DOES MOTHER’S EDUCATION MATTER IN CHILD’S HEALTH? EVIDENCE FROM SOUTH AFRICA doc

South African Journal of Economics Vol 76:4 December 2008 DOES MOTHER’S EDUCATION MATTER IN CHILD’S HEALTH? EVIDENCE FROM SOUTH AFRICA1 patricia medrano*, catherine rodri guez† and edgar villa‡ ´ Abstract Using the 1993 South Africa Integrated Household Survey, this paper studies the effect that mother’s education through the knowledge channel has on children’s health using height for age Z-scores as health measure Under a two-stage least square methodology we find that an increase in years on mother’s education (approximately standard deviation) will lead to an increase of 0.6 standard deviations on her child’s height for age Z-score We also find, as the medical literature suggests, support for the hypothesis that mother’s education is more important for children older than 24 months of age JEL Classification: I12, I21, O15, D1 Keywords: Education, health, Z-score INTRODUCTION Poverty can be thought of as a vicious circle Low-income households have less education and are probably less healthy than wealthier ones, making it very difficult for them to leave that poverty state through their own work and effort It has been previously established that investments in children’s health and education could help break this circle by enhancing individual’s future income through their future productivity For instance, Currie and Hyson (1999), Case et al (2003) and Behrman and Rosenzweig (2004) have found strong links between child’s health and future income, education attainment and adult health Not surprisingly, many of the Millennium Development Goals are related to the improvement of children’s education and health.2 In this paper we touch on this important subject and asses the impact that investment in mother’s education through the knowledge channel has on children’s health measured as height for age Conditions within a household are important determinants of a child’s health and as one might expect parents play a central role in “producing children with good health” Specifically, the “mother” has been described as the most important health worker in the household Nonetheless, how well a mother performs this task may depend on factors We are grateful for the advice and helpful comments of Julie Anderson Schaffner and the participants of the Development Seminar at Boston University on an earlier draft We also thank the comments and suggestions received from an anonymous referee All views and errors are exclusively those of the authors The authors also acknowledge the financial support from the Iniciativa Científica Milenio to the Centro de Microdatos (Project P07S-023-F) * Centro de Microdatos, Department of Economics, University of Chile; pmedrano@ econ.uchile.cl † Universidad de los Andes; corresponding author: cathrodr@uniandes.edu.co ‡ Pontificia Universidad Javeriana; e.villa@javeriana.edu.co Among the goals we find the reduction of child mortality, eradicate hunger, achieve universal primary education and improve maternal health © 2008 The Authors Journal compilation © 2008 Economic Society of South Africa Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA 612 South African Journal of Economics Vol 76:4 December 2008 613 such as her own health, the resources that are available to her and her schooling In particular, it has sometimes been argued that schooling equips the mother with a specific knowledge that enhances her ability to generate healthier children This paper quantifies the relationship between this key factor and child’s health using South African data from the 1993 Integrated Household Survey (LSMS) The magnitude of this relationship has important policy implications If there is a positive effect of mother’s education as knowledge on children’s health, certain type of policies concerning the diffusion of specific information at community levels can be called for For example, one important channel through which mother’s education as knowledge can affect child’s health at a community level is through the usage of health facilities which can serve as a complement or even as a substitute for the mother’s education Therefore, a policy concern that we examine is the impact of health facilities interacted with mother’s education as knowledge Finally, motivated by the U shape pattern in children’s health that the medical and public health literature suggests exists, we study whether mother’s education affects in different ways children of different age groups Even though this question has been previously addressed in the literature, the South Africa of the early 90s is an interesting place to re-examine it because of the wide disparity in income and health between poor and rich households During the Apartheid era, South Africa was an environment where the Black communities in the rural areas did poorly on most of the indicators of well-being, one of them being health status The results found show that mother’s education has a positive and significant impact on children’s long-run health proxied by their height for age Z-score We find for an ordinary least squares (OLS) regression that mother’s with more years of education (approximately standard deviation) increase on average 0.1 standard deviations a child’s height for age Z-score This effect increases to 0.6 standard deviations when mother’s education, household disposable income and father’s education are instrumented adequately We find that this effect is maintained for children between 24 and 72 months of age while vanishing for younger children Following this introduction we review some of the existing literature on this and related topics We then proceed to develop a conceptual framework to explain the possible transmission channels of mother’s education and the estimation problems that should be acknowledged in the empirical specifications Section four describes the data set we use for our empirical application and section five presents our findings Section six ends with conclusions LITERATURE REVIEW The empirical literature on human capital investment generally focuses on education and health investments using reduced form models that integrate the health production process with a model of household choice The demand studies for health outcomes usually evaluate the impact that household and community level characteristics, like the usage and availability of health facilities, may have on distinct health measures Among these, many have focused on the effect that mothers’ education has on children’s health measures such as birth weight, height and weight for age Z-scores or nutrient intake A comprehensive review of earlier studies can be found in Behrman and Deolalikar (1988) and Strauss and Thomas (1995) In this section, however, we review some studies that use © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 614 South African Journal of Economics Vol 76:4 December 2008 height for age Z-scores as a proxy of children’s health and hence are closely related to the present paper Probably, the closest related paper in the literature to this study is Glewwe (1999) Using cross-sectional data from Morocco, he estimates the impact that mothers’ health knowledge has on children’s long-term health status proxied by height for age Z-scores The empirical approach taken by the author is careful in two distinct aspects First, the database provides information that can help the researcher distinguish between general numeracy and literacy skills of the mother and their specific knowledge on health issues Second, given that variables such as health knowledge, household income and literacy and numeracy skills may be endogenous, the author uses an instrumental variable approach to correct for such possibility.3 While simple OLS coefficients suggest that the knowledge channel is not important in determining child’s health; when endogeneity issues are addressed the IV coefficient on mother’s knowledge increases and is highly significant In another study, Barrera (1990) assesses the efficiency and allocative effects of maternal education Using data from the Philippines he finds a positive impact of maternal education, which is stronger for younger children Similarly, Thomas et al (1991) study the impact of mothers’ education in child’s height for age Z-score The authors argue there are three channels through which mother’s education can improve a child’s health: income augmenting effects, information processing effects and interactive effects with community services Using information on Brazilian children they find that almost all the impact of maternal education can be explained by indicators of access to information which are proxied by the availability of media services in the community Using information on children from Côte d’Ivoire, Strauss (1990) evaluates whether differences in height and weight for age Z-scores can be explained by differences in the level of education of the parents, local wages and the availability in health, schooling and water infrastructure As the previously cited studies, the author worries about the assumption of exogeneity of observable control variables In order to reduce any potential bias to the coefficient of parents’ education caused by such endogeneity problems, Strauss (1990) includes mother’s standardised height to capture genetic and background characteristics of the family Moreover, he also presents fixed and random effects estimations in order to test for the sources of correlation The results show that after controlling for mother’s height, her education has a positive but small effect on child’s height Contrary to all these studies, Wolfe and Behrman (1987) not find an important effect of mother’s education on a child’s height for age Z-score Using a special sister sample from Nicaragua they can control for the mother’s background variables and find that the significance and magnitude of the impact of education disappears They conclude then that mothers’ education is simply serving as a proxy of her background and only has a significant effect on calorie intake measures In a novel and recent study, Chen and Li (2006) estimate the nurturing effect of mothers’ education on child’s health again proxied by height for age Z-scores According to the authors, while the nurturing effect should be related to mother’s education, the nature effect is caused by selection or omitted variables In order to obtain the former effect, the authors use information on Chinese adopted children which are of course Among the different instruments used by the author one finds household assets, exposure to mass media and the education of siblings of the mother among others © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 South African Journal of Economics Vol 76:4 December 2008 615 genetically unrelated to the nurturing parents implying that endogeneity problems should no longer exist Using simple OLS regressions the authors find a positive and significant nurturing effect of the mother Moreover, comparing the estimates with a sample of biological children they conclude that most of the impact on children’s health is not given by genetic factors but through the education channel This literature has also addressed the question of gender bias Thomas (1994) using data from United States, Brazil and Ghana finds that maternal education and non-labour income have a bigger impact on the height of a daughter relative to a son while paternal education has the opposite effect Namely father’s education has a bigger impact on a son relative to a daughter Duflo (2000) gives evidence that households not function as a unitary entity Using data from South Africa she finds a positive effect on girl’s anthropometric measures if a grandmother, rather than a grandfather, receives cash transfers by a social security program while no apparent effect is found for boy’s health status The general evidence found in the literature allows us to argue in favour of the hypothesis that there exists a positive effect of mother’s education on height for age measures of children The biggest concern is probably that reduced form estimates may understate the impact of this variable given its probable correlation with a child’s genetic background The following sections will address these concerns and evaluate whether the positive effects are also found in a representative sample of South African children which, to the best of our knowledge, has not been carried out yet CONCEPTUAL FRAMEWORK As mentioned above, in this paper we focus on the role that mother’s knowledge plays in enhancing future household productivity through child health investments However, we should be cautious in interpreting the different channels through which mother’s education could affect children’s health It is generally accepted that mother’s education can affect child health through the following five channels: (i) (ii) (iii) (iv) (v) It increases economic resources of the family by increasing own earnings It increases efficiency in the usage of available health facilities It can affect household preferences It improves allocation of resources due to better knowledge and information It can indicate wealth status and assortative mating Disentangling these different channels is crucial to assess the specific impact of mother’s education as knowledge on children’s health (channel iv) Health Production and Household Model In order to study the determinants of children’s health, the literature has traditionally used a standard household utility maximisation approach that depends on child’s health, which in turn is determined by a health production function.4 Following this literature, we assume that household i’s utility is an increasing function of the consumption of child health (Hi) given other non-health-related commodities of the household (Zi) and observable household characteristics Ci That is, utility will be described by the following A detailed description of these models can be found in Strauss and Thomas (1995) and Thomas et al (1991) © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 616 South African Journal of Economics Vol 76:4 December 2008 standard function Ui = U(Hi, Zi; Ci) As mentioned, child’s health is the output of a production function that depends on a child’s biological endowment (e), the effective units of mothers input (X*) and health services at the community level (HF) We summarise it as: Hi = h(ε i , X i *, HFi ) (1) Reasonably we assume that greater biological endowment as well as effective units of mother’s education and usage of health facilities infrastructure increase a child’s health, i.e Dh/De > 0, Dh/DX* > and Dh/DHF > Moreover, we assume that the effective units of mothers input depend on two factors: household goods such as appropriate food that serve as inputs for child care (W) and household child care knowledge (CK) which uses all these inputs in an efficient manner Under this assumption we can express Xi* = k(Wi, CKi) where k(·) represents the functional form by which the effective units of mothers input is formed Crucially we assume that household child care knowledge is determined by formal education of household members This can be justified in the sense that skills to read and write within a household generate better child care knowledge and therefore a more efficient technology This can be formalised as CK i = r(MEDi , FEDi ) (2) where years of mother’s and father’s formal education are denoted by MED and FED, respectively We conjecture that greater formal education increases household child care knowledge, i.e Dr/DMED > and Dr/DFED > Hence, a functional relation can be represented by: X * = k(Wi , r(MEDi , FEDi )) i (3) We conjecture about the following relations that affect effective units of mother’s input: (i) Inputs W should have a marginal positive effect on effective units of mother’s input (ii) Mother’s and father’s formal education should have a positive effect on effective units of mother’s input only through child care knowledge, i.e DXi*/DMED > and DXi*/DFED > To close the model, we assume a traditional budget constraint for the household of the form: Yi = PWWi + PZZi + PHFHFi; where Pj is the vector price of goods j = W, Z, HF and Yi is disposable income of household i The household will naturally maximise its utility subject to the production function of health (1) and its budget constraint The model thus yields a reduced form demand function for child health given by: Hi = g(Yi , PW , PZ , PHF ; HFi , MEDi , FEDi , C i , ε i ) (4) where recall that ei represents the unobservable biological health endowment of a child living in household i Notice that HF usage increases directly the overall utility of the household as it increases child health but can have an ambiguous effect on overall utility since households have to pay for using these facilities as assumed in (4) © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 South African Journal of Economics Vol 76:4 December 2008 617 Empirical Specifications and Methodology A linear specification of the demand function for child health is given by the following equation: Hi = β0 + β1MEDi + δ1 Yi + C i d + Pd + ε i (5) where prices are lumped together as a row vector P and Ci is a vector of observable child and household characteristics such as father’s education; usages of health facilities; gender of child, race and age of child; age of mother; if head is a widow mother; if father of child is absent; number of siblings and the quality of the house they live in captured by number of rooms, household usage of electricity, usage of tap water and availability of a toilet The parameter of interest is b1 which we conjecture to be non-negative Importantly, we interpret b1 as a marginal causal effect For a given cross-section of households equation (5) may be estimated by OLS Nonetheless, several parameters would be inconsistently estimated due to endogeneity problems However, notice that disposable income Yi can be endogenous since it can be correlated with ei for various reasons One such reason is that households that have had greater wealth in the past tend to have greater disposable income currently This greater wealth in the past could have generated healthier household members, in particular the mothers, which in turn inherit these health endowments to their children when born generating a correlation between Yi and ei MEDi and FEDi can also be correlated indirectly with ei since both are surely positively correlated with disposable income Yi Moreover, MED and FED can be directly correlated with ei since formal education can be correlated with mothers health, which in turn is correlated with child’s health Finally, HFi usage can be negatively correlated with ei since health facilities are required more for unhealthier children The vector P can be correlated with ei through disposable income and HF Notice that omitting P and HF in (5) still allows us to consistently estimate b1 if we are willing to assume (something that is reasonable) that HF and P are only correlated with ei through Yi and not directly correlated with MED and FED To solve these endogeneity problems we propose an instrumental variables approach where the instruments for MED and FED are: (i) exposure of the household to mass media; (ii) expenditure in newspapers and entertainment; (iii) if the main language in the household is Afrikaan or English; and (iv) whether someone in the household has moved or migrated for academic reasons.5 The first two instruments have been previously used and proven useful in the literature by authors such as Glewwe (1999) and Thomas et al (1991) The third instrument is a valid one in the South African context given its particular history As expressed by Keswell (2004) one of the biggest effects of Apartheid was the extreme economic inequality generated between race groups Such inequality is clearly observed in the differences between average educational attainments across races For instance, using information from 1995 Lam (1999) shows that while the average years of education for non-Whites was 7.22, for Whites it was 11.63 Given that language is closely related with race, it is expected that its correlation with education should be high The fourth instrument could be correlated with the household preference for education and with the household disposable income which is also instrumented It would have been ideal to use as additional instruments variables such as mother’s or father’s siblings’ education Unfortunately such information is not available to us © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 618 South African Journal of Economics Vol 76:4 December 2008 Moreover, we propose ownership of durable goods by the household as instruments for disposable income (Yi) Specifically, we use the following variables: if the household owns the house in which they live in and the number of motor vehicles, refrigerators, stoves, telephones and television sets that they report to own This approach to instrument disposable household income is also taken in Glewwe (1999) All instruments can be criticised depending on what one is willing to assume For example, a possible critique to our language instrument for MED or FED is that language of the White minority can also be correlated with accumulated wealth and correlated with disposable income which in turn is correlated with a child’s endowment Moreover, one can believe that household language can be correlated with a child’s biological health endowment through other channels like a child’s race and gender Nonetheless, recall that a child’s race and gender are control variables included in (5) and therefore this potential pitfall is avoided Given that more instruments are available than potential endogenous variables, standard tests of over-identification will be provided in the Results section in order to empirically try to validate their usage DATA DESCRIPTION Although South Africa has better human development indicators than other African countries such as Kenya and Nigeria, its people still lack basic services and there is much work needed in order to achieve better development results Some basic comparative indicators for 2005 can be seen in Table below As observed, while clearly South Africa is more developed than its Sub-Saharan peers, when compared to other developing countries such as those from Latin America the reality is the opposite For instance, life expectancy is only 48 years while the average in Latin America reaches 72 Related to children’s health the situation is not different either Specifically, child’s health still is far behind acceptable standards While infant mortality of children under years of age in South Africa is 68 per 1,000 live births, this statistic in Latin America and high-income countries is 26 and 5.8, respectively Moreover, according to information in the World Development Indicators, child mortality in South Africa has increased in the past years.6 These simple statistics imply that the improvement of children’s health in the country is imperative One such way is perhaps through the increase in mothers’ education if it is proven to be an important variable To answer this question, the data used in this paper Table Comparative human development indicators, 2005 South Africa Life expectancy at birth (years) Fertility rate (total births per woman) Mortality rate, infant (per 1,000 live births) Mortality rate, under (per 1,000 live births) Infant mortality (per 1,000 live births) Primary completion rate School enrollment/secondary education (gross) School enrollment/tertiary education (gross) Sub-Sahara Africa Latin America High-income countries 48 2.5 2.8 55 68 89 84.9 14.4 47 2.3 5.3 96.4 163 60.8 31.7 5.1 72.5 1.3 2.4 26.2 30.8 98.5 87.6 29.3 79 0.7 1.7 5.8 6.9 97.4 101.4 66.8 Source: World Bank, World Development Indicators Specifically, the estimates suggest that infant mortality and mortality under in the year 2000 was 50 and 63 per 1,000, respectively © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 South African Journal of Economics Vol 76:4 December 2008 619 comes from the 1993 South Africa LSMS This is a nationally representative, multipurpose household survey conducted by the World Bank which randomly selected 9,000 households from all races and areas throughout the country It contains data on several subjects including socioeconomic variables for all members of the households, anthropometric measures for all children aged less than years as well as community level characteristics Although the LSMS data set contained weight and height measures for each child, our study is not based on these simple measures There are several reasons for this decision First, the weight of a child relative to the age is a short-term measure of child nutrition since it responds to changes in health and nutritional status very quickly Second, the effect of health status on height is cumulative and can be perceived only in the long run making it difficult to study the effect for younger children Since none of these two anthropometric measures were adequate, we decided to base our measure of child’s health on one that would allow us to assess the relative poverty in South African children’s health compared to a standard well-nourished population such as that of the children in the United States Hence, we used the height for age Z-scores based on the information provided by the National Center for Health Statistics from the United States This measure reports the standardised deviation of the height of the child with respect to the median height of the age and gender group in a reference population of well-nourished children such as that of the United States and is computed in the following manner: Hi = height i − mean g std.dev g (6) where i indexes each child in the survey and g indicates the age and gender group that he/she is part of This measure is commonly used in the literature and has the advantage that provides useful information of both short- and long-term health status (Thomas et al., 1991).7 Under this specification a Z-score of zero implies that the child’s height is equal to the median height of a well-nourished child; while a negative score would imply that the child has a poorer health compared to that of the median health of a well-nourished population Following the recommendation of the World Health Organization we focused on children between months and years of age Limiting the analysis of height measures in this interval is due to measurement errors for the new born and environmental factors that affect the older ones We also dropped from the study children in the highest and lowest 1% of the Z-scores since their values seemed very unrealistic and were probably due to measurement errors.8 After all these recommendations were taken, In principle, another alternative measure that can be used to answer the question of interest is the analogous weight for age Z-score However, it is normally catalogued as a short-run measure that heavily depends on family’s current income It is expected then that the effect of mothers’ education on this variables should arise through the income channel This would imply that regressions that control for this channel should yield an insignificant coefficient on mothers’ education We carried out these regressions and find that this is in fact the case for South Africa They are available upon request This last restriction in the data used made us loose a total of 73 observations Children with Z-scores values such as -112.28 were dropped It is simply unrealistic to believe a South African child is 112.28 standard deviations behind a well-nourished American child However, as © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 620 South African Journal of Economics Vol 76:4 December 2008 Table Height for age Z-scores and mothers’ average education Number of children Mean child height Z-score Mother’s average years of education All children 3,912 -1.154 (1.976) 7.405 (3.966) By race Blacks 3,220 -1.291 (1.944) -1.114 (1.784) -0.283 (2.091) 0.090 (2.006) 6.917 (3.859) 8.249 (2.779) 10.212 (2.757) 11.124 (4.183) -1.387 (1.953) -0.990 (1.912) -0.657 (1.990) 6.465 (3.877) 8.388 (3.683) 9.124 (3.652) -1.056 (1.986) -1.247 (1.962) 7.395 (3.911) 7.415 (4.018) Coloured 317 Indian 100 Whites 275 By location Rural 2,325 Urban 737 Metro 850 By gender Female 1,907 Male 2,005 Standard deviations in parenthesis Source: 1993 South Africa Integrated Household Survey we were left with the Z-scores of 3,912 children between the ages of months and years that lived in 1,729 different households As previously mentioned, the most important aspect that characterised the recent history of South Africa is the legacy of Apartheid It was a discriminatory system institutionalised in the late 1940s that significantly hindered the development of the country and specially that of its Black population Race laws touched every aspect of social life and the population was mainly classified into one of three categories: White, Black (African) or Coloured (of mixed decent) Two important aspects influenced by such policy were both education and health of their citizens Indeed, descriptive statistics in Table show us that South African children in 1993 had a poorer health status compared to that of American children Among the four races that can be distinguished in the survey, the difference with a well-nourished child is substantially smaller for White children while Black children have the lowest Z-scores of the four races Dividing the sample according to the region in which the household resided shows that children from rural regions appeared to be better-off than those who live in urban areas Finally, it is worth mentioning that in this society girls seem to be relative better off than boys in regards to their height for age Z-scores measures As mentioned before our prime interest is the effect that mother’s education has on children’s health Table also shows that the mean education for the women in the sample is around years This average is considerably different among races; while White women attain on average 11 years of education, Black women only attain years This is an expected result given that during the Apartheid era Black women were discriminated by their race, class and gender There are also some differences between the average robustness check all estimations were carried out maintaining these children and the main conclusions remain intact Results are available upon request © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 South African Journal of Economics Vol 76:4 December 2008 621 Table Socioeconomic statistics of the households Mean Child’s height (24 months) Child’s height (36 months) Child’s height (72 months) Child’s age (months) Child’s weight Mother’s age Father absent Father’s education Father’s age Total number of children in the household Total pc consumption Ownership of house they live in Drinks tapwater Proper sanitation system Access to hospital Access to health facilities Household expenditure in mass media Number of motor vehicles Number of bicycles Number of radios Number of fridges Number of stoves Standard deviation 82.14 90.75 106.87 36.86 13.16 30.15 39.32 7.29 37.81 1.83 95.64 76.41% 42.31% 33.61% 7.00% 37.50% 37.811 0.29 0.31 1.03 0.39 0.30 6.105 5.837 6.715 18.363 4.127 7.495 4.531 9.017 1.012 73.944 – – – – – 9.017 0.671 0.699 0.858 0.616 0.480 Source: 1993 South Africa Integrated Household Survey education levels of the mothers depending on the area of residence Mothers of children living in urban areas attain on average higher education levels Other socioeconomic variables that will be used in the empirical study are displayed in Table From the sample used in the study one can observe that almost 40% of children have an absent father and that fathers’ average education is approximately 7.9 years.9 Looking at other characteristics of the household we observe that total per capita monthly consumption is 95.45 Rands,10 only 43% of the households drink water from the pipe or from water vendors and 35% has either a toilet or an improved latrine as their sanitation system inside the house Access to hospitals in 1993 was very limited since only 7% of the households had access to them while 37% had access to other health facilities such as Maternity Clinics and Pharmacies As for the descriptive statistics of the instruments used, it can be observed that the monthly expenditure in mass media was 13.7 with a high standard deviation.11 The ownership of durable goods are highly related with current income such as number of motor vehicles, while number of television sets or number of fridges is generally low with the exception of number of radios EMPIRICAL RESULTS FOR MOTHER’S EDUCATION AS KNOWLEDGE OLS Results As mentioned above our primary interest is to understand the reduced form impact that mother’s education (through the knowledge channel) has on children’s health between Following Glewwe (1999) and in order to avoid loosing the high proportion of children without fathers, which could lead to important selection bias, the average population values of education were replaced instead of the missing and a dummy variable indicating that the father was absent was included in all regressions 10 This amounts to almost 237 Rands of 2008 or US$30 11 This amounts to almost 39 Rands of 2008 or US$4.13 © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 622 South African Journal of Economics Vol 76:4 December 2008 months and years of age The first three specifications of Table show OLS estimates of equation (5) where the first specification is a benchmark OLS regression of child’s health (measured by height for age Z-score) on years of mother’s education controlling for basic child and mother characteristics such as race, gender and weight of the child, age of child and mother; and if the mother is a widow and if father of the child is absent As seen from the table, one more year of mother’s education has a positive and statistically significant (1%) effect of approximately 0.049 standard deviations on average on a child’s height for age However, the estimator of the mother’s education coefficient in the benchmark can suffer from omitted variables bias as discussed above This justifies controlling for household characteristics such as fathers education, disposable household income, number of siblings and the quality of the house they live in captured by number of rooms, household usage of electricity and tap water; and availability of a toilet The second specification in Table reports this regression Moreover, community characteristics are also called for, such as whether the household resides in an urban area and if the community in which the household resides has health facilities As seen, the coefficient on years of mother’s education is reduced in half but still has a positive and statistically significant (1%) effect of approximately 0.026 standard deviations on average on a child’s Table Dependent variable is children’s height for age Z-score OLS estimates 2SLS estimates I Mother’s education White (= if child is White) Female (= if child is a girl) Age in months Age in months squared Weight of child Mother’s age Widow (= if mother of child is a widow) Absent (= if father of child is absent) II III IV V VI 0.049 (0.008)** 0.763 (0.143)** 0.229 (0.064)** -0.061 (0.012)** 0.000 (0.000)** 0.188 (0.055)** 0.015 (0.005)** -0.001 (0.136) -0.147 (0.066)* 0.026 (0.009)** 0.318 (0.144)* 0.220 (0.065)** -0.059 (0.012)** 0.000 (0.000)** 0.183 (0.055)** 0.013 (0.005)** 0.039 (0.139) -0.088 (0.067) 0.007 (0.011) 0.001 (0.000)* -0.194 (0.126) 0.049 (0.064) 0.147 (0.038)** 0.359 (0.188)+ 0.228 (0.065)** -0.058 (0.012)** 0.000 (0.000)** 0.180 (0.054)** 0.028 (0.006)** 0.004 (0.138) -0.210 (0.075)** 0.151 (0.087)+ 0.415 (0.262) 0.225 (0.066)** -0.058 (0.012)** 0.000 (0.000)** 0.181 (0.055)** 0.026 (0.009)** 0.013 (0.164) -0.217 (0.127)+ -0.027 (0.081) -0.001 (0.003) -0.177 (0.130) 0.039 (0.070) No – 3,919 0.14 Yes – 3,912 0.15 0.020 (0.011)† 0.310 (0.144)* 0.219 (0.065)** -0.059 (0.012)** 0.000 (0.000)** 0.183 (0.055)** 0.013 (0.005)** 0.050 (0.139) -0.085 (0.067) 0.006 (0.011) 0.001 (0.000)* -0.042 (0.294) -0.105 (0.136) -0.019 (0.031) 0.020 (0.016) Yes – 3,912 0.15 No 0.60 3,919 – Yes 0.63 3,912 – 0.137 (0.091)† 0.358 (0.277) 0.252 (0.071)** -0.062 (0.013)** 0.000 (0.000)** 0.185 (0.056)** 0.018 (0.012) -0.155 (0.237) -0.191 (0.137) 0.072 (0.120) 0.000 (0.003) -0.348 -2,871 2,110 -2,064 0.036 (0.335) -0.277 (0.275) Yes 0.46 3,912 – Father’s education Household disposable income H = if hh resides in community with a hospital OHF = if hh resides in community with other health facilities Mother’s educ ¥ H Mother’s educ ¥ OHF Comunity and household characteristics Overidentification test of IV list (p-value) N R2 Robust standard errors in parentheses + Significant at 10%; * significant at 5%; ** significant at 1%; † significant at 10% one tail Source: 1993 South Africa Integrated Household Survey © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 South African Journal of Economics Vol 76:4 December 2008 623 height for age for every year of formal education Intuitively, increasing mothers’ education by one standard deviation (4 years) would imply that on average a 2-year-old child would be approximately 0.5 centimeters taller The third specification in Table includes interaction terms between MED and health facilities to assess the impact of the efficiency channel in the usage of health inputs The coefficients of the interaction terms are not statistically significant The lack of significance could be due to the cost of using these health inputs Even if health facilities are available, mothers may choose not to use them for various reasons such as transportation costs and high monetary costs Moreover, the estimators of the interaction coefficients can be biased due to the potential endogeneity of MED In all three OLS specifications of Table one can observe that heavier children tend to have a higher height for age Z-score This is not surprising since both measures are clearly positively correlated It is interesting to notice that girls tend to have better health status relative to boys which is statistically significant while White South African children are relatively healthier than children from other races in the country Furthermore, we find that the child age coefficient is negative and significant This result by itself implies that as children get older, differences in the height for age measures for American and South African children increase Since the coefficient on child’s age squared is positive and significant it appears that the negative impact of age on child’s health increases in a decreasing rate When analysing the impact that households’ characteristics have on children’s health the first thing to notice is that even though household disposable income is positive, its magnitude is quite small once mother’s and father’s education are controlled for This result goes in line with previous findings such as Thomas et al (1991) Interestingly, children with more educated fathers and who lived in wealthier households not seem to have a better health status Moreover, children that had a younger mother and an absent father had a relatively poorer health status in a statistically significant way It’s not the same to be a mother at age 16 than at age 30, where the women are more mature and physically better prepared Finally, in the second specification of Table unhealthier children seem to reside in communities with hospitals and other health facilities which clearly cannot be interpreted as a causal effect since it is possible that these facilities could suffer of endogenous placement policies 2SLS Results As discussed above mother’s education, father’s education and disposable household income could be endogenous Under this scenario the last three columns of Table report two-stage least square (2SLS) estimates of the respectively three OLS specifications In the fourth specification of Table mother’s education is the only endogenous variable and is instrumented by the following variables: exposure of the household to mass media, expenditure in newspapers and entertainment, if the main language in the household is Afrikaans or English and whether someone in the household has moved or migrated for academic reasons The estimate coefficient on MED is positive and statistically significant but with a much greater magnitude compared to the first OLS specification.12 Interpreted 12 Even though the first stage regression is not reported the four instrumental variables are jointly significant at the 1% © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 624 South African Journal of Economics Vol 76:4 December 2008 Table OLS first stage regressions Mother’s educ English or Afrikaan is language of household Media (= if household buys newspaper and/or entertainment media) Expenditure in newspapers and/or media Studying away(= if household has member studying away) Own house (= if hh resides in own house) Number of motor vehicles Number of TV sets Number of telephones Number of stoves Number of fridges Includes all other variables in second stage Robust LM test for joint significance of relevant instruments (p-value) N R2 Dep variable father’s educ -0.522 (0.202)** 0.589 (0.129)** 0.001 (0.001) 4,162 (0.311)** 0.022 (0.141) 0.402 (0.115)** 0.560 (0.121)** -0.179 (0.254) 0.267 (0.192) 0.537 (0.131)** Yes 0.0001 3,912 0.29 0.739 (0.172)** 0.337 (0.115)** 0.002 (0.002) 0.609 (0.275)* -0.084 (0.126) 0.653 (0.112)** 0.267 (0.113)* 0.069 (0.161) 0.297 (0.163)+ (3.838)* 0.083 (0.124) Yes 0.0001 3,912 0.34 Disp hh income -11,765 (4.012)** 21,048 (2.566)** 0.127 (0.064)* 410,930 (5.654)** -6,339 (2.439)** 7,273 (2.850)* 9,544 (2.930)** 2,274 -4,197 7,571 1992 -3,076 Yes 0.0001 3,912 0.49 Robust standard errors in parentheses + Significant at 10%; * significant at 5%; ** significant at 1% causally one more year of a mother’s education increases approximately 0.14 standard deviations on average a child’s height for age When household and community characteristics are included as in the fifth specification of Table 4, where MED as well as FED and household disposable income are instrumented, the magnitude of the MED coefficient remains similar but notice that standard errors increase due possibly to both the 2SLS procedure and multicollinearity of the three endogenous variables The first stage of this 2SLS procedure is reported in Table where a robust Lagrange Multiplier statistic to test joint significance of the relevant instruments of each endogenous variable is reported In all cases the relevant instruments are jointly significant at 1%.13 The last column of Table reports 2SLS estimates when the interaction terms of MED with health facilities available at the community level are included The coefficients of the interaction terms are again not statistically significant Moreover, it is worth mentioning that all 2SLS estimates satisfied an over identification test for the list of instrumental variables For any specification the null of joint exogeneity of the instrumental variables is not rejected at even 40% of statistical significance This gives confidence on the exclusion assumptions necessary for the consistency of the 2SLS estimator of b1 Under these results, our preferred specification is the fifth one in Table As can be observed in this model the 2SLS estimates maintain the same sign as the OLS estimates 13 In this specification the instruments for mother’s education are media and expenditure in newspapers and/or media; while for father’s education the corresponding instruments are studying away and English/Afrikaans as household language; finally, the instruments for disposable income correspond to the rest of the variables (i.e number of motor vehicles, stoves, television sets, telephones and fridges owned by the household) © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 South African Journal of Economics Vol 76:4 December 2008 625 As is well known a 2SLS estimation procedure increases standard errors; nonetheless, mother’s education is statistically significant at 10% Importantly, if we believe that the parameter of interest b is estimated consistently then the magnitude of this estimate increases strongly: one more year of a mother’s education increases on average the height for age Z-score of a child in 0.15 standard deviations This finding is similar to that of Glewwe (1999) in which after instrumenting maternal education and disposable income the magnitude of the coefficient of interest significantly increased Compared to the OLS case, the 2SLS coefficient suggests that increasing mothers’ education by one standard deviation (4 years) would imply that on average a 2-year-old child would be approximately 3.5 centimeters taller Although this finding seems quite large, the result is quite similar to that reported in Chen and Li (2006) for Chinese adopted children: on average a Chinese woman with more years of education would increase the height of the adopted child in centimeters Finally, we explored with the second and fifth specifications of Table the differential impact of mother’s education on children’s health depending on their age The medical and public health literature shows that young children (ages to 2) undergo a critical transition period, namely they have underdeveloped immune systems and are relatively more vulnerable to infections and disease This age period is characterised by fast growth that coincides with a changing diet from breast milk to prepared foods At a certain time, breastfeeding alone becomes inadequate for their nutrient requirements which can generate trauma and stress in their transition to prepared foods if the diet deteriorates in quality and perhaps in quantity It is not surprising therefore that child’s health outcomes have generally exhibited a decline from months of age through the second year of life, followed by a turnover and a continuous improvement thereafter.14 This suggests a U shape pattern in the transition period from breastfeeding intakes to prepared food intakes Following the U shape pattern idea, the sample was divided in two age groups of children: those between months and 24 months of age and those between 24 and 72 months of age We conjecture that children between and 24 months, compared to older ones, are more dependent on their mother’s presence (e.g breast feeding) but not on her education level Hence, the coefficient on MED should be smaller than those obtained using the full sample of children The results found are summarised in Table for both OLS and 2SLS estimations We find that MED is important for older children while not for younger ones which is in line with the literature Interestingly, the OLS and 2SLS for the older aged group is similar in practical terms to the findings for the whole sample which supports the conjecture that mother’s education is important only for children that not breastfeed and which need well prepared food CONCLUSIONS Several studies in different countries have found that maternal education affects positively and significantly child’s health outcomes In this paper we show that South Africa is no exception to that rule Children with more educated mothers have a better long-term relative health status measured by height for age Z-score The 2SLS coefficient suggests that increasing mothers’ education by one standard deviation (4 years) would imply that on average a 2-year-old child would be approximately 3.5 centimeters taller This finding is 14 Barrera [1990] © 2008 The Authors Journal compilation © Economic Society of South Africa 2008 626 South African Journal of Economics Vol 76:4 December 2008 Table Dependent variable is height for age Z-score Յ 24 months >24 months I Father’s education Household disposable income White (= if child is White) Female (= if child is a girl) Age in months Age in months squared Weight of child Mother’s age Widow (= if mother of child is a widow) Absent (= if father of child is absent) Observations R2 III IV OLS Mother’s education II 2SLS OLS 2SLS 0.010 (0.017) 0.013 (0.024) 0.001 (0.001) 0.315 (0.267) 0.441 (0.107)** -0.229 (0.065)** 0.003 (0.002) 0.512 (0.047)** 0.011 (0.009) 0.544 (0.350) -0.062 (0.128) 1,181 0.28 0.073 (0.177) -0.135 (0.147) 0.004 (0.006) 0.454 (0.646) 0.451 (0.123)** -0.205 (0.072)** 0.002 (0.002) 0.505 (0.049)** 0.010 (0.021) 0.653 (0.411) -0.014 (0.261) 1,181 – 0.027 (0.010)** 0.006 (0.012) 0.001 (0.001)* 0.316 (0.166)+ 0.142 (0.073)+ -0.044 (0.022)* 0.000 (0.000) 0.135 (0.051)** 0.016 (0.005)** -0.071 (0.147) -0.072 (0.075) 2,731 0.14 0.157 (0.093)+ 0.010 (0.083) -0.003 (0.002) 0.412 (0.263) 0.136 (0.077)+ -0.044 (0.022)* 0.000 (0.000) 0.135 (0.051)** 0.030 (0.009)** -0.138 (0.178) -0.268 (0.128)* 2,731 – Robust standard errors in parentheses + Significant at 10%; * significant at 5%; ** significant at 1% important since, as mentioned, previous studies have found that healthier status during childhood are related to better education and labour market outcomes in adulthood Across the different specifications considered we gradually controlled for several channels through which mother’s education can affect child’s health The objective was to isolate the knowledge channel in order to assess the reallocation of resources due to better knowledge and information Several interesting results emerge from here Father’s education is not important once mother’s education is controlled for Secondly, neither health facilities nor their interaction with mother’s education appear to have a positive significant effect on the height for age of the children between months and years of age in South Africa The intuition is that even if health facilities are available to the households these may choose not to use them if the cost of doing so is too high The two results have important policy implications since they suggest that governments should invest the scarce resources primarily in women’s education and perhaps subsidise the use of local health facilities for the poorer households Finally, following the medical literature that reports a U pattern in children’s health we divided the sample in two different age groups: those between and 24 months of age and those between 24 and 72 months We find that while mother’s education is not important during the 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