DRAFT. Katleen Van den Broeck *

Determinants of stunting: The role of education, information and childcare practices in Mozambique DRAFT Katleen Van den Broeck * This version February, 2007 Abstract: Stunting prevalence rates in Mozambique are very high (41 percent), especially in rural areas (46 percent). Recent research shows that consumption growth alone will not be sufficient to solve the problem of malnutrition. To investigate the role of other determinants of stunting I use a two step approach with specific attention to the role of childcare knowledge and practices, education and access to information. In rural areas I find that the positive effect of mother s education works entirely through its effect on better childcare knowledge and practices, which then have a positive effect on anthropometric outcomes. Hence, continuous investment in girls education will lead to better childcare practices and better anthropometrics in areas where community services are in short supply and household level practices play a more important role. In the shortterm the education channel may be complemented by improving mothers knowledge through other information channels such as radio programmes or direct visits by health workers. * Department of Economics, University of Copenhagen. All comments highly welcome. Please send to kvdb@econ.ku.dk The work was initially prepared as a background paper to the Mozambique Poverty, Social and Gender Assessment while working at the World Bank in Maputo.

Acknowledgements A first draft of the work has been prepared as a background study to the Mozambique Poverty, Social and Gender Assessment by the World Bank. I am thankful to Louise Fox for her continuous support, to Ken Simler for his advice on the use and interpretation of the data and to Mikkel Barslund for fruitful discussions on the topic. Permission to use the Mozambique Demographic and Health Survey 2003 from MEASURE DHS (www.measuredhs.com) is greatly acknowledged. 1. Introduction The latest Demographic and Health Survey (DHS, 2003) in Mozambique showed that 41 percent of children younger than five year old are stunted (low height for age) and nearly half of them (18 percent) are severely stunted 1. Stunting is generally used as a sign of chronic under nourishment reflecting a history of problematic dietary and/or health situations. Strong regional differences exist with the lowest levels of stunting in Maputo City and Maputo Province (21 and 24 %) in the south and the highest in Cabo Delgado in the north where 56 percent of under 5 year old children are stunted 2. There is also a strong difference between rural and urban areas (46 and 29 percent respectively). Not only is malnutrition affecting the child s current health situation but it has both individual and national future development effects. A bad nutritional status increases the susceptibility to diseases and ultimately death 3. Not only is the child s physical but also its psycho-intellectual development likely to be affected by a low nutritional status (see for example case studies in Behrman, 1999). Early childhood development has long-term consequences in terms of educational achievements and health status, hence future adult productivity and earning potential as shown for example in Glewwe, Jacoby and King 1 Stunted: Height-for-age more than 2 standard deviations (SD) below the median of the reference population; Severely stunted: Height-for-age more than 3 SD below the median of the reference population. An international reference population defined by the U.S. National Centre for Health Statistics is used for comparison. 2 In Zambezia, Niassa, Tete, Sofala and Nampula stunting is higher than 40 percent (47, 47, 46, 42 and 42 respectively). In Cabo Delgado 30 percent of the under 5 year old children are severely stunted. 3 Poor nutritional status contributes to 53 percent of deaths associated with infectious diseases among children under 5 year old (WHO, 2005). 2

(2001) and Alderman et al (1999). By affecting adult health and educational status, early childhood nutrition has consequences for long term economic development too. 4 Low chronic nutritional status (stunting) is not always visible. A short five year old child can easily be mistaken for a healthy three or four year old child by the untrained eye and stunted children are not as strikingly thin as underweight children. Moreover, its long term human and economic development effects have not always been acknowledged. But recently malnutrition is being recognised as a key to fighting poverty (Worldbank, 2006). A chronic situation of hunger (due to food availability and/or food access problems) was long believed to be the major cause of stunting. However, more and more evidence shows that child malnutrition continues to exist even in food-secure households or with supplementary feeding programmes 5, suggesting that food utilisation 6 and other care factors play a significant role in children s nutritional status. Although stunting is especially high in low-wealth groups, for example in Mozambique 49 percent of the under five year old children in the lowest wealth quintile are stunted compared to 20 percent in the highest wealth quintile (DHS, 2003), research suggests that income growth alone will not be sufficient to solve the malnutrition problems (Wolfe and Behrman, 1982; Haddad et al, 2003; Ranjan, 2004; Glewwe et al, 2004; Alderman, Hoogeveen and Rossi, 2005). Specifically for Mozambique Haddad et al (2003) find a projected decline from 23 percent underweight children in 1997 to 16 percent in 2015, assuming a steady 2.5 percent annual consumption growth. Anthropometric analysis by Simler and Ibraimo (2005), using four Mozambican surveys between 1996 and 2003, confirms the results obtained by Haddad et al: despite significant reductions in poverty and steady 4 For example, Alderman and Behrman (2004) analyse the development effects of increasing low birth weight and Strauss and Thomas (1998) analyse the link between adult nutrition and health status and economic development. 5 In India, levels of child malnutrition fell only slowly during the 1990s, even with significant economic growth and substantial public spending on an Integrated Child Development programme of which the main component was supplementary feeding to malnourished children. Das Gupta et al (2005) assess programme placement and the programme s outcomes. They find little evidence of a programme impact on child nutritional status. 6 Deficiencies of key vitamins and minerals are very prominent (it is estimated that 35% of people lack adequate iodine, 40% suffer from iron deficiency, more than 40% of children are vitamin A deficient; World Bank, 2006) 3

consumption growth, 7 they find only little improvement in children s anthropometric status 8. After controlling for missing data and changes in child mortality they find stunting has only declined from 42 percent in 1996/97 to 38 percent in 2003. Contrary to the role of income, the effect of mother s education has always been acknowledged (Behrman and Wolfe, 1984 or Smith and Haddad, 2000) although the channels through which it materialises can be different. Channels through which parental education affects child health include, besides its possible income augmenting effect, better utilisation of resources available in the community such as health infrastructure or better capability of processing information. Thomas et al (1991) show for Brazil that the maternal education effect largely works through the extent to which the mother accesses and processes information. In a study of Morocco, Glewwe (1999) also finds that the effect of mothers education mainly works through their health knowledge. Behrman and Hoddinott (2005) also find that children with mothers with at least 5 years of schooling achieve greater gains in height from PROGRESA. They suggest that these mothers (who are functionally literate) are better able to process information and hence can benefit more from PROGRESA. Besides the better information processing capacities of educated mothers, information can have beneficial effects also for the children of uneducated mothers. For example, in their study of the nutritional effects of nutrition information and formal schooling in Central Java, Webb and Block (2004) find the following effects: while mothers formal schooling is a direct determinant of the long-term nutritional status of children, it is a determinant of short-term nutritional status indirectly through its effect on nutrition information. Moreover, other determinants than schooling play an important role in mothers nutrition information such as distance to health centres. While the nutritional effect of income growth in Mozambique seems marginal, an investigation into the role of other possible determinants of chronic malnutrition has not been undertaken. In this paper I study the nutritional effects of education and childcare 7 Annual mean (percentile) growth rates of 4.1 percent between 1997 and 2003 (Fox et al, 2005). 8 Simler and Ibraimo correct for several confounding factors and differences in the samples (such as different age distributions in the samples, correlation of incomplete anthropometric data with household economic status, variable truncation of the samples because of child mortality), after which the improvement over time in malnutrition outcomes is slightly larger but remains small. 4

and whether there is a role for informational programmes beyond the role of education to improve child care. Informational programmes may substitute or complement education and may be especially useful to improve childcare practices amongst illiterate mothers. In a similar study on urban Accra, Ruel et al (1999) find that good care practices can compensate for low maternal schooling. The contribution of this paper is twofold. First of all, I aim to assess the relative importance of different determinants of child anthropometric status with specific attention to the role of income/wealth, mother s education and illness prevention childcare knowledge and practice. The second question is how exactly mother s education affects anthropometric status in the rural Mozambican situation, meaning whether most of its impact works through good childcare or whether any additional benefits of education exist, and whether there is a role for information policies beyond the role of formal education in the determination of childcare. As stunting is a much larger problem in rural areas and access to resources and services and educational levels are very different in rural and urban areas, we do the analysis separately for each area with a specific focus on the determinants of stunting in rural Mozambique. In the following sections I present the methodology and the data used for the analysis. Section four shows the results and section five concludes. 2. Methodology Child nutritional status (N) is a home produced good and the nutrition production function can be written as N = f( K, M, H, I, µ ) (1) where K is a vector of child exogenous characteristics, M and H are vectors of exogenous mother and household characteristics, I represents nutrition inputs such as medical care and µ reflects children heterogeneity in terms of unobserved health endowments. Adopting a linear form of the nutrition production function, the empirical version of (1) becomes 5

N α1 β1x γ1c 1 = + + +ε (2) where X is a vector of exogenous controls, C is childcare, ε 1 is a random error term, identically and independently distributed, containing the child s unobserved health endowment effect µ. Childcare C will be represented by a composed index of good childcare practice evaluating the mother s answers to a range of childcare related questions. Examples are whether the child has been given any vitamin A containing fruits in the last 24 hours or whether the mother knows that a child with diarrhoea needs more liquids than usual. However, childcare inputs are arguably endogenous. The endogeneity of health inputs in a health production function was shown by Rosenzweig and Schultz (1983). The possible source of endogeneity here is omitted variable bias. It is likely that unobserved mothers characteristics such as mother ability, which are captured in ε 1, also affect the explanatory variable C. Moreover, there may also be a measurement error problem since the self-constructed childcare index used will only be an imperfect measurement of the true childcare practices of the mother. This measurement error can result in a downward bias on the effect of childcare (Wooldridge, 2006). Since the error term ε 1 is most likely correlated with C, ordinary least squares (OLS) estimation would yield inconsistent estimates of the parameter of interest γ 1. In order to obtain consistent estimates for γ 1, C needs to be instrumented C α2 β2x γ2z 2 = + + +ε (3) with ε 2 is an iid error term and Z is a vector of instrumental variables. In that case N can be estimated via two-stage least squares (2SLS). Proper instrumental variables need to be found in order for the 2SLS estimator to behave well and solve the endogeneity problems. It is necessary that Z is uncorrelated with ε 1 and (partially) correlated with C. Cov( Z, ε ) = 0; Cov( Z, C) 0. 1 (4) However, it is often not straightforward to find good instruments and the IV estimates may still be biased in the same direction as the OLS estimates or the IV estimates can 6

have large standard errors if the correlation between Z and C is weak, even in large samples. Thus the validity of instruments should be checked. Bound, Jaeger and Baker (1995) suggest using the partial 2 R and F statistic of the identifying instruments in the first-stage estimation as indicators of the quality of the IV estimates. With respect to the requirement of zero correlation between the instruments in the reduced equation and the error process in the structural equation, Baum, Schaffer and Stillman (2002) suggest the use of the Hansen-Sargan overidentification test. 3. Data The data used for the analysis are the Demographic and Health Survey data for Mozambique collected in 2003. 9 The survey is nationally representative of women of reproductive age (age 15 to 49), and was especially designed to obtain information on fertility, family planning, child survival and child health. The survey was implemented by the Instituto Nacional de Estatistica (INE) with the technical assistance of ORC Macro. 12,315 households were interviewed; 12,418 women and 2,900 men. The following paragraphs present anthropometrics data of under five year old children 10 and an overview of selected childcare practices which are considered as health and nutrition enhancing. Some of the tables contain child specific information while others contain information on the children s mothers, sometimes reflecting knowledge and sometimes practices which were asked about the lastborn child. Data are presented nationally, by area of residence - rural or urban, and by wealth quintiles. 11 To capture the child s relative height-for-age the Z-score is used, which is the number of standard deviations a child s height-for-age differs from the median height-for-age of 9 Mozambique Demographic and Health Survey 2003 (DHS2003). Data obtained from Measuredhs. 10 Anthropometric data was collected for all children in the household which were under 6 years of age. 11 Weights are used for calculations and regressions throughout the paper; Wealth quintiles are quintiles of individuals rather than households. They are based on an asset index (wealth factor score) which is the weighted sum of household asset scores which were standardised in relation to a normal distribution with zero mean and standard deviation one. The household assets include ownership of consumer goods such as a radio, a television, a bicycle or car, as well as dwelling characteristics such as source of drinking water, sanitation facilities, and material of the floor. For more detail on the construction of wealth factor scores based on Mozambique DHS 1997 data, see Gwatkin et al (2000). 7

children of the same age and sex in the reference population. 12 In table 1 three different indicators of nutritional status are presented: height-for-age (stunting) is an indicator of long-term or chronic malnutrition, weight for height (wasting) is a short-term indicator reflecting acute malnutrition, and weight for age (underweight) is a combination of both. It is generally accepted that Z-scores below a value of -2 indicate stunting, wasting and underweight respectively. 13 Table 1: Averages of anthropometric measures, DHS 2003 Height/age Weight/height Weight/age z-score % z<-2 z-score % z<-2 z-score % z<-2 Rural -1.84 46-0.12 4-1.24 27 Urban -1.32 29 0.04 3-0.80 14 Boys -1.78 43-0.08 3-1.16 24 Girls -1.62 39-0.07 4-1.08 22 Poorest -1.92 49-0.16 5-1.33 30 2 nd -1.86 46-0.17 3-1.29 26 3 rd -1.85 46-0.06 3-1.19 26 4 th -1.51 35-0.03 3-0.96 19 Richest -1.10 20 0.11 2-0.61 9 Total -1.70 41-0.08 4-1.12 23 Source: Calculations from DHS 2003 dataset The average Z-score for height-for-age is -1.7 and 41 percent of the under five year old children have Z-scores below -2. Stunting is much worse in rural than in urban areas (17 percent more children are stunted in rural areas). Girls have a higher Z-score than boys, which is commonly found. There is a strong difference in the Z-scores of children in the three lowest wealth quintiles and those in the two richest quintiles, the difference being nearly 30 percent more stunted children in the lowest compared to the richest quintile. The picture evoked by the acute malnutrition indicator is less grave but the Z-score is negative on average except in urban areas and in the richest quintile. The Z-score for weight for age is also negative on average and the same trends as in the height-for-age Z- score are repeated. 12 Z-score=(observed value-median reference value)/standard deviation of reference population. Most commonly used reference standards were developed by the US National Center for Health Statistics (NCHS). These are recommended by WHO for international use (see Food and Nutrition Technical Assistance website: www.fantaproject.org). 13 A Z-score below -1 indicates mild stunting, wasting or underweight; a Z-score below -2 indicates moderate malnutrition and a Z-score below -3 indicates severe malnutrition. 8

For the rest of the analysis focus is on the long term indicator of malnutrition, height-forage, which shows the persistently worst levels. In this paper I assess the role of simple illness prevention and curative techniques (such as intake of micronutrients - vitamins and minerals, use of mosquito nets, vaccinations, and liquid providing habits for children with diarrhoea). 14 I focus specifically on rural areas where the problem of stunted children is a lot more severe, but compare the results with those for urban areas. The setting is similar to Ruel et al (1999), where the authors analyse the determinants of child height for age in urban Accra using a childcare index concentrated on child feeding and hygiene practices. The index used here is less concentrated on feeding and more on illness prevention based on services and simple home care. Additionally, the role of education, childcare and access to information are made more explicit by instrumenting the childcare index. The following tables contain information on selected knowledge and childcare practices of the mothers of under five year old children. In table 2 percentages of children who received preventive care such as being vaccinated or receiving Vitamin A supplements are shown. The numbers in the table suggest that there is no substantial difference in treatment between boys and girls. There are strong differences between rural and urban areas; and strong differences between the lowest wealth quintile, the next two middle quintiles and the two richest quintiles with preventive care practices steadily improving per quintile. 14 For a good overview of studies on the effects of different types of childcare practices, see Arimond and Ruel, 2002. 9

Table 2: Preventive care practices for children younger than 5 years child specific Percentage of all <5 year olds Health card a Vaccinated against DPT b Vaccinated against polio b Vaccinated against measles b Vitamin A supplements last 6 months Rural 72 74 73 56 39 Urban 92 91 89 74 61 Boys 77 78 76 61 45 Girls 79 79 78 61 46 Poorest 62 64 64 47 35 2 nd 75 75 75 57 38 3 rd 78 80 78 63 45 4 th 91 90 88 73 54 Richest 95 93 91 77 63 Total 78 79 77 61 45 a Child has health card (either effectively seen by enumerator or mother states child has health card) b Reported is only DPT1, Polio1, Measles1 (vaccination repetition rates are much lower) Source: Calculations from DHS 2003 dataset Table 3 shows a mix of preventive and curative knowledge and practice variables which were collected at the mother s level. The same pattern emerges: knowledge about health practices (columns 1 and 3) is higher in urban than in rural areas and is firmly increasing by wealth quintile. For example, in rural areas only 44 percent of the children have mothers who know that a child needs more liquid when it has diarrhoea whereas this is 70 percent in urban areas. The difference between the lowest and the highest wealth quintile is strikingly high at nearly 40 percent. An additional point that the numbers in Table 3 show is that, although mothers may have heard about a practice (88 percent of children have a mother who heard about ORS) they may not actually practice it (only 10 percent of the children have mothers who ever used ORS). This may indicate problems of availability, or of household constraints of monetary or other nature. For all under five year olds sleeping under mosquito nets and giving Vitamin A containing fruits to the lastborn child, the rural-urban or wealth differences are not so striking. Having children sleep under mosquito nets is in general not a widely practiced habit, with the richest quintile and the urban areas showing only slightly higher numbers than rural and poor groups. The practice of giving Vitamin A fruit shows the same pattern in all groups. 10

Table 3: Knowledge and practices of mothers of under 5 year old children Percentage of all <5 year olds a Heard of Oral Rehydration Salts (ORS) Ever used ORS Knows more liquid when diarrhoea All < 5 year olds under mosquito net Vit.A fruit last 24 hours (lastborn) Rural 86 8 44 1 21 Urban 96 14 70 4 20 Poorest 79 7 40 1 19 2 nd 86 8 40 1 20 3 rd 90 13 49 2 22 4 th 95 12 59 3 25 Richest 98 12 78 6 20 Total 88 10 51 2 21 a Base for percentage calculations are children under 5 years i.e. statistics are % of children under 5 with a mother who Source: Calculations from DHS 2003 dataset As mother s care during pregnancy also has an important effect on the child, I present a table with some pregnancy related variables (Table 4). Additionally, another type of knowledge is presented, namely whether the mothers have heard about family planning. Again, the rural-urban and wealth quintile differences are strongly apparent: much fewer children in rural areas and poorer quintiles have mothers who have heard of family planning, or who have taken iron during the pregnancy of the last child. The difference is smaller where it concerns prenatal check-ups although in rural and poorer quintiles more women visit a traditional birth attendant, who often have only very basic equipment or none at all, rather than visiting a doctor or nurse in a health facility with better equipment, to rightly assess the evolution of the pregnancy. The latter option is more costly and often at a larger distance. Table 4: Knowledge and practices of mothers of under 5 year old children - pregnancy Percentage of all <5 year olds a Heard of family planning Taken iron while pregnant of lastborn Any prenatal checkup for lastborn Formal prenatal care c for lastborn Rural 57 50 97 81 Urban 75 80 100 b 97 Poorest 47 40 95 69 2 nd 53 51 97 84 3 rd 69 57 97 87 4 th 72 73 99 97 Richest 81 88 100 b 98 Total 62 59 97 86 a Base for percentage calculations are children under 5 years i.e. statistics are % of children under 5 with a mother who b Actual numbers: 99.5% in urban and 99.6% in richest wealth quintile c Formal prenatal care: doctor, nurse/midwife, auxiliary midwife (excluding traditional birth attendants) Source: Calculations from DHS 2003 dataset 11

Besides availability and cost of products and services, information may play an important role in mothers childcare behaviour. Different access to media or other information sources can explain variation in mothers knowledge. Table 5 shows differences in access to different types of media and Table 6 shows different sources of information about family planning. 91 percent of the children have mothers who never read a newspaper; 97 and 77 percent in rural and urban areas respectively. The percentage is very high for all quintiles but less for the two richest quintiles. Watching TV produces better results, especially in urban areas and the richest quintile. But the medium that is most widely used is radio. 38 percent of children in rural areas have a mother who listens to the radio at least once a week and only 20 percent have a mother who never listens to the radio. In the poorest quintiles 25 percent never listens to the radio. Table 5: Information access of mothers of under 5 year old children Percentage Newspaper TV Radio of all <5 year olds a Never At least once a week Never At least once a week Never At least once a week Rural 96.9 0.3 89.3 1.4 20.4 37.8 Urban 76.9 5.7 47.8 27.5 11.6 60.4 Poorest 97.5 0 94.6 0.1 25.4 22.5 2 nd 98.1 0.1 92.8 1.0 25.9 32.6 3 rd 96.9 0.2 85.6 1.5 11.9 50.4 4 th 88.6 1.4 66.6 10.1 13.5 59.0 Richest 67.4 9.8 30.6 41.5 8.3 71.8 Total b 91.4 1.8 77.8 8.6 18.0 44.1 a Base for percentage calculations are children under 5 years i.e. statistics are % of children under 5 with a mother who b Columns per category do not add up to 100: missing categories are less than once a week and missing Source: Calculations from DHS 2003 dataset In the Mozambique DHS no questions were asked about whether the mother received some information on childcare practices. But questions on family planning were included so I will use this information to proxy for information on childcare specifically. The percentages of children with a mother who heard about family planning through different sources suggest the importance of different media channels in acquiring information. It is apparent from the data that radio is the best source of information for family planning followed by the health facilities (mind that the question for health facilities was asked over the last 12 months while radio was asked over the last month) while the other sources (TV, newspaper, visits by family planning workers) are of minor importance. 12

Especially in rural areas and in the two bottom wealth quintiles the radio is the most important source while in urban areas and richer quintiles there are multiple sources. Table 6: Importance of different sources of family planning (FP) information Percentage of all <5 year olds a Heard of family planning On the radio last month On TV last month In the newspaper last month Visited by FP worker last 12m At health facility last 12m Rural 57 43 5 4 7 34 Urban 75 61 30 17 10 42 Poorest 47 33 4 4 5 28 2 nd 53 36 4 4 6 31 3 rd 69 56 7 4 12 40 4 th 72 55 13 8 9 45 Richest 81 71 43 23 11 44 Total 62 48 12 8 8 36 a Base for percentage calculations are children under 5 years i.e. statistics are % of children under 5 with a mother who Source: Calculations from DHS 2003 dataset All tables lead to the same conclusion: knowledge and practice and access to information are much lower in rural than in urban areas and much lower in poorer quintiles, steadily improving towards the higher quintiles. But given the evidence cited supra, it may not be wealth as such that affects anthropometric outcomes but specific variables correlated with wealth such as education, living conditions or access to information. I will explore the effects of these variables on anthropometric status either directly or indirectly through childcare. 4. Variables and estimation results To construct the index which will serve as a proxy of the true illness prevention related childcare behaviour of the mother, I select several variables available in the Mozambique DHS 2003 and score the answers given by the respondent according to their relevance in child health (e.g. when never breastfed the score is reduced by one as it is particularly important to feed the child the first breast milk which strengthens the child s immune system). The construction of such index is not straightforward. Part of the index is not specific to the child but to the mother, and some questions were only asked for the last born child so we can only assume these practices are representative of the mother s general childcare behaviour. For the child specific practices, it becomes even more 13

complicated as some childcare practices are dependent on the age of the child, for example vaccinations. In annex Table A.1 the variables and the scoring method used to construct the childcare index are presented. The list of variables is not exhaustive but merely a selection of knowledge and practice variables. The childcare index has an illness preventive focus: for example, it includes pregnancy related variables as the period during and even before pregnancy through the first two years of life has a long-term impact on the child s growth (Worldbank, 2006); it also includes vitamin A practices (fruits or supplements); furthermore, it includes the use of mosquito nets which is an important protection against malaria. And it includes knowledge variables such as whether the mother has heard about ORS, or knows whether to give more or less liquids in case a child has diarrhoea. Bearing all these selection and scoring problems in mind, substantial measurement error in the childcare variable is expected. In Figure 1 the distribution of childcare knowledge and practice is shown, by area of residence and for the poorest and richest wealth quintiles (averages and median values of the childcare index, knowledge and practice parts separately and the combination of both, are presented in annex Table A.2). The observed combined childcare score ranges from minus 5 to 9. Unsurprisingly, the mean of the score is much higher in urban compared to rural areas and in the rich compared to poor group. The fact that scores are lower in rural and poorer groups may be due to lack of information on the one hand but on the other hand also to lower availability of or access to the different elements included in the scores such as iron tablets or Vitamin A supplements. Besides the childcare knowledge and practice score (childcare score) a number of exogenous variables enter both stages of the 2SLS regression. I include a vector of child specific variables including age and age squared (age months and age months sq) and sex (sex), whether the father of the child is still alive or the child is a paternal orphan (orphan) and birth order (birth order). Also included is a vector of the child s mother s characteristics such as her physical characteristics (Height and BMI), her age (age), her education measured in years and a squared term (education and education sq), her status 14

in the family, namely whether she is the head of the household or the wife of the head (household head and wife of head). Figure 1: Distribution of childcare knowledge and practice score, by area and wealth 1. Rural 2. Urban Density 0.05.1.15-5 0 5 10 childcare knowledge and practice score Density 0.05.1.15.2-5 0 5 10 childcare knowledge and practice score 3. Poorest quintile 4. Richest quintile Density 0.05.1.15 Density 0.05.1.15.2.25-5 0 5 10 childcare knowledge and practice score -5 0 5 10 childcare knowledge and practice score Source: Childcare knowledge and practice score based on DHS 2003 variables. Next, a vector of household characteristics reflecting the household health environment is included, with access to water captured by the time it takes to get water (distance to water, in minutes), and sanitation facilities, namely whether the household uses any type of toilet or latrine compared to the base situation of no sanitation facilities (latrine or toilet) and whether the house has a hard floor made of wood or concrete (hard floor). A hard floor is typically considered as better for a child s health especially with respect to respiratory diseases. Lastly, we also include a measure of household wealth (wealth factor score). There might be a multicollinearity problem since this wealth factor score in the DHS data is not a direct income or consumption measure but an asset score 15

constructed from the data through principal component analysis. It is based on a weighted sum of household asset scores including ownership of consumer goods such as a radio, a television, a bicycle or car, as well as dwelling characteristics such as source of drinking water, sanitation facilities, and material of the floor (Gwatkin et al, 2000). Ideally the regression should also be augmented with community resources (Strauss, 1990; Smith and Haddad, 2001). Unfortunately, community level information such as the presence of a health centre is not available. 15 Instead, province dummies are included to correct for provincial fixed effects such as average distance to and quality of health centres. Provincial dummies may also capture cultural differences which may affect feeding and other childcare practices. For example, Gillespie et al (2004) find that traditional beliefs in the Lao People s Democratic Republic determine how young children are fed and what type of foods pregnant or lactating women can eat. Also for Bolivia, Morales et al (2005) find that Quechua children have significant lower nutritional status compared to Aymara and Spanish children (correcting for other characteristics). Ethnicity is not available in the Mozambique DHS data so the cultural differences can be captured by the province dummies. As suggested in the methodology section a 2SLS analysis is applied to correct for measurement error and omitted variable bias. The first step estimating the childcare knowledge and practice score of the mother is also presented to get a view of the roles of education and other information sources. There is another econometric issue to be tackled, namely the choice of instruments for the childcare index. There are two candidates that can serve as instruments capturing access to information namely the possession of a radio in the household (HH has radio) and whether the mother has heard about family planning (mother heard about FP). Living in a house with a radio or having a mother who has heard about family planning does not have any direct positive health effects for children hence should not be used in the structural equation. However, they may affect the mother s knowledge about childcare. So I assume these instruments can be excluded from the structural regression and do not have any effects directly on child 15 Some countries in the MeasureDHS database do have a Service Provision Assessment or SPA (however, information on services is collected not for the clusters but in a sample of health providers; Mozambique does not have one). 16

anthropometric status but for their effect on better childcare practices of the mother. Test results for the validity of the instruments are presented. Table 7 presents summary statistics of the exogenous variables used in the regressions. As discussed before, the children s height for age, their mothers and household characteristics are very different in rural and urban areas. Regarding mothers characteristics, especially status in the household and years of education show strong rural-urban differences. Urban mothers are more likely to be neither a household head nor the wife of the head than rural mothers. There is a large education gap with rural mothers having on average two years of education less than urban mothers. 59 percent of the rural and 24 percent of the urban mothers have zero years of education and 79 and 38 percent respectively are illiterate. The household environment of the children is again very different by area with less healthy circumstances for the rural children (lower latrine/toilet coverage, more clay/dusty floors). It confirms the importance of finding alternative ways through which the child s general health environment can be improved. The econometric analysis is restricted to the sample of under two year old children. The reasons for doing so are: (1) some of the practices captured in the childcare index apply only to the lastborn child. So the mother may have only recently learned them and applied them only to the youngest child(ren), (2) especially early childhood developments (infants and young children under the age of two) are important for adult health and productivity status, and this two year time period is considered the window of opportunity to affect the child s long-term health status, (3) in the same line, it is likely that the older the child gets the more the influence from other inputs, such as food availability or psychological environment, will play a role and the effects of home care may fade. The rural and urban divide is maintained throughout the analysis because the areas are strongly different in characteristics. For example, accessing health or information services is less complicated in urban areas which may affect the role of home childcare or there could be a different influence of traditional norms and practices in urban versus rural areas. Moreover, some of the typical rural-urban differences such as educational levels, ownership of a TV or fridge are reflected in the wealth score (which shows large 17

differences in averages between rural and urban) which may in turn stand proxy for all rural-urban differences and give biased estimates on the wealth effects. Table 7: Summary statistics of variables used in the regressions Variables Rural Urban Mean SD Mean SD Child characteristics Z-score height for age -1.8 1.5-1.3 1.3 Percentage stunted 45.6 49.8 28.7 45.2 Age (in months) 27.8 17.5 27.4 17.4 Sex (% male) 50.3 50.0 47.2 49.9 Orphan (% father died) 2.2 14.6 3.0 17.2 Birth order (number) 3.9 2.4 3.5 2.3 Mother characteristics Height mother (in cm) 154.7 6.2 156.1 6.0 Body Mass Index of mother (BMI) 24.3 14.5 27.6 18.7 Age (in years) 28.2 7.3 26.9 7.1 Mother is head of HH (% of mothers) 11.9 32.3 8.2 27.4 Mother is wife of HH head (% of mothers) 66.2 47.3 49.6 50.0 Mother s education (average years) 1.3 1.9 3.9 3.1 Household characteristics Distance to water (in minutes) 36.3 34.9 24.5 32.0 Latrine or toilet (% households) 37.0 48.3 78.9 40.8 Hard floor (% households) 10.6 30.8 58.9 49.2 Wealth score -50530 29920 66195 112638 Instrumental variables Radio (% households) 53.0 49.9 69.8 45.9 Heard about family planning (% mothers) 57.1 49.5 74.2 43.7 Source: Calculations from DHS 2003 dataset Table 8 shows the results of the first stage of the 2SLS estimations where the effect of education and other information sources such as radio and family planning institutions on childcare knowledge and practice can be compared. Tests for the validity of the instruments are also presented. The instruments pass both the test of correlation with the endogenous variable and orthogonality to the error process in the second stage in the rural sample but are much weaker in the urban sample. In rural areas the mother s characteristics are very important determinants of childcare knowledge and practices. There is a positive effect of her being the household head and a very significantly positive effect of her years of education. The other sources of 18

information, having a radio and having heard of family planning, are as significant. It is difficult to judge the importance of the latter two compared to the education effect, which is dependent on its level. However, an F-test shows that the effect of a mother who has heard about family planning is definitely larger than the effect of having a radio in the house. There is also a significant effect of wealth. 16 Corrected for the effects of higher education and better access to information sources, the positive effect of wealth may indicate that there are monetary constraints. For example, vitamin A or iron pills or mosquito nets have a certain monetary cost if not distributed freely by the community or NGOs. The effects of most of the variables are quite different in urban areas. In urban areas, where childcare knowledge and practices is generally higher than in rural areas, mothers education does not seem to affect care. On the contrary, having a radio in the house or having a mother who heard about family planning, are still important determinants of the childcare index. This could indicate that education is important to reach a certain level of childcare but once that level is reached other information sources become more important. Table 9 presents the results for the second step of the 2SLS model, including the instrumented childcare index, together with the uninstrumented effects in the OLS model. I discuss the 2SLS results first. 16 The coefficient is 1.3*10-5 but there is no straightforward way of interpreting the magnitude due to the construction of this wealth factor score. 19

Table 8: Determinants of childcare knowledge and practice score, first step of 2SLS Dependent: Childcare Rural Urban for children<=23 months Coefficient SE a Sign. Coefficient SE a Sign. Child characteristics Age (months) 0.364 0.039 *** 0.379 0.042 *** Age (months) sq -0.010 0.002 *** -0.010 0.002 *** Sex (1 if male) -0.014 0.116 0.343 0.150 ** Orphan (father not alive) 0.302 0.398 0.009 0.463 Birth order -0.006 0.050-0.006 0.054 Mother characteristics Height (cm) 0.006 0.011 0.005 0.015 BMI -0.011 0.022 0.027 0.025 Age (years) 0.011 0.017-0.022 0.016 Household head 0.390 0.217 * 0.440 0.286 Wife of head -0.169 0.192 0.221 0.180 Education (years) 0.475 0.103 *** 0.035 0.065 Education (years) sq -0.038 0.015 *** 0.008 0.006 Household characteristics Distance to water (min) -0.001 0.002 0.004 0.002 * Latrine or toilet 0.348 0.217 0.496 0.246 ** Hard floor -0.698 0.316 ** -0.179 0.253 Wealth factor score 0.000 0.000 ** 0.000 0.000 * Instrumental variables HH has radio 0.380 0.148 *** 0.375 0.208 * Mother heard about FP 0.789 0.158 *** 0.499 0.234 ** Constant -2.411 1.828-1.299 2.273 Observations 2190 909 R-squared 0.344 0.460 Instrumental variable tests Partial R-squared 0.030 0.027 F(2, 368) / F(2, 209) b 15.30 2.60 Prob > F 0.000 0.077 Overidentification test 0.869 3.024 Chi sq (1) p-value 0.351 0.082 * significant at 10%; ** significant at 5%; *** significant at 1%. Provincial fixed effects included but not shown: rural Manica, Gaza and Maputo Province have significantly higher scores than the base province Niassa. Urban Cabo Delgado, Nampula and Tete have significantly lower scores than the base urban Niassa. a Standard errors are robust, corrected for clustering and other sample design effects such as stratification. b Baum, Schaffer and Stillman suggest an F-statistic below 10 is cause for concern. In rural Mozambique, height-for-age worsens by age of the child at decreasing speed and boys have worse outcomes than girls. An age effect is often found: the older a child, the more severe his height-for-age situation becomes showing the accumulated effects of dietary and health deficiencies (it is not a mere age effect as the reference values used to 20

construct Z-score are age and gender specific). Girls usually show better height-for-age outcomes. It confirms that the first months are crucial as an opportunity to improve children s nutritional status. The results suggest that the age specific effects are more negative in rural than in urban areas. Whether the father is still alive or not does not seem to have negative effects on the child s height-for-age. 17 Physical characteristics of the mother (height and body mass index) strongly determine the height-for-age Z-score of the child in rural and urban areas. The status of the mother (whether she is the household head or wife of the head) only appears to be important in urban areas where children of women who are the head of the household have significantly better height-for-age Z-scores. Surprisingly, we do not find the expected positive effect of sanitary facilities. However, this seems to be the case in other Sub-Saharan African countries as well. Smith et al (2004) use DHS datasets of 36 countries to investigate rural-urban differences in determinants of height-for-age and for the Sub-Saharan Africa subset of the sample they find insignificant effects of water (wells and piped water) and sanitary facilities (pit latrine and flush toilet) in rural areas. Turning to the variables of interest, childcare, education and wealth, they seem to play different roles in rural and urban areas. In rural areas, childcare is the most important factor through which a child s height-for-age can be positively influenced. 18 There appears not to be any additional anthropometric effect of education and wealth but their positive effect through better childcare knowledge and practices. 19 17 Also in other child outcomes such as school enrolment and years of schooling it is found that maternal orphans are particularly worse off compared to paternal orphans. In Kenya school participation substantially decreases after a parental death, especially when it is a maternal death, when the child is a young girl and when he/she is a weak student (Evans and Miguel, 2005). In South Africa, a maternal death not only negatively affects the child s probability of being enrolled in school but also the average years of schooling and the average money spent on education (Case and Ardington, 2004). 18 Expanding the sample to the under 3 year old children and under 4 year olds, the childcare index is significantly positive at 10 percent in rural areas, expanding further to the under 5 year olds childcare is not significant anymore (insignificant in urban areas for any of the groups). Decreasing effects of maternal nutrition knowledge on dietary intakes of American children between 2 and 17 years old were also found in Variyam et al (1999). 19 Mackinnon (1995) also found that the effect of primary education only became significant once hygiene practices and beliefs were removed from a regression investigating child mortality in Uganda, suggesting that the effect of primary education mainly works through beliefs. 21

In urban areas this particular combination of childcare knowledge and practices does not seem to have any significant effects on height-for-age nor seems education to be important. Wealth and whether the mother is head of the household are the strongest determinants in urban Mozambique. For urban Accra, Ruel et al (1999) find that good care practices do not provide additional benefit to children with higher educated and wealthier mothers, but they do find a positive effect of education. The 2SLS and OLS results are very similar in urban areas but in rural areas there are strong differences with respect to the magnitude and significance of coefficients of childcare and education. For urban areas, the OLS regression is actually preferred over the 2SLS technique since exogeneity of the childcare index can not be rejected (hence OLS is more efficient). In rural areas, exogeneity is rejected and the 2SLS estimates have to be used. Under the OLS, education has a significantly positive effect on children s height-for-age in rural Mozambique while the childcare index has no significant effect at all. The measurement error problem may lead to an underestimation of the coefficient on childcare in the OLS regression but the bias is quite large. This large bias suggests that the variance of the measurement error is large compared to the variance in the true childcare variable (Verbeek, 2000). 22

Table 9: Determinants of under two year old height-for-age (2SLS and OLS results) Z-height-for-age Rural Urban 2SLS OLS 2SLS OLS Coeff. SE a Sign. Coeff. SE a Sign. Coeff. SE a Sign. Coeff. SE a Sign. Childcare score 0.243 0.117 ** 0.020 0.017 0.053 0.157 0.029 0.030 Child characteristics Age (months) -0.247 0.051 *** -0.164 0.021 *** -0.118 0.063 * -0.109 0.030 *** Age (months) sq 0.005 0.002 *** 0.002 0.001 *** 0.001 0.002 0.001 0.001 Sex (1 if male) -0.222 0.076 *** -0.218 0.070 *** -0.153 0.098-0.144 0.095 Orphan (father not alive) 0.466 0.400 0.504 0.386-0.160 0.306-0.157 0.303 Birth order -0.004 0.033-0.005 0.030-0.022 0.046-0.023 0.046 Mother characteristics Height (cm) 0.043 0.008 *** 0.045 0.008 *** 0.040 0.012 *** 0.040 0.012 *** BMI 0.043 0.017 *** 0.041 0.015 *** 0.036 0.018 ** 0.037 0.016 ** Age (years) 0.003 0.010 0.005 0.009-0.007 0.015-0.007 0.015 Household head -0.065 0.168 0.017 0.151 0.555 0.278 ** 0.566 0.277 ** Wife of head 0.205 0.135 0.183 0.120 0.151 0.139 0.159 0.140 Education (years) -0.009 0.093 0.106 0.054 ** 0.040 0.053 0.041 0.052 Education (years) sq -0.001 0.011-0.010 0.009-0.007 0.008 *** -0.007 0.008 Household characteristics Distance to water (min) 0.000 0.001 0.000 0.001 0.003 0.002 0.003 0.002 Latrine or toilet -0.223 0.107 ** -0.143 0.097-0.069 0.153-0.058 0.160 Hard floor -0.027 0.198-0.233 0.162-0.054 0.119-0.059 0.129 Wealth factor score 0.000 0.000 0.000 0.000 * 0.000 0.000 *** 0.000 0.000 *** Constant -7.567 1.316 *** -8.052 1.212 *** -7.450 1.867 *** -7.459 1.864 *** Observations 2190 2191 909 909 R-squared 0.169 0.280 0.278 0.278 H0: childcare is exogenous (Durbin-Wu-Hausman Chisq) 13.241 p-val 0.000 1.380 p-val 0.240 * significant at 10%; ** at 5%; *** at 1%; a Standard errors are robust, corrected for clustering and other sample design effects such as stratification. Provincial dummies included; rural Cabo Delgado and Maputo Province dummies significantly lower than base rural Niassa (at 10%); urban Tete, Manica, Gaza and Maputo Province dummies significantly higher than base urban Niassa (at 1, 5, 10 and 10 % respectively). 23