Health disparities between racial groups in South Africa: A decomposition analysis

ARTICLE IN PRESS

0277-9536/$ - se

doi:10.1016/j.so

�CorrespondE-mail addr

Social Science & Medicine 62 (2006) 2897–2914

www.elsevier.com/locate/socscimed

Health disparities between racial groups in South Africa:A decomposition analysis

Cecile Charasse-Pouelea, Martin Fournierb,�

aMaıtre de Conferences, Centre d’Etudes et de Recherches sur le Developpement International, 65 bd Franc-ois Mitterrand,

63 000 Clermont-Ferrand, FrancebCEFC, Room 304, Yu Yuet Lai Building, 43-55 Wyndham Street, Central, Hong Kong

Available online 23 January 2006

Abstract

This article explores the sources of self-rated health status inequalities among South Africans. We propose a

decomposition of observed health disparities between racial groups using a procedure based on an extended version of

Oaxaca–Blinder decomposition methods which addresses explicitly the specificities associated with the analysis of

subjective measures. This method allows for a decompositions of differences in self-rated health status between races and

isolates what may be due to observed socioeconomic inequality between racial groups, i.e. an ‘‘indirect racial effect’’

(Whites and Africans with different socio-economical characteristics have different health), from what could be due to

unexplained racial differences, i.e. a ‘‘direct racial effect’’ (Whites and Africans with similar socio-economical

characteristics have different health). Unsurprisingly, we find a strong indirect racial effect in favor of Whites. However,

our analysis tends to show that the issue of direct racial discrimination on health is more complex and closely linked with

that of economical inequality and discrimination. Our results thus stress the necessity not only to open access for Africans

to the more sophisticated sector of health care but also to provide them with the economical opportunity to use it.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: South Africa; Self-rated health; Oaxaca–Blinder decomposition; Segregation

Introduction

A large number of theoretical and empiricalpapers have recently been devoted to the measure-ment and the explanation of health and social healthinequalities (Kakwani, Wagstaff, & van Doorslaer,1997; Pradhan, Sahn, & Younger, 2003; Wagstaff,Paci, & van Doorslaer, 1991; Wagstaff & vanDoorslaer, 2004). This question is all the morecrucial in the case of South Africa, especiallyconcerning health disparities between racial groups.

e front matter r 2005 Elsevier Ltd. All rights reserved

cscimed.2005.11.020

ing author. Tel.: +852 2815 1773.

ess: [email protected] (M. Fournier).

Indeed, since the colonization of the country, aselective restriction of economical, political, socialand curative rights and an institutional discrimina-tion have been implemented. It has created aracially stratified society where discriminations,differences in poverty levels, in health care supply,and in education have been taking place betweenracial groups. Different racial groups have thusbeen facing very different health risks factors.

Since 1990, Apartheid has been officially takenover by a multiracial democracy confronted topoverty and inequality. In 1994, the Government ofNational Unity defined five key human develop-ment priorities: employment, housing, education,

.

www.elsevier.com/locate/socscimed

ARTICLE IN PRESS

1The term ‘Black’ refers collectively to all radically oppressed

groups and not just to ‘Africans’. However, since the social,

economic and political institutions in South Africa were

structured along legally defined racial categories which divided

‘Blacks’ into ‘Indians’, ‘Colored’ and ‘Africans’, the daily

experience and consequent health patterns could not be described

without recourse to such racial terminology. Their use in this

paper, however, does not imply their legitimacy. Note that

population group classification, which used to be based on a legal

definition, is now based on self-perceptions and self-classification.

An African is thus someone who classifies him/herself as such.2The health system had been fragmented into not contiguous

National, Colored, Indian and White ‘‘own affairs’’, four

provincial and 10 homeland health departments.

C. Charasse-Pouele, M. Fournier / Social Science & Medicine 62 (2006) 2897–29142898

nutrition and health. Five programs have thus beenimplemented to combine reconstruction with devel-opment and growth, and redistribution with demo-cratization. Health reforms aimed at an entirereorganization of the health care system, based onincreased community participation and focused onthe promotion of primary health care. Today, adecade after South Africa democratization, severalhuman development objectives still remain to beachieved, and discrimination may persist. Indeed,some evidence suggests that racial and economicaldiscrimination may still have significant discrimina-tory impacts on under-five mortality, anthropome-trical measures and demand for curative health care(Charasse, 1999). In South Africa, racial group stillseems to be a strong determinant of individualincome, educational attainment and individualhealth care demand, but also of community healthcare supply and of the quality of medical treatment(Bradshaw, Matiseng, & Nannan, 2001; Burgard,2004; Van Rensburg & Fourie, 1994).

The objectives of this paper are twofold. First, weintend to measure health disparities between racialgroups in 1999 using a self-assessed health indicator.This indicator is not only a complex humanjudgment on current health of the individual, butalso a representation of the history of his/her family,as well as an indication on life expectancy andpotential health problems (Idler & Benyamini,1997). Second, we propose to explore the sourcesof health related inequality. A decomposition ofobserved health disparities between racial groups isproposed using an extended version of Oaxaca–Blinder decomposition methods (Blinder, 1973;Oaxaca, 1973) allowing for decompositions ofnon-linear models and addressing specific issuesrelated to the analysis of subjective measures. Ouraim is to isolate what is due to structural socio-economical differences between racial groups (whatwe will name in the following ‘‘indirect racialeffect’’), from what is due to a permanent racialdifference in use, access, type/quality of treatment(called in the following ‘‘direct racial effect’’).

Our work is based on the 1999 October House-hold Survey covering more than 100,000 indivi-duals, which provides valuable information onhealth and structural explanatory variables at theindividual as well as at the household level. Thepaper is organized as follows. Section 1 presents thebackground of health and discrimination in SouthAfrica. Section 2 presents the data used anddiscusses the indicator chosen for health. Section 3

presents the decomposition method applied to thedata. Section 4 discusses estimation results andSection 5 presents our decomposition results anddiscusses the segregation issue.

Segregation, discrimination and health in South

Africa

Under Apartheid, cultural pluralism, which wasfirst officially aimed at creating a developmentprocess respective of the African culture, actuallycreated racism and cultural imperialism in SouthAfrica (Giliomee, 1993). Until the 1970s, whitesuperiority enabled prejudices and discrimination.Afterwards, the principle of ‘‘separation in equal-ity’’ led to systematic racial segregation and to apositive apartheid based on a separated develop-ment of potentially equal nations. But, racism,discriminations and human dignity shortfalls werereal. South Africans were separated and discrimi-nated on a racial basis. The policies of Apartheidand the whole social system in South Africa havecreated institutional and structural discriminationsand large disparities between racial groups1 in termsof socio-economic status, employment, education,housing and health services. Africans were the mostdiscriminated group.

On the health care side, the political andinstitutional development of the country created afragmented public health care system2 (predomi-nantly focused on hospital care) with a discrimi-nated access on a racial and geographical basis. Atthe same time, a private sector providing betterquality of care permitted discrimination by incometo join official discrimination by race. Resources,health care supply and quality of care have beendistributed along racial, economical and geographi-cal bases in the private as well as in the publicsector.

ARTICLE IN PRESSC. Charasse-Pouele, M. Fournier / Social Science & Medicine 62 (2006) 2897–2914 2899

Citizens could be under or over serviced by ahealth care system where basic needs were omittedand economical and racial discrimination coexisted.Thus, at the end of Apartheid, disparities betweenracial groups were huge. In 1993, the averagemonthly income of an African household was 679rands, seven times less than that of a whitehousehold (Statistics South Africa, 1999). In SouthAfrica, Whites enjoyed a better socio-economic andsanitary environment and better access to high-quality health care services. Unequal access tohealth care services and bad socio-economic condi-tions induced a large burden of diseases amongstAfricans, especially infectious and parasitic diseases.McIntyre, Bloom, Doherty, and Brijlal (1995)reported that social diseases conducted seven timesmore to death amongst Africans (14%) than amongWhites (2%) while cardio-vascular diseases ac-counted for 40% of Whites’ deaths against 12%for Africans. As Burgard (2004) noted, ‘‘race playeda central role in determining life chances and healthoutcomes’’.

On the 27th of April 1994, the new South Africawas born. At this date, the first multiracialgovernment was democratically elected. A newhealth ministry was put in place and the firstAfrican Minister of Health was designated, Nkosa-zana Dlamini Zuma. She was in charge of elaborat-ing a unitary and egalitarian health care system forall South Africans. Between 1994 and 1999,progress has been made in overcoming the Apart-heid legacy. Since April 1996, free basic curativehealth care has been available for all in dayhospitals and public clinics. Buch (2001) noticedthat achievements in the health sector from 1994 to2000 also included a number of important featuresconcerning both a better and broader supply ofhealth services among the poorest and most remotecommunities and a better management of healthservices at the national level.

In 1996, negative discrimination was officiallytaken out of the new South African constitution andhealth became a fundamental right. However, theprinciple of legal equality does not imply effectiveequity, justice or immediate disappearance ofdiscriminations, racial inequality of socioeconomicstatus and opportunities for social achievement. In2001, the General Household Survey was conductedto measure the level of development and perfor-mance of various government programs. Thissurvey reported disparities in health and in theresources that are associated with healthy outcomes

between racial groups. First of all, the majority ofWhites had access to a medical aid scheme (68%),followed by Indians/Asians (29%), and then colored(19%). The African population had the smallestproportion of people with access to a medical aidscheme (8%). Whites had the highest proportion ofpeople who consulted in the private sector (83%),followed by Indians/Asians, Colored and Africans(36%). In all 13% of the people who consulted inthe public sector were dissatisfied with the servicethey received. On the other hand, only 3% whoconsulted in the private sector were dissatisfied withthe service. Long waiting time was experienced by31% of Africans, and 6% of Whites: 13% ofAfricans experienced that drugs they needed werenot available, but only 2% of Whites were in thiscase and while 8% of Africans experienced rudestaff or were uncared or turned away, 2% of Whitesexperienced this. At the end, 38% of Africansdecided to consult no health worker during themonth prior the survey (38% of which did notconsult for monetary reasons and 8% because it wastoo far). For their white counterparts, the percen-tages were, respectively, 5% and 0%.

In this context, the objective of this paper is toevaluate to what extent racial group may stillrepresent a strong direct and/or indirect determi-nant of health status.

Data set and variables definition

The data set

Our data come from the 1999 survey of theOctober Household Survey (OHS). The surveysampling intends to provide representative data,covering more than 100,000 individuals and provid-ing reliable and extensive information on indivi-duals as well as households. In this work, we focuson Africans and Whites only, which represent a sub-sample of 92,471 individuals.

As shown by Table 1, strong differences arereadily visible between Africans and Whites con-cerning both wealth, education and living condi-tions. These differences, of course, imply thatAfricans and Whites face very different health riskconditions. Indeed, Whites are much better edu-cated, largely richer, more urbanized, enjoy betterhousing conditions and are much better covered interms of health insurance. Moreover, Table 1 alsoshows strong demographic differences between thetwo racial groups with a large younger African

ARTICLE IN PRESS

Table 1

Socio-economic conditions and access to health care by racial

group in 1999

Africans Whites

Average number of years of education 5.4 10.2

Average age 25.4 35.1

Percentage of individuals living in rural

areas

54.1 13.9

Share of households with monthly income

below 399 rands

20.6 1.03

Average age of household head 49.8 46.1

Percentage of households with male head 55 87.5

Percentage of household with health

insurance coverage

8.7 66.8

Percentage of households living in bricks,

cement or concrete houses

61 98


cohort and numerous mono parental familiescaused by migrations of men, since most jobopportunities were concentrated in Whites’ neigh-borhood where African families were not allowed tostay. Moreover, the large number of household ranby women amongst Africans also reflects thedamage of AIDS within the male part of thecommunity.

The indicator of health

Theoretically and empirically, different measuresof health status may be considered to study healthdisparities between sub-populations. Measuringhealth status is complex due to the multidimen-sional nature of health and the lack of objectiveordering except in the case of life and death.Empirical papers often rely on self-evaluations ofhealth status and morbidity, which are veryinformative and are now routinely collected inhousehold surveys. In this paper, we use anindicator of self-assessed health (SAH). SAH is asimple subjective measure of health that provides anordinal ranking of perceived health status. In the1999 OHS, every person in the household was askedto describe his/her health as very poor, poor,average, good or excellent.

On the basis of several epidemiological, econom-ical or sociological surveys conducted in developedcountries, Idler and Benyamini (1997) showed thatthis type of indicator is not only a complex humanjudgment on the current health of the individual,but also a representation of the history of his/herfamily, as well as a prediction of life expectancy andpotential health problems. They conclude, ‘‘self-

ratings represent a source of very valuable data onhealth status’’ and ‘‘provide the respondents’ viewsof global health status in a way that nothing elsecan’’. Furthermore, the predictive power of SAHdoes not appear to vary across socio economicgroups (Burstrom & Fredlund, 2001). Fylkesnes andHelge Forde (1991) and Van Doorslaer et al. (2000)also pointed out that SAH is among the bestpredictors of patient-initiated visits.

However, the measure and use of SAH also sufferfrom a number of drawbacks (Butler, Burkauser,Mitchell, & Pincus, 1987). Indeed, SAH is oftenassociated to a reporting behavior bias, whichentails that the mapping of ‘‘true’’ health intoSAH categories may vary with respondent char-acteristics. This source of measurement error hasbeen termed ‘‘state-dependent reporting bias’’ (Ker-khofs & Lindeboom, 1995), ‘‘scale of referencebias’’ (Groot, 2000) or ‘‘response category cut-pointshift’’ (Murray, Tandon, Salomon, & Mathers,2001). This occurs if sub-groups of the populationsystematically use different cut points for healthevaluation depending on cultural, economical,social or gender expectations for health. Thesebiases can also be linked to health servicesaccessibility and availability, to the use of healthsystem, to individual income and to employmentstatus (Strauss & Thomas, 1998). Indeed, as shownby Iburg, Salomon, Tandon, and Murray (2001) onUS data, the cut points (on the question of self-reported difficulty with regard to walking for aquarter of a mile) for a black, poor, less educated,older female were below those reported for a white,rich, well-educated, younger male. As Lindeboomand van Doorslaer (2004) noted, these biases mayhave important implications for the measurement orthe explanation of disparities in health. This issuemay be of particular importance in South Africa,where social and economic discriminations betweenracial groups have historically induced a number ofpeople to hide their diseases (Charasse, 1999).

In what follows, we will thus try to take explicitlyinto account potential differences in cut-pointsbetween Whites and Africans when reporting SAH.

The methodology

The methodology developed in this work has twomain objectives. First, it addresses the issue ofthe extension of Oaxaca–Blinder decompositionmethods to qualitative response models throughmicro-simulation-based decomposition procedures.

ARTICLE IN PRESS

3A variety of factors at the household and community levels

have a direct influence on individual health outcomes. They cover

household actions and risk factors, household assets, community

factors, health service provision, health finance, supply in related

sectors and public policies. All these information are unfortu-

nately not available in the 1999 OHS.

C. Charasse-Pouele, M. Fournier / Social Science & Medicine 62 (2006) 2897–2914 2901

Second, it proposes a specific procedure to try toaddress the drawbacks associated with SAH mea-sures discussed above.

Decomposing observed differences in health

Several interesting papers have analyzed pure orsocio-economic inequalities in the health sector(Kakwani et al., 1997; Wagstaff et al., 1991;Wagstaff, van Doorslaer, & Watanabe, 2003;Wagstaff & van Doorslaer, 2004). The mosttraditional approach has been to think of differ-ences in health status according to individualincome or economic standing (Castro-Leal et al.,2000; Wagstaff, 2000). Gender inequalities in healthstatus have also received a great deal of attention(Arber, 1997; MacIntyre, Hunt, & Sweeting, 1996;Montoya Diaz, 2002) and ethnic inequalities inhealth have been of concern in recent years(Brockerhoff & Hewitt, 2000; Burgard, 2004;Charasse, 1999). However, decomposition analysesof racial disparities remain limited and are oftensubject to caution.

In this paper, we develop a procedure based on thegeneral Oaxaca–Blinder decompositions framework(Blinder, 1973; Oaxaca, 1973) to assess non-linearmodels necessary to address the issue of SAHmeasures. Our aim is to disentangle the two path-ways through which belonging to a certain racialgroup may impact health: the impact of structuralsocio-economical differences between racial groups(indirect racial effect), and the direct impact of race,leading individuals endowed with similar socio-economic characteristics to be in different healthconditions across racial groups (direct racial effect).Direct racial effect is to be understood here as acomposite measure incorporating unequal use of,and segregation on access to health care and type/quality of treatment due to differences in preferencesin demand for health induced by long-term institu-tional discrimination and/or persisting discrimina-tion phenomena against non-Whites. In order toevaluate the respective magnitude of these twofactors, we propose counterfactual situations corre-sponding to health levels that would be observed forAfricans, had they been faced with the ‘‘healthgenerating process’’ observed for Whites and reci-procally. By ‘‘health-generating process’’ we under-stand the mechanisms through which individualhealth is affected by socio-economic characteristics.A difference for Africans between observed healthand the counterfactual health under the ‘‘Whites’

model’’ thus provides an evaluation of the directracial effect taking place. Indeed, if there was nodirect racial effect, health under the Africans’ model(observed health) should be equal to health under theWhites’ model (counterfactual) for any given socio-economic characteristics.

Formally, lets call hij the health level of individuali belonging to racial group j, hij may be assumed todepend on three sets of arguments: individual andhousehold observable socio-demographic character-istics (x)3, unobservable characteristics and healthhazard (summarized by e) and a set of parameterscorresponding to the health-generating processlinking socio-demographic characteristics and ob-served health (b):

hij ¼ Hðxij ; eij; bjÞ. (1)

Within this framework, observed differences inaverage health between two racial groups may comefrom two potential sources:

(i)
A difference in average socio-demographiccharacteristics between the two racial groups(differences in xij),
(ii)
A difference in health generating processesbetween the two racial groups (differences in bj).
The first source can be understood as an indirectracial effect since it is the indirect impact ofbelonging to a racial group on socio-demographiccharacteristics such as education or living condi-tions that leads to differences in average health. Thelatter corresponds to a direct racial effect sincedifferent models provide different health levels toindividuals endowed with the same socio-demo-graphic characteristics.

It is thus possible to decompose observed healthdifferences into these two components as follows (2racial groups: a for Africans and w for Whites):

Indirect racial effect I iwa ¼ Hðxiw; eiw; bwÞ

�Hðxia; eia; bwÞ, ð2Þ

Direct racial effect Diwa ¼ Hðxiw; eiw; bwÞ

�Hðxiw; eiw; baÞ. ð3Þ

ARTICLE IN PRESSC. Charasse-Pouele, M. Fournier / Social Science & Medicine 62 (2006) 2897–29142902

Indirect racial effect, I, is obtained by comparingthe observed health for racial group w and thehypothetical health obtained by simulating on racialgroup a the health generating process of racialgroup w. Likewise, direct racial effect is obtained bycomparing observed health for racial group w andthe hypothetical health obtained by simulating onracial group w the health-generating model of racialgroup a.

Decomposing observed health differences intodirect and indirect racial effects has strong policyimplications since the evidence of direct racial effectwould reflect that Africans and Whites endowedwith the same socio-demographic characteristics donot enjoy the same level of health risks. Three maindimensions may be at stake here: inequality inaccess to health care, disparity in the quality ofhealth care and racial differences in demand forhealth care inherited from years of Apartheid and/or from persisting discrimination against non-White. In any case, if this type of direct racial effectis observed, institutional measures can be imple-mented to reduce the gap in health level betweenraces in the medium turn. Indirect racial effect isequally important since it highlights the impact oflong-term inequality in life chances, which isaffecting key determinants of health such aseducation or income. Addressing the issue of thereduction of indirect racial effect however calls fordifferent types of measures, either through long-term programs aiming at equalizing socio-economicdeterminants of health or through relevant andtargeted help or subsidy programs.

General technical issues

Our approach differs from the way Oaxaca–Blin-der decomposition procedures are usually adaptedto non-linear models (Fairlie, 2003; Myeong-Su,2000; Wagstaff & Nguyen, 2001). Some relatedtechnical issues thus need to be discussed here.

Non-linearity

Since SAH measures are qualitative variables,estimating the model presented in Eq. (1) implies theuse of non-linear estimation models. If the usualOaxaca–Blinder decomposition relies on modellinearity, the use of micro-simulations howeverallows for any type of model since counterfactualsare simulated at the individual level before aggrega-tion. We believe this approach is more general andmore flexible than usual alternatives consisting in

decomposing the latent index (Wagstaff & Nguyen,2001) or in Taylor expansion approximations(Myeong-Su, 2000).

Residuals

Health shocks included in the e terms play acentral role here. Residual terms can be seen asincorporating exogenous health shocks (uncorre-lated with observed characteristics) experienced bythe individual. It is thus necessary to computedecompositions through micro-simulations at theindividual level, keeping residuals constant for eachobserved individual, and not through group char-acteristics averages. Average effects can then beobtained through the aggregation of decomposi-tions run at the individual level.

Path dependence

This approach falls in the line of the well-knownBlinder–Oaxaca decomposition methodology. Acommon issue related to this type of methods ispath-dependence. Indeed, the two effects are likelyto depend on the reference (population or para-meters) used for the simulation. In other words, it isgenerally the case that, in Eqs. (2) and (3):

IwaaIaw and DwaaDaw.

In the application that follows, the ambiguity istaken into account by considering simultaneouslyalternative definitions of the various effects andanalyzing observed differences between alternativespecifications.

SAH and potential biases

As discussed above, SAH variables may incorpo-rate significant biases in terms of response categorycut-point differences between races. The followingdetails implications of these biases on decomposi-tion results and proposes a general procedure toaddress these issues.

The general model

For simplicity, let us start with a simple linearunderlying model, where reported health takes onlytwo values: good and bad. Extensions will then bestraightforward. Unobserved health ( H�iw) for in-dividual i belonging to race group w can be written as

H�iw ¼ xiw bw þ gw þ eiw, (4)

where bw and gw are race specific parameters, xiw

refers to individual observable characteristics


and eiw to unobservable characteristics. Using thisformulation, individual i belonging to race w willdeclare being in good health if and only if his/her real(unobserved) health is above some race-specificthreshold level Tw. Formally, observed self-assessedhealth status (ho) can be modeled as follows:

ho¼ 1ðgoodhealthÞ if andonly if

H�iw ¼ xiwbw þ gw þ eiw4Tw. ð5Þ

Model (5) is however unidentified. Indeed the varianceof eiw cannot be identified nor can gw and Tw

separately. The only estimable models (through aProbit model for example) are of the following type:

xiw

bw

sw

þðgw � TwÞ

sw

þeiw

sw

40, (6)

where sw is the standard error of eiw.

Simulations

Simulating Whites’ health if confronted toAfricans’ model should formally be done throughthe computation of the following model:

xiwba þ ga þ eiw4Tw

) Good ðcounterfactualÞhealth; ð7Þ

xiwba þ ga þ eiwpTw

) Bad ðcounterfactualÞhealth:

This corresponds to applying the ‘‘Africans model’’to Whites observed and unobserved characteristicstaking as a base Whites’ conception of being in‘‘good’’ or ‘‘bad’’ health. Model (7) is in turn strictlyidentical to:

xiwba

sw

þga � Tw

sw

þeiw

sw

40

) Good ðcounterfactualÞhealth; ð7aÞ

xiwba

sw

þga � Tw

sw

þeiw

sw

o0

) Bad ðcounterfactualÞhealth

or, symmetrically,

xiwba

sa

þga � Tw

sa

þsw

sa

� �eiw

sw

40

) Good ðcounterfactualÞhealth; ð7bÞ

xiwba

sa

þga � Tw

sa

þsw

sa

� �eiw

sw

40

) Bad ðcounterfactualÞhealth:

In order to compute the decomposition proposed in(2) and (3), three issues are thus at stake: (i) thecomputation of ðeiw=swÞ, (ii) the approximation ofðsw=saÞ, and (iii) the identification of gi and Ti.

Drawing normalized residuals

Following the estimation of (6) ðeiw=swÞ can easilybe drawn for all observed individuals from therelevant conditional distributions. In the case ofa Probit model, residuals can be drawn fromNð0; 1 u4�xiwaw � bwÞ or Nð0; 1 uo�xiw:aw � bwÞ,where aw and bw refer to estimated coefficientsfor, respectively, ðbw=swÞ and ððgw � TwÞ=swÞ.

Approximation of ðsw=saÞ

Even though residual variances cannot be directlyidentified in qualitative response models, somevaluable information can be obtained from thefive-level answer to the question on self-assessedhealth. Indeed, it can be assumed that a first-orderapproximation of the ratio of unobserved health(H*) variances between races can be provided by theratio of self-assessed health declaration (ho) var-iances. Formally, this assumption writes:

VarðH�wÞ

VarðH�aÞ�

VarðhowÞ

VarðhoaÞ. (A1)

Under assumption A1, it is then straightforwardfrom (4) that:

sw

sa

� Dswa ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiVarðho

wÞ

VarðhoaÞ

1þ Varðxia:aa þ baÞ

1þ Varðxiw:aw þ bwÞ:

s(8)

Although imperfect, this step is absolutely necessaryto the analysis since, as shown in (7b), bothpredicted residuals and estimated thresholds are tobe corrected by the ratio of standard errors in orderto derive consistent simulations. By nature, assump-tion A1 is not formally testable. However, it seemsquite intuitive and tests can be run to check ifderived results are sensitive to variations in thevalue of ðsw=saÞ around Dsw

a . It should be stressedthat assumption A1 does not imply that variances ofunobserved health and SAH are equal but only thatthe variance ratios between Whites and African aresimilar.

In the application that follows, we have run thedecomposition procedures with various values forðsw=saÞ, assuming that Dsw

a may under or over-estimate the true value of ðsw=saÞ by up to 20%.


Identification of gi and Ti

More problematic is the issue of the identificationof gi and Ti in model (7b). Indeed, there is no way toidentify separately model constants and thresholdsin this type of model. The only available approxi-mations of ðga � Tw=saÞ are thus estimates of ðga �

Ta=saÞ and ðgw � Tw=saÞ.It could be assumed that thresholds for Africans

are lower than for whites and thus thatðga � Tw=saÞoðga � Ta=saÞ, however no similarex-ante assumption can be made concerning therelative values of ga and gw, and thus of ðga �

Tw=saÞ and ðgw � Tw=saÞ. Using a min–max intervalmay thus not be a sufficient robustness test.

In the following, we propose to test the robust-ness of the results by running decompositions basedon values for ðga � Tw=saÞ over an interval coveringðga � Ta=saÞ and ðgw � Tw=saÞ and extended onboth ends by half its total size (half ofðga � Ta=saÞ � ðgw � Tw=saÞ�� ).

4The instrumentation of household income is presented in

Appendix 1. Instruments include household assets, reflecting

household wealth (television, vehicle, telephone, building materi-

al for house) as well as specific income sources (State child

Econometric estimations

The econometric estimation of health functions isdone through an ordered Probit model on self-evaluated health status including five health levels(very poor health, poor health, average health, goodhealth, very good health). Observed distributions ofWhites and African are reported Table 2.

On both Whites’ and Africans’ sub-samples, ourestimation results show usual and intuitive impactsfor the main socio-demographic variables. To be amale is a positive determinant of health status(Feachem, Kjellstrom, Murray, Over, & Philips,1992; Lavy, Strauss, Thomas, & De Vreyer, 1995);being educated is in favor of declaring a high levelof health (Grossman, 1972; Strauss & Thomas,1995); age is a negative and concave determinant ofhealth level (Grossman, 1972).

Table 2

Self-assessment of health status by racial group in 1999

Level of health Africans Whites

Very poor 0.8% 0.3%

Poor 4.6% 2.1%

Average 8.0% 6.5%

Good 35.8% 34.6%

Excellent 50.8% 57.1%

Number of observations 82,601 7876

Source: OHS 1999

Economical variables seem to be discriminatingelements for health care access in South Africa.Indeed, the instrumented level of monthly incomewas used as an indicator of resources4. It ispositively correlated to health levels and has theusual concave form for Africans. This aspect is alsoconfirmed by the estimated effect of family size,large families penalizing South Africans in terms ofhealth. Indeed, the size of the household partlydetermines the family financial capacity and in-creases sanitary risks5.

Although the data used does not provide thenecessary information to formally address neigh-borhood effects, we have tried to capture somedimensions of local life quality by introducingvariables on the local availability of clinics andschools6. We find that having a clinic/hospital in thevicinity is positively related to a higher health statusfor Africans. In South Africa, public clinics are themain supplier of basic health care. Our estimationconfirms the fundamental role of primary healthcare supply7. The negative sign observed for Whiteson this dimension also reflects neighborhood effects.Indeed, as clinics are usually located in poorquarters, living close to one for a White signals alower than average neighborhood quality within theracial group.

Signs of a direct racial effect can be seen from thecomparison of estimations conducted separately forWhites and Africans. However, as stressed above(Section 3), estimation results cannot be directlycompared since ordered Probit models can onlyidentify coefficients up to a multiplying factor.Table 3 relies on assumption A1 to provide compar-able estimates, Whites’ estimates being corrected by aproxi for the residual variances ratio between Whites

support) and savings types (formal savings, financial assets).

Details are provided in Appendix 1.5Family size may also foster learning effects for self-medication

(Ellis & Mwabu, 1991; Heller, 1982). Here, however, negative

aspects seem to dominate.6Provincial dummies are also included to take into account

large differences in health care facilities across provinces (Viljoen

et al., 2000).7Nevertheless, after the implementation of the free health care

policy in public clinics, evaluations showed that a lot of sick

people were asked to go back home without being cared for.

Since 1995, the development of medical facilities by construction,

rehabilitation or transformation of hospitals in clinics has tried to

correct the free health care policy failures.

ARTICLE IN PRESS

Table 3

Ordered Probit estimation of a reduced health demand function

Whites Africans

Male 0.0161 0.0629*

0.0320 0.0082

Education 0.0153* 0.0278*

0.0060 0.0012

Age �0.0020 �0.0121*

0.0041 0.0009

Age2 �2.17E�04* �1.06E�04*

4.82E�05 1.17E�05

Household size �0.0522* �0.0061*

0.0136 0.0014

Male head �0.0529 0.0432*

0.0511 0.0091

Age of the head �0.0068* �0.0033*

0.0017 0.0003

Hunger in the household during

the preceding year

�0.1941* �0.1521*

0.0917 0.0100

Health insurance coverage in the

household

�0.0484 0.0283

0.0422 0.0219

School within 30min walk 0.0494 0.0629*

0.0472 0.0117

Clinic/hospital within 30min walk �0.0675*** 0.0157***

0.0389 0.0091

Instrumented total monthly

income

0.2025*** 0.1290*

0.1172 0.0186

(Instrumented total monthly

income)20.0028 �0.0161*

0.0146 0.0037

Urban location �0.0782 �0.0030

0.0547 0.0103

Cut 1 �4.0823 �3.0623

0.2955 0.0386

Cut 2 �2.9657 �2.1546

0.2855 0.0361

Cut 3 �2.0787 �1.5660

0.2845 0.0356

Cut 4 �0.5108 �0.3593

0.2840 0.0353

Number of observations 8144 83,327

LR chi2 (22) 1189 11,733

Pseudo-R2 0.08 0.06

Residual variance ratio (Whites/

Africans)

0.84

Notes: Standards errors are given in italics. *, ** and *** refer to

significance at respectively 1%, 5% and 10% (before correction).

Reported estimates for Whites have been corrected by a proxi for

the ratio of residual variances in order to make both sets of

estimated coefficients comparable (see Section 3 for details).

Provincial dummies have been omitted for display purposes.


and Africans. We will however restrict the followinganalysis to major observed differences, keeping inmind that comparisons remain delicate.

First, concerning individual characteristics, gen-der is a significant and important factor for Africansbut not for Whites, education seems to play agreater role for Africans, and aging causes morehealth degradation for Africans. These resultssuggest that Whites have better protection againststructural health factors.

Second, important differences seem to arise fromeconomic factors. Indeed, income appears to play amuch stronger role for Whites than for Africans,and the gap increases with age. The higher impact ofincome for Whites may appear quite counter-intuitive since the income effect is widely found toshow a concave pattern. Since Whites have muchhigher incomes than Africans, the opposite resultmay be expected. If Table 3 shows that the concaveform can be found for the Africans’ sub-sample, itseems that the segmentation of the South Africanhealth care system introduces some non-linearity athigher incomes. Our results are however consistentwith field observation suggesting that higher healthcare quality is provided to Whites at a highmonetary cost through structures that very fewAfricans may be liable to use. Indeed, Africans havea very limited access to the most sophisticated typesof treatment dispensed by private practitioners andat private hospitals due to high prices demanded bythis sector, mostly devoted to insured and richpatients. Public clinics are thus their main suppliersof essential health care. Similarly, among Whites,being cared for in a public hospital or a clinic isactually a sign of poverty and very bad health statusor severity of illness.

To conclude, our estimation results seem to showstrong differences in health determinants betweenAfricans and Whites, concerning both structuralhealth factors and economic variables. They suggestthat Africans and Whites, even if endowed withidentical characteristics, may nonetheless be facingdifferent health risks, which reveals some form ofdirect racial effect. Indeed, our results suggest thatcare provided to Whites allows for a better manage-ment of structural health risks factors such as loweducation, aging or gender. Similarly, higher coeffi-cients estimated for Whites for income suggests thatthe South African health care system is profoundlysegmented between low price–low quality publicfacilities and a high price–higher quality privatesector. However, estimations alone do not allow forany direct evaluation of discrimination phenomena.Next section further addresses this issue throughdecomposition analyses.

ARTICLE IN PRESS

Table 4

Simulation resultsa

(1) (2) (3) (4) (5)

Whites’ Whites’ Africans’ Indirect Direct

Population population population discriminationd discriminationd

Whites’ model Africans’ model Africans’ model [(3)�(2)] [(2)�(1)]

(Observed)b (Simulated)c (Observed)b

Very poor 0.2% 0.3% 1.0% 0.6 0.1

Poor 2.3% 3.1% 5.6% 2.5 0.8

Average 8.1% 10.0% 14.3% 4.3 1.9

Good 43.5% 45.8% 49.1% 3.2 2.3

Africans’

population

Africans’ population Whites’ population Indirect discrimination

[(2)�(3)]

Direct discrimination

[(1)�(2)]

Africans’ model Whites’ model Whites’ model

(Observed) (Simulated) (Observed)

Very poor 1.0% 1.5% 0.2% 1.3 �0.5

Poor 5.6% 7.6% 2.3% 5.3 �2.1

Average 14.3% 20.1% 8.1% 12.0 �5.9

Good 49.1% 59.5% 43.5% 16.0 �10.4

aResults in Table 4 are based on ‘‘average’’ cut-points (ððga � TaÞ þ ðgw � TwÞ=2Þ, using notations from Section 3). This change in cut-

points in turn affects observed values, which are thus slightly different from those presented Table 2.bBold figures refer to the observed situation.cItalic figures are counter-factual simulation results.dDiscrimination is to be understood here against Africans. A negative figure thus reflects discrimination against Whites.


Decomposition results

Table 2 indicates how unequally health isdistributed in South Africa. Indeed, there is amarked difference between Whites and Africans interms of SAH. Africans report significantly lowerhealth levels than Whites, but not as much as onecould have expected with regard to inequality of lifechances in disfavor of Africans pointed out by Table1. This in turn suggests that sizeable reporting biasesmay be affecting SAH measures8.

Table 4 reports decomposition results computedfrom ordered Probit estimates discussed in thepreceding section and based on the procedurepresented Section 3. These decomposition resultsallow for a distinction between direct and indirectracial effects in observed health differences betweenWhites and Africans. Moreover, Appendix 1 pre-sents the necessary robustness tests discussed in themethodology section, which seem to validate theresults reported Table 4.

Simulation results present the two possible waysof evaluating direct and indirect racial effects.Observed and simulated cumulated percentages ofindividuals of each race group have been computedfor health status equal or below four threshold

8We are indebted to an anonymous referee for this observation.

levels: ‘very poor’, ‘poor’, ‘average’ and ‘good’. Theremaining percentage represents individuals enjoy-ing ‘excellent’ health.

Differences between the first two columns corre-spond to direct racial effect since the populationstructure is held constant and the only effect atwork concerns differences in health models betweenrace groups. Symmetrically, differences between thelast two columns correspond to indirect racial effectsince the health model is kept constant and the onlysource of variation comes from differences in socio-demographic characteristics between race groups.

As expected, indirect racial effect in favor ofWhites is clearly observable here for both basepopulations (comparisons between columns 2 and3). Indeed, the upper part of Table 4 shows that, ifWhites’ health was determined by the same modelas Africans’, differences in average socio-demo-graphic characteristics alone would explain between57% and 76% of observed differences in healthbetween the two race groups, depending on healthlevels considered. Similarly, the lower part of Table4 shows that, if Africans were facing the Whites’health model, differences in socio-demographiccharacteristics would lead to 5.3% points moreAfricans in ‘poor’ or ‘very poor’ health than Whitesand 12% points more Africans at or below ‘average’health than Whites.


Results concerning direct racial effect are morepuzzling (comparisons between columns 1 and 2).Indeed, the upper part of Table 4 clearly pleads infavor of direct racial effect against Africans sinceWhites faced with the African model would tend tohave a lower health status for any given healththresholds. The lower part of Table 4 howeverleaves much ambiguity since simulations based onthe African population structure tend to show adiscrimination phenomenon against Whites. Indeed,according to these results, Africans would be inpoorer health if faced with the Whites’ model for allhealth thresholds considered. This result is of coursequite counter-intuitive and requires further discus-sion.

First, as our results strongly depend on theassumptions made in order to identify the variousdimensions of the decomposition procedure, wetried to check thoroughly the robustness of theresults to deviations from the two main assump-tions: the assumption concerning threshold levelsand the assumption concerning the ratio of theore-tical models’ residual variances. Graphs reported inAppendix 1 show that our main results appear to bequite robust. Indeed, Graphs 1–3 shows that ourtwo main findings remain valid over a large intervalof cut-points used for simulations, covering thewhole interval defined by the estimated cut-pointsfor the two race groups. Similarly, graphs 2 and 3report robustness tests for departures from the proxivalue proposed in Section 3 for theoretical modelsresidual variances ratio. The proxi value used in thegeneral calculation presented Table 4 is 0.83,alternative decompositions assuming a 20% under-evaluation of the ratio (1.01) and a symmetric 20%over-evaluation (0.67) both confirm a strong indir-ect racial effect as well as the more surprising resultthat Africans may be in worse health if faced withthe Whites’ model.

It should be stressed that, as mentioned earlier,decompositions of the Oaxaca–Blinder type do notnecessarily provide symmetrical results dependingon the population upon which computations aredrawn. It may well be the case that Whites’ healthmodel ‘‘better fits’’ Whites’ population structurewhile Africans’ health model ‘‘better fits’’ Africans’population structure, leading to poorer health forboth groups, were they to be faced with the othergroup model.

Earlier discussions on estimation results may helpto provide a better understanding of our findings.Indeed, major differences between Whites’ and

Africans’ health models can be summarized asfollows. First, ‘‘structural’’ health risks linked withsocio-demographic characteristics such as pooreducation, old age or gender have a greater impacton Africans than on Whites. Second, income has agreater impact on Whites’ health. As discussed inmore details above, these findings correspond toprofound differences in demand for, and access tohealth care between the two race groups. Healthrisks linked with socio-demographic characteristicsare attenuated for Whites who have a better accessto a high quality but costly private health caresystem while Africans have little choice but to turnto low cost but lower quality health care. In thisrespect, if Africans were to adopt Whites’ healthdemand model, low income and poor insurancecoverage would leave most of them in poorer healthsince they could only afford a very low level ofhealth care. Symmetrically, if Whites were to adoptAfricans’ health demand model, high income andbetter endowments would not be sufficient tomaintain the higher health levels that they obtainthrough the more expensive and better qualityprivate health care system.

Our results thus point out at a strong segmenta-tion of the South African health sector in which therichest part of the population (most of which areWhites) have access to high-quality health carethrough expensive private health institutions, whilethe poorest (most of which are Africans) have nochoice but to turn to the lower quality and lowerprice public health sector.

Conclusions

In this paper, we tried to address the question ofhealth inequality in South Africa using a self-assessed health (SAH) indicator. Unsurprisingly,our results show strong evidence of Whites being inbetter health than Africans even though theindicator has good chances to under-evaluate thisgap due to differences in SAH cut-points across racegroups.

In a second step, we estimated health equationsusing an ordered Probit specification. Estimationresults, which are consistent with theories andformer empirical results on health determinants,provide a first set of explanations for healthdifferences between racial groups. Indeed, majorhealth determinant variables such as education,income, living conditions or household size seem toplay against poorer endowed Africans compared to


richer and better-educated Whites. This line ofexplanation is what we call the ‘‘indirect racialeffect’’ since differences in health result fromdifferences in socio-demographic characteristicsinherited from the former system.

Our results also suggest that there are strongdifferences in health models between Africans andWhites. We find, in particular, that gender is animportant factor for Africans but not for Whites,that education plays a greater role for Africans andthat facing ageing causes more health degradationfor Africans. These differences already seem toreveal that some discrimination is taking place.Indeed, usual health risks associated with loweducation, gender or aging appear to be muchbetter dealt with for Whites than for Africans.Moreover, economic variables seem to play astronger role for Whites, suggesting that, evencontrolling for other dimensions, Whites’ healthmodel relies more strongly on economic factorsthan Africans’. This result is consistent with fieldobservation that Africans have a very limited accessto the most sophisticated type of treatment dis-pensed at private practitioners and private hospitalsand usually turn to public clinics, which provideaffordable but lower quality health care.

Finally, this article proposes an evaluation of therespective contribution of direct and indirect racialeffects on observed health differences betweenAfricans and Whites through an extension ofOaxaca–Blinder decomposition procedures. Theprocedure proposed explicitly takes into accountpotential biases induced by the use of a subjectivemeasure for health and proposes a set of robustnesstests in order to validate the results.

Our results provide strong support for an over-whelming indirect racial effect against Africans.Indeed, simulations show that differences in socio-demographic characteristics alone lead to between81% and 230% more individuals in ‘poor’ or ‘verypoor’ health amongst Africans than amongstWhites depending on the reference model used.

Our results are however more puzzling concern-ing the evaluation of ‘‘direct racial effect’’. Indeed,we find that Whites would be in poorer health ifthey were faced with the Africans’ health model.This result pleads in favor of inequality in access tohealth care, disparity in the quality of health careand racial differences in demand for health careinherited from years of Apartheid and/or frompersisting discrimination against Africans. How-ever, we also find that Africans would be in poorer

health if they were faced with Whites’ health model,which would point out at a discrimination phenom-enon against Whites. Moreover, this result appearsquite robust to changes in the main underlyingassumptions of the decomposition procedure. Weclaim that this apparently counter-intuitive resultactually reflects the specificities of the South Africanhealth system. Indeed, under the Whites’ model,poor income and poor insurance coverage wouldconduct most Africans to be in poorer healthbecause they could only afford a very limited levelof the expensive higher quality private health caresystem that most Whites are turning to.

Our results have strong policy implications.Indeed, they suggest that, even though Apartheidhas been officially abandoned since 1990, a persist-ing racial discrimination on health may still betaking place in 1999 and influence health behaviorbetween racial groups. Indeed, differences in healthbetween Africans and Whites may not solely be dueto differences in health determinants such aseducation or income, which can be imputed to theformer regime, but also seem to be caused bypersisting direct discrimination on access to healthfacilities and on the type and quality of health careprovided. Our findings also suggest that reducingrigidities in the access of Africans to facilities usedby Whites may not be sufficient. Indeed, if somemonetary compensation schemes and insurancecoverage are not put in place, only richer Africanswill have the opportunity to enjoy the benefits ofbetter health care institutions.

Finally, our results stress that discrimination is amulti-dimensional phenomenon. Our estimationresults suggest that it affects major factors such asreturns to education, income or access to hospitals.Each of these dimensions would still need to befurther analyzed and evaluated.

Acknowledgments

We wish to thank Matt Sutton as well as twoanonymous referees for useful comments andsuggestions.

Original data sets are available on request toStatistics South Africa; October Household Survey(South Africa), 1999; Pretoria, South Africa:Statistics South Africa (producer); Pretoria : SouthAfrican Data Archive (distributor), 2001. http://www.nrf.ac.za/sada. SADA 0114.

http://www.nrf.ac.za/sada

http://www.nrf.ac.za/sada


Appendix 1. Robustness tests

See Graphs 1–3

Very Poor Health

-0.01

-0.005

0

0.005

0.01

0.015

0.02

-0.5 0 0.5 1 1.5

Poor Health and below

-0.01

-0.02

-0.03

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

-0.5 0 0.5 1 1.5

Average Health and below

-0.1

-0.05

0

0.05

0.1

0.15

-0.5 0 0.5 1.5

Good Health and below

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

-0.5 0 0.5 1 1.5

Indirect racial effect (Whites' model)

Direct racial effect (Whites' model)

Indirect racial effect (Africans' model)

Direct racial effect (Africans' model)

1

Graph 1. Decomposition results over various hypothesis concerning race-specific cut-points. Notes: Values on the horizontal axis refer to

threshold values upon which decompositions are based. Values of 0 and 1 correspond to threshold values estimated, respectively, for

Africans and Whites. Results presented in Table 4 are based on a value of 0.5.

ARTICLE IN PRESS

Very Poor Health

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-0.5 0 0.5 1 1.5


-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

-0.50

0.5 1 1.5


-0.1

-0.05

0

0.05

0.1

0.15

0.2

-0.5 0 0.5 1 1.5

-0.1

-0.05

0.05

0.1

0.15

0.2

-0.5 0 0.5 1 1.5


0





Graph 2. Decomposition results over various hypothesis concerning race specific cut-points under the hypothesis of a 20% over-

evaluation of residuals’ standard errors ratio


ARTICLE IN PRESS

Very Poor Health

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

-0.5 0 0.5 1 1.5


-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

-0.5 0 0.5 1 1.5


-0.15

-0.1

-0.05

0

0.05

0.1

0.15

-0.5 0 0.5 1 1.5


-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

-0.5 0 0.5 1 1.5





Graph 3. Decomposition results over various hypotheses concerning race-specific cut-point under the hypothesis of a 20% under-

evaluation of residuals’ standard errors ratio.



Appendix 2. Instrumentation of income

See Table 5

Table 5

Instrumentation of income

Whites Africans

Coef. Sd. Err. Coef. Sd. Err.

Household size 0.0894 0.0291 0.0323 0.0066

Number of children under 5 0.0319 0.0572 �0.0148** 0.0128

Number of hh members between 15 and 65 0.2135 0.0340 0.0682 0.0091

Average education of members aged 15 and above 0.1344 0.0106 0.0618 0.0026

Male head 0.3116 0.0605 0.3962 0.0158

Age of the head �0.0040 0.0019 0.0059 0.0007

Hunger in the household during the preceding year �0.0785 0.1362 �0.3645 0.0178

Health insurance coverage in the household 0.4341 0.0533 0.8279 0.0276

Urban location �0.1468 0.0845 0.0886 0.0194

School within 30min walk 0.0399 0.0694 �0.0734 0.0210

Clinic/hospital within 30min walk �0.0267 0.0568 0.0608 0.0168

Brick, concrete or cement house 0.4484 0.1634 0.0682* 0.0237

Tile roof 0.1984 0.0495 0.3375 0.0272

Hh owns house 0.2291 0.0534 �0.0790 0.0170

Hh owns financial assets �0.2675** 0.2740 0.3231 0.0385

Hh owns formal savings 0.8335*** 0.2669 0.5015*** 0.0392

Hh receives pension �0.3554 0.0686 0.1936 0.0250

Hh receives State child support 0.5486** 0.3501 0.0022 0.0740

Hh owns telephone 0.3093* 0.0706 0.2914 0.0205

Hh owns radio 0.1128 0.1222 0.1089 0.0183

Hh owns vehicle 0.3996* 0.0747 0.4454 0.0229

Hh owns television 0.3216** 0.0992 0.1508* 0.0176

Cut 1 0.9836 0.5530 0.8928 0.0510

Cut 2 1.7594 0.5503 1.8983 0.0519

Cut 3 2.1594 0.5504 2.4690 0.0525

Cut 4 2.6144 0.5509 3.0849 0.0535

Cut 5 2.9947 0.5513 3.5447 0.0544

Cut 6 3.9141 0.5528 4.4896 0.0572

Cut 7 4.8968 0.5548 5.4770 0.0631

Number of observations 2290 21,632

LR chi2 (22) 1287 14,973

Pseudo-R2 0.17 0.18

Note: *, ** and *** denote that variable which do not appear significant, respectively, at the 50%, 20% and 10% level when included in the

health function jointly with income instrumented with all other variables presented in the table.

Provincial dummies have been omitted for display purposes.


References

Arber, S. (1997). Comparing inequalities in women’s and men’s

health: Britain in the 1990s. Social Science and Medicine,

44(6), 773–787.

Blinder, A. S. (1973). Wage discrimination: Reduced form and

structural estimates. Journal of Human Resources, 8, 436–455.

Bradshaw, D., Matiseng, K., Nannan, N. (2001). Health status

and determinants. In A. Ntuli, N. Crisp, E. Clarke, &

P. Barron, (Eds.), South African health review 2000. Health

System Trust.

Brockerhoff, M., & Hewitt, P. (2000). Inequality of child

mortality among ethnic groups in Sub-Saharan Africa.

Bulletin of the World Health Organization, 78, 30–41.

Buch, E. (2001). The health sector strategic framework : A

review. In A. Ntuli, N. Crisp, E. Clarke, & P. Barron, (Eds.)

South African health review 2000. Health System Trust.

Burgard, S. (2004). Race and pregnancy-related care in Brazil and

South Africa. Social Science and Medicine, 59(6), 1127–1146.

Butler, J. S., Burkauser, R. V., Mitchell, J. M., & Pincus, T. P.

(1987). Measurement errors in self-reported health variables.

The Review of Economics and Statistics, 69(4), 644–650.

Burstrom, B., & Fredlund, P. (2001). Self rated health: Is a good

predictor of subsequent mortality among adults in lower as

well as in higher social classes? Journal of Epidemiology and

Community Health, 55, 836–840.

Castro-Leal, F., et al. (2000). Public spending on health care in

Africa: Do the poor benefit. Bulletin of the World Health

Organization, 78, 66–74.

Charasse, C. (1999). Health and discriminations: The case of South

Africa (350pp). PhD in Economics, CERDI, Universite de

Clermont I.

Ellis, R.P., & Mwabu, G. (1991). The demand for outpatient

medical care in rural Kenya. Mimeo.

Feachem, R. A. G., Kjellstrom, T., Murray, C. J. L., Over, M., &

Philips, M. (1992). The health of adults in the developing world.

A World Bank book. Oxford: Oxford Publication.

Fairlie, R. W. (2003). An extension of the Blinder–Oaxaca

decomposition techniques to logit and probit models.

Economic growth center discussion paper ( p. 873). Yale Uni-

versity, http://www.econ.yale.edu/growth_pdf/cdp873.pdf.

Fylkesnes, K., & Helge Forde, O. (1991). The Tromso Study:

Predictors of self-evaluated health—has society adopted the

expanded health concept. Social Science and Medicine, 32(2),

141–146.

Giliomee, H. (1993). Racisme et Apartheid. In Wieviorka, M.,

Racisme et Modernite, Actes du colloque Trois jours sur le

Racisme, 5–7 juin 1991 (pp. 195–224). La Decouverte.

Groot, W. (2000). Adaptation and scale of reference bias in self-

assessments of quality of life. Journal of Health Economics,

19(3), 403–420.

Grossman, M. (1972). The demand for health: A theoretical and

empirical investigation. NBER occasional paper 119, The City

University of New York, National Bureau of Economic

Research. New York: Columbia University Press.

Heller, P. S. (1982). A model of the demand for medical and

health services in peninsular Malaysia. Social Science and

Medicine, 16, 267–284.

Iburg, K. M., Salomon, J. A., Tandon, A., & Murray, C. J. L.

(2001). Cross-population comparability of physician-assessed

and self-reported measures of health. In C. J. L. Murray, J. A.

Salomon, C. D. Mathers, & A. D. Lopez (Eds.), Summary

measures of population health (pp. 433–448). World Health

Organization: Geneva.

Idler, E. L., & Benyamini, Y. (1997). Self-rated health and

mortality : A review of twenty-seven community studies.

Journal of Health and Social Behavior, 38, 21–37.

Kakwani, N., Wagstaff, A., & van Doorslaer, E. (1997). Socio-

economic inequalities in health: Measurement, computation,

and statistical inference. Journal of Econometrics, 77, 87–103.

Kerkhofs, M., & Lindeboom, M. (1995). Subjective health

measures and state dependent reporting errors. Health

Economics, 4, 221–235.

Lavy, V., Strauss, J., Thomas, D., & De Vreyer, P. (1995). The

impact of the quality of health care on children’s nutrition and

survival in Ghana, LSMS Working Paper no 106. World Bank:

Washington DC.

Lindeboom, M., & van Doorslaer, E. (2004). Cut-point shift and

index shift in self-reported health. Journal of Health

Economics, 23(6), 1083–1099.

MacIntyre, S., Hunt, K., & Sweeting, H. (1996). Gender

differences in health: Are there gender differences? Social

Science and Medicine, 48(1), 49–60.

McIntyre, D., Bloom, G., Doherty, J., Brijlal, P. (1995). Health

expenditure and finance in South Africa. Health System Trust

and World Bank.

Montoya Diaz, M. D. (2002). Socio-economic health inequality

in Brazil. Health Economics, 11, 141–154.

Murray, C.J.L., Tandon, A., Salomon, J., & Mathers, C.D.

(2001). Enhancing cross-population comparability of survey

results. GPE discussion paper series no. 35, WHO/EIP.

Myeong-Su Y. (2000). Decomposition analysis for a binary

choice model. Working paper, Department of Economics,

Rutgers University, http://netec.mcc.ac.uk/WoPEc.

Oaxaca, R. L. (1973). Male–female wage differentials in

urban labor markets. International Economic Review, 14(3),

693–709.

Pradhan, M., Sahn, D. E., & Younger, S. D. (2003). Decompos-

ing world health inequality. Journal of Health Economics,

22(2), 271–293.

Statistics South Africa (1999). Mid year estimates. Statistical

release P0302. (Available at http://www.statssa.gov.za/Statis

tical_releases/Statistical_releases.htm.

Strauss, J., & Thomas, D. (1995). Human resources:empirical

modeling of household and family decisions. In H. Chenery,

& T. N. Srinivasian (Eds.), Handbook of development

economics (Vol. 3). Amsterdam: North-Holland.

Strauss, J., & Thomas, D. (1998). Health, nutrition and economic

development. Journal of Economic Literature, 36, 766–817.

Van Doorslaer, E., Wagstaff, A., Van der Burg, H., Christiansen,

T., De Graeve, D., Duchesne, I., et al. (2000). Equity in the

delivery of health care in Europe and the US. Journal of

Health Economics, 19(5), 553–583.

Van Rensburg, H. C. J., & Fourie, A. (1994). Inequalities in

South Africa health care, Part I: The problem-manifestations

and origins. South African Medical Journal, 84, 96.

Viljoen, R., Heunis, C., Janse van Rensburg, E., Van Rensburg,

D., Engelbrecht, M., Fourie, A., et al. (2000). National

Primary Health Care Facilities Survey 2000. Durban : Health

Systems Trust.

Wagstaff, A. (2000). Socioeconomic inequalities in child mortal-

ity: Comparisons across nine developing countries. Bulletin of

the World Health Organization, 78, 19–29.

http://www.econ.yale.edu/growth_pdf/cdp873.pdf

http://netec.mcc.ac.uk/WoPEc

http://www.statssa.gov.za/Statistical_releases/Statistical_releases.htm

http://www.statssa.gov.za/Statistical_releases/Statistical_releases.htm


Wagstaff, A., & Nguyen, N. N. (2001). Poverty and survival

propects of Vietnamese children under Doi Moi. Working

Paper, World Bank, http://www.econ.worldbank.org/files/

15027_wps2832.pdf.

Wagstaff, A., Paci, P., & van Doorslaer, E. (1991). On the

measurement of inequalities in health. Social Science and

Medicine, 33(5), 545–557.

Wagstaff, A., van Doorslaer, E., & Watanabe, N. (2003). On

decomposing health sector inequalities, with an application to

malnutrition inequalities in Vietnam. Journal of Econometrics,

112(1), 207–223.

Wagstaff, A., & van Doorslaer, E. (2004). Overall versus socio-

economic inequality in health: A measurement framework and

two empirical applications. Health Economics, 13, 297–301.

http://www.econ.worldbank.org/files/15027_wps2832.pdf

http://www.econ.worldbank.org/files/15027_wps2832.pdf

Health disparities between racial groups in South Africa: A decomposition analysis

Documents

Transcript of Health disparities between racial groups in South Africa: A decomposition analysis