Discussion Paper No. 768

ACCESS TO HEALTH INFRASTRUCTURE
AND CHILD HEALTH DEVELOPMENT:EVIDENCE FROM POST-APARTHEID
SOUTH AFRICA
Shinsuke Tanaka
The 12th ISER-Moriguchi Prize (2009)
Awarded Paper

February 2010

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Access to Health Infrastructure and Child Health
Development: Evidence from Post-Apartheid South
AfricaShinsuke Tanakay
Boston University
January 2010

Abstract
A growing body of literature shows that child health has substantial long-term
economic impacts. This study examines whether, and to what extent, increased accessto health infrastructure leads to better child health status as measured by weight-forage z-scores. To assess the causal relationship, I exploit plausibly exogenous variationin access induced by the dramatic change in health policy in South Africa immediately
after the end of apartheid. Using longitudinal household data, health services are foundto improve the nutritional status of not only newly born babies but also children who
were already born at low health status. Yet, the e¤ects are signi…cant only for boys.

JEL classi…cation: I18, H54, J13, H51, O15
Keywords: child health, health infrastructure, South Africa
I am extremely indebted to my advisors, Randall Ellis, Daniele Paserman, and Tavneet Suri, for extensive discussions and suggesions on this paper. I am also thankful to Alok Bhargava, Nicola Branson, Alfredo
Burlando, Kenneth Chay, Susan Cleary, Taryn Dinkelman, Jane Fortson, Emily Gee, Gordon Hanson, Todd
Idson, Marika Imagawa, Hirokazu Ishise, Takahiro Ito, Yukie Kamiya, David Lam, Kevin Lang, David Lee,
Ken Leonard, Joshua Lustig, Di McIntyre, Omer Moav, Dilip Mookherjee, Keisuke Nakao, Tatsushi Oka,
Emily Oster, Nancy Qian, Gail Thomas, Ingrid Woolard, Futoshi Yamauchi, Wesley Yin and seminar participants at Boston University, the Institute of Social and Economic Research (ISER) of Osaka University,theSouthern Africa Labour and Development Research Unit (SALDRU) of the University of Cape Town, the2009 North American Summer Meeting of Econometric Society, the 30th Nordic Health Economist’s Study
Group, the 24th Annual Congress of the European Economic Association, and the 2009 Northeastern Universities Development Consortium Conference for helpful comments and advice. Part of this research was
carried out while the author was visiting the SALDRU. Financial support from the Graduate School of Arts
and Sciences at Boston Univerity is gratefully acknowledged. The previous version of this paper was awarded
the 12th Moriguchi Prize by the ISER at Osaka University. All errors are my own.
yDepartment of Economics, Boston University. 270 Bay State Road, Boston, MA 02215. Email:stanaka@bu.edu.

1Introduction

A considerable amount of public resources have been spent in constructing health facilities
and providing health services in low income countries, yet it remains unclear whether theseinvestments lead to increased utilization in health services and improved health status among
the poor. Two empirical di¢ culties complicate answering these questions. First, little is
known about how poor households respond to the provision of public services. Second,
many health services are provided in rich urban areas, and many of the social programs
are targeted at certain groups of people. Hence, availability of health institutions or theintensity of access to health care services systematically varies across regions and often
prevents researchers from credibly identifying the impacts of medical services.
In this paper I provide new evidence on the extent to which increased access to healthservices contributes to health status among poor children. To assess this, I exploit plausibly
exogenous variations in access to medical care generated by a new reproductive health policyin South Africa. Historically, black Africans had severely limited access to health services
under apartheid. The new democratic government, established in 1994, started providing
free medical services to pregnant mothers and children under 6 years old. Two salientfeatures under apartheid help address typical endogeneity problems that make it di¢ cultto disentangle the e¤ects of health services from the e¤ects through other unobservablevariables. First, the allocation of clinics was controlled by whites, over which black Africans
had little control. Second, black Africans had low mobility from the regions in which they
were forced to live during apartheid.
Using the KwaZulu-Natal Income Dynamics Study (KIDS), I merge longitudinal household surveys on health status during the early childhood with community information on
infrastructure. I exploit two dimensions of variations in access across individuals. One variation comes from birth timing. Evidence supports that the new policy granted the poor
with better access by relaxing budget constraints, which had been previously identi…ed as
the major barrier to undergoing medical treatment. Thus, comparing health status of chil1

dren before and after the policy change provides an estimate of between-cohorts impact of
substantial improvements in access. Another dimension is within-cohort variation that isdetermined by the community that a household resided in during the apartheid era. Households in communities with a clinic (hereafter the high treatment region) immediately gained
access to health services as soon as the policy was initiated in 1994, whereas those who did
not have a clinic in their communities (hereafter the low treatment region) continued tohave poor access.1 With this exogenous variation, I compare health improvements between
children living in the high and low treatment regions.
The results show that children who obtained access to health services achieved signi…cantimprovements in health status as measured by weight-for-age z-scores (WAZ). The point
estimates suggest that the policy increased WAZ of newly born babies by 0.64 standard
deviations and that of pre-primary school children by 0.57 standard deviations. This not
only indicates that pre- and post-natal services are important determinants of health status
but also suggests that health care during early childhood helps mitigate the low health status
experienced at birth. However, the signi…cant e¤ects are found only for boys. This highlights
a potential mechanism of unequal intrahousehold allocation of resources; boys are more likely
to receive medical treatment than girls.
The e¤ects would be spurious if there were intrinsic di¤erences in household attributes
leading to a di¤erential health pattern in the absence of the policy change. This would betrue if whites endogenously allocated clinics among black African’s communities under theapartheid regime. In addition, the estimates would be biased if there were any unobservablechanges that were concurrent with the policy reform and systematically varied between the
two types of regions. This would be likely if post-apartheid investments were disproportionately made in the high treatment region. Thus, I conduct two placebo experiments toprovide testable implications of each case. To test pre-treatment endogeneity, I investigate
1

Throughout the paper, I use the term "community" to refer to a census enummerator subdistrict and"region" to refer to a group of communities that share the same characteristics, which is whether there wasa clinic or not as of 1993.

2

whether there is a systematic di¤erence in health trends using only observations before thepolicy change. Further, making use of the fact that the policy a¤ects only children under 6
years old, the basic idea to address post-treatment omitted variables is to investigate healthimprovements for older children who are aged 6 years old and above as of 1994. These cohorts
provide appropriate counterfactual evidence during the post-apartheid era in the absence ofthe policy change because they are not entitled to receive free health services, but they musthave still been a¤ected by other changes in households or communities, if any. Both results
indicate that the estimates are likely to be underestimated and to mark a lower bound of
the overall impact of the policy.
By looking at the impacts of the dramatic change in health policy, the study contributes
to knowledge in the realm of the e¤ectiveness of health services and facilities in improving
child health in low income countries. Many studies have shown strong correlations between
the two as we often observe people with high income level in areas with better services
and more quali…ed doctors (Jishnu Das and Je¤rey Hammer 2007). On the other hand,poor areas are more likely to have fewer health facilities, high absenteeism, and unhealthypeople. As a consequence, there is ubiquitous phenomena of "wealthier is healthier" and"healthier is wealthier" in cross-sectional and some panel settings, but …nding the causal
relationship is not trivial (Lant Pritchett and Lawrence H. Summers 1996; Sarah L. Barberand Paul J. Gertler 2002; Anne Case, Darren Lubotsky, and Christina Paxson 2002; Abhijit
Banerjee, Angus Deaton, and Esther Du‡o 2004; Deaton 2006; Janet Currie and Wanchuan
Lin 2007; Currie 2008). There are several reasons to expect that increased access may notnecessarily improve health status. First, quality of health services in poor countries is often
perceived to be low and ine¢ cient. Simply expanding quantity of health facilities may notsolve this problem. Second, the linkage between access and health status is theoreticallyand empirically ambiguous; Emily Oster (2009) presents a model with evidence that a mere
increase in access may widen inequality and hurt disadvantaged group of people, particularly3

at a low level of access.2
It is of interest for many policy makers in developing countries to understand the extent
to which providing access to medical care contributes to health improvement. There is added
importance in exploring the topic because a growing body of literature has shown that healthstatus, particularly early childhood nutrition, has substantial long-term economic impacts onlater outcomes. It has been shown that children who experienced positive (negative) healthshocks during early childhood perform better (worse) in school, earn higher (lower) income
during young adulthood, and achieve higher (lower) health status and socioeconomics status
up to the middle age (Harold Alderman, Jere R. Behrman, Victor Lavy, and Rekha Menon
2001; Paul Glewwe, Hanan G. Jacoby, and Elizabeth M. King 2001; Glewwe and King 2001;
Case, Angela Fertig, and Paxson 2005; Alderman, John Hoddinott, and Bill Kinsey 2006;
Almond 2006; Hoyt Bleakley 2007; Futoshi Yamauchi 2008; Taryn Dinkelman 2008; Sharon
Maccini and Dean Yang 2009).
This paper also contributes to the contentious debate over the impact of price on take-up
behavior among the poor. Many developing countries introduced or increased user fees tohealth services based on the argument that free provision of public services does not increaseusage because even the poor do not value free or cheap public services and bypass them (Alaka
Holla and Michael Kremer 2008; Klaus Deininger and Paul Mpuga 2004). Proponents also
argue that imposition of user fees should sustain or improve quality of services. On the other
hand, there is also evidence that imposing user fees on the poor had signi…cantly negative
impacts on their usages (Asenso-Okyere et al. 1998; see further discussions in Deininger andMpuga 2004). The new reproductive health policy that I focus on removed user fees fromhealth services, providing a rare environment to observe behavioral responses to free medicalcare.3
2

The relationship appears to be relatively monotonic in developed countries. Studies have found that
increased access contributed to a sharp reduction in the relative infant mortality of blacks in the UnitedStates (Kenneth Chay and Michael Greenstone 2000; Douglas Almond, Chay, and Greenstone 2007).
3
Another example of free health services is Deininger and Mpuga (2004), where they focus on the abolitionof health user fees in public hospitals in 2001 in Uganda. This study di¤ers from their study in a key aspectthat the policy in South Africa exhibits a discontinuous entitlement at age 6, which enables me to address

4

The rest of the paper is structured as follows. Section 2 introduces the new healthpolicy in the historical context, and Section 3 describes the data and the summary statistics.
Section 4 presents the empirical strategy, and Section 5 discusses the results. Section 6 tests
the validity of the identi…cation assumptions, and Section 7 concludes.

2

Reproductive Health Policy in South Africa

The South Africa’s apartheid policy has shaped it as one of the most discriminatory countries
in the world. Society was characterized by racial and spatial disparities and disintegration.
Geographical segregation driven by the Bantu Authorities Act of 1951 forced black Africans
to move into underdeveloped and infertile areas, called "homelands," where few infrastructures existed. Racial discrimination was intensi…ed by the Bantu Homelands Citizenship Act
of 1970, which deprived South African citizenship from black Africans in homelands.
The legacy of apartheid ideology also formulated political, social, and economic institutions. For example, government institutions were built for each race de…ned as white,colored, Indians/Asians, and Africans. Most of the resources were controlled by the white
minority in urban areas without any representatives from the black majority (Diane Cooper
et al. 2004). Among these geographical and racial inequalities in resources and services
allocations, the health sector in black communities was also short of facilities and …nances.
The absence of comprehensive health policies prior to 1994 led to a signi…cant lack of coordination and integration in providing health services, symbolized by as many as eighteen
Departments of Health at the central and regional government levels.4 Black Africans constitute the most disadvantaged and underserved group; their access to health services wasconstrained by a lack of facilities, doctors, and medicines at public health institutions as wellunobservable time e¤ects. Because Uganda policy provided universal free services, their study interprets
the time e¤ect as the impact of the policy change.
4
Speci…cally, these include the Department of National Health and Population Development, three "owna¤airs" Department of Health for white, colored, and Indian people, 10 health departments for African
people in each "homelands," and the departments for four provinces (Klugman and McIntyre 2000).

5as by the …nancial burden on transportation to health facilities and out-of-pocket payments
for health services (Department of Health 2000).
Under the pressure from the international community, a series of negotiations to dismantle apartheid took place starting in 1990. In April 1994, apartheid was formally abolished,
and a new democratic government was established under the leadership of President Nelson
Mandela. In an e¤ort to eliminate substantial racial and geographic inequalities, various newpolicies were passed to ensure improved access to health care with priority for previously
disadvantaged black mothers, women, and children (Department of Health 1995). Most notably, the Reconstruction and Development Programme (RDP), along with National HealthPlan for South Africa (African National Congress 1994), proclaimed free health care services
to pregnant mothers and children under 6 years old in the public sector (Republic of South
Africa 1994). Free services to mothers included pre- and post-natal care from con…rmationof pregnancy until 42 days after delivery. The policy was implemented in June 1994.
The free health care provision granted a signi…cant increase in access to health services
to black African, who previously identi…ed cost of medical services as the major barrierfor seeking treatment. Community Agency for Social Enquiry (CASE) (1995), a national
representative survey of households before the policy change, notes that "not a¤ordable"
was the most common reason for foregoing health care (73.8 percent) among black families,
followed by lack of transportation modes (11.5 percent). This contrasts to the fact that only
23.2 percent of whites listed the cost of medical care as the primary constraint. In addition,
90 percent of black Africans did not have health insurance, whereas 76 percent of whites did(CASE, 1995). Various studies have found that removing user fees extended antenatal caredelivery to women who were not previously reached and increased the number of patients
under 6 years old (Robert Cameron 1996; David McCoy 1996; Department of Health 1998;
Helen Schneider and Lucy Gilson 1999; David Wilkinson et al. 2001; Cooper et al. 2004).

6

3

Data Source and Descriptive Statistics

The analysis in this paper is based on the data from KIDS, a longitudinal household survey
in the KwaZulu-Natal province of South Africa.5 The …rst wave of 1993 (hereafter KIDS93)
is part of the 1993 Project for Statistics on Living Standards and Development (PSLSD),
the …rst comprehensive household survey in South Africa. To address dramatic changes inSouth African society since 1994, African and Indian households of the PSLSD sample in the
KwaZulu-Natal province were revisited in 1998 (hereafter KIDS98). As the largest province
in South Africa, KwaZulu-Natal is home for approximately 9.4 million of total 40 million
national population and is characterized by high rates of poverty and deprivation from basic
needs. Though it is not the poorest province in South Africa, it certainly shares many
characteristics with other former homelands (Stephan Klasen 1997; Murray Leibbrandt andIngrid Woolard 1999; May et al. 2000). The original sample contains 1,389 households (215
are Indian and 1,174 are Africans), of which 1,178 households (85 percent) were followed
up in 1998, which involves limited concern about attrition (Michael R. Carter and John A.
Maluccio 2003).
There are several advantages to using KIDS. First, it contains household level of information on demographics and socioeconomic status. In addition, anthropometric data areprovided for all children aged 6 years old and below in KIDS93 and for all children aged 11
years old and below in KIDS98. The community survey provides information of available
health institutions in each community. Second, the timing of data collection is suitable;comparison of the two waves captures changes in child health status from right before toafter the policy change.
For the analysis of the present study, the samples are restricted to black Africans. Thisis appropriate because black Africans not only constitute the majority of the South African5

The surveys were jointly conducted by the University of KwaZulu-Natal (UKZN), the University of
Wisconsin-Madison, and the International Food Policy Research Institute (IFPRI), the London School ofHygiene & Tropical Medicine, and the Norwegian Institute of Urban and Regional Studies.
A more detailed description of KIDS is provided in Julian May et al. (2000) and May et al. (2007).

7

population but also account for most of the poor. Because the new government aimed at
protecting and equalizing opportunities for the most disadvantaged group, black Africans
are most likely to have bene…ted from the new health policy.
The primary goal of the study is to assess the e¤ects of gaining access to health care on
health status. Health status is measured using the anthropometric indicators, from which
WAZ is calculated.6;7 To address impacts of increasing access to health care services onchildren, I link their health status with the infrastructure information from the community
survey. In this paper, I focus on clinics as the primary health care facility to provide healthservices to pregnant mothers and children. Anecdotal evidence supports this approach sincemost black Africans refer to public clinics for the child’s …rst treatment or for antenatalcare.8
Table 1 presents baseline summary statistics on the individual level of health status anda variety of household and community characteristics in 1993 by region. Each community in
1993 had either zero or one clinic. I refer to the communities with a clinic as high treatment
region. The exposure is high in a sense that households gained immediate access to health6

Detailed descriptions of how to construct WAZ and how reference groups were sampled are discussed
in WHO Multicentre Growth Reference Study Group (2006). Simply summarizing, the WAZ of child i arecalculated as:
WAZ =

Wi Wg
SDg

where Wg is the median weight of the reference group in the world population, and SDg is the standard
deviation of the reference group.
7
Many other studies also use height-for-age z-scores (HAZ) to measure health status. It is widely recognized in the literature that WAZ characterizes short-run ‡uctuations in nutritional status, while HAZ captures long-term accumulation of nutritional history. The main reason why I focus on WAZ in this paper isthat most of the samples in the analysis are too young, such as ages less than three years old, and height
does not di¤er much among these children. Gustavo J. Bobonis, Edward Miguel, and Charu Puri-Sharma(2006) provide support on this. They …nd the e¤ects of iron supplementation and deworming drugs on WAZ
among preschool children, yet no e¤ect is observed on HAZ.
Another concern with height is that it is subjected to large measurement error particularly for infants who
are not able to stand up yet, whereas weight can be measured relatively more accurately. Glewwe, Jacoby,
and King (2001) …nd that about 52 percent of the total variance in HAZ, even for children at the school
enrollment, is caused by measurement error.
I used HAZ, nonetheless, and found no signi…cant e¤ect. Because HAZ is a dependent variable in myanalysis, the measurement error leads to larger standard error.
8
Evidence shows that Africans visit public clinics for antenatal services (69 percent) and for the …rst
treatment (40 percent), whereas whites seek private doctors for antenatal care (79 percent) and for the …rst
treatment (74 percent) (CASE 1995). See also, Pranitha Maharaj and John Cleland (2005)

8

services as soon as the policy was initiated. On the other hand, families in the communities
without a clinic, referred as the low treatment region, needed to wait until facilities became
ready or travel for a long distance to receive treatment.

[Table 1 about here]

It shows that twenty-six communities belong to the high treatment region, while twentyeight communities are in the low treatment region. The third column presents the di¤erencesin means between the regions. It shows that none of the individual and household characteristics is signi…cantly di¤erent between the two regions. This suggests that black families
had limited ability to determine the extent of infrastructure in their communities, bolstering
the view that the allocation of clinics among black communities was orthogonal to family attributes. There is some evidence that communities in the high treatment region are
more likely to have other types of social facilities. Consequently, cross-sectional estimatesof the relationship between health facilities and health status may confound unobservabledi¤erences in communities and may involve severe omitted variable bias. The next section
discusses this paper’s challenge to develop an appropriate counterfactual for health statusamong children in the absence of access to health institutions.

4

Empirical Strategy

The empirical framework is established to compare children’s health growth between the tworegions before and after the policy change. The new reproductive policy in 1994 provides exogenous variations in access to health infrastructure, which helps avert potential endogeneity
between underlying family background and availability of services. Consequently, the treatment is determined by the two dimensions of variation in exposure to the new health policy.
First variation comes from birth timing. Because free health service started in 1994, any
9children observed in KIDS93 are not exposed to the policy. On the other hand, all children
born after 1994 in KIDS98 are eligible for free health services. Second variation across individuals within the same cohort is determined by the residential community: whether thehousehold lived in the high or low treatment region as of 1993. This leads to the baseline
regression:

Wihcl =0+

1 (Highc

0

Y oungl )+ 2 Highc + 3 Y oungl +Xh

0

4 +(XcY oungl ) 5 +"ihcl (1)where Wihcl is the weight-for-age z-score of individual i, in household h, in residential community c, in cohort l. Y oungl = 1 if cohort l is a¤ected by the policy (post-treatment
cohorts, hereafter referred as young cohort), while Y oungl = 0 for pre-treatment cohorts,
hereafter referred as old cohort. Highc = 1 if community c had a clinic in 1993 (often known
as treatment group), and is otherwise equal to 0 (control group).9 Xh is an additional vectorof household characteristics, Xc contains a vector of community characteristics, and "ihcl isan unobserved individual disturbance. Xc is interacted with a young cohort dummy because
it takes into account other types of health facilities and programs that may be correlatedwith the existence of clinics and may potentially confound e¤ects on health outcomes in
1998. For example, if other governmental programs, such as immunization campaigns provided through clinics, were the key determinants of health improvement, the results would
be misleading unless they are explicitly controlled.10 The parameter of interest is

1 , whichrepresents the di¤erence-in-di¤erences e¤ect of a clinic on an outcome variable.
To further re…ne variations across cohorts and communities, the main strategy employedis:Wihcl =0+

1 (Highc00

Y oungl ) + Xh 2 + (Xc Y oungl ) 3 +c+l + eihcl(2)

9
A similar speci…cation is used in Du‡o (2001), which examines the causal e¤ects of school construction
on educational attainment and income level in Indonesia.
10
Although it is important to control for these community characteristics, the key assumption that black
families did not have in‡uence over the availability of clinics suggests that 1 can be estimated even without
controlling for these attributes. However, the inclusion of them still enhances the precision of the estimateswith lower standard error. Nonetheless, the results are presented with and without control variables. This
helps test the validity to the identi…cation assumption.

10

whereandcl

is …xed e¤ects of a residential community that the household resided in 1993,

is cohort …xed e¤ects. All standard errors are clustered at c

k 2 fY oung; Oldg.11 The coe¢ cient of interest,1,

k level, whereis expected to be positive if a clinic

delivers bene…cial health services to children, or equivalently if gaining access to health careleads to improved health status. The e¤ect can be interpreted as causal based on the identi…cation assumption that, after controlling for the birth timing and residential location, theintensity of the change in access to health infrastructure is exogenously determined by thepolicy change. The potential threat is to confound any cohort-speci…c and community-levelunobservable variables that are correlated with the availability of clinics and have direct
impacts on an outcome.
This raises several potential concerns that this identi…cation assumption may be violated. First issue is pre-treatment bias. For example, if the initial allocation of clinics underapartheid was endogenously determined by whites, targeting to build facilities in communities where people are somehow better (worse) at responding to health growth, then simple
comparison of outcomes between treated and non-treated regions would be upwardly (downwardly) biased. This should be characterized by di¤erential pre-trends of health development
between the regions.
Second concern is omitted variables that correlate with the initial allocation of clinics.
For instance, Table 1 shows that communities with a clinic as of 1993 were more likely tohave various other types of infrastructure. In addition, if these communities are politicallyimportant and are likely to receive further investments or projects after 1994, then theestimates mistakenly pick up these e¤ects. I take two approaches of placebo experiments
in pre- and post-treatment period to address testable implications of the validity of theidenti…cation strategy, as discussed in Section 6. Evidence shows that confounding variables,
11

This means that any correlations between households within the same community during the same period
are allowed, but it assumes no correlation between households within the same community over time. This
accomodates the fact that South Africa experienced drastic changes in various facets of society before andafter 1994. Nonetheless, standard errors are also clustered at community level, allowing for correlations
across time, but the results did not signi…cantly change.

11

if any, tend to understate the estimates.

5

Empirical Results

5.1

Age Level Analysis

The …rst primary question posed in this study is whether gaining access to health infrastructures, such as clinics, improves health status among children. Figure 1 presents the average
health status of children across ages. Panel A exhibits somewhat parallel trend between thetwo regions in KIDS93. This provides graphical support that, prior to the policy change,children in both regions share a similar pattern of health development. Panel B, which uses
observations in KIDS98, tells di¤erent episodes. Children aged right to the dashed line comprise non-exposed cohorts because they are more than 6 years old upon the policy start. A
sharp increase in WAZ is depicted in the high treatment region when the cohorts begin tobe a¤ected by the new health policy.

[Figure 1 about here]

Panel A of Table 2 illustrates the identi…cation strategy, laid out by the equation 1,
using a simple two-by-two matrix. It compares means of health status between the sameage groups across the two types of regions. I consider children aged 0 to 3 in KIDS93 as
the old cohort. This provides baseline health status among non-exposed cohorts, as they areobserved prior to the policy change. I then refer to children aged 0 to 3 in KIDS98 as the
young cohort. They can be considered as treated cohorts since they have full exposure to
the new policy since birth.12 While the average health status increases in both regions, it
increases signi…cantly more in the high treatment region. It is also worth noting that the12

I omit 4-year-old children from the fully exposed group because the exposure to the policy starts whenbabies are still in utero to account for ante-natal services.

12

average health status in 1993is lower in the high-treatment region, though the di¤erence
is not statistically signi…cant. This evidence reassures that children in the high-treatment
region did not bene…t from clinics prior to 1994 possibly due to barriers to access thatprevented them from receiving health care.
[Table 2 about here]
Table 3 presents estimates from a series of regression analyses, whose sample corresponds
to Panel A of Table 2. Column (1) reproduces the results from Table 2 as the benchmark
speci…cation. Column (2) provides baseline results of equation 2, introducing communityand cohort …xed e¤ects. Column (3) and (4) control for various household or community
characteristics. Of other variables in household characteristics, the logarithm of monthly
income, which is a proxy of household wealth, is strongly associated with the health statusof children. This can be interpreted in terms of the resource constraint; cost of health care
was the major constraint for undergoing services. Both of the community characteristics
are highly correlated with child health status. The estimate of interest becomes even larger
with the inclusion of community characteristics, indicating that the health e¤ect throughclinical services are not overstated by other governmental health programs or health facilities.
Although these variables are markedly important in themselves, their inclusion does notalter the di¤erence-in-di¤erences estimates. This is suggestive that the availability of clinics
in 1993 is not related to any of these background characteristics. Column (5) provides thepreferred estimate, indicating that, after controlling for all variables, gaining access to healthservices improved the health status of newly born babies by 0.64 standard deviation away interms of WAZ. This highlights particularly the importance of ante- and post-natal services.
[Table 3 about here]
I now consider di¤erential e¤ects by gender; it is commonly observed in developing countries that most bene…cial e¤ects are enjoyed only among boys but not among girls. This is13often explained by an unequal intrahousehold allocation of resources, as is often prevalent in
Asian countries in the context of consumption behaviors. To assess this, I divide the sample
by gender.

[Table 4 about here]

Panel A of Table 4 reveals that positive e¤ects found in Table 3 are driven mostly by boys;
the point estimates are even larger and are highly signi…cant. On the other hand, the e¤ects
are consistently positive for girls yet never statistically di¤erent from zero. This highlights apotential mechanism of unequal intrahousehold allocation of resources; boys are more likely
to receive medical treatments. Figure A1, which presents similar pre-trends in health status
between boys and girls, provides graphical support that the results are not driven by intrinsic
biological di¤erences by gender. Further, Table A2, which shows the results based on the
regressions allowing for di¤erential impacts across ages,13 uncovers a result that positive
health improvement is observed for girls aged 0. Consistent with previous …ndings, noneof the other cohorts among girls experienced improved health status. This provides further
support of an unequal allocation of resources for girls; all children, regardless of their sex,
bene…t from gaining access to prenatal services, during which a child’s gender is unknown,
yet girls fall behind in later years.

5.2

Cohort Level Analysis

This subsection aims to answer a di¤erent aspect of the question: whether gaining access tohealth care improves health status of children even after they undergo low health status at
13

The regression is based on interacting the region dummy by each cohort dummy;X
00
Yihrk = 0 + il
(Highril ) + Xh 1 + (Xr Y oungk ) 2 + r + l + ihrklwhere il = 1 if individual i is age l in 1998. Each coe¢ cientcohort with respect to the reference cohorts.

14

1l can be interpreted as an e¤ect on a given

birth. To mitigate di¤erent cohort-speci…c variation, this can be addressed by tracking thesame cohorts. A longitudinal setting of the dataset allows me to trace the same individuals
over time.14 Panel B of Table 2 provides means of health status among each cohort by
region, and regression estimates are presented in Table 5. In both tables, children aged 0
to 3 in KIDS93 are referred as the old cohort, and children aged 5 to 8 in KIDS98 as the
young cohort.15 The advantage of this comparison as opposed to the age level analysis is thatthese cohorts were already born when the policy started. Thus, it helps identify whether
gaining access to post-natal services or health services during early childhood also brings
about health improvement.
Results in Table 5 are in line with the age level analysis; the di¤erence-in-di¤erences
estimates are robust to the inclusion of various household and community characteristics,
and household income illustrates an important avenue of resource e¤ects. One interesting
disparity is that the household size is now negatively associated with health status.

[Table 5 about here]

Note that these cohorts experienced signi…cantly low health status in early childhood;
Panel B of Table 2 shows that the average WAZ is -0.414 in the low treatment region andeven lower at -0.545 in the high treatment region. The results provide added signi…canceto gaining access to clinics; it not only helps newly born infants through ante-natal services
but also partially o¤sets the poor health status experienced at birth. Panel B of Table 4
illustrates di¤erential e¤ects of policy by gender. Again, positive and signi…cant e¤ects arefound only for boys, and no statistically signi…cant e¤ects are found for girls.
14

Panel B of Table 4 show slighly di¤erent numbers of observations in each cell. This should indicatethat I do not refer to exactly the same individuals but instead follow the cohorts by age. Di¤erent timingsof birthday and survey collection generate slightly di¤erent ages even within the same cohorts, but because
these timings are exogenously determined, the bias should be negligible.
15
Although, as mentioned, they are the same cohort, I still use the term "young" and "old" cohort to beconsistent throughout the paper. They are simply the same cohort at "younger" and "older" times.

15

6

Threats to Validity

6.1

Endogenous Allocation of Health Facilities in Pre-treatment
PeriodThe key to the identi…cation strategy is that initial allocation of clinics is exogenously determined and does not confound variables that are correlated with health outcomes. There
are two important sources of selection bias. The obvious, and foremost, important concern
is whether it is plausible to believe that black African families had limited control overavailability of health institutions in their communities. South Africa under apartheid wascharacterized by extreme domination by the white minority over the black majority; mostof the resources were allocated for white communities, and little was distributed to black
communities. For example, Case and Deaton (1999) describe that school quality, measured
by pupil-teacher ratios, was extensively dispersed across black districts immediately before
the end of apartheid. Using evidence that socioeconomic characteristics among black families are not associated with school resources, OLS regressions of various school outcomes onschool quality can estimate the class size e¤ects. Following their work,16 I test whether theobservable characteristics of black households are associated with the availability of healthfacilities. If black families could exert extensive power to allocate health facilities in theirneighbors, there should be a signi…cant correlation between the two. Table 6 explores thisbalancing test. It shows that no household attributes is the signi…cant determinant of initial
allocation of health facilities in the pre-treatment period. This is reassuring that there isno selection bias from household’s side; black families could not in‡uence the intensity of
infrastructure in their communities.

[Table 6 about here]
16

Case and Deaton (1999) use data from PSLSD in 1993. As mentioned, KIDS93 focuses on observations
in KwaZulu-Natal from PSLSD.

16

Yet, this does not directly rule out the concern that there are unobservable rules that
determined the allocation of health facilities- this is the second source of selection mechanism.17 I have exchanged excessive discussions with local economists and doctors, but it
still remains unclear how the allocation was determined in the …rst place. However, even if
there was some systematic rule on how to build clinics among black communities, this would
invalidate the identi…cation strategy only if, in the absence of the policy change, it led todi¤erential trends in health growth between the two regions. In other words, the empiricalstrategy is still valid if the pattern of health growth among children is not systematicallydi¤erent across regions. This is violated if households with access to clinics in 1993 had ahigher rate of health accumulation by taking an advantage of initial access to health services. The …rst placebo experiment is designed to test whether children exhibit the same
trend across ages during the pre-treatment period.
As previously shown, Panel A of Figure 1 depicts such trends in KIDS93; it illustrates that
health development across ages appears to follow the same trend between the two regions.
Panel C of Table 2 presents testable implications of this placebo experiment. Note that themost appropriate comparison of cohorts may be the one between the same age groups both
before the policy change.18 However, because the survey was conducted only once prior to
1994, I compare the closest cohorts examined in Panel A. I refer to cohorts aged 0 to 3
in KIDS93 as the young cohort and to cohorts aged 4 to 7 in KIDS93 as the old cohort.
Such comparison serves as an appropriate falsi…cation test because both age groups have no
exposure to the policy. The di¤erence-in-di¤erences estimate do not show any signi…cantpositive trends in the high treatment region. Panel C of Table 7 provides regression resultsof this comparison. The evidence supports that children in the high-treatment region do
17

Case and Deaton (1999) also raise the similar concern and have discussed with South African academics,
the then Ministry of Education, and the Department of Education and Training on the allocation rule ofteachers across schools. Their conclusion is that, although there were some guildelines, observations fromdata do not support them, and most of the allocations appear to be determined by bureaucratic minds anddid not meet regional needs. See further discussions in their paper.
18
For example, the ideal comparison is between children aged 0-3 in the 1993 dataset and chidren aged
0-3 in the 1990 dataset, if data would have been collected in 1990.

17

not have higher rates of health improvement; if they did, the estimates would show spurious
positive correlations. The negative estimates, though insigni…cant, indicate that confounding
variables in pre-treatment period, if anything, would underestimate the bene…ts generated
by the policy change. The results are suggestive that availability of clinics in 1993 brought
about limited access or bene…ts to children in the pre-treatment period, and the e¤ects are
not driven by the inappropriate identi…cation assumptions.

[Table 7 about here]

6.2

Omitted Variable Bias in Post-treatment Period

One of the di¢ culties associated with evaluating social policies is that there may be several
other changes happening concurrently, and it is di¢ cult to disentangle these e¤ects. The
new health policy in South Africa is not exceptional; there may be unobservable changes
in policies or infrastructure in post-apartheid era speci…c in the areas with clinics. This is
likely if new investments are made based on the initial existence of clinics. This subsection
discusses several issues in post-treatment period, which potentially lead to omitted variablebias in the treatment e¤ects.
First concern is the construction of new clinics between 1994 and 1998. The policy emphasized previously deprived and underserved areas to meet the goal of racial and geographicalequality. Table 8 presents evidence to support this aim that by 1998, a signi…cantly larger
number of clinics were built in communities without a clinic as of 1993.

[Table 8 about here]

However, I refer to areas with a clinic in 1993 as the high treatment region for several
reasons. The average change in the number of clinics per community between KIDS93 and18

KIDS98 is 0.65 (with the standard deviation of 0.95), yet the data contain no information
on exactly when each clinic was built or started provision of services. This makes it dif…cult to know how many clinics were available when each cohort was born. Rather, thegovernment reported that Clinic Upgrading and Building Programme, initiated as a Presidential Lead Project in 1994, did not bring about noteworthy outcomes at the early stage.19
Further evidence shows that construction of clinics was substantially slow in the KwaZuluNatal province due to political instability and violence (Cameron 1996; Sultan Khan, Benoit
Lootvoet, and Shahid Vawda 2006); even the …rst democratic election did not take place
until 1996. These pieces of evidence are consistent with the assumption that clinics observedin 1998 are more likely to have been built in later years, namely close to 1998, and thus
it validates the assumption that areas with a clinic in 1993 granted immediate and higher
exposure to the policy. It is also worth noting that even if clinics were built at earlier stages,
the results mark the lower bound of the estimates.
Second concern is that the treatment variable simply proxies new investments conditional on availability of clinics, such as quality change in health facilities (i.e. range of
medical services, availability of drugs or equipment, workload of physicians, number of beds,
or interactions between doctors and patients). If so, this would lead to a di¤erent policy implication; the government should allocate more resources to improving the quality ofservices at existing facilities, rather than to expanding the physical availability of healthinstitutions. Another possibility is that communities initially with a clinic are politicallyimportant and thus are more likely to receive further infrastructure or projects, if not clinics. Then, the positive e¤ects presented so far may be actually picking up these e¤ects,
acting as a reduced-form analysis of the new investments. The second placebo experiment
investigates such potential bias during the post-treatment period. The basic framework ofsuch falsi…cation test is that If any other changes have a …rst-order e¤ect, I should observe
19

Advancement was made after August 1995, when a Fast Track Clinic Building Process was initiated toaccelerate constructing and upgrading clinics, particularly in rural areas. By March 1998, approximately
R710m had been spent (Department of Health 1995).

19improvements in health status even among people who are not directly a¤ected by the newhealth policy. This can be tested by comparing cohorts aged 5 to 7 in KIDS93 and cohorts
aged 10 to 12 in KIDS98. Because these children are 6 years old and above as of 1994, they
have no exposure to the health policy through 1998.20 However, even these cohorts shouldbe a¤ected by new investments in any other sorts. If these changes were to confound healthe¤ects, the coe¢ cient of the di¤erence-in-di¤erences estimate should be positive. As shownin Panel D of Table 2 as well as in regression results in Panel D of Table 7, this is not the case.
The coe¢ cients are negative and marginally insigni…cant, indicating that children in the lowtreatment region improved health slightly more. The results are rather consistent with thefact that post-apartheid policies were disproportionately targeted into deprived areas. In
terms of the quality change in health services, the results re‡ect anecdotal evidence that
quality improvement was an even more di¢ cult task than constructing new clinics in theSouth African labor market context. Constrained by the existing labor agreements, transferring sta¤ from one place or facility to another was restricted (Lucy Gilson et al. 1999).
The working conditions worsened partly because the new health policy did not amelioratesta¢ ng costs, which remained to account for a large amount of health expenditure (McCoy
1996). As a result, it led to sta¤ shortages, and overburdened existing personnel, whichfurther contributed to lowering sta¤ morale and to raising barriers to providing high-quality
services. Furthermore, the overcrowding of patients and the lack of drugs were left unsolved.
Various studies show that patients perceived the quality of services deteriorated since 1994
(McCoy 1996; Sharon Fonn et al. 1998; CASE 1999; Gilson et al. 1999; Di McIntyre andBarbara Klugman 2003; Gilson and McIntyre 2007).
In summary, unobservable changes in other aspects of society do not appear to be confounding factors. Instead, removing fees, particular to young children, had immediate e¤ects
on lowering barriers in access to health care and contributed to health development. This
20

Actually, a new policy was passed in 1997, and it eliminated all user fees at primary care in the public
sector. This means that even these cohorts, who were certainly not a¤ected by the new reproductive healthpolicy in 1994, were a¤ected and gained access to health services. Yet, I simply do not account for thispolicy since it does not alter my assumptions or results.

20

does not rule out the importance of quality improvement. Rather, this simply indicates thatthe environment in South Africa helps separate the pure e¤ect of access from quality change,
and interactions between quantity and quality should certainly promote further health development. The trade-o¤ between these two should be an interesting topic for future research.

6.3

Identifying the Mechanism: Access versus Alternative Hypotheses

The e¤ects, presented so far, suggest that gaining access to health care, by removing the…nancial burden, is a driving factor of health improvement. As robustness check of the causal
mechanism, this subsection considers alternative channels for the results.

6.3.1

The Migration to Regions near Clinics

The presumption that black families had limited impacts over the initial allocation of healthfacilities does not rule out the possibility that they could still migrate to regions nearby
clinics. Yet, this is not a case for blacks prior to the fall of apartheid. As described, underapartheid, millions of black Africans were forced into homelands. They had little freedom
to choose a place to live, and their mobility was severely restricted, making it di¢ cultto migrate to areas with better health infrastructure. However, the demise of apartheid
also freed black Africans to migrate out, which leads to a change in composition of sample
population in the high and low treatment regions. To avoid this geographical self-selection
bias after 1994, I use communities that households lived in 1993 as their residential location,
instead of current communities. Both are highly correlated, as approximately 95.3 percentof the sample households still live in the same community as in 1993, yet the former ispredetermined and not endogenous to selection into the high treatment region. Nonetheless,I repeat the main analysis using only the samples who did not migrate by 1998, shown in

21

Table A1. The main results do not alter, suggesting that migration itself cannot explain themain results.

6.3.2

Health Services versus Health Education

Another issue is that the e¤ect of gaining access to clinics is not due to health services but
due to health education provided at clinics. It is widely observed in developing countries thatparents lack basic knowledge or information on sanitation and child health.21 For example,
CASE (1995) shows that 23 percent of black African households use unpiped water, such as
from a river, stream, or dam, and 89 percent of them drink it without boiling it. To spreadbasic health information to the public, clinics and other public health facilities served asimportant bases of educational campaign. While it is di¢ cult to isolate e¤ects through suchchannel, aforementioned two pieces of evidence support the argument that health education
does not appear to be a key factor. One is that, as shown in Table 4, positive healthimprovements are observed only for boys. If health education mattered more, this would
be conceivably implausible because implementing sanitary behaviors is mostly costless, e.g.,boiling water or washing hands, and thus it involves no need to exclude girls. Furthermore,as Panel D of Table 7 presents, there was no e¤ect on non-exposed cohorts. Even if there
existed gender bias in sanitary controls, this placebo experiment would show positive e¤ects
if health education were a key determinant. Therefore, health education cannot simply
explain the positive association between availability of clinics and health improvements.

6.3.3

Fertility

A further concern is that the free antenatal services might have altered black African women’s
fertility behaviors. The change could be either positive or negative. It could be positive if
women take advantage of the policy and become pregnant more frequently. It could be
21

It is well-known that PROGRESSA in Mexico required parents to receive health education in order toobtain a cash transfer.

22

negative if women take into account a decrease in infant mortality through antenatal careand lower the number of births. The reason why this mechanism could potentially bias
the results is that it shifts the distribution of health status among newly born babies; withincreased fertility, the estimates provide lower bounds since less healthy babies, who had notbeen born alive previously, were born alive now. Or, with decreased fertility, the estimates
are upwardly biased, erroneously picking up e¤ects of expanded intrahousehold allocationof resources to each child. Nonetheless, extensive studies show that there has not been any
changes in fertility (McCoy 1996; Schneider and Gilson 1999; Tom A. Moultrie and Ian M.
Timaeus 2003), suggesting that fertility is unlikely to generate bias in the analyses.

7

Conclusion

This paper exploits exogenous variation in access to health infrastructure brought by the
new reproductive health policy in the post-apartheid period in South Africa. This quasiexperimental design provides a rare opportunity to address the e¤ects of gaining access to
medical services on health status among children. The analysis employs a di¤erence-indi¤erences approach to examine how the health status of children who obtained access tohealth services improves.
The results suggest that free health services have improved the health status of not only
newly born babies but also of children who were already born when the policy was initiated.
Such positive e¤ects are observed only for boys but not for girls. The results highlightan unequal intrahousehold allocation of resources that favors boys. Further evidence helps
identify that access to health services is the key mechanism through which child health status
has been improved.
These results present several important policy implications for South Africa and other lessdeveloped countries. First, freeing user fees is e¤ective in increasing access in an environment
where poor households face signi…cant budget constraint. Second, bene…ts of increasing
23

access are substantial during early childhood even when some quality deterioration of theservices are evident. While this promotes further expansion in investing the quantity of …scal
infrastructure, the combination of quantity and quality betterments should boost child healthstatus. Third, merely increasing access to health services does not solve gender inequality.
The pattern of this …nding is consistent with Oster (2009), who argues that a simple increasein overall level of access to social programs is not su¢ cient; it may not bene…t women but
even lead to greater gender inequality. Understanding the precise mechanism of gender bias
in medical resources and how interventions should be made to bene…t girls is an important
topic for future research.

24

References[1] African National Congress. 1994. A National Health Plan for South Africa. Paper prepared by the ANC with the technical support of WHO and UNICEF, Johannesburg.
[2] Alderman, Harold, Jere R. Behrman, Victor Lavy, and Rekha Menon. 2001. "Child
Health and School Enrollment: A Longitudinal Analysis." Journal of Human Resources, 36(1): 185-205.
[3] Alderman, Harold, John Hoddinott, and Bill Kinsey. 2006. "Long Term Consequencesof Early Childhood Malnutrition." Oxford Economic Papers, 58: 450-474.
[4] Almond, Douglas. 2006. "Is the 1918 In‡uenza Pandemic Over? Long-term E¤ects of
In Utero In‡uenza Exposure in the Post-1940 U.S. Population." Journal of Political
Economy, 114(4): 672-712.
[5] Almond, Douglas, Kenneth Y. Chay, and Michael Greenstone. 2007. "Civil Rights, theWar on Poverty, and Black-White Convergence in Infant Mortality in the Rural
South and Mississippi." Unpublished.
[6] Asenso-Okyere, W. K., Adote Anum, Isaac Osei-Akoto, and Augustina Adukonu. 1998.
"Cost Recovery in Ghana: Are There Any Changes in Health Care Seeking Behaviour?" Health Policy and Planning, 13(2): 181-188.
[7] Banerjee, Abhijit, Angus Deaton, Esther Du‡o. 2004. "Wealth, Health, and HealthServices in Rural Rajasthan." American Economic Review Papers and Proceedings,
94(2): 326-330.
[8] Barber, Sarah L., and Paul J. Gertler. 2002. "Child Health and the Quality of Medical
Care." Unpublished.
[9] Bleakley, Hoyt. 2007. "Disease and Development: Evidence from Hookworm Eradicationin the American South." Quarterly Journal of Economics, 122(1): 73-117.

25

[10] Bobonis, Gustavo J., Edward Miguel, and Charu Puri-Sharma. 2006. "Anemia andSchool Participation." Journal of Human Resources, 41(4): 692-721.
[11] Cameron, Robert. 1996. "The Reconstruction and Development Programme." Journalof Theoretical Politics, 8(2), 283-294.
[12] Carter, Michael R., and John A. Maluccio. 2003. "Social Capital and Coping with
Economic Shocks: An Analysis of Stunting of South African Children." World Development, 31(7): 1147-1163.
[13] Case, Anne, and Angus Deaton. 1999. "School Inputs and Educational Outcomes inSouth Africa." Quarterly Journal of Economics, 114(3): 1047-1084.
[14] Case, Anne, Darren Lubotsky, and Christina Paxson. 2002. "Economic Status and
Health in Childhood: The Origins of the Gradient." American Economic Review,
92(5): 1308-1334.
[15] Case, Anne, Angela Fertig, and Christina Paxon. 2005. "The Lasting Impact of Childhood Health and Circumstance." Journal of Health Economics, 24: 365-389.
[16] Chay, Kenneth, and Michael Greenstone. 2000. "The Convergence in Black-White InfantMortality Rates during the 1960s." American Economic Review, 90(2): 326-332.
[17] Community Agency for Social Enquiry (CASE). 1995. A National Household Surveyof Health Inequalities in South Africa. Prepared for the Henry J. Kaiser Family
Foundation: Johannesburg.
[18] Community Agency for Social Enquiry (CASE). 1999. The Second Kaiser Family Foundation Survey of Health Care in South Africa. Prepared for the Henry J. Kaiser
Family Foundation: Johannesburg[19] Cooper, Diane, Chelsea Morroni, Phyllis Orner, Jennifer Moodley, Jane Harries, Lee
Cullingworth, and Margaret Ho¤man. 2004. "Ten Years of Democracy in South
Africa: Documenting Transformation in Reproductive Health Policy and Status."
Reproductive Health Matters, 12(24), 70-85.
26

[20] Currie, Janet. 2008. "Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Healthin Childhood, and Human Capital Development." National Bureau of Economic
Research Working Paper 13987.
[21] Currie, Janet and Wanchuan Lin. 2007. "Chipping Away at Health: More on the Relationship between Income and Child Health." Health A¤airs, 26(2): 331-344.
[22] Das, Jishnu and Je¤rey Hammer. 2007. "Money for Nothing: The Dire Straits of Medical
Practice in Delhi, India." Journal of Development Economics, 83(1): 1-36.
[23] Deaton, Angus. 2006. "Global Patterns of Income and Health: Facts, Interpretations,and Policies." National Bureau of Economic Research Working Paper 12735.
[24] Deininger, Klaus and Paul Mpuga. 2004. "Economic and Welfare E¤ects of the Abolitionof Health User Fees: Evidence from Uganda." World Bank Policy Research Working
Paper 3276. The World Bank: Washington D.C.
[25] Department of Health. 1995. Maternal Child and Women’s Health Draft Policy Document. Department of Health: Pretoria.
[26] Department of Health. 1998. Department of Health Achievements and Highlights of 1997.
Department of Health: Pretoria.
[27] Department of Health. 2000. Health Sector Strategic Framework 1999-2004. Department
of Health: Pretoria.
[28] Dinkelman, Taryn. 2008. "The Long-term E¤ects of Being Born in a Drought: Evidence
from the Cape Area Panel Study 2002-2006." Unpublished.
[29] Du‡o, Esther. 2001. "Schooling and Labor Market Consequences of School Constructionin Indonesia: Evidence from the Unusual Policy Experiment." American Economic
Review, 91(4): 795-813.

27

[30] Fonn, Sharon, Makosazana Xaba, Kin San Tint, Daphney Conco, Sanjani Varkey. 1998.
"Reproductive Health Services in South Africa: From Phetoric to Implementation."
Reproductive Health Matters, 6(11): 22-32.
[31] Gilson, Lucy, Jane Doherty, Di McIntyre, Stephen Thomas, Vishal Briljal, Chris Bowa.
1999. The Dynamics of Policy Change: Health Care Financing in South Africa, 19941999. Centre for Health Policy (University of the Witwatersrand) and the Health
Economics Unit (University of Cape Town):Johannesburg.
[32] Gilson, Lucy and Di McIntyre. 2007. "Post-Apartheid Challenges: Household Accessand Use of Health Care in South Africa." International Journal of Health Services,37(4): 673-691.
[33] Glewwe, Paul and Elizabeth M. King. 2001. "The Impact of Early Childhood Nutritional
Status on Cognitive Development: Does the Timing of Malnutrition Matter?" WorldBank Economic Review, 15(1): 81-113.
[34] Glewwe, Paul, Hanan G. Jacoby, and Elizabeth M. King. 2001. "Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis." Journal of Public
Economics, 81(3): 345-368.
[35] Holla, Alaka, and Michael Kremer. 2009. "Pricing and Access: Lessons from Randomized Evaluations in Education and Health." Center for Global Development Working
Paper 158. Center for Global Development:Washington, D.C.
[36] Khan, Sultan, Benoit Lootvoet, and Shahid Vawda. 2006. "Transcending Traditional
Forms of Governance: Prospects for Co-operative Governance and Service Delivery
in Durban’s Tribal Authority Areas." Transformation, 62: 84-117.
[37] Klasen, Stephan. 1997. "Poverty, Inequality and Deprivation in South Africa: An Analysis of the 1993 Saldru Survey." Social Indicator Research, 41: 51-94.

28

[38] Klugman, Barbara, and Di McIntyre. 2000. "From Policy, Through Budgets, to Implementation: Delivering Quality Health Care Services.”In The Fifth Women’s Budget,ed. Budlender D, Cape Town: IDASA[39] Leibbrandt, Murray, and Ingrid Woolard. 1999. "A Comparison of Poverty in South
Africa’s Nine Provinces." Development South Africa, 16(1): 37-54.
[40] Maccini, Sharon and Dean Yang. 2009. "Under the Weather: Health, Schooling, andEconomic Consequences of Early-Life Rainfall." American Economic Review, 99(3):1006-1026.
[41] Maharaj, Pranitha, and John Cleland. 2005. "Integration of Sexual and Reproductive
Health Services in KwaZulu-Natal, South Africa." Health Policy and Planning, 20(5),310-318.
[42] May, Julian, Michael R. Carter, Lawrence Haddad, and John Maluccio. 2000. "KwaZuluNatal Income Dynamics Study (KIDS) 1993-1998: A Longitudinal Household Data
Set for South Africa Policy Analysis." Development Southern Africa, 17(4), 567-581.
[43] May, Julian, Jorge Aguero, Michael R. Carter, and Ian M. Timaeus. 2007. "TheKwaZulu-Natal Income Dynamics Study (KIDS) Third Wave: Methods, First Findings and an Agenda for Future Research." Development Southern Africa, 24(5),629-648.
[44] McCoy, David. 1996. Free Health Care for Pregnant Women and Children Under Six inSouth Africa: An Impact Assessment. Durban: Health Systems Trust, Child Health
Unit (University of Cape Town).
[45] McIntyre, Di, and Barbara Klugman. 2003. "The Human Face of Decentralization andIntegration of Health Services: Experience from South Africa." Reproductive HealthMatters, 11(21), 108-119.

29

[46] Moultrie, Tom A. and Ian M. Timaeus. 2003. "The South African Fertility Decline:
Evidence from Two Censes and a Demographic and Health Survey." Population
Studies, 57(3): 265-283.
[47] Oster, Emily. 2009. "Does Increased Access Increase Equality? Gender and Child HealthInvestments in India." Journal of Development Economics, 89(1): 62-76.
[48] Pritchett, Lant, and Lawrence H. Summers. 1996. "Wealthier is Healthier." Journal of
Human Resources, 31(4): 841-868.
[49] Republic of South Africa. 1994. White Paper on Reconstruction and Development. Government’s Strategy for Fundamental Transformation. Pretoria: Government Printer.
Cape Town: WPJ/1994.
[50] Schneider, Helen and Lucy Gilson. 1999. "The Impact of Free Maternal Health Care in
South Africa." In Safe motherhood Initiatives: Critical Issues, ed. Marge Bere andT.K. Sundari Ravindran, 93-101. Oxford, England: Blackwell Science.
[51] WHO Multicentre Growth Reference Study Group. 2006. WHO Child Growth Standards: Length/Height-for-age, Weight-for-age, Weight-for-length, Weight-for-heightand Body Mass Index-for-age: Methods and Development. Geneva: World HealthOrganization.
[52] Wilkinson, David, Eleanor Gouws, Marlene Sach, and Salim S. Abdool Karim. 2001.
"E¤ect of Removing User Fees on Attendance for Curative and Preventive Primary
Health Care Services in Rural South Africa.” Bulletin of the World Health Organization, 79(7), 665-671.
[53] Yamauchi, Futoshi. 2008. "Early Childhood Nutrition, Schooling and Sibling Inequality
in a Dynamic Context: Evidence from South Africa." Economic Development andCultural Change, 56:657-682.

30

31

Notes: The …gure plots the mean value of WAZ by age and region. WAZ
below -6 and above 5 are removed as outliers, as these numbers are seen
as biologically implausible. The sample contains children aged 0 to 7 in
KIDS93. Ages are calculated from ages in month, from which I denote age
0 month to 6 months as 0 year old, 6 months to 12 months as 0.5 year old,and so forth.

Panel A: 1993

Panel B: 1998

Notes: The …gure plots the mean value of WAZ by age and region. WAZ
below -6 and above 5 are removed as outliers, as these numbers are seen
as biologically implausible. The sample contains children aged 0 to 12 in
KIDS98. The dashed line indicates the timing of policy change; cohorts
to the right are not a¤ected by the policy, whereas cohorts to the left are
partially or fully a¤ected by the policy. The dashed line is drawn at age 9.5
year old since the policy was iniated in June 1994, and most of the samples
in KIDS98 were surveyed from March to May in 1996. According to mycalculation of ages, children at exactly 6 years old at the time of policychange are 9.5 years old in KIDS98.

Figure 1: Average weight-for-age z-scores by region

Table 1: Summary Statistics of Baseline Sample in KIDS93
Variables
High
Low
Di¤.
Panel A: Child characteristics
Age
3.28
3.39
-0.111[1.60][1.64](.114)
Weight (kg)
13.02
13.04
-0.022[4.11][4.09](.290)Height (cm)
87.6
88.15
-0.551[14.29][15.25](1.062)
Panel B: Household characteristics
Monthly income
1037.36 1038.61-1.250[984.66] [1150.63] (99.25)
Father’s education
4.33
3.91
0.426
[3.43][3.24](.448)
Mother’s education
5.34
4.85
0.494
[3.39][3.36](.329)
Panel C: Community characteristics
No. of communities
26
28
No. of observations
350
482
No. of primary schools
2.42
1.39
1.03**[2.35][1.34](.52)
No. of other types of
0.462
0.036
0.426***
health infrastructures[.81][.036](.158)
Notes: The table provides summary statistics of variable means as of 1993.
Panel A reports characteristics of children under age 7 whose anthropometric data are available. Panel B presents household characteristics of thesechildren, and Panel C shows characteritics of communities these households
resided. Other types of health infrastructure include public hospital, dispensaries, and maternity home. Column 1 consists of the high treatment
region, where there was a clinic in 1993, whereas Column 2 contains the lowtreatment region, where there was no clinic. Column 3 estimates the di¤erence in means between the two regions under the null hypothesis that there
is no di¤erence in the means. Standard deviations are in square brackets
and standard errors are in parentheses.
Signi…cant at p<0.01***, p<0.05**, p<0.1* levels.

32

Table 2: Di¤erence-in-di¤erences matrix in means of health status by cohort and level of
exposure to the policy
Panel A: Age level analysis
weight-for-age z-score
High
Low
Di¤.
Young Cohort:
Aged 0 to 3 in KIDS98
Observations

0.321(.106)[212]-0.069(.079)[288]

0.391(.130)Old Cohort:-0.545 -0.414 -0.131
Aged 0 to 3 in KIDS93 (.110) (.089) (.140)
Observations
[246]
[325]
Di¤erence

0.867(.154)

0.345(.121)

0.522(.258)

Notes: Panel A compares means of each variable for respective cohort and region forthe same age group. The young cohort consists of children aged 0 to 3 in KIDS98,
who are fully exposed to the policy. The old cohort consists of children aged 0 to 3 in
KIDS93, who have no exposure to the policy. Standard errors are in parentheses, andnumber of observations in each cell is provided in square brackets. Standard errors forthe di¤erence-in-di¤erences are clustered at the community*Cohort level.

Panel B: Cohort level analysis
weight-for-age z-score
High
Low
Di¤.
Young Cohort:
Aged 5 to 8 in KIDS98
Observations

0.045(.077)[283]-0.345(.062)[341]

0.39(.098)Old Cohort:-0.545 -0.414 -0.131
Aged 0 to 3 in KIDS93 (.110) (.089) (.140)
Observations
[246]
[325]
Di¤erence

0.59(.131)

0.069(.108)

0.521(.244)

Notes: Panel B compares means of each variable for the same cohort. The young cohort
consists of children aged 5 to 8 in KIDS98. The old cohort consists of children aged 0 to
3 in KIDS93. Although I use the term "young cohort" and "old cohort" to be consistentthroughout the paper, these are the same cohort observed across time.

33

Panel C : Placebo experiment in pre-treatment period
weight-for-age z-score
High
Low
Di¤.
Young Cohort:-0.545 -0.414 -0.131
Aged 0 to 3 in KIDS93 (.110) (.089) (.140)
Observations
[246]
[325]
Old Cohort:-0.668 -0.754
Aged 4 to 7 in KIDS93 (.140) (.120)
Observations
[103]
[147]

0.086
(.185)

Di¤erence-0.217(.272)

0.122(.192)

0.339(.155)

Notes: Panel C compares means of each variable in the placebo experiment as of 1993.
The young cohort consists of children aged 0 to 3 in KIDS93, and the old cohort
consists of children aged 4 to 7 in KIDS93. Both of the cohorts are not exposed tothe policy.

Panel D: Placebo experiment in post-treatment period
weight-for-age z-score
High
Low
Di¤.
Young Cohort:-1.016 -0.830 -0.186
Aged 10 to 12 in KIDS98 (.095) (.079) (.124)
Observations
[76]
[114]
Old Cohort:
Aged 5 to 7 in KIDS93
Observations-0.653 -0.981(.193) (.174)
[52]
[66]

0.328
(.261)

Di¤erence-0.362(.197)-0.514(.298)

0.151(.168)

Notes: Panel D compares means of each variable in the placebo experiment acrosstime. The young cohort consists of children aged 10 to 12 in KIDS98, and the oldcohort consists of children aged 5 to 7 in KIDS93. Samples are not entirely the same
individuals due to minor di¤erences in the timing of data collection, which slightly
changed individuals contained in each cohort since it is sorted by age in years at thetime of data collection. However, it intends to trace the same cohort who has no
exposure to the policy. Age 7 years old as of 1993, and thus age 12 years old as of1998, is the oldest children whose anthropometric data are available.

34

Table 3: Determinants of health status in age level analysis
Dependent Variable
Weight-for-age z-scores
Variables
(1)
(2)
(3)
(4)
(5)
High*Young
0.522**
0.571*** 0.515*** 0.692*** 0.638***(.258)(.186)(.179)(.212)(.203)
Young cohort
0.345**(.136)
High-treatment regions-0.131(.196)
Household characteristics
Father’s education
0.015
0.016
(.018)(.017)
Mother’s education-0.014-0.014(.016)(.016)
Household size
-0.014-0.014(.010)(.011)
Log monthly income
0.154***
0.155***(.055)(.054)
Community characteristics
Immunization campaign
0.902*** 0.925***
since 1993(.225)(.224)
No. of other health-0.321** -0.330**
centers in 1993(.136)(.136)
Constant-0.414*** -2.313*** -3.31*** -3.217*** -4.298***(.115)(.674)(.792)(.736)(.848)
Cohort FE
No
Yes
Yes
Yes
Yes
Community FE
No
Yes
Yes
Yes
Yes
Observations
1071
1071
1071
1071
1071
2
R
0.04
0.2
0.21
0.20
0.21
Notes: The table presents estimates of the e¤ect of gaining access to health infrastructure on
health status among children aged 0 to 3 in KIDS98. The sample corresponds to Panel A of
Table 2. The dependent variable is health status, measured by WAZ. WAZ below -6 and above
5 are removed as outliers, as these numbers are seen as biologically implausible. Observations
are at the individual level. Robust standard errors are clustered at the community*Cohort
level. Column 1 provides estimates of equation 1 which simply controls for young and highdummy variables, whereas other columns provide estimates of equation 2, which includes eachcohort and community …xed e¤ects.
Signi…cant at p<0.01***, p<0.05**, p<0.1* levels.

35

36

(5)

0.323
0.43
0.384
(.220) (.267) (.255)
0.24
0.22
0.24

(4)

0.385 0.329* 0.263 0.314 0.238(.269) (.192) (.186) (.210) (.205)
0.01
0.18
0.2
0.18
0.2
No
No
Yes
No
Yes
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes

0.374
(.228)
0.22

(2)

Girls
(3)

Notes: The table estimates whether there are any di¤erences in the e¤ect of gaining access to health infrastructure by gender. Only thedi¤erence-in-di¤erences estimates are shown. The sample of Panel A corresponds to Panel A of Table 2, and the sample of Panel B corresponds
to Panel B of Table 2, both of which are now sorted by gender. The numbers of observations are 551 for boys and 520 for girls in Panel A,
and 611 for boys and 584 for girls in Panel B. The dependent variable is health status, measured by WAZ. WAZ below -6 and above 5 areremoved as outliers, as these numbers are seen as biologically implausible. Observations are at the individual level. Household and community
characteristics are the same as before. Robust standard errors in the parentheses are clustered at the community*cohort level.
Signi…cant at p<0.01***, p<0.05**, p<0.1* levels.

Panel B: Cohort level analysis
High*Young
0.642** 0.842*** 0.781*** 1.127*** 1.049***(.331)(.278)(.259)(.353)(.334)
2
R
0.03
0.25
0.26
0.26
0.27
Household characteristics
No
No
Yes
No
Yes
Community characteristics
No
No
No
Yes
Yes
Cohort FE
No
Yes
Yes
Yes
Yes
Community FE
No
Yes
Yes
Yes
Yes

Table 4: Determinants of health status in age level analysis by gender
Dependent Variableweight-for-age z-score
Boys
Variables
(1)
(2)
(3)
(4)
(5)
(1)
Panel A: Age level analysis
High*Young
0.640** 0.741*** 0.697*** 1.012*** 0.969***
0.39(.326)(.265)(.259)(.311)(.305)(.312)
2
R
0.06
0.29
0.30
0.30
0.31
0.02

Table 5: Determinants of health status in cohort level analysis
Dependent Variable
Weight-for-age z-scores
Variables
(1)
(2)
(3)
(4)
(5)
High*Young
0.521** 0.544*** 0.484*** 0.638*** 0.566**(.244)(.192)(.179)(.240)(.228)
Young cohort
0.069(.134)
High-treatment regions-0.131(.196)
Household characteristics
Father’s education
0.005
0.005
(.014)(.014)
Mother’s education-0.008-0.008(.014)(.014)
Household size
-0.028***-0.028***(.010)(.010)
Log monthly income
0.148***
0.148***(.055)(.055)
Community characteristics
Immunization campaign
0.213
0.265
since 1993(.164)(.168)
No. of other health-0.205-0.190centers in 1993(.139)(.136)
Constant-0.414*** 3.435*** 2.44*** 2.567*** 1.445**(.115)(.219)(.601)(.255)(.594)
Cohort FE
No
Yes
Yes
Yes
Yes
Community FE
No
Yes
Yes
Yes
Yes
Observations
1195
1195
1192
1195
1192
2
R
0.02
0.16
0.17
0.16
0.18
Notes: The table estimates the e¤ect of gaining access to health infrastructure on health statusamong children aged 5 to 8 in KIDS98. The sample corresponds to Panel B of Table 2. The
rest is the same as Table 3
Signi…cant at p<0.01***, p<0.05**, p<0.1* levels.

37

Table 6: Determinants of clinical availability
Independent variable
Mean total household income
0.049(thousands)(.092)
Mean household size
-0.069(.044)
Mean years of paternal education
0.005(.052)
Mean years of maternal education-0.021(.061)
2R
0.11
Notes: This table provides estimates of whether thereis evidence that initial availability of clinics as of 1993
is based on any household characteristics. Observations are at the household levels, from which eachvariable mean is calculated at the community level.
Robust standard errors are in parentheses, clustering
at the community level. The regression includes indicators for metro/urban/rural area. The dependent
variable is a dummy taking a value of 1 if a community
is in the high treatment region, and 0 otherwise.

38

Table 7: Placebo Experiment

Variables(1)
Panel C: Pre-treatment period
High*Young-0.217(.272)
2R
0.01

Dependent Variableweight-for-age z-score
(2)
(3)
(4)
-0.145(.188)
0.19-0.157(.190)
0.20(5)-0.145 -0.157(.188) (.190)
0.19
0.20

Panel D: Post-treatment period
High*Young-0.513* -0.444 -0.479* -0.479 -0.506(.298) (.273) (.274) (.315) (.316)
2
R
0.01
0.23
0.24
0.23
0.24
Household characteristics
No
No
Yes
No
Yes
Community characteristics
No
No
No
Yes
Yes
Cohort FE
No
Yes
Yes
Yes
Yes
Community FE
No
Yes
Yes
Yes
Yes
Notes: Panel C tests whether there is evidence that children in each region show
any di¤erential trends in health development. The sample corresponds to Panel Cof Table 2. Panel D estimates whether the availability of clinics in the community asof 1993 had any impacts on cohorts who were not exposed to the policy. The sample
corresponds to Panel D in Table 2. The rest is the same as Table 3.
Signi…cant at p<0.01***, p<0.05**, p<0.1* levels.

Table 8: Construction of new clinics
Independent variableHigh-0.879***(.220)
Constant
1.071***(.153)
Observations
54
2
R
0.24
Notes: The table tests whether there is evidence that clinical constructions conducted
after 1994 are based on the initial availability of clinics as of 1993. The dependent
variable is the number of clinics built between 1993 and 1998, and the observation
is at the community level. High refers to
the high treatment region.
Signi…cant at p<0.01***.

39

Appendix
Figure A1: Average weight-for-age z-scores by gender in 1993

Notes: The …gure plots the mean value of WAZ by age and gender. The rests
are the same as Figure 1 Panel A.

40

Table A1: Removing migrants from the main analyses
Panel A: Age level analysis
Weight-for-age z-scores
High
Low
Di¤.
Young Cohort:
Aged 0 to 3 in KIDS98
Observations

0.346(.110)[204]-0.055(.080)[282]

0.401(.133)Old Cohort:-0.545 -0.414
Aged 0 to 3 in KIDS93 (.110) (.089)
Observations
[246]
[325]

-0.131(.140)

Di¤erence

0.532(.261)

0.891(.157)

0.359(.121)

Notes: The observation in 1998 are restricted to thehouseholds who resided in the same communities as in1993. The rests are the same as Table 2.

Panel B: Cohort level analysis
Weight-for-age z-scores
High
Low
Di¤.
Young Cohort:
Aged 5 to 8 in KIDS98
Observations

0.068(.077)[277]-0.348(.063)[331]

0.416(.099)Old Cohort:-0.545 -0.414
Aged 0 to 3 in KIDS93 (.110) (.089)
Observations
[246]
[325]

-0.131(.140)

Di¤erence

0.547(.243)

0.613(.132)

Notes: Same as Panel A.

41

0.066
(.111)

Table A2: Cohort-by-cohort e¤ects of clinics on health status
Dependent variableweight-for-age z-score
Speci…cation 1
Age in 1998 Yrs of exp.
(1)
(2)
(3)
0
1
1.187*** 1.140**
1.862***(.449)(.539)(.604)
1
2
0.388
0.774*
0.137(.289)(.409)(.421)
2
3
0.235
0.909*-0.384(.298)(.468)(.292)
3
4
0.773*** 1.225***0.347(.265)(.379)(.301)
45
0.235
0.657*-0.175(.303)(.386)(.384)
5
50.705** 0.936**
0.471(.328)(.458)(.395)
6
4
0.670** 1.444***-0.129(.284)(.396)(.398)
7
3
0.269
0.51
0.071
(.257)(.401)(.300)
82
0.418
0.883**-0.022(.266)(.419)(.290)
9
1
0.181
0.361
0.12
(.243)(.340)(.253)
10
0-0.290-0.070-0.505*(.233)(.334)(.268)
Samples:
Boys
Yes
Yes
No
Girls
Yes
No
Yes
R2
0.14
0.17
0.18
Observations
2345
1191
1154
Notes: The table reports di¤erent magnitudes of e¤ects
P across each age.0 The
estimates are based on regression of Wihcl = 0 + il l High c
il + Xh 1 +(Xc0 Youngl ) 2 + c + l + "ihcl , where il = 1 if individual i is at age l.
Robust standard errors in the parentheses are clustered at the community*cohort level.

42