest cuts. Two regions with notable changes were Melaky and South

Atsinanana, where poverty significantly increased while per capita expenditure of the MoH decreased by

about one half. Overall, all regions were affected by the decrease in both the range and variance per capita

of MoH expenditures.

Figure 31. MoH Recurrent Expenditure Per Capita by Region and Poverty Rate, 2006-2010

Sources: Data from MFB, INSTAT (population), EPM (poverty).

77. Expenditures are strongly negatively correlated to poverty ratios and strongly positively

correlated to per capita consumption, which is a proxy indicator for income. The relationship becomes

slightly more regressive in the later period when using consumption per capita but not using poverty ratios

(Table 13).

Table 13. Correlation Between MoH Current Expenditure and Regional Poverty

Poverty ratio Per capital consumption

2006-2008 -0.72 0.71

2009-2013 -0.71 0.74

Note: All coefficients are significant at α<0.001.

Source: Data from MFB, INSTAT (population), EPM (poverty).

0102030405060708090100

0.0

2.0

4.0

6.0

8.0

10.0

12.0

AN

ALA

MA

…

BO

ENI

DIA

NA

ALA

OTR

A-…

MEN

AB

E

MEL

AK

Y

BO

NG

OLA

VA

ITA

SY

BET

SIB

OK

A

HA

UTE

…

SAV

A

AN

OSY

VA

KIN

AN

K…

ATS

IMO

-…

AM

OR

ON

'I…

IHO

RO

MB

E

ATS

INA

NA

NA

AN

ALA

NJI

R…

SOFI

A

VA

TOV

AV

Y…

AN

DR

OY

ATS

IMO

-…

%

Tho

usa

nd

of

20

13

Ari

ary

Average 2006-2008Average 2009-2013Poverty Ratio 2005Poverty Ratio 2010


59

78. The regional distribution presented above is necessarily affected by the geographical location

of tertiary care facilities. Contrary to primary care activities, there is an important trade-off between

efficiency and regional equity for tertiary care facilities because of large economies of scale in activities

that requires expensive capital inputs. In order to get at the notion of equity within the constraints of

feasibility, one needs to concentrate on the provision of primary health care.

79. The analysis below looks at a snapshot of the situation (rather than its evolution, as above), to

compare the 2013 primary care current expenses per capita to 2010 poverty rates by region.56 To obtain

total current expenses for primary care, non-investment expenses recorded in the basic health provision

program of SIGFP were added to salary expenses in primary health care centers, primary hospitals, and

district public health services.57 The finding is that salaries largely dominate, with non-salary expenses

accounting for just 3 percent of the total, on average, and no more than 8 percent for a given region. A

second type of analysis was done using salaries only, based on our estimates of primary care provision in

all facilities (not just PHC facilities).

80. Even when restricting expenditures to the primary health care level, regional distribution is

strongly regressive. Regions with lower poverty rates receive more per capita, while regions with higher

poverty rates receive less (Figure 32). Although there are wide differences in per capita MoH expenditures

in the middle range of poverty rates, differences are marked at the two extremes. The same pattern obtains

when using per capita consumption instead of poverty rates, with higher MoH expenditures per capita in

richer regions (Annex 2, Figure A.1). Correlation coefficients, although slightly lower than before, still

show strong regressivity whether they are calculated against poverty ratios or consumption per capita (Table

14).

56 Although salaries by function were estimated down to 2006, the estimation could not take account of changes in the

number of personnel by type of facility for 2006-2012. The 2013 estimates have the least margin of error. 57 The salary breakdowns by facilities group district hospitals of category 1 and 2 (CHD1 and CHD2) in a single

category (CHDR) while the budget programs for 2013 include CHD1 expenses in basic care and CHD2 expenses in

hospital care. The ratios of specialized to non-specialized staff salaries in CHRD by regions are used to estimate the

salaries going to CHD2s, which excluded from the total presented here.


60

Figure 32. MoH Recurrent Expenditure Per Capita on Primary Care by Region, 2013

Data sources: Authors calculations based on MFB/SIGFP, INSTAT (population), EPM (poverty), and MoH/DRH.

Table 14. Correlation Between MoH Expenditures on PHC and Regional Poverty

Poverty ratio

Per capita

consumption

Current expenditure in basic health care facilities and district services -0.63 0.59

Salary expenditures on PHC/PH (based on type of personnel) -0.71 0.74 Note: All coefficient significant at α<0.01.

81. Given the importance of primary care dispensed in tertiary hospitals, as discussed in section

C, the argument for excluding secondary and tertiary care facilities when assessing regional equity needs

to be qualified. Although it is not desirable to increase the number of tertiary care facilities due to

economies of scale, primary care staff in these facilities could be deployed to different facilities at no cost

in terms of efficiency. Moving forward, there needs to be a more rational allocation of primary care staff.

82. In summary, whether restricting expenditures to primary health care facilities, primary

health care personnel, or including all current expenditures, expenditures of the MoH are

characterized by a strongly regressive regional distribution. It is very likely, especially considering

that the distribution of total health expenditures is less regressive than MoH expenditures, that externally

financed health expenditures not going through government accounts are progressively distributed.

Nevertheless, it is clear that efforts need to be made on the part of the Government to reach poorer regions.

In particular, given that the MoH covers most expenditures on medical personnel salaries, efforts to better

deploy medical staff to poorer regions need to be made. Indeed, given the large weight of salaries in MoH

expenditures, these results are consistent with an unequal distribution of primary health care personnel

across regions.

0

20

40

60

80

100

0

2

4

6

8

10%

Tho

usa

nd

of

20

13

Ari

ary

Current exp/c on basic health care services (based on type of facility)exp/c on primary care based on personnel type (salaries only)Poverty rate (2010 EPM) - Secondary axis


61

3. Rural/urban differences

83. Given the differences in poverty ratios and consumption per capita between urban and rural areas

(Table 15), it is important to look more closely at rural and semi-rural disadvantaged areas. In order to do

the analysis by type of residence, we used the official delineation of communes into rural and urban

categories according to the 2011 Decree.58 Communes were classified into four levels of urbanization so

as to minimize the “border” effects and avoid over-identifying communes as rural.59 Although salaries

cannot be identified by type of residence, the share of non-wage recurrent expenditure that can be identified

in the data is sufficient to be indicative of the level of activity.60

Table 15. Poverty and Consumption by Type of Residence, 2005 and 2010

Poverty rate

%

Per capita consumption

2013 Ar, capital city

2005 2010 2005 2010

Capital city 34.4 30.1 266014 680428

Large urban centers 43.4 31.6 230112 662635

Secondary urban centers 63.4 62.1 167412 426743

Rural areas 73.5 82.2 137847 283781 Source: Household Surveys 2005 and 2010.

84. Analysis of expenditure shares by type of residence show that less five percent goes to rural

communes. Considering that approximately two-thirds of the population live in rural areas, this

represents a highly unequal distribution of expenditure shares. On average 36 percent going to large

urban centers, 46 percent to smaller urban areas and 13 percent to the semi-rural or peri-urban areas

(Figure 33 and Table 16).

58Décret-n°2011-0042-portant-classement-des-Communes-en-Communes-urbaines-ou-en-Communes-rurales

(MFB). 59 Out of a total of 1,549 classified communes, 9 were identified as large urban centers (including the capital city,

which is classified separately), 63 as urban (category 2 urban), 104 communes as semi-rural or peri-urban (category

1 rural), and 1,373 as rural (category 2 rural). 60 The government expenditure data cannot be directly divided into rural and urban locations but it identifies activity

units by commune code.


62

Figure 33. Rural/Urban Shares of Non-Wage Recurrent Expenditures Directly Assigned to

Communes

Note: Population shares are based on standard definition used in the World Development Indicators, which may or

may not include the semi-rural or peri-urban areas indicated here as rural/urban.


Table 16. Share of Expenditures to Rural Areas versus Share of the Population

2006 2008 2010 2011 2012 2013

share of exp to rural* 4% 4% 5% 5% 5% 5%

share of exp to rural-extended * 16% 17% 19% 17% 18% 18%

Approx share of rural population 71% 69% 69% 68% 67% 67% *Note: Rural only includes the most rural communes (category 2). Peri-urban communes (category1) are included in

the rural-extended category.

Data source: WDI, MFB/SIGFP.

D2. Out-of-Pocket Household Expenditures: Do They Impact Regional and Income Inequalities?

85. Looking at the size and evolution of household expenditures in a context of increasing poverty, it

is important to gauge the need for the Government to relieve some of the financial burden on poorer

households. The results of this section need to be interpreted in the context of changes in utilization, in

particular whether people tend to seek care when they are sick. The analysis below is based on data from

the 2005 and 2010 Household Surveys and the UN Household Survey on progress towards the Millennium

Development Goals in 2012.

1. Distribution of OOP expenditure by income level and region

86. Households in Madagascar spent less than 1 percent of their budget on health in 2005, just

above 1 percent in 2010, and 1.4 percent in 2012. The distribution of consumption by quintile is given in

0%10%20%30%40%50%60%70%80%90%

100%

2006 2008 2009 2010 2011 2012 2013

Rural 674 1003 1023 875 880 527 288

Rural/Urban 1896 3321 3275 2491 2298 1405 815

Urban 7199 12685 11059 8830 8179 5116 2757

Large urban centers 6081 8374 6929 7048 6543 3631 2744

Million 2013 Ar.


63

Figure 34. In interpreting the data, it is important to note that the poverty level is situated around the

average level of consumption in the fourth quintile (and closer to the high end of the fourth quintile in

2010). In 2005, the distribution of health expenditure as a share of the household budget was slightly

increasing with consumption, except in the middle range. In 2010, however, the distribution changed very

much to the disadvantage of the poorest quintile. In absolute terms, average household expenditure per

person increased 22 percent in the poorest quintile, 19 percent in the top quintile, 9 percent in the fourth,

and 5 percent in the second, while it decreased 5 percent in the third quintile. In 2012, however, the increase

for the poorest quintile slowed to 15 percent, while the average household expenditures increased 18 and

37 percent for the second and third quintiles and over 50 percent for the richest quintile.

Figure 34. Household Out-of-Pocket Expenditure, 2005, 2010 and 2012

Source: Data from household surveys 2005, 2010 and ENSOMD 2012.

87. Average per capita OOP across regions increases as regional poverty decreases, which is most

noticeable in the richest three regions (Diana, Boeny, Analamanga). Figure 35). The correlation

between OOP spending per capita and poverty rates is negative, statistically significant, and increasing

from 2005 to 2010 (from 0.44 to 0.68). There are, however, large difference between regions as well as

unequal gains and losses between 2005 and 2010.

0

2

4

6

8

10

12

14

16

18

20

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Lowestquintile

Secondquintile

Thirdquintile

Fouthquintile

RichestTh

ou

snad

co

nst

ant

20

13

Ar.

% o

f h

ou

seh

old

bu

dge

t

% of budget2005

% of budget,2010

% of budget,2012

Avg exp. perperson, 2005

Avg Exp. perperson, 2010

Avg Exp. perperson, 2012

people under poverty level


64

Figure 35. Per Capita OOP Health Expenditure by Region and Poverty Level, 2005 and 2010

Note: Expenditure per capita adjusted to reflect prices in the capital.

Source: Data from household surveys 2005, 2010 and ENSOMD 2012.

88. Four of the six poorest regions experienced larger than average OOP. Some regions in

particular stand out. Androy, which is the second poorest region, had one of the higher OOP rates in the

country in 2005, and despite a decrease in 2010, it still has higher than average OOP per capita. In 2010,

expenditure per capita in Sofia reached the same level as in Analamanga, with the lowest poverty rate.

Menabe had an OOP rate well below expected, given its relative poverty rate in 2005, but fully lost its

advantage in 2010 after its OOP per capita rate more than doubled.

89. No statistically significant correlation was found between the average OOP burden (share of

OOP in the household budget) and poverty rates. As Figure 36 shows, there are large variations between

regions, and Androy as an important outlier, with a OOP burden much higher than all other regions and the

highest poverty rate in 2010 and in 2012 (94.5 and 96.7 percent).

0

20

40

60

80

100

0.02.04.06.08.0

10.012.0

Average HE per person, 2005 Average HE per person, 2010

Poverty Rate 2005 (Axis 2) Poverty Rate 2010


65

Figure 36. Burden of OOP Spending by Region and Poverty Level, 2005, 2010 and 2012

Source: Data from Household Surveys 2005, 2010 and ENSOMD 2012.

2. Relationship between OOP spending and MoH expenditures using regional data

90. The relationship between MOH recurrent expenditures and OOP per capita is positive

overall (Figure 37). This is mainly due to differences in the richest three and poorest three regions. The

correlation coefficients between average OOP per person and per capita recurrent expenditures of the MoH

by region are positive and increase when comparing 2005 and 2010 (0.42 to 0.59). When looking at average

OOP expenditure as a share of the household budget, however, the relationship is no longer statistically

significant.

0

20

40

60

80

100

120

0

0.5

1

1.5

2

2.5

3

3.5

An

alam

anga

Bo

en

y

DIA

NA

Ala

otr

a M

ango

ro

Me

nab

e

Me

laky

Bo

ngo

lava

Iho

rom

be

Be

tsib

oka

Itas

y

SAV

A

An

osy

Vak

inan

kara

tra

Ats

imo

Ats

inan

ana

Am

oro

n’i

Man

ia

Hau

te M

atsi

atra

Ats

inan

ana

An

alan

jiro

fo

Sofi

a

Vat

ova

vy F

ito

vin

any

An

dro

y

Ats

imo

An

dre

fan

a

% o

f h

ou

seh

old

bu

dge

t

HE/budget, 2005 HE/budget, 2010 HE/budget, 2012

Poverty Rate 2005 (Axis 2) Poverty Rate 2010 Poverty Rate 2012


66

Figure 37. Average OOP Per Capita vs MoH Expenditure by Region, 2005 and 2010

Note: The expenditure data are averaged over 2006 to 2008 for salaries by region. Other current expenditures do not

include 2007 data. Values are not deflated to reflect prices in the capital.

Source: Data from Household Surveys, MFB, INSTAT Madagascar (population).

3. Analysis of “catastrophic” OOP expenditure

91. The scarcity of risk pooling mechanisms in Madagascar, combined with a cost recovery

system that makes public health care expensive for the poorer quintiles, leaves poor households

especially vulnerable to catastrophic health expenditures. Health expenditures are considered catastrophic

if they force the individual or household to engage in behavior that has long-term negative effects on their

economic well-being. This could involve selling capital assets, heavy borrowing to pay for medical care,

lost wages during an illness, or reducing the consumption of subsistence goods. Following Xu and al.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Vak

inan

kara

tra

Itas

y

Vat

ova

vy F

ito

vin

any

An

dro

y

Sofi

a

Bo

ngo

lava

Ats

imo

Ats

inan

ana

SAV

A

Am

oro

n’i

Man

ia

An

alan

jiro

fo

An

osy

Ala

otr

a M

ango

ro

Be

tsib

oka

Hau

te M

atsi

atra

Iho

rom

be

Ats

inan

ana

Ats

imo

An

dre

fan

a

Me

nab

e

Me

laky

An

alam

anga

Bo

en

y

DIA

NA

OOP/person, 2005 (thousand 2013 Ar.)

OOP/person, 2010 (thousand 2013 Ar.)

Average of 2006-2008 recurrent MOH expenditure per capita (secondary axis, thousand 2013 Ar.)


67

(2003) and the approach used by the WHO, health expenditure is defined as catastrophic if payments for

health care exceed 40 percent of the household’s non-subsistence expenditure or its capacity to pay.61 62

92. Few households are subject to catastrophic expenditure, but prevalence increased in all

quintiles between 2005 and 2010, and in the middle class in 2012. The proportion of households with

catastrophic health expenditure is low in Madagascar, affecting less than 2.5 percent of household overall,

with significantly higher prevalence in the highest consumption quintile (up to 4 percent in 2010) and very

low prevalence in the lowest quintiles (Figure 38).63 The results for 2010 show increasing overall

proportions of households with catastrophic OOP spending, but the prevalence is slightly decreasing in

2012, except for households in the middle quintile, with the most noticeable increases in the fourth quintile.

Figure 38. Incidence of Catastrophic OOP Expenditure by Wealth Quintile

Source: Data from Household Surveys 2005, 2010 and ENSOMD 2012.

93. Very low percentages of households with catastrophic expenditures in the poorest quintiles

are usually indicative of “system failure”; i.e., the poor just do not seek care. In Madagascar, the

proportion of people not seeking care when ill is fairly high, an average of 70 percent in 2012, across all

61 Following the work of Xu and al. (2003), this capacity is defined as the household consumption expenditure

available after basic needs have been met. Given that the poorer the household the higher the share of consumption

devoted to food, the subsistence expenditure is defined as the average of food expenditure of households whose food

share was in the 45th to 55th percentile. Capacity to pay of the ith household is defined as:

CTPi = EXPi – SE45-55i

where SE45-55i corresponds to the average food expenditures of households in the 45th to 55th percentiles and adjusted

for the size of the ith household. In cases when total expenditure was less than this basic subsistence level, the own

household’s observed food expenditure was used instead. 62 The World Bank defines catastrophic expenditures as spending 10 percent or more of total expenditure at household

level on health care costs. (Pradhan and Prescott 2002; Ranson 2002; Wagstaff and van Doorslaer 2003) 63 A recent analysis of catastrophic spending in Zimbabwe (PER) returned similar results, with catastrophic health

expenditures varying from 0.3 percent in the poorest quintile to 1.9 percent in the richest quintile (although the

subsistence income was calculated including some basic clothing and shelter) Catastrophic expenditure can, however,

be very high in some countries where people actually use the health system. In Ukraine, for example, 25 percent of

households in the two lowest quintiles faced catastrophic health spending, but the prevalence decreased in highest

wealth quintiles.

0.0

2.0

4.0

6.0

Poorest(Q1)

Q2 Q3 Q4 Richest(Q5)

Catastrophic = OOP health share greater than or equal to 40% of non-subsitence expenditure

2005

2010

2012


68

the quintiles. More than 55 percent of the richest households and 65 percent of the poorest do not seek

health care. Low utilization (rather than high cost) is therefore a plausible explanation for the low incidence

of catastrophic expenditure. When looking at people who do not seek care for financial reasons, only the

poorest quintile clearly stands out in 2010, at 25 percent, while the range in the other quintiles is 10 (richest

quintile) to 15 (second and third quintile) (Figure 39).

Figure 39. Percentage of People Who Did Not Seek Care when Sick for Financial Reasons, 2005 and

2010

Source: Data from households surveys 2005, 2010 and ENSOMD 2012.

94. Catastrophic expenditures may cause 4.5 percent of people in the richest quintile and 3

percent in the fourth quintile to fall below the poverty line. The results above need to be put in

perspective. Nearly 80 percent of the population in Madagascar lives under the poverty line. Most of the

people in the fourth quintile are poor, and the minimum consumption level in the richest quintile is only 9

percent above the poverty line. Therefore, households at the bottom of the fifth and top of the fourth quintile

are most in danger of falling into poverty and may experience negative long-term financial impacts from

catastrophic expenditures (given that the poor have little to lose in terms of assets). In addition, OOP health

expenditures are also impoverishing because they can cause those that are already poor to call deeper into

poverty.

D3. Distribution of MoH Expenditures by Socioeconomic Category of User: Benefit

Incident Analysis

95. A Benefit Incidence Analysis (BIA) can be carried out using the simple utilization (or usage)

approach, assuming that all individuals using the service receive the same benefits.64 The analysis is based

on (a) decomposing users of public health facilities by income level, and (b) decomposing public health

expenditures by type of health facility.65 For Madagascar, the first part of this process relies on good quality

64 This is the approach used in Glick and Razakamanantsoa (2002) in their BIA for health and education using 1990

data for Madagascar. 65 The usage approach assumes that all individuals using the service receive the same benefits. It also assumes that the

quality of health services does not depend on average income levels in the area. Although these assumptions are not

realistic, the approach has the advantage of being less demanding in terms of data quantity and quality. The unit cost

approach to BIA is not be feasible for health given the structure of expenditure accounting.

11.5

19.215.1

11.68.3

25.3

14.6 14.411.2 10.0

24.120.7 21.9

19.0

10.7

Poorest quintile Quintile 2 Quintile 3 Quintile 4 Richest quintile

2005 2010 2012


69

data from the periodic Household Survey, which provides information on the marginal benefits of

government expenditure and how each segment of the population would benefit from additional spending

directed to different types of facilities. The second part of the BIA depends principally on the ability to

decompose public expenditures by type of facility to match the type of facilities identified in the Household

Survey. As noted in section C2, data are not readily available to break down salaries by type of facility,

but a reconstruction can be done using multiple data sources and approximations using average salaries by

type of personnel (Annex 3).

1. Marginal benefit of government expenditure by type of facility

96. Additional spending on CSB1s (basic health centers) is pro-poor. The distribution of benefits

is consistent on the primary level. The distribution of users by quintile of consumption for different types

of facilities in 2005, 2010 and 2012, using Lorenz curves, are shown in figures 40 and 41. Spending on

CSB2s benefited all quintiles approximately equally in 2010 (equality appears to have slightly improved

compared to 2005, likely due to the transformation of CSB1 into CSB2). Inequality becomes apparent at

the CHD level and clearly increases as we move up into levels of care toward CHUs, which are clearly

utilized by the rich. No clear conclusion can be drawn about changes in inequality across 2005, 2010 and

2012, especially at the hospital level, but this could be due to the fact that results are based on much lower

sample sizes in 2005.66 Consistent with the catastrophic expenditure analysis above, the poorest households

in the lowest two quintiles rarely use secondary and tertiary hospitals.

Figure 40. Lorenz Curves by Quintile for Utilization of Public Health Facilities, 2005-2010

Source: Data from household surveys 2005 and 2010.

66 Results regarding hospital utilization rest on very small unweighted sample sizes. The Household Survey question

on visits to health facilities was asked of respondents who had had a health problem in the previous two weeks, which

is a very short time period to assess hospital usage. A total of only 13 respondents went to a CHU, 30 to a CHRR, and

193 to a CHD in 2005; and a total of 38, 107 and 423 in 2010. These numbers were 58, 117 and 316 in the Development

Goals National Monitoring Survey (ENSOMD) of 2012.

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5

2005 to 2010 (2005 superimposed in dotted lines)

CSB1

CSB2

CHRR

CHD

CHU

45 Degree line


70

Figure 41. Lorenz Curves by quintile for Utilization of Public Health Facilities, 2005-2012

Source: Data from Household Survey 2005 and ENSOMD 2012.

2. Benefit incidence of MoH expenditure

97. On average between 2010 and 2013, the MoH spent about 20 billion 2013 Ar. on non-wage

expenditures to basic health and hospital services (based on program budgets), and 67 billion on salaries to

personnel working in health facilities (excluding all administrative units).67 It is important to understand

how much of this money benefited the poor. The goal of the BIA is to estimate benefits across consumption

quintiles in terms of utilization. Given differences in classifications and the fact that wage expenditures are

so large relative to non-wage expenditures (7 times larger in 2013), the analysis is done in two parts: a)

allocating non-wage expenditures by quintile and b) allocating wage expenditures.

98. The BIA for MOH non-wage expenditures reveals that benefits are found to be regressive

with the average benefits going to individuals in the richest quintile two to four times higher than

those going to individuals in the poorest two quintiles (Table 17). The two poorest quintiles were found

to benefit the least in most cases. The BIA rests on two groups: users of primary health care facilities and

users of hospital facilities.68 Whether we consider CHR users as benefiting from basic health services

(method 1) or hospital services (method 2), and whether we look at 2013 or at average expenditures over

the last four years, benefits are found to be regressive. Results from 2013 are more regressive when users

of CHRs are assumed to benefit from basic health care expenditures rather than hospital-related

expenditures (method 1), given that basic health care expenditures were lower than hospital expenditures

in 2013 (0.8:1 ratio compared to 1.7:1 on average since 2010). In addition, the total benefit was substantially

lower in 2013. Figures 42 and 43 illustrate the regressive nature of MoH non-wage spending. The data

67 In 2013, the numbers were 11 billion for non-wage expenditures and 73 billion for wages. 68 Programs in the 2010-2013 budgets group all expenditures for basic health centers (CSB1 and CSB2) and for level

1district hospital into basic health care services, while the Household Survey separates CSB1 and CSB2 but does not

distinguish between district hospitals with and without surgery (CHD1 and CHD2).

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5

2005 to 2012 (2005 superimposed in dotted lines)

CSB1

CSB2

CHD

CHRR

CHU

45 Degree line


71

indicates that 40-60 percent of these expenditures benefited people living under the poverty line (fourth

quintile and below).

Table 17. Benefits from Non-Wage Current Expenditures of the MoH by Consumption Quintile

Billions of 2013 Ar. 2010-2013 2013

Method (a) Method 1 Method 2 Method 1 Method 2

Lowest quintile 3.2 2.5 1.2 1.7

Second quintile 3.0 2.5 1.1 1.6

Third quintile 3.8 3.6 2.0 2.1

Fourth quintile 4.3 4.1 2.3 2.4

Richest 6.0 7.6 4.5 3.4

Richest to poorest 1.9 3.1 3.9 2.0

Richest to second quintile 2.0 3.0 4.0 2.1

Note: Method 1 calculates benefits across quintiles assuming that CHRs provide all basic care services (preferred

method). Method 2 assumes that CHRs provide only hospital services. Source: Data from household surveys, MFB/SIGFP.

Figure 42. Estimated Distribution of Benefits from MoH Non-Wage Expenditures

Note: Current expenditures are restricted to basic care and hospital services budget programs in SIGFP (mandated).

Utilization by quintile is based on 2010 Household Survey. Method 1 calculates benefits across quintiles, assuming

that CHRDs provide all basic care services. Method 2 assumes that CHRDs provide all hospital services.

Source: Data from MFB/SIGFP and Household Survey 2010.


72

Figure 43. Estimated Distribution of Benefits from MoH Non-Wage Expenditures

Source: Data from Household Survey (utilization), MoH//Human Resources Department, MFB/SIGFP.

99. Benefits from MoH wage expenditures on personnel in health facilities (excluding all

personnel in administrative units) show that the richest quintile benefits 3.6 times more than the

poorest quintile, and at least twice as much as households in any other quintile.

100. Distribution of benefits from all MoH expenditures directed to primary health and hospital

care is pro-rich with the richest quintile benefiting from 40 percent of total expenditures. Focusing

on the distribution of expenditures rather than on actual amounts, benefits can be presented in a Lorenz-

curve type graph that accumulates benefits across quintiles, the 45 degree line being the line of perfect

equality (Figure 44). Although the distribution of wages by facility drives the result, non-wage expenditures

also benefit the rich disproportionately. 69

69 It would have been interesting to conduct this analysis over two time periods. Unfortunately, the lack of budget programs prior to 2009 does not

allow for reproduction of the analysis of non-wage expenditures. Although wage expenditures could be traced back to 2006, the estimation was

based on strong assumptions that limit the extent to which the allocation of personnel across facilities could change, and this would greatly limit the interpretation. This exercise, however, should be repeated in the future using these results as benchmarks.

8.2 8.9 13.5 13.2

29.6

0.0

10.0

20.0

30.0

Lowestquintile

Secondquintile

Third quintile Fouthquintile

Richest

Bill

ion

20

13

Ar.

2013


73

Figure 44. Estimated Distribution of Benefits from MoH Expenditures

Note: The distribution is based on benefits from utilization by type of facility and expenditures directed to primary and hospital care (for non-wage expenditure). Method 1 calculates the benefits across quintiles, assuming that CHRs provide basic care services. Method 2 assumes that CHRs

provide all hospital services. The total take the average of method 1 and 2.

Source: Data from Household Survey (utilization), MoH/Human Resources Department, MFB/SIGFP.

101. CHUs absorb about one third of all MoH expenditures directed to health facilities but are

very sparingly used by the poor, while CSB1s are the only facilities that are primarily used by the

poor. In fact, all other facilities are used by the richest quintile in a greater proportion than their population

share. The distribution of benefits depends greatly on the utilization rates and size of expenditures at the

CHU and CSB1 levels. A few data manipulations reveal that changing the distribution of benefits to be

more equitable is not an easy task. Transferring expenditures from CHUs to CSB1s could reduce the

inequality, although, given the low utilization of health facilities by the poor in Madagascar, it is not feasible

to generate a progressive distribution scenario. Several scenarios were examined and the resulting

distribution of benefits compared to the 2013 benchmark.

Scenario 1 – Reallocate general practitioners: Deploy two general practitioners per district from

the CHUs to the CSB1s (775 general doctors were employed in CHUs in 2013).

Scenario 2 – Increase the number of CSB1s: Add 300 new primary care facilities and staff them

with one doctor and one support person (this scenario assumes that the utilization profile stays as

for existing CSB1).

Scenario 3 – Reallocate CHU staff time to CSB1s: Dispatch one third of the CHU staff each year

to provide health care in CSB1 facilities.

Scenario 4 – Relocate CSB2 facilities to reach more poor households, and increase staff:

Strategically relocate half of the CSB2 facilities so they are closer to poorer segments of the

0%10%20%30%40%50%60%70%80%90%

100%

Cu

mu

lati

ve b

en

efi

ts

Cumulative population by quintiles of consumption

2013

45 degree line

Wage expenditure

non-wage, method1

non-wage, method2

Total


74

population (equivalent to allocating half of CSB2 expenditures to CSB1s), and increase the CSB2

staff so there is at least one general practitioner per facility (currently 1,190 doctors for 1,620

facilities) by deploying the equivalent number of CHU general practitioners to CSB2.

102. The system clearly needs to improve its ability to reach the poor using some of the resources

currently used to staff tertiary facilities. Figure 45 presents each scenario, given 2013 wage expenditures

and the same utilization profile as above (except for scenario 4). It is obvious that marginal changes to

expenditure allocation would not make a large difference in inequality. Scenarios 1 and 2, which are

strongly equity enhancing in a marginal sense, barely change the overall picture. Scenarios 3 and 4 do make

a difference, reducing inequality by more than half, but are unrealistic to implement without large-scale

structural reforms and economic reforms. A reduction of the share of salaries in MoH expenditures would

also create more opportunities for change.

Figure 45. Estimated Distribution of Benefits: Simulations of Four Scenarios

Source: Data from Household Survey 2010 and MFB/SIGFP.

D4. Assessment of Gender Equality

103. Overall, there is no discrimination against girls and women in health care. Gender is another

dimension of inequality that has been shown to have important implications for growth relates to the ability

to provide care to girls and women. On average, females visit all levels of health facilities in greater

numbers than men (Figure 46). The CHU result is highest but cannot be compared to the other facilities

because it is based on a much smaller sample.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 Lowestquintile

Secondquintile

Thirdquintile

Fouthquintile

Richest

Cu

mu

lati

ve b

en

efi

ts

Cumulative population by quintiles of consumption

2013

45 degreelineBenchmark

Scenario 1

Scenario 2


75

Figure 46. Utilization of Health Facilities by Gender, 2005 and 2010

Note: Values for 2005 CHRR and CHU use are not reported because the samples are too small to make meaningful

inferences (<30 total visits).

Source: Data from Household Surveys 2005 and 2010.

104. The higher overall proportion of females using health centers is explained by the larger

proportion of female who were sick (7-8 percentage points more than males in both surveys). Out of

those who were sick in the two weeks period to the surveys, the same proportion of males and females

sought care, which excludes the reverse possibility of a male bias against seeking care (Table 18).

Table 18. Gender Differences in Health-Seeking Behavior when Sick, 2005 and 2010

2005 2010

Percentages from weighted sample Female Male Female Male

Sick in last two weeks 54.1% 45.9% 53.8% 46.2%

Sick who did not seek care 60.1% 60.1% 67.6% 67.5%

Sick who did not seek care for financial reasons 14.4% 11.0% 16.1% 14.1%

Source: Household Surveys 2005 and 2010 (EPM).

105. Overall averages gender differences across consumption quintiles are not significant. Looking

at primary health care facilities for which number of visits are sufficiently large by quintile, the only female

disadvantage is in the second quintile for primary hospitals, but the difference is not large enough to be

significant (Figure 47).

5.0 4.59.18.0

3.79.2

3.012.2

CSB1 CSB2 CHD CHRR CHU

percent female using the service-50Above 0 = Female Advantage

2005 2010


76

Figure 47. Gender Equality in Utilization of Primary Health facilities by Quintile, 2005 and 2010

Source: Data from Household Surveys 2005 and 2010.

106. Given these results, and in particular the fact that there is no significant differences in female

advantage by type of facility, an analysis of the distribution of benefits of MoH expenditure would not

yield additional information than what is presented here.

Key Findings

Overall, the distribution of public spending is highly inequitable with per capita total health expenditures

negatively correlated with poverty rates. This has significant implications for the overall health of the population

especially in the current context of Madagascar where over 80% of the population is living in absolute poverty.

Distribution of public spending

Looking at changes in the distribution over time, Antananarivo alone has been driving the increase in current

expenditures. Expenditures in other provinces have been relatively stable.

Salary shares have remained stable over time but show some inequality with respect to population shares. Most

receive less than their estimated population shares.

Expenditures are strongly negatively correlated to poverty ratios.

Whether restricting expenditures to primary health care facilities, primary health care personnel, or including

all current expenditures, expenditures of the MoH are characterized by a strongly regressive regional

distribution. Regions with lower poverty rates receive more per capita, while regions with higher poverty rates

receive less.

With regards to non-wage expenditures, average benefits going to individuals in the richest quintile are two

to four times higher than those going to individuals in the poorest two quintiles with the two poorest quintiles

were found to benefit the least in most cases.

Likewise, benefits from MoH wage expenditures on personnel in health facilities (excluding all personnel in

administrative units) are also clearly regressively distributed. The richest quintile benefits 3.6 times more than

the poorest quintile, and at least twice as much as households in any other quintile.

Non-wage recurrent expenditure shares by type of residence have remained relatively stable with only 13

percent to semi-rural or peri-urban areas, and less than 5 percent to the rural communes. Considering that

approximately two-thirds of the population live in rural areas, this represents a highly unequal distribution of

expenditure shares.

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5

Consumption quintiles

CSB1

CSB2

CHD


77

Household spending and utilization by quintile

The scarcity of prepayment mechanisms in Madagascar, combined with a cost recovery system that makes

public health care expensive for the poor.

Few households are subject to catastrophic expenditure in Madagascar, but prevalence increased in all quintiles

between 2005 and 2010, and in the middle class in 2012.

Very low percentages of households with catastrophic expenditures in the poorest quintiles are usually

indicative of “system failure”; i.e., the poor just do not seek care. Low utilization (rather than high cost) is

therefore a plausible explanation for the low incidence of catastrophic expenditure.

Inequality becomes apparent at the district hospital level (CHD) and clearly increases moving into upper levels

of care toward regional hospital level (CHU), which are clearly utilized by the rich. Consistent with the

catastrophic expenditure analysis, the poorest households in the lowest two quintiles rarely use secondary and

tertiary hospitals.

Policy implications

Given the strong inverse relationship between poverty and good health, the need for publicly provided health

care is greater among the poorer populations. Better equity in the distribution of spending in the health sector

will need to take into consideration better resource allocation and targeting of the population, improvement in

access to health care especially in rural areas and reducing the financial burden on households.

More equitable distribution of resources

In the current context, Madagascar needs to urgently agree on and implement pro-poor strategies to ensure better

equity of health expenditure and health services amongst the population. This includes:

Redistribution of health expenditure according to geographic distribution of the population to also benefit

the poor

Better allocation of existing human resources to be more equitable to the poor. Consider a diagnostic of

the current human resource system.

Additional spending on first line health facilities (CSB1s), which are utilized more by the poor and

maintaining spending on second line health facilities (CSB2s), which benefits all quintiles approximately

equally. In addition, consider reallocation of CSB2 so they are more accessible to households.

Reallocate regional hospital staff time to CSB1s and CSB2s.

In this context, an updated census, poverty map and a Demographic Health Survey are needed to help inform

decisions on resource distribution.

Tailor interventions for the poorest quintiles

This analysis reiterates the need to implement interventions to improve health seeking behavior tailored

specifically to the poor with mechanisms such as vouchers, conditional/non-conditional cash transfers,

exemption schemes, non-monetary incentives (e.g. safe delivery kits) and support to the expanding the reach

of health practitioners and health workers into the community.

Redesigning existing mechanisms to be more effective such as the Equity Fund and social health insurance

schemes

78

REFERENCES

Baldacci, E., Gupta. S. Clements, B., and Cui, Q. (2008), “Social Spending, Human Capital and Growth

in Developing Countries,” World Development. Vol. 36(8), pp. 1317-1341

Efficiency of Public Spending in Developing Countries: An Efficiency Frontier Approach: Santiago

Herrera & Gaobo Pang – Policy Research Working Paper Series 3645, The World Bank.

http://siteresources.worldbank.org/INTQFA/Resources/EfficiencyofPublicSpendinginDevelopingCount

ries_MAY05.pdf

Gaudin, S. and Yazbeck (2013). Health Sector Policy Challenges in Low and Middle Income Countries:

Learning from Public Expenditure Reviews. Background paper for the health and economy program.

Mimeo

Glick, P. and M. Razakamanantsoa (2002) ‘The Distribution of Social Services in Madagascar, 1993–99’,

Working Paper No. 128, Cornell University Food and Nutrition Policy Program

http://www.cfnpp.cornell.edu/images/wp128.pdf or http://www.instat.mg/pdf/iloinstat_7.pdf

Glick, P. and M. Razakamanantsoa (2005) . The Distribution of Education and Health Services in

Madagascar over the 1990s: Increasing Progressivity in an Era of Low Growth. Jl of African

Economies, 15(3):pp. 399-433 (published version of the 2002 paper but with less specific information)

Glick P., R. Saha , and S. D. Younger (2004). Integrating Gender into Benefit Incidence and Demand

Analysis. Working Paper of the Food and Nutrition Policy Program, Cornell University.

http://www.cfnpp.cornell.edu/images/wp167.pdf

O’Donnell, O, E van Doorslaer, A Wagstaff, M Lindelöw, (2007) Analyzing Health Equity using

Household Survey Data: a Guide to Techniques and their Implementation, World Development

Institute, World Bank, Washington DC, 2007. www.worldbank.org/analyzinghealthequity (ISBN: 0-

8213-6933-4)

Xu, K., D.E. Evans, K. Kawabate, R. Zeramdini, J. Klavus, and C.J.L. Murray (2003), “Household

Catastrophic Health Expenditure: a multicountry analysis”, Lancet 362: 111-17

World Bank. (2006) Djibouti - Public Expenditure Review (PER) - making public finances work for

growth and poverty reduction. World Bank Report #34624

Hernandez Patricia, Sigrid Dräger, David B. Evans, Tessa Tan-Torres Edejer and Mario R. Dal Poz

(2006) Measuring expenditure for the health workforce: evidence and challenges. Background paper

prepared for The world health report 2006 - working together for health.

http://www.who.int/hrh/documents/measuring_expenditure.pdf

Vujicic, Marco, Kelechi Ohiri, and Susan Sparkes (2009) Working in Health: Financing and Managing

the Public Sector. The World Bank, Directions in Development, Human development

series. http://go.worldbank.org/PU86PVIEU0 [PDF version on WHO website:

http://www.who.int/workforcealliance/knowledge/publications/partner/workinginhealth_vujicic_worldb

ank_2009.pdf)

http://www.instat.mg/pdf/iloinstat_7.pdf

http://www.cfnpp.cornell.edu/images/wp167.pdf

http://www.who.int/hrh/documents/measuring_expenditure.pdf

http://go.worldbank.org/PU86PVIEU0

http://www.who.int/workforcealliance/knowledge/publications/partner/workinginhealth_vujicic_worldbank_2009.pdf

http://www.who.int/workforcealliance/knowledge/publications/partner/workinginhealth_vujicic_worldbank_2009.pdf

Madagascar PER – Health |Annexes

79

ANNEXES

ANNEX 1. TABLE SUPPLEMENT

Table A1. International Comparisons of Public Health Expenditures (including external funds)

Country/Comparison Group Percent of GDP

2000-2012 2005-2008 2009-2012

Rwanda 4.18 4.69 6.04

Zambia 3.67 3.64 3.85

Mozambique 3.60 3.67 2.93

Tanzania 2.52 3.19 2.99

DRC 2.48 2.05 3.25

Madagascar 2.40 2.52 2.31

Mauritius 2.12 1.96 2.41

Comoros 1.98 2.65 1.77

Kenya 1.87 1.81 1.82

Sub-Saharan Africa (developing only) 2.66 2.72 3.02

Low income 2.31 2.42 2.67

Low & middle income 3.30 3.30 3.60

High income 5.26 5.21 5.73

% of General Government Expenditure

2000-2012 2005-2008 2009-2012

Rwanda 17.7 20.2 13.2

Zambia 14.7 14.9 7.5

Madagascar 13.0 12.3 14.5

Mozambique 12.9 14.2 6.2

Tanzania 11.3 13.7 9.8

Mauritius 9.4 8.8 8.9

DRC 9.1 9.3 23.0

Comoros 9.0 11.8 11.1

Kenya 7.5 7.1 16.5

Sub-Saharan Africa (developing only) 10.2 10.6 10.8

Low income 10.0 10.4 10.4

Low & middle income 10.7 10.7 11.1

High income 13.3 13.5 13.5

Data source: WHO (GHED)


80

Table A2. Evolution of Budget Allocations by Administrative Levels Reported by MoH

Reproduced from PDSS 2007-2101,MoH,2007.

http://www.internationalhealthpartnership.net/fileadmin/uploads/ihp/Documents/Country_Pages/Madagas

car/MadagascarPDSS_25mars_2007.pdf

Table A3. List of Categories Used in the Alternative Classification of MoH Expenditures

Classifications by level of care or specific program Classifications by type of activity

General administration Curative

Primary Health Care (PHC) Preventive

Primary and Secondary Health Care Curative and preventive

Secondary and Tertiary health care Pharmacy/laboratories

Public Health Administration

Maternal & Child Health (inc. reproduction/FP) Maintenance and logistics

Health: non differentiated by level Training

Buildings and equipment (PIP only)

Additional classification by focus of activity

Type of admin (general, decentralized, planning and evaluation, finance, Information technology, etc.)

Procurement type activities

Type of facility

Type of disease (CD, NCD)

Type of public health care activity

Type of primary health care Other specific focus (vulnerable people, env. health, nutrition, health promotion, outreach, etc.) Transfers to hospitals identified separately

Note: Salaries were included in a specific SOA in the administrative function and identified separately. Social

protection and population type SOAs were also identified as such but are not included above.

http://www.internationalhealthpartnership.net/fileadmin/uploads/ihp/Documents/Country_Pages/Madagascar/MadagascarPDSS_25mars_2007.pdf

http://www.internationalhealthpartnership.net/fileadmin/uploads/ihp/Documents/Country_Pages/Madagascar/MadagascarPDSS_25mars_2007.pdf


81

Table A4. Tracking of Specific Allocation of Interest in Non-Wage Recurrent Expenditure

In Million of constant 2013 Ar. 2006 2008 2009 2010 2011 2012 2013

Health priority/specific program Communicable Diseases 2970 6600 349 2852 5490 3560 6099 MCH/Reproductive health - all 1745 1911 37 1017 4893 960 925

of which reproductive health 716 115 0 60 54 180 82 Neglected Tropical Diseases 0 0 0 2112 1074 315 641 Non-Communicable Diseases 102 316 113 105 99 150 94 Environmental health 0 0 35 33 30 18 10 Governance and quality Partnerships with private sector 139 311 209 1832 2921 1259 757 Statistics and data management 301 980 591 240 463 129 90 Quality control activities 78 77 43 38 36 75 42 Large items Transfers to hospitals 3055 7848 7016 5416 4873 4502 4114 Paramedical school 2520 2525 2198 3238 2909 2593 2120

Note: The choice of categories is determined and limited by what could be identified in the accounts rather than what

we would like to measure relative to specific needs. In addition, the amounts obtained are not exhaustive of all

expenditures in the specific program (except for the MCH program), because expenditures could be included in some

other general administrative units and therefore not identified by specific target. Some expenditure were identified as

directed to specific target groups (vulnerable people in particular) based on SOA created in 2012 but the amount were

small.

Data source: MFB/SIGFP.

Table A5. PIP Expenditures in Specific Categories, 2008-2013

In Million 2013 Ar. 2008 2009 2010 2011 2012 2013

Communicable diseases 21052 107 7294 3910 775 24446 Non Communicable diseases 0 3491 3905 800 2642 8882 Neglected Tropical Diseases 0 0 0 1 82 77

Note: These amounts are limited to investment expenditure included in SIGFP. The SOA used in this classification

did not exist in 2006.

Data source: MFB/SIGFP.


82

Table A6. Annual MoH Salaries by Province as a Ratio to SIGFP Remunerations by Province

2006 2008 2009 2010 2011 2012 2013 Total salaries in wage data/ total remunerations in SIGFP 1.11 1.35 n/a 0.86 0.89 0.91 0.89 CENTRAL 1.09 1.12 0.83 0.88 0.89 0.86 ANTANANARIVO 1.17 1.44 0.83 0.86 0.85 0.85 ANTSIRANANA 1.06 1.54 0.94 0.97 0.97 0.89 FIANARANTSOA 1.09 1.40 0.84 0.87 0.99 1.00 MAHAJANGA 0.96 1.56 0.86 0.85 0.87 0.89 TOAMASINA 1.21 1.32 0.86 0.95 0.96 0.94 TOLIARA 1.08 1.34 0.99 0.93 0.98 1.00 Total salaries in wage data/ total salaries and charges in SIGFP 0.99 1.19 n/a 0.75 0.79 0.85 0.80 CENTRAL 0.99 1.02 0.75 0.79 0.83 0.76 ANTANANARIVO 1.02 1.29 0.74 0.77 0.81 0.75 ANTSIRANANA 1.04 1.29 0.84 0.86 0.91 0.79 FIANARANTSOA 0.98 1.24 0.73 0.75 0.86 0.89 MAHAJANGA 0.84 1.38 0.70 0.75 0.81 0.78 TOAMASINA 1.07 1.05 0.75 0.83 0.87 0.88 TOLIARA* 0.97 1.22 0.84 0.93 0.98 1.00

*Note: no social charges were entered in the SIGFP for Toliara in 2011-2013. The allocation by province in the salary

data are done using the region rather than the section code which had salaries of the same regions spread into several

provinces; in the end the difference in method did not produce noticeable differences as the difference in allocations

cancelled out.

Data sources: MFB, SIGFF and Service de la Solde.


83

ANNEX 2. FIGURE SUPPLEMENT

Figure A.1. MoH Recurrent Expenditure Per Capita by Region vs Per Capita Consumption, 2006-

2010

Data sources: MoF, INSTAT (population), EPM (poverty).

-100

100

300

500

700

900

1100

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

BO

ENI

AN

ALA

MA

NG

A

DIA

NA

ALA

OTR

A-M

AN

GO

RO

BO

NG

OLA

VA

MEL

AK

Y

MEN

AB

E

BET

SIB

OK

A

ITA

SY

SAV

A

AN

OSY

VA

KIN

AN

KA

RA

TRA

HA

UTE

MA

TSIA

TRA

AN

ALA

NJI

RO

FO

IHO

RO

MB

E

ATS

IMO

-AN

DR

EFA

NA

ATS

INA

NA

NA

ATS

IMO

-ATS

INA

NA

NA

AM

OR

ON

'I M

AN

IA

SOFI

A

VA

TOV

AV

Y-FI

TOV

INA

NY

AN

DR

OY

Tho

usa

nd

20

13

Ar

Average 2006-2008 Average 2009-2013 p.c. consumption, 2005 p.c. consumption, 2010


84

Figure A.2. MoH Recurrent Expenditure Per Capita on PHC by Region vs Per Capita

Consumption (2003)

Note: Per capita consumption in PPP from EPM 2010.

Data sources: MFB, MINSANP, INSTAT, EPM.

0

100

200

300

400

500

600

700

0

1

2

3

4

5

6

7

8

9

Tho

usa

nd

20

13

Ar.

Tho

usa

nd

of

20

13

Ari

ary

Current exp/c on basic health care services (based on type of facility)

exp/c on primary care based on personnel type (salaries only)

Consumption/capita (2010 EPM) Secondary axis

Date post:	30-Jul-2020
Category:	Documents
Upload:	others
View:	18 times
Download:	0 times