the impact of public health spending on poverty and ......(university of cape town) h-africans...

SOUTH AFRICA: POVERTY AND INEQUALITYINFORMAL DISCUSSION PAPER SERIES

19333February 1999

The Impact of Public HealthSpending on Poverty and Inequality

in South Africa

Florencia Castro-Leal

WORLD BANKCOUNTRY DEPARTMENT IAFRICA REGION

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SOUTH AFRICA: POVERTY AND INEQUALITYINFORMAL DISCU,JSSION PAPER SERIES

* Poverty and Inequality in the Distribution ofPublic Education Spending in South Africa

Florencia Castro-Leal, February 1999

* The Impact of Public Health Spending on Povertyand Inequality in South Africa

Florencia Castro-Leal, February 1999

* Review of Public Expenditures: Efficiency andPoverty Focus

Gurushri Swamy and Richard Ketley, February 1999

* Safety Nets and Income Transfers in South Africa

Harold Alderman, February 1999

* Poverty Issues for Zero Rating Value-Added Tax (VAT)in South Africa

Harold Alderman and Carlo del Ninno, February 1999

* Violence and Poverty in South Africa: Their Impacton Household Relations and Social Capital

Caroline Moser, February 1999

* Women Workers in South Africa: Participation, Pay andPrejudice in the Formal Labor Market

Carolyn Winter, February 1999

FOREWORD

This paper is one of a series of informal discussion papers on poverty andinequality issues in South Africa, which were produced as contributions to the Povertyand Inequality Report (PIR). The PIR was commissioned by the Deputy President'sOffice of the Government of the Republic of South Africa (and was published in 1998 byPraxis Publishing, South Africa). As these papers were written at different times over theyears 1996-1998, the analysis in each paper covers different periods; however, for ease ofreference, they are now being disseminated in one series.

A complementary report, which gathers the views of the poor themselves, waswritten by a team of South Africans and also published by Praxis Publishing. "TheExperience and Perceptions of Poverty in South Africa" (1998) gives voice to the poor,who describe what poverty is to them, how they get trapped in it, and how they mightescape from it. This study was initiated and funded by the World Bank (through a DutchTrust Fund) and by the Overseas Development Administration of the U.K. Government.

The papers in this series were written under the direction of Ann Duncan (TaskManager) and under the overall guidance of Pamela Cox (Country Director) and RuthKagia (Sector Manager). The series was edited by Barbara Koeppel, and the finalpresentation was managed and executed by Lori Geurts.

Country Department IThe World BankFebruary 1999

Copyright 1999The World Bank1818 "H' Street, N.W.Washington, DC 20433, U.S.A.

This is an informal study by World Bank staff, publishedfor discussion purposes. It is not an official World

Bank document.

ACKNOWLEDGEMENTS

This paper was written by Florencia Castro-Leal, Economist, PRMPO. Theauthor gratefully acknowledges the support from Ann Duncan in producing this paper.Special thanks are due to Stephan Klasen for his valuable comments and assistance incoordinating the review process. The author also wishes to thank Jane Doherty, DavidHarrison, Gurushri Swamy and Trina Haque for very helpful comments on an earlierdraft. Keith Hansen provided timely information on the review on Health Expenditureand Finance in South Africa. Yisgedullish Amde and Kalpana Mehra provided excellentcomputational analysis of household survey data. Many thanks to Precy Lizarondo forproviding quick and efficient word processing assistance.

The views expressed in this study are those of the author and should not beattributed to the World Bank, to members of its Board of Executive Directors, or thecountries they represent.

ABSTRACT

From 1983 to 1993, public health spending increased by almost one percentagepoint of the gross domestic product (GDP) and the share of public health resources toGDP increased to 3.3% from 2.6%. This was equivalent to a 3% annual increase in realtenns and a 0.5% annual increase in per capita terms. Although this represented amoderate rate of growth, a larger share of public resources was dedicated to the healthsector at a time when GDP growth was low. This paper explores how these expenditureswere distributed across socioeconomic groups, races and regions and who benefited. Themethodology used, Benefit Incidence Analysis, measures how well public services weretargeted to certain groups.

The analysis suggests that budget allocations by levels of care conceal severeinequities and inefficiencies in the public health system: Indeed, since spending is largelydirected to high levels of care, most public health resources have benefited the highincome groups, while the poor remain under-served. For example, the 25% of thepopulation who live in Gauteng and Western Cape, where poverty rates are lowest,receive over 40% of public health resources. By contrast, nearly 66% of the populationlive in Mpumalanga, Northern Province, KwaZulu Natal and the Eastern Cape (thepoorest provinces), but receive less than half the resources.

Over 75% of South Africans have access to public health facilities--but theseindividuals are mainly in the middle income quintiles. Conversely, the poor, who areconcentrated in provinces with a high proportion of residents from the former homelands,lack affordable public health care: First, they do not have easy access to facilities; second,although fees are quite low, they are still too high for the ultra-poor. Thus, those with thelowest incomes, especially women and children, suffer from poor health status.

For these reasons, primary health care needs to be a higher priority within thesystem if public spending is to reduce inequities and inefficiencies. Specifically,targeting mechanisms need to be devised to reach the poor. While a progressive feestructure exists in theory, the income threshold is so high that almost everyone receivescare for a low fee. This suggests the means tests do not distinguish between the poor andnon-poor. Reforming these practices could improve the poverty focus of the healthsystem.

ABBREVIATIONS AND ACRONYMS

DPT Diptheria, Pertussis, Tetanus (immunization)

HEF Health Expenditure and Finance in South Africa(1995 Study)

SALDRU Southern Africa Labour & Development Research Unit(University of Cape Town)

H-Africans Africans living in the former Homelands

NH-Africans Africans living in the former non-Homeland areas

CURRENCY EQUIVALENTS

Rand/US$

1990 2.6

1991 2.7

1992 3.1

1993 3.4

1994 3.5

1995 3.6

1996 4.7

1997 4.9

1998 5.9

TABLE OF CONTENTS

I. INTRODUCTION ............................................. 35

II. SOCIOECONOMIC AND DEMOGRAPHIC GROUPS ......................................... 35

IH. USE OF HEALTH CARE SERVICES .............. ............................... 3 5

IV. MATERNAL AND CHILD HEALTH CARE ............................................ 35

V. PUBLIC SPENDING ON HEALTH ............................................. 35

VI. THE DISTRIBUTION OF PUBLIC HEALTH SPENDING .................................. 3 5

REFERENCES ............................................ 35

APPENDIX A ............................................. 32

The Impact of Public Health Spending on Povery and Inequality in South Afiica

I. INTRODUCTION

From 1983 to 1993, public health spending increased by almost one percentagepoint of gross domestic product (GDP) and the share of public health resources to GDPincreased from 2.6% to 3.3%. 1 This was equivalent to a 3% annual increase in real termsand 0.5% in per capita terms.2 Although this represents a modest rate of growth, it isnoteworthy that a larger share of public resources was dedicated to the health sector at atime when GDP growth rates were low. This paper explores how the expenditures weredistributed across socioeconomic groups, races and regions and who benefited. Themethodology used, Benefit Incidence Analysis,3 measured how well public services weretargeted to groups such as the poor, regions, women and children.

The distribution of public health resources was and remains inequitable: (a) theyare unevenly allocated because of the unequal consumption of public services bydifferent socioeconomic groups, regions, races and genders; and (b) the pattern ofgovernment spending within the health sector maintains the status quo. The distributioncan be examined across groups through the allocation of per unit public subsidiesaccording to individual utilization rates of public services.

Detailed public spending information was drawn from the review of HealthExpenditure and Finance in South Africa published jointly by the Health Systems Trustand the World Bank (HEF, 1996). Also, data on individual patterns of access and use ofpublic services were obtained from the South Africa Living Standards and DevelopmentSurvey, coordinated by the University of Cape Town's Southern Africa Labour andDevelopment Research Unit (SALDRU, 1993).4 BEF and SALDRU data were used tocalculate per capita health subsidies for different population groups.

II. SOCIOECONOMIC AND DEMOGRAPHIC GROUPS

Because the poverty profile ("Key Indicators of Poverty in South Africa") foundthat nearly 70% of the poor live in the former homelands, 5 this group has been studiedseparately. These geographical areas are almost 100% African (see Appendix Table A.1),and account for 66% of the African population overall (see Appendix Table A.2), 63% ofthe households in the poorest quintile and 55% in the second poorest (see Figure 1). 6

Due to regional income disparities, the study focused on three of the new provinces, the

I Health Expenditure and Finance in South Africa (1995).2 lbid.

3 See Van de Walle and Nead (1994), Meerman (1979), and Selowsky (1979).

4 The survey was funded by the Governiments of Demnark, the Netherlands and Norway, with technical assistanceprovided by the World Bank.

5 "Key Indicators of Poverty in South Africa", RDP (1995), p. 12

6 The "Key Indicators" (see above) determined that about 40 percent of South African households (equivalent to about

53% of the population) are poor. Households are ranked by per adult equivalent expenditures; the poorest 20 percentconstitutes the poorest quintile and contains the poorest 20 percent of all households, and so on for the next quintile, upto the top quintile which contains the richest 20 percent of all households. Since households in poor quintiles have alarger number of members in them than households in rich quintiles, then the total population found in poverty is largerthan the number of poor households (RDP, 1995).

1

The Impact of Public Health Spending on Poverty and Inequality in South Afrca

Eastern Cape, Northern Province and KwaZulu Natal, where more than 75% of Africansfrom the former homelands (H-Africans) live (see Table 1).

Figure 1Racial Composition within Quintiles

1 100 a~~~~~~Africamlex-

80 Ho_muiard

0I 0 Wkite

40 *In

l 20 . 11 ll 1111 1 Coloured

12; l bbiNAficnNon-ex-.- Ho_nd

Source: SALDRU (1993)

Table 1: Percentage Share of Former Homelands Population by New Province

New province ShareN.Cape 0.00Free State 2.34North West 11.07Mpumalanga 10.13W.Cape 0.00N.Province 22.88E.Cape 23.42Gauteng 0.00KwaZulu Natal 30.15Total 100.00Source: SALDRU (1993)

The analysis of demographic groups was feasible because data on both publichealth spending and the use of health care services were available: HEF data was used tocalculate per unit subsidies by new province, while the SALDRU household surveysupplied data on residence by new province and racial composition.

2

The Impact of Public Health Spending on Poverty and Inequality in South Africa

IIl. USE OF HEALTH CARE SERVICES

The poorest report half the illness rate of the richest

In most developing countries, self-reported illness is positively correlated withhousehold income and educational.level. In South Africa, as elsewhere, the poor reportconsiderably lower rates of illness than do the non-poor. The self-reported illness rate inthe poorest income quintile is less than half that of the richest quintile (see Table 2). 7 Inaddition, Africans have the lowest rates, which corresponds to the behavior of the bottomtwo income quintiles-because these groups are only minimally represented in the topthree quintiles. Conversely, reporting rates for Indians and Whites correspond to thebehavior of the top two income quintiles. No important differences in rates were foundbetween males and females, while differences across new provinces were closely relatedto their racial composition.

Table 2: Incidence of Illness by Quintile and Race(self-reported, % of total)

African/Non- African/ex-Hh quintile Total ex-Homeland Homeland Colored Indian WhitePoorest 6 5 6 8 ** **

II 8 6 8 9 ** **

III 9 8 10 10 14 12IV 10 9 10 11 14 11Richest 13 13 11 13 20 12Average 8 7 8 10 15 12

** Sample <= 30 observationsSource: SALDRU (1993)

One fourth of the poorest who get ill do not seek health care

Of those who reported illness, almost 20% did not seek or obtain health care (seeTable 3). This phenomenon is exactly the reverse of self-reported illness across incomequintiles: 25% of those in the poorest income quintile did not seek health care, comparedto one-seventh in the richest. The highest rates for those who reported illness but did notseek care are for non-homelands Africans (NH-Africans) and Coloreds in the poorestincome quintile, 28% and 3 5%, respectively. Gender disaggregation showed no importantdifferences.

7 The incidence of illness by quintile and race is calculated on the basis of self-reported illness in a two week recallperiod contained in SALDRU (1993).

3


Table 3: Individuals Who Sought No Health Care by Quintile and Race(as % of those reporting illness)

H African/non- African/ex-quintile Total Ex-homeland Homeland Colored Indian WhitePoorest 25 28 24 35 ** **II 20 24 20 11 ** **III 13 15 12 10 15 0IV 17 13 13 18 26 23Richest 14 0 11 8 3 18Average 18 18 19 15 16 19** Sample <= 30 observationsSource: SALDRU (1993)

About half the people who do notget health care say it is too costly

By far the most important reason why half the population and almost 75% of thepoorest do not seek medical care when ill was that treatment was too expensive (seeTable 4); even half the Africans in the fourth income quintile gave this response.

Table 4: No Care Because Treatment Is Too Expensive, by Quintile and Race(% of ill reporting 'no care')

African/non- African/ex-Hh quintile Total ex-homeland Homeland Colored Indian WhitePoorest 72 75 74 33 ** **II 55 49 58 29 ** **III 43 53 44 11 ** **IV 42 56 47 22 ** **Richest 9 ** ** ** ** 8Average 48 59 61 22 31 13** Sample <= 30 observationsSource: SALDRU (1993)

Poor women are more likely than men to forego care because of costs

Affordability was considerably more important for poor females than males (seeTable 5): More than 80% in the poorest income quintile did not seek care when illbecause it was too expensive compared to about 60% of males. The second poorestincome quintile also had a larger number of females who did not get health care for thisreason. Thus, affordable medical care is urgently needed for both poor males andfemales, but particularly for the latter.

4


Table 5: No Care Because Treatment Is Too Expensiveby Quintile and Gender

(% of ill reporting 'no care')

Hh quintile Total Male FemalePoorest 72 57 81II 55 46 61II 43 43 43IV 42 39 44Richest 9 13 5Average 48 41 54Source: SALDRU (1993)

Residents from poor provinces are more likely to forego care because of costs

Lack of health care because of its high cost was noted more frequently in the poorprovinces (see Table 6): More than half the residents in new provinces, where over halfthe population is poor, did not seek or obtain care because of the cost. 8 In the NorthemProvince, the province with the second highest poverty rate, almost 33% did not gethealth care, and 75% said it was for monetary reasons.

Table 6: Those Who Did Not Seek Care for Monetary Reasons by New Province(percentage)

No care Treatment too costlyNewprovince (as % of those ill) (as % of those ill reporting

no care)lN.Cape 21 14ree State 17 75

North West 23 77Mpumalanga 20 46W.Cape 18 31N.Province 27 75E.Cape 15 46Gauteng 17 27KwaZulu Natal 16 51Total 19 48Source: SALDRU (1993)

8 The "Key Indicators of Poverty in South Africa" reports the poverty rates by new province as follows (percentage ofthe population of each province who are poor): E. Cape, 78 percentN.Province, 77 percent; Free State, 66 percent N.Cape, 57 percent North West, 57 percentKwaZulu Natal, 53 percent;Mpumalanga, 52 percent; W. Cape, 23 percent;Gauteng, 19 percent (RDP, 1995).

5

The Impact ofPublic Health Spending on Poverty and Inequality in South Africa

Medical care carries a high opportunity costfor the poor and Africans from theformerhomelands

Further, the opportunity cost of getting medical treatment was higher for the poor:It took those in the poorest quintile an average of two hours, round-trip, to get medicalattention compared with only about a half hour for the richest (see Table 7).

Table 7: Round-Trip to Medical Care(time in hours and minutes)

Hh Total African/Non- African/ex- Colored Indian Whitequintile Ex-Homeland HomelandPoorest lhr.54 lhr.42 2hr.00 lhr.08 ** **II lhr.42 lhr.10 lhr.56 48 46 54IIl lhr.14 56 lhr.34 46 34 16V 52 52 lhr.14 40 30 50

Richest 34 lhr.22 lhr.24 26 26 28Average lhr.18 lhr.08 lhr.46 44 28 30


Medical attention was more expensive for the poor because of the longer timethey need to reach health care, in addition to the higher monetary equivalent of foregoneearnings and the fees paid for treatment and drugs. For example, H-Africans needed totravel longer distances for treatment, across all income quintiles: Round-trips tookbetween two hours for the poorest, and (excluding in the former Homelands) about a halfhour for the richest.

The opportunity cost of getting health care needs to incorporate waiting time, inaddition to travel time. Residents in the Northern Province and Eastern Cape traveled thelongest time (including the wait for medical attention), which was about two and a halfhours (see Table 8).

6


Table 8: Distance to and Waiting Time for Medical Attention by New Province(time in hours and minutes)

New province Distance Waiting time Totall__________ (round-trip) for treatment

N.Cape 52 - lhr.08 2hr.00Free State lhr.26 44 2hr.10North West lhr.18 33 lhr.48Mpumalanga lhr.24 50 2hr.14W.Cape 46 51 lhr.37N.Province lhr.44 48 2hr.321E.Cape lhr.42 39 2hr.21Gauteng 44 36 lhr.20KwaZulu Natal lhr.28 39 2hr.07

l Total lhr.18 42 2hr.00


Au income groups and races are more likely to use private doctors/nurses than healthcenters

Regardless of income quintiles and race, the largest shares of patient visits are toprivate doctors and nurses. The next largest share went to hospitals, and the lowestproportion used health centers (see Tables 9, 10 and 11): On average, more than half thehealth care visits were to private doctors and nurses, but disparities across incomequintiles were considerable. About 33% of the poorest visited private doctors and nurses,compared to almost 90% of the richest.9

Table 9: Hospital Use by Quintile and Race(as % of those seeking care)

Hh Total African/Non- African/ex- Colored Indian Whitequintile ex-Homeland HomelandPoorest 37 43 35 59 ** **

II 33 27 33 45 ** **

lIII 28 25 27 39 ** **

IV 21 19 23 34 9 17Richest 8 8 21 16 10 6,verage 26 25 31 38 13 8** Sample <= 30 observations Source: SALDRU (1993)

9 Although SALDRU does not contain information about health insurance, it is likely that the non-poor are using thisfinancing mechanism to cover their health expenses.

7

The Impact ofPublic Health Spending on Poverty and Inequality in South Afiica

Table 10: Health Center/Clinic Use by Quintile and Race(as % of those seeking care)

H Total African/Non- African/ex- Colored Indian Whitequintile Ex-Homeland HomelandPoorest 24 17 27 6 ** **

II 22 23 24 7 ** **

III 19 18 25 5 ** **

IV 11 15 16 4 11 2Richest 3 10 4 4 2 2Average 16 17 24 5 5 3** Sample <= 30 observations Source: SALDRU (1993)

Table 11: Private Doctor/Nurse Use by Quintile and Race(as % of those seeking care)

Hh Total African/Non- African/ex- Colored Indian Whitequintile ex-Homeland HomelandPoorest 31 31 31 35 ** **

II 39 49 36 43 ** **

III 47 52 43 55 57 42IV 65 63 57 61 79 81Richest 86 72 75 80 89 89Average 53 52 39 55 79 86

** Sample <= 30 observations Source: SALDRU (1993)

About 25% of all visits for medical care were to hospitals compared to one-seventh to health centers; again, disparities across income quintiles were large. Forexample, the poor had higher utilization rates for hospitals and health centers than thenon-poor: The poorest were three times more likely to visit them when ill than therichest.

These patterns--mainly high utilization of hospitals and low use of health centers--suggest at least two sources of inefficiencies. First, outpatient services could beovercrowding high level, specialty facilities. Thus, high-level personnel at high-levelhospitals, with high fixed costs are treating relatively minor illnesses. Therefore, lowlevel, less complex health facilities tend to be more inefficient and even less costeffective since they attract relatively few patients--largely due to lack of funds, drugs, andqualified personnel. Thus, the referral system could be generally not functioning.

8


IV. MATERNAL AND CEIILD HEALTH CARE

Poor women have about twice as many children as the non-poor

Based on the data, high poverty also translates into a high number of births perwoman: Those aged 15-49 in the poorest quintile had almost twice as many as those inthe richest quintile (see Table 12), with the highest mean number of births among Africanwomen in the former homelands. Women in the Eastern Cape and Northern Provincehad the highest number of births. KwaZulu Natal, the most populated new province, hadthe third highest number.

Table 12: Fertility and Child/Infant Mortality by Quintile, Race and New Province

Infant Mortality Child MortalityHh quintile, Race and New Total Fertility (Children per (Children per

rovince (Births per woman who died woman who diedwoman) before 1 yr,). before 5yr)

Hh quintileslPoorest 4.25 0.36 0.55II 3.48 0.24 0.35III 3.27 0.17 0.26IV 2.90 0.10 0.16Richest 2.21 0.04 0.04RaceAfrican/Non-ex-Homeland 3.25 0.22 0.31African/ex-Homeland 3.75 0.27 0.43Colored 3.26 0.07 0.12Indian 2.59 0.08 0.09White 2.32 0.03 0.03ProvinceE. Cape 3.73 0.32 0.50Mpumalanga 3.49 0.16 0.26Gauteng 2.76 0.08 0.12KwaZulu Natal 3.55 0.21 0.34N. Cape 3.04 0.07 0.15N. Province 3.73 0.22 0.33North West 3.11 0.22 0.31Free State 3.27 0.33 0.46W. Cape 3.00 0.10 0.15lAverage .30 |0.19 0.29Source: SALDRU (1993)

9

The Impact of Public Health Spending on Poverty and Inequality in South Afiica

Infant and child mortality are highest among the poor and Africans

Infant and child mortality were also highest for the poorest quintile, close to 0.4and 0.6 per woman, respectively (see Table 12). 10 The Eastem Cape and Free State hadthe highest rates, and Africans had the largest average number of children per womanwho died before the ages of 1 and 5. Child mortality was also highest in these twoprovinces, while KwaZulu Natal and the Northern Province reported the second highestnumbers. Conversely, the richest quintile reported only 0.04 for infant and childmortality.

Malnutrition among the poorest and Africans from the former homelands is similar tothat in the Africa region

For the country as a whole, the proportion of malnourished children was wellbelow that of the Africa region and all developing countries (see Table 13); however, theproportion was about the same for children in the poorest income quintile and amongthose from the former homelands.

The nutritional status of young children can be measured by three basicindicators: underweight (weight for age indicates current malnutrition), stunting (heightfor age indicates long-term malnutrition) and wasting (weight for height indicates acutecurrent malnutrition). These are among the most sensitive measures of changes in earlyhealth status and the availability of food. "I Severe, moderate and total malnutrition (thesum of moderate and severe) were calculated by income quintiles, race and new provincefor children up to 6.12

Inequality in stunting, the indicator of long-term malnutrition, was the highestacross quintiles. Overall, 25% of children were stunted. But in the poorest quintile, thefigure was over 33%. In the four new provinces (Northern Cape, Eastern Cape, NorthernProvince, and KwaZulu Natal), more children than the national average suffer fromstunting: Over 33% have long-term malnutrition. Conversely, fewer than 10% werestunted in the richest quintile.

10 Infant and child mortality indicators are the mean number of children per woman who died before I and 5 years old,respectively. These indicators are proxies of infant mortality rate (IMR) and child mortality rate (CMR), properlymeasured they would almost certainly show the same patterns reported in Table 23.11 UNICEF (1990). An Assessment of Child Mainutrition.

12 Severe values are less than -3 standard deviations of the median of the population and moderate values are between -2 and -3 standard deviations of the median of the population.

10

The Impact ofPublic Health Spending on Poverty and Inequality in South Aica

Table 13: Prevalence of Malnutrition by Quintile, Race and New Province(children up to 6 years of age)

Underweight Stunting WastingHh quintile, Race (weight-for-age) (height-for-age) (weight-for-height)

And New Province Severe Moderate Total evere Moderate Total re Moderate Total

Hh quintilePoorest 7.8 15.7 23.5 13.3 21.5 34.8 4.1 6.3 10.4II 5.3 12.2 17.5 9.3 14.7 24.0 3.8 6.0 9.8III 6.0 10.6 16.6 7.6 15.2 22.8 4.3 6.4 10.7IV 3.1 12.0 15.1 5.7 10.7 16.4 4.5 6.3 10.8Richest 0.8 5.0 5.8 2.3 4.0 6.3 1.4 5.6 7.0Race

frican/Non-ex- 5.2 13.7 18.9 7.9 15.9 23.8 5.6 7.7 13.3HomelandAfrican/ex- 6.8 13.7 20.5 11.6 17.6 29.2 3.9 6.0 9.9HomelandColored 4.4 10.3 14.7 5.4 15.6 21.0 1.8 4.7 6.5Indian 0 7.4 7.4 2.0 5.7 7.7 3.8 4.8 8.6White 0.3 3.1 3.4 2.6 3.5 6.1 0.3 5.0 5.3Province

Cape 3.2 8.4 11.6 5.4 12.6 18.0 3.1 4.4 7.5Cape 6.5 14.6 21.1 10.0 25.1 35.1 1.6 6.5 8.1

. Cape 2.7 8.2 10.9 14.0 19.0 33.0 1.0 2.4 3.4KwaZuluNatal 6.2 11.1 17.3 11.4 13.7 25.1 4.1 5.8 9.9Free State 7.4 13.4 20.8 4.6 19.7 24.3 7.2 6.1 13.3Mpumalanga 3.7 13.1 16.8 6.8 13.3 20.1 2.6 5.0 7.6N. Province 10.3 16.9 27.2 9.8 18.4 28.2 6.1 9.0 15.1North West 9.0 21.4 30.4 8.5 16.0 24.5 5.3 11.3 16.6Gauteng 3.5 12.0 15.5 5.8 11.9 17.7 4.2 7.7 11.9

South Africa 5.6 12.5 18.1 .3 15.7 25.0 3.9 6.2 10.1Average i lAfrica Region 26.6 35.3 10.2Total Developin I 35.7 139.0 I 8.4Country I I I I I__ llll

Note: Severe values are less than -3 standard deviations of the median of the reference populationand moderate values are between -2 and -3 standard deviations.Source: SALDRU (1993); UNICEF (1990).

The proportions of children with a hospital/clinic card were considerably lowerfor the poor than the non-poor, the lowest for African children in the former homelandsand for those in the Eastern Cape, KwaZulu Natal, Free State, Northern Province andNorthern Cape (see Appendix Table A.3).

11


About 66% of children have immunization cards; of these, fewer than 80% are fullyimmunized

About 66% of the children have an immunization card. The proportions ofchildren with hospital/clinic cards are considerably lower for the poor, the lowest forAfrican children from the former homelands and for children in the Eastern Cape,KwaZulu Natal, the Free State, the Northern Province and the Northern Cape (seeAppendix Table A.3)

The most sensitive indicator of immunization coverage is the extent of DPTvaccinations for children between 12-23 months of age: However, fewer than eight in 10with immunization cards had been fully immunized (see Table 14).

Table 14: DPT Immunization by Quintile, Race and New Province(% of 12-23 months of age, with a hospital/clinic card)

Hh quintile, Race Times Immunizedand New Province 0 1 2 3+Hh quintilePoorest 4.9 6.9 15.6 72.6II 4.7 5.8 11.8 77.8II 2.9 2.3 9.4 85.5V 1.1 3.4 12.6 82.8

Richest 1.4 4.6 11.5 82.6Rce

African/Non-ex-Homeland 1.2 4 12.6 82.2African/ex-Homeland 5.2 6.4 14.7 73.6Colored 2.7 0 1.5 95.8Indian ** ** ** **White 2.2 6.5 9.8 81.5ProvinceE. Cape 6.5 11.4 12 70.2Mpumalanga 6.3 1.7 14.2 77.8Gauteng 0 3.7 9.2 87.1KwaZulu Natal 5.6 4.9 19.6 69.9N. Cape ** ** ** **N. Province 2.7 3.8 14.5 79

orthWest 2.8 10.4 14.9 72Free State 4.5 4.2 8.5 82.8W. Cape 0 0 4.2 95.8lAverage 5.2 6.4 14.7 78.8

** Sample <= 30 observations Source: SALDRU (1993)

A very high proportion of children (5% on average) older than one but under twoyears old, had no immunizations against DPT (although they had immunization cards):This occurred most frequently among the poorest and African children in the former

12


homelands. Children between 12-23 months in the Eastern Cape, Mpumalanga andKwaZulu Natal also had above-average rates of non-immunization.

New provinces where over half the residents were poor or where many migratedfrom the former homelands lagged considerably behind in matemal and child healthindicators:'3 For example, the Northern Cape, Eastem Cape, Northern Province andKwaZulu Natal had the worst health indicators for women and children of all newprovinces. Thus, although geographic targeting of maternal and child health programscould be recommended, low-income women and children in all provinces also needaffordable health care.

V. PUBLIC SPENDING ON HEALTH

Public health spending increased at a time of low GDPgrowth

Public health spending increased by almost one percentage point of GDP from1983 to 1993, and the ratio of public health resources to GDP increased from 2.6% to3.3% (see Table 15). 14 This is equivalent to a 3% annual increase in real terms and a0.5% annual increase in per capita terms.15 Although such growth was moderate, itoccurred when GDP growth was slow.

Table 15: Public recurrent health care expenditures

Year Public Expenditures Expenditures PerHealth (nominal R m) (R m in capitaSpending 1983/84 (real*)(% of GDP) prices.)

1983/84 2.6 2,453.6 2,453.6 761992/93 3.3 11,114.9 3,199.9 79* Deflated to 1983/84 prices using the CPI deflatorSource: HEF (1996)

About 66% of health resources were privately financed and 33% were from thepublic sector. The two largest sources of finance were private medical schemes andgeneral taxes (see Figure 2): 66% of private sector funds were from medical schemes(with private employees contributing more than 75%), about 25% were directly frompatients, and the rest were from industry and medical insurance. Public sector fundingwas almost entirely financed from general taxes, with a small contribution from user fees,local taxes, utility sales and rates.16 -17

13 See footnote 7 for provincial poverty rates. All provinces, but W. Cape and3auteng, have more than 50 percent ofthe population living in poverty.14 BEF (1996).15 Ibid.

16 Donor funding is not included in the HEF (1996) because of data deficiencies in aggregate figures.17 Refer to the BEF (1996) for a discussion of the characteristics of medical schemes and medical insurance. Ingeneral, medical schemes are non-profit associations funded by contributions of employees and employers. The size ofthese contributions varies according to the member's income and number of dependents.

13


Figure 2Health sector financing, public and private, 1992-1993

Medical Schemes41.1% Medical

Insurance 3.1%

Industry 3.6%

Local rates, utiityHoshlsales and t6°es payments 14.3%

0.6%UsrFees 1. 7%/

General TwaxRevenue 35.6%

Source: Data obtained from HEF (1996)

Public health spending was highly concentrated in acute-care hospitals, concealingsevere inefficiencies in the health system

Acute-care hospitals received more than 75% of all public health recurrentspending,18 while only about 10% was dedicated to primary health care (see Figure 3).

Figure 3Allocation of public recurrent health care expenditures by level of care, 1992-1993

Chronic-careHospitals 5% Community

SecondaryHospitals Hospitals 21%11%

Tertiary Hospitals14%1 Non-Hospiata

Primay Care 11%

Academic Hospitals

Source: HEF (1996)

Academic and tertiary hospitals accounted for almost half of all public healthrecurrent expenditures, which indicates a high concentration of services at complexhealth facilities. This reflects the system's inefficiencies, since these facilities, where careis more expensive per unit of service, are being used to treat relatively minor diseases andas the entry point into the health system. This pattern of spending by level of health care

18 Acute-care hospitals include academic hospitals, tertiary hospitals, secondary hospitals and community hospitals.

14

The Impact of Public Health Spending on Poverty andInequality in South Afica

closely resembles the pattern of health facility use reported in the 1993 SALDRU (seeTables 9, 10 and 11 above). 19

Public health spending is highest in Gauteng and Western Cape

Western Cape, Gauteng and Free State provinces recorded above average totalpublic recurrent health expenditures per capita (see Table 16).20-21 This was partly due tothe concentration of academic and tertiary hospitals in major urban areas of Gauteng andthe Western Cape. The rest of the new provinces averaged about 200 Rand per capita.

Spending was lowest in the Mpumalanga, second. lowest in the Northern Province andthird lowest in the North West.

19 It likely that the concentration of public health funds in high-level facilities is driving the patterns of facility use

since lack of resources at low-level health facilities is likely to decrease the quality of service.

20 Although disaggregated public recurrent expenditures by level of health care and province cannot be found in the

final draft of the HEF, the 1995 draft of the BEF is the only data source currently available that provides this level of

disaggregation.

21 The 1995 draft of the HEF explains the methodology for calculating national expenditures on non-hospital prinary

care (refer to Appendix B in HEF, 1995). The allocation of primary care costs outside and inside the ex-homelands

follows different procedures. Outside the ex-homelands, budgets for individual clinics were readily available and were

used as the basis for each district's allocation of administrative costs and vertical programs. Inside the ex-homelands,

the proportion of total primary care recurrent expenditures at the district level was estimated based upon the allocationsto health wards and the number of registered nurses working in community services. The procedure was devised from

two case studies for KaNgwane and Ciskei (Priceet al, 1995 quoted by BEF, 1995). Recurrent expenditures inside the

ex-homelands also include vertical preventive programs from the ex-homeland head office.

15

The Impact of Public elalth Spending on Poverty and Inequality in South Africa

Table 16: Provincial population and public recurrent expenditures by level ofhealth care and province, 1992/1993 (Rand)

Public Recurrent Public Recurrent Expenditures byExpenditures by Level of Level ofHealth Care (Total) *Health Care (per capita) *

Province Pop. Hospital Clinic! Total*| Hospital Clinic! Total**(million) Health * Health

Ctr Ctr(Rand) (Rand)

Eastern Cape 6.7 206.39 16.36 226.98 1,382,813 109,612 1,520,766

Mpumalanga 2.8 106.20 20.36 136.60 297,360 57,008 382,480

Gauteng 6.8 346.53 24.32 381.66 2,356,404 165,376 2,595,288

KwaZulu 8.5 199.24 26.74 236.88 1,693,540 227,290 2,013,480Natal

Northem 0.7 172.44 26.60 221.15 120,708 18,620 154,80Cape

Northern 5.1 136.10 22.53 164.07 694,100 114,903 836,757Province

North West 3.5 138.26 34.03 178.91 483,910 119,105 626,185

Free State 2.8 235.56 30.93 266.49 659,568 86,604 746,172

Western 3.6 400.32 50.4 491.13 1,441,152 181,584 1,768,068

Cape

Total 40.7 224.31 26.54 261.52 J[ 9,129,565 1,080,102 [ 10,644,001* Although disaggregated public recurrent expenditures by level of health care and province cannotbe found in the final draft of the HEF (HEF, 1996), this is the only data source currently availablethat provides this level of disaggregation.* * Total also includes general administration and other non-attributable items to either hospitals orclinicsSource: HEF (1995)

16

The Impact of Public Health Spending on Poverty and Inequality in South Aica

Disparities are substantial in public spending per health visit

Disparities in public spending per health visit by new province are substantial (seeTable 17).22-23-24 At the hospital level, per visit expenditures were below average in newprovinces where many residents were from the former homelands: The Eastern Provincewas the lowest, the Eastern Cape the second lowest, and KwaZulu Natal the third lowest.Disparities were even wider at the health center/clinic level: All public expenditures pervisit were below average, except for the Western Cape, which recorded the highestspending, the North West, the second highest, and the Free State, the third highest.Conversely, new provinces that do not contain large urban centers or large populationsfrom the former homelands received lower-than-average public health resources percapita and per visit. Such disparities generally reflect important quality differentials inservices.

22 Table 16 brings together two types of information in order to compute per health visit expenditures: (i) publicrecurrent expenditures by level of health care for 1992/93 from the HEF (1995); and, (ii) the total number of visits byfacility type from the SALDRU household survey (1993). Public spending per health visit calculated in this wayaccounts for the average cost, which is different from the marginal cost that would be obtained by requesting theinformation directly from the hospitals or health centers/clinics. Average costs per health visit include overhead costsand research.

23 SALDRU's health section includes curative single visits only (e.g., visits to seek treatment for illness, injury,disability or ailment). The recall period in SALDRU is two weeks and the questionnaire allows the respondent to reportonly a single visit in that period. Thus, only one visit is reported in the household survey even if a patient attended ahealth facility more than once within a two week period.

24 At present there are four national data sets available in South Africa that report health facility visits which could beaggregated at the provincial level, unfortunately none of them agree. These are: SALDRU, October Household Survey,CASE survey and ReHBIS. Differences in the proportions of visits by health facility type can be partly explainedbecause of differences in the methodology (recall period, inclusion/exclusion of preventive visits and multiple visits,etc.) and partly because of difficulties to collect information from all new provinces in South Africa. SALDRUpresents several advantages for analyzing the distribution of all health resources across socioeconomic anddemographic groups: (i) it is a nationally representative survey, which covered 9,000 households nation-wide; (ii) itprovides the linkage between health indicators and household expenditure and thus, it allows for the analysis ofdistributional issues across famnilies from different income groups; and, (iii) the analysis of the distribution of healthresources based on SALDRU visits is consistent with the poverty profile published by the RDP, "Key Indicators ofPoverty in South Africa" (I 995).

17


Table 17: Public recurrent expenditures by level of health care and province,1992/1993

Province Level of Health CareHospitals Health Center/Clinics

Eastern CapeExpenditures (R'000) 1,382,813 109,612SALDRU No. of visits 4,140,370 2,021,708Subsidy per visit 334 54

MpumalangaExpenditures (R'000) 297,360 57,008SALDRU No. of visits 1,536,236 1,165,918Subsidy per visit 194 49

GautengExpenditures (R'000) 2,356,404 165,376SALDRU No. of visits 1,620,606 2,195,700Subsidy per visit 1,454 75

KwaZulu NatalExpenditures (R'000) 1,693,540 227,290SALDRU No. of visits 4,900,246 2,940,002Subsidy per visit 346 77

Northern CapeExpenditures (R'000) 120,708 18,620SALDRU No. of visits 129,870 51,012Subsidy per visit 929 365

Northern ProvinceExpenditures (R'000) 694,110 114,903SALDRU No. of visits 1,254,032 1,324,518Subsidy per visit 554 87

North WestExpenditures (R'000) 483,910 119,105SALDRUNo. of visits 715,416 521,014Subsidy per visit 676 229

Free StateExpenditures (R'000) 659,568 86,604SALDRU No. of visits 912,418 422,968Subsidy per visit 723 205

Western CapeExpenditures (R'000) 1,441,152 181,584SALDRU No. of visits 2,487,108 368,784Subsidy per visit 579 492

TotalExpenditures (R'000) 9,129,565 1,080,102SALDRU No. of visits 17,696,302 11,011,624Subsidy per visit 516 98

Source: SALDRU (1993) and HEF (1995)

18

The Impact of Public Health Spending on Povert and Inequality in South Aica

VI. THE DISTRIBUTION OF PUBLIC HEALTH SPENDING

This study used the Benefit Incidence Analysis methodology to examine thedistribution of public spending across different socioeconomic and demographic groups.Its most important advantage was that it could measure how well public services weretargeted to certain groups, such as the poor, geographic regions, girls and women.

Figure 4Annual per visit public health spending by level of care and new province, 1992-1993

1600

1400

1200

1000

800- *Hospital

600 U CfinihHeaIthCtr

400

200

0


The actual allocation of public spending to different socioeconomic anddemographic groups was determined by a combination of supply and demand factors.First, the study analyzed the pattem of govemment spending to and within the sector foreach type of public service. Second, it explored household behavior regarding use ofpublic services by socioeconomic groups. Thus, it integrated two sources of information:unitary subsidies by type of service (i.e., the per visit annual subsidy for the healthsector), and individual utilization rates of public services (i.e., total visits by level of care)disaggregated by socioeconomic group, region and gender. In this way, public spendingon health was distributed across socioeconomic groups subject to their visits to healthfacilities and the annual public subsidy per visit by level of care. The incidence of publicspending was the result of: (a) a public policy decision on the allocation of publicexpenditures to and within each sector, and (b) a private decision based on the behaviorof households.

The targeting of public health resources across socioeconomic and demographicgroups was analyzed with information about visits from the 1993 SALDRU householdsurvey and with data about govemment recurrent spending by level of health care and

19


new province from the 1995 HEF. The combination of the variability of visits and publichealth spending determined the disparities (see Figure 4).25

Public health spending patterns under-serve the poorest and allow leakage to the non-poor

If public health resources were equally distributed across the population, eachhousehold quintile would receive a percentage share equal to its share of the population.Although the resources are more heavily distributed towards poor households than thenon-poor, the poorest are still under-served and leakage to rich households persists (seeFigure 5). For example, the ultra-poor received a smaller share of health resources (27%)than their share of the population (29%).26 In addition, as much as 20% of the resourcesbenefits rich households in the top two income quintiles, although they have access toprivate health care and most likely belong to either an insurance or medical scheme.

Figure 5All public health spending by household quintile and race

10 0~~~~~~~lAfricanl ex-4 Alek ~ 80 Homeland(N 20 Sedn

48 Whiteo ~ g 60

A~~~~~~~~~~~~~

I lo (5~~~~~~~'~~ 40 *Indian

0 d d 20 °Coloured

X z-, -,z t t t bAfricanNon-ex- Homeland


Because 25% of the poor seek no care when they are ill (see Table 3) and most(nearly 75%) stated that treatment is too expensive (see Table 4), it can be concluded thathealth care is not affordable for the poorest families. To correct for such under-coverage

25 The benefit incidence analysis allocates annual public spending per visit by level of health care and new province tohouseholds according to visits to health facilities reported in SALDRU (1993). Annual public spending per visit bylevel of health care and new province is calculated on the basis of total public spending information obtained from BEF(1995 draft) divided by the number of visits obtained from SALDRU (1993)(see Tables 15 and 16).26 Quintiles are created by ranking households, on the basis of per adult equivalent expenditures, from poorest torichest and then aggregating them into five groups with 20 percent of households in each quintile. The "Key Indicatorsof Poverty in South Africa" defined as "poor" the poorest 40 percent of households and as "ultra-poor" the poorest 20percent of households (RDP, 1995). The poor constitute 53 percent of the population living in the poorest 40 percent ofhouseholds and the ultra-poor constitute 29 percent of the population living in the poorest 20 percent of households. Alarger concentration of the population among poorer household quintiles occurs because poorer households tend tohave larger famnilies than richer households (see Appendix Figure A. 1).

20


and reduce leakage to non-poor families, more accurate targeting mechanisms need to bedevised.

Figure 6Hospital-level public health spending by household quintile and race

! 30 .4 -Africal ex-

80 Homeland

iz 20 Hospital | 60 .White

oSUpe nd2 0 L i ng~ 1 I 40_Indian10 A uato L 0*Ida

20 SCU3oured

0 ~~~~~~~0z ~~~~~~~~~~~~~frican/Non-

ex- Homeland

Source: SALDRU (1 993) and HEF (1 995)

A major problem is that spending on hospitals (most South African hospitals arepublic) benefits rich households more than all public health spending: More than 20% ofpublic resources at the hospital level is received by families in the richest and secondrichest income quintiles, households in the second and third poorest income quintilesreceive more than their share of the population, while the poorest families are under-covered (see Figure 6).

21


Figure 7Health center/clinic-level public health spending by household quintile and race

29.7 W~~~~~~~~~~~~~~~~Africanl ex-30 ~ 2.

8 zi0 Homeland* White

20 *HeatC. ~ 602.urce: SSALDRU (1993) and HEF )ndian

~ 4010 te suintil 0 Coloured

from under-coverage of public resources at this level, but le Africang ton-

hoseorei thno haslfrge pulcspening honpitals.Thi cntbeers/lnicsinried byth factithat

households in rich income quintiles do not tend to use these facilities, while householdsin the second and third income quintiles record high utilization rates.

Targeting new provinces with large populations from the former homelands canMmprove the poverty focus ofspublic health spenaing

Public health spending is lowest for new provinces with many residents from theformer homelands: Mpumalanga, Northern Province, KwaZulu Natal and Eastern Capeaccount for almost 66% of the population, but received less than half of all public healthresources (see Figure 8). By contrast, Gauteng and Westein Cape received considerablymore than their population share: They have about 25% of the population and the lowestpoverty rates in the country, but received more than 40% of all public health resources.

22

The Impact of Public Health Spending on Poverty and inequality in South Afiica

Figure 8Public health spending by new province, 1992-1993(% of total by new province)

30.0

2 30.- ,7

25.0 0.6

1 0.0 6..B *9.

IS.0 12.3 1 0.0

30.0

2 .00 16.6 1

| 50 5.3- -

15.0

I20.0- - 1.

0.0

30.0 ~~~~~~~~~~~~~~~~~~2 5.8

23.0

15.8~~~~~~~~8

20.0 13.1 1.1 -

1510 f.p11 pnMt m

p0

10.0 7. . .

S. 01.3

0.0 I h

S 'ource: SALDRU (1993) and F (1995)

23


The fee structure does not effectively target health resources

Despite a fee structure that is theoretically progressive, on average, patients frompoor as well as non-poor households pay about the same fees for hospital and healthcenters/clinics visits. The fees paid are as evenly distributed across the bottom fourquintiles as the fees paid to private doctors. This means that everybody in these quintileshas access to public health services at the lowest fee; based on the SALDRU survey,average family expenditures for the fourth quintile are Rand 16,014 (see Table 20). Thus,the fee structure and income thresholds do not differentiate between poor and non-poorpatients (see Table 19).

Table 18: Household expenditures on health care, 1992/1993:Fees per visit by quintile2 7 (Rand)

Qitle Hospitals Health Private DoctorCenters!clinics

Poorest 1 7 7 3 8P e 19 6 44III 48 6 38IV 30 8 47Richest 1,131 256 553Average 117 27 232


Patients from all quintiles except the richest pay relatively low fees per visit tohospitals and health centers (see Table 18). However, the lowest family incomethreshold (H1) of the current fee structure at public health facilities is still too high whencompared to actual family income. The HI applies to all patients from families with anincome level from zero to Rand 18,000 for inpatient services and from zero to Rand20,000 for outpatient services. In addition, the current means tests reinforce the inabilityof the public health system to distinguish between poor and non-poor users, and is anissue that needs to be studied further.28

27 In this table, quintiles are created by ranking individuals, instead of households, on the basis of per adult equivalentexpenditures, from poorest to richest and then aggregating them into five groups with 20 percent of individuals in eachquintile.28 The placement system for patients has two main drawbacks: (i) a patient is placed on a specific income category (HIto H3 or private), solely on the basis of an interview and no supporting documentation or proof of income level isrequired; and, (ii) although private patients or members of medical schemes are not supposed to use public healthfacilities, unless private health facilities are inaccessible in the area, this is generally not enforced (REF, 1996).

24

The Impact ofPubhlic Health Spending on Poverty and Inequality in South Afica

Table 19: Inpatient and outpatient public fees by family income level

Annual Family Income (in Rand), 1993/94Inpatient Services in HI H2 H3 PrivateHospitals (per admission) 0-18,000 18,001-26,000 26,001- 35, 001+ or

35,000 medicalscheme*

Community andSecondary 21 101 152 202Regional and Academic 26 129 194 258

Annual Family Income, 1994/95Outpatient Services Hi H3 Private

0-20,000 20,001-29,000 29,001- 39,001+ or39,000 medical

schemeClinicDoctor visit 4 4 4 31Nurse visit 2 2 2 31Mobile clinic 0 0 0 31Hospital OPDCommunity Hospital 8 16 24 31Tertiary or Academic 13 26 39 51HospitalLaboratory and Drugs**Community Hospital 0 0 0 31

ertiary or AcademicHospital 0 0 0 51

Charges include other charges, e.g. intensive care, diagnostic tests and drugs* * Charge per request and per prescription, respectivelySource: HEF (1996)

Reforming the current fee structure and means tests can improve the povertyfocus of public health spending. At present, the fee structure creates a higher burden forthe poor than the non-poor (see Fig. 9), since families in the poorest quintile spend morethan twice as much on health care as families in the fourth income quintile and almost asmuch as families in the richest income quintile. The share of health care expenditures fornon-food household expenditures is about the same for families in the poorest and therichest income quintiles; the difference is that, after satisfying food expenditures, poorhouseholds' income is dedicated to basic needs.

25


Table 20: Household expenditures on health care by quintile29 , 1993(average per family and per capita)

Quintile Household ExpendituresPerfamily Per capita

Poorest 5,002 745II 8,323 1,417III 11,258 2,290IV 16,014 4,113Richest 45,774 15,396Overall 21,480 4,794Source: SALDRU (1993)

Figure 9Household expenditures on health care by quintile3 0,1993(share of fees and drugs to non-food expenditures)

9.0 ~~~~8.79.0 76 7 .78. 0 7.

03.0

29 Ibid.

30 Ibid.

26

The Impact ofPublic Health Spending on Povery and Inequality in South Aica

VII. CROSS-COUNTRY COMPARISONS

For the reasons listed above, public health benefits in South Africa are not widelybenefiting the poor and leak to the non-poor: 66% of public health resources benefit thethree richest quintiles, while those in the two poorest receive a smaller share than theirproportion of the population (see Figure 10). 31

Figure 10Distribution of Public Health Spending by Quintile, 1993(% of total)

Richest 17% ---

In _ ~~~24°4III24

II 19%

Poorest 16%

0 5 10 i5 20 25

Public healh pendin (X of total)


The distributions of health resources by level of care across income quintiles,together with the Lorenz distribution of total household expenditures, are shown inFigure 11. The diagonal line (or 450 line) is also known as the line of absolute equalitysince it goes through those points where the cumulative share of the population equals thecumulative share of public education spending.

31 In this section, quintiles are created by ranking individuals on the basis of per adult equivalent expenditures, frompoorest to richest and then aggregating them into five groups with 20 percent of individuals in each quintile. This wayof creating population quintiles, by ranking individuals instead of households, is commonly used in most countrieswhere incidence analysis of public spending is available and therefore it allows for cross-country comparisons.

27


Figure 11Distribution of Public Health Spending and Lorenz Distribution, 1993

Q 100Health

l 8() l Center/Clnic

Hospti tal60r

40 All Heath

20 - / Tl 6 Housei old0 / expenditures

0 20 40 60 80 100

Cumulative share ofpopulaion


The distributions of public spending by level of health care and for all health carecross the diagonal line at about the third income quintile, this indicates that families inhigh income groups benefit relatively more than poor families.

Table 21: Incidence of Public Health Spending on the Poorest and RichestQuintiles, Selected Countries, Percentage Share

Health spending benefiting:Country Year Poorest 20% of the Richest 20% of theAFRICA_______ population (°/) population (/o)AFRICACote d'Ivoire 1995 11 32Ghana 1992 12 33Kenya 1992 14 24Madagascar 1993 12 30South Africa 1993 16 17

ATIN AMERICAChile 1982 22 11Colombia 1992 27 13Uruguay 1989 37 11

Note: If public health spending were equally distributed across population quintiles,then the poorest and richest income quintiles (every number in the table above) wouldreceive a 20% share of spending.Source: Castro-Leal, Dayton and Demery (1996)

28

The Impact ofPublic Health Spending on Poverty and Inequality in South Aica

Most public health resources are received by high income groups

While the share of health resources received by the poorest and richest quintiles isabout the same, most of the resources are enjoyed by middle-income groups (in thesecond through the fourth quintiles).32 By comparison, the distribution of healthresources in Chile, Colombia and Uruguay is strongly pro-poor (see Table 21). Thecross-country comparison is particularly significant with Chile because the Lorenzdistribution, or distribution of household expenditures, is as inequitable as in SouthAfrica. However, Chile has reformed its health sector to target low income groups.

When compared to other African countries, South Africa's poorest receive alarger share of public health resources. However, significant disparities exist in healthspending across the new provinces: The smallest share goes to those where a large part ofthe population is from the former homelands.

VIH. POLICY IMPLICATIONS AND MAIN FINDINGS

Prioritizing primary care services can reduce inequities and inefficiencies in the publichealth system

The public health sector is largely dominated by high levels of care: Acute-carehospitals obtain more than 75% of all public recurrent health spending, of which morethan half goes to academic and tertiary hospitals in large urban centers. This allocation ofresources by level of care conceals severe inequities and inefficiencies in the system.

Because hospitals in the new provinces absorb the bulk of public health spending,most of it benefits high income groups, while the poor are under-served: New provinceswith large urban centers and highly specialized hospitals receive a larger share of thebudget than those with a high concentration of rural inhabitants. For their part, poorprovinces lack public health resources and their residents have poor health status. Thisproblem is particularly severe in new provinces (such as the Eastern Cape, NorthernProvince and KwaZulu Natal) where a large proportion of residents are from the formerhomelands.

Large budget allocations to hospitals are also inefficient: Outpatient services forrelatively minor illnesses and preventive care are rendered by high level personnel at highfixed costs. Low level, less complex facilities tend to be even more inefficient and thequality of service tends to decline due to lack of resources.

Affordable primary care for poor women and children is urgently needed

Disparities in maternal and child health indicators are wider across income groupsthan overall health indicators. High fertility and poor child health status are strongly

32 If public spending were equally distributed across the population, then every quintile would receive an exactly equalshare of total public resources in the sector.

29


associated with poverty. For example, the Eastern Cape and Northern Province record thehighest child and infant mortality rates as well as the lowest nutritional status andimmunization rates.

Women and children are a disadvantaged group among the poor in term of healthoutcomes. They urgently need affordable primary health care, particularly in the newprovinces mentioned above.

Reforming the fee structure and means tests can improve the poverty focus of publichealth resources

More than 75% of the population has access to public health facilities almost freeof charge. Although fees are low, the ultra poor still cannot afford to pay. And, while thefee structure for public health services is theoretically progressive, the current incomethresholds do not distinguish between poor and non-poor patients. Thus, what emerges isa de facto uniform fee structure with a de jure progressive fee structure. In addition,current means tests reinforce the inability of the public health system to categorize usersby income group. As a result, high-income groups, mainly from urban areas, benefitrelatively more than low-income groups, mainly from rural areas. If these practices werereformed, it would improve the poverty focus of public health resources.

30


References

Castro-Leal, Florencia, Julia Dayton, Lionel Demery and Kalpana Mehra. 1998. PublicSocial Spending in Africa: Do the Poor Benefit? The World Bank, Washington,D.C. (mimeo).

Meerman, Jacob. 1979. Public Expenditure in Malaysia: Who Benefits and Why? OUPfor the World Bank, Oxford.

Ministry in the Office of the President: Reconstruction and Development Programme(RDP). 1995. Key Indicators of Poverty in South Africa. South Africa.

University of Cape Town's Southern Africa Labour and Development Research Unit(SALDRU). 1993. South Africa Living Standards and Development Survey. SouthAfrica.

Selowsky, Marcelo. 1979. Who Benefits from Government Expenditure? A Case Study ofColombia. A World Bank Research Publication, Washington, D.C.

McIntyre, Di, Gerald Bloom, Jane Doherty and Prem Brijlal. 1996. Health Expenditureand Finance in South Africa (HEI). Health Systems Trust and the World Bank.South Africa.

. January 1995. Health Expenditure and Finance in South Africa (HEF). SouthAfrica (mimeo).

UNICEF. 1990. An Assessment of Child Malnutrition.

Van de Walle, Dominique and Kimberly Nead. 1994. Public Spending and the Poor:7heory and Evidence. PRDPE. World Bank, Washington, D.C.

31


APPENDIX A

Table A. 1: Racial composition offormer homelands

Race %African 99.95Colored 0.01Indian 0.02White 0.02Total 100.00


Table A.2: Africans by area of residence

RaceH-Africans 65.68NH-Africans 34.32Total 100.00


32

The Impact of Public Health Spending on Poverty and Inequality in Souhi Africa

Table A.3: Children with hospitallclinic card by quintile, race and new province(% under 6years of age)

Race and Hh quintile TotalNew Province oorest II III IV RichestRaceArican/Non- 7 70 77 81 90 74HomelandAfrican/Homeland 57 61 70 71 ** 62Colored 85 82 89 88 82 86Indian * ** ** 98 ** 99|White * ** ** 92 90 89New provinceE. Cape 45 54 76 74 ** 51Mpumalanga 73 76 73 69 84 74Gauteng 84 82 79 89 92 86

waZulu Natal 57 56 69 84 ** 65.Cape * 74 ** ** ** 69

N. Province 69 64 75 78 ** 69North West 70 69 75 74 ** 71

.S. 69 59 72 ** ** 67W. Cape 95 88 82 89 92 88

Average 60 65 74 82 88 69


33

The Impact of Public Health Spending on Poverty and Inequality in South Afca

Figure A.lPopulation distribution by household quintiles

30.0 9-0 _ Household share

20.0 l 94

10.0 1.

0.0 [


Figure A.2Distribution of the population by race withinhousehold quintiles

rAfrican/ex-80 Homdia,d

*J'hgte60

40 M Indian

20 0 Coloured

M bAfticaWnion- -.~Homeland


34

The Impact of Public Health Spending on Poverty and Inequality in South Aica

Table A.4: Household expenditures on health care fees per visit, 1992/93by race (Rand per visit)

Race Hospitals Health Private DoctorCenters! IClinicsl

African/Non- 25 41 57HomelandAfrican/Homeland 91 6 45Colored 40 12 97Indian 96 10 73White 1,468 868 794Average 117 27 232


35

the impact of public health spending on poverty and ......(university of cape town) h-africans...

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