Maternal status and child health outcomes
Maternal Socio-economic Status and Child Nutritional Outcomes: Evidence from Rural Nepal
|Diane Dancer* |Anu Rammohan** |
|Econometrics and Business Statistics |Economics |
|University of Sydney |University of Sydney |
ABSTRACT
Using data from the Nepal Demographic and Health Survey (2001), we examine the effect of maternal socio-economic status on pre-school age children’s nutritional outcomes. Z-scores of child weight-for-height and height-for-age are used to measure child health outcomes. Our estimation results show that maternal education and autonomy exert a far greater influence on child nutrition than father’s education. We also find large gender and regional differences in the various nutritional outcomes. Interestingly, our results show that for females, the z-score for weight-for-age remains negative for all age categories, whereas for males it becomes positive from when they are 3 years or older. In the height-for-age model we find that for females their z-scores steadily worsen with each age category, so that even if they do not start with a disadvantage, by the time they are 3 years of age, their height-for-age is over 1 standard deviation below that of a 6-12 month female child.
JEL classification: J22, J23, O15, I21
Malnutrition among young children is a chronic problem in Nepal and according to United Nations estimates, in 2002 it affected approximately 48% of all pre-school age children (UNDP, 2004). Both maternal education and autonomy are considered to be influential in improving child health. However, despite the plethora of studies linking improvements in maternal education to child nutritional outcomes (see Glewwe, 2000; Behrman, 1990; Strauss and Thomas, 1995; Strauss, 1990; Thomas et al, 1991), the influence of maternal social status (or autonomy) on child nutrition remains a less researched area. The few studies that examine the link between maternal autonomy and child health, do so in the context of infant mortality (Murti et al, 1995; Kishor, 1993) or access to health care (Bloom et al, 2001, Maitra, 2004).
Additionally, studies from India also find gender differentials in intrahousehold resource allocations and child mortality rates (Bardhan 1988; Behrman 1998; Harris 1999), that exhibit a distinct geographical pattern attributable to mother’s social status (see Murthi et al, 1995; Kishor, 1993; Bloom et al, 2001; Pande, 2003).[1] A common pattern flowing through all these studies from South Asia is that the female child is disadvantaged both in terms of intrahousehold allocation of health and food resources and this therefore leads to excess female mortality. Thus, although the excess mortality of females is well-established in studies from India (Basu 1989; Das Gupta 1987; Pebley and Amin 1991; Sen and Sengupta 1983), few studies from South Asia examine the health status of surviving children and explore if there is a gender bias (see DeRose et al, 2000 for a review). Moreover, studies such as Basu (1989) and Pelletier (1998) argue that the link between malnutrition and mortality is not so clear-cut. Both studies find higher malnutrition rates among boys relative to girls. This is somewhat surprising since it not only contradicts the long held view that the female disadvantage in child health is greater in South Asia than in other regions, but it also contrasts with research by Kishor (1998) and Caldwell et al (1988) who argue that excess female mortality is primarily due to malnutrition in children.
These above studies however, do not study the role of maternal autonomy on the health status of surviving children, despite its considerable importance in a region where female socio-economic status is typically considered to be poor. Moreover, the nutrition status of surviving children is an important indicator of long-term child health, and is therefore also likely to have important policy implications.
This paper attempts to address some of the gaps in this literature by analysing the main determinants of child nutrition in rural Nepal, focusing on the influential role of maternal socio-economic autonomy. In particular we examine if there are any systematic regional or gender differences attributable to maternal socio-economic status.
We use child weight-for-height (a measure of wasting) and height-for-age (a measure of stunting), two commonly used anthropometric measures of child nutrition to measure child health status. These measures are used by nutritionists to measure both short-term (weight-for-height) and long term (height-for-age) nutritional deprivation among children.
BACKGROUND
The setting for our analysis is Nepal, which is a landlocked country in the foothills of the Himalaya mountains. With a population of approximately 24.1 million in 2002 and Gross Domestic Income per capita of $240 US, Nepal is one of the poorest countries in the world, with a predominantly rural population (World Bank, 2003). Geographically, Nepal can be divided into three distinct ecological zones: the mountains, the hills and the terai (plains). Only 7% of the population lives in the mountains where communication and transport facilities are fairly limited because of the harsh terrain. The hills region, on the other hand, accounts for 44% of the population and also includes the two major areas of the Kathmandu and Pokhara valleys. Approximately 49% of the population lives in the terai, which is basically an extension of the flat Indian Gangetic plains. This area has the most developed transportation and communication facilities.
Nepal performs poorly in terms of child nutritional measures, with 48% and 51% of pre-school age children being underweight and under height for their age respectively in 2002 (UNDP, 2004). Furthermore despite Nepal facing similar socio-economic characteristics to India, there is relatively little focus on maternal autonomy and its influence on child nutrition in the Nepalese context.
Women’s status in Nepal, as measured by the degree of autonomy they enjoy both in the household and outside is typically very low. In typical Nepalese households, not only do women have very little decision making power, but there are also several restrictions placed on women working outside, travelling alone without being accompanied by male or elderly relatives or accessing health care provided by males. Like in India, Nepal also exhibits large regional differences in women’s status, and previous studies such as Niraula and Morgan (1996) and Morgan and Niraula (1995), find that in terms of autonomy, women from the hill region fare better than women living in the terai.
Since low levels of female autonomy may also mask a greater preference for sons, regional differences in autonomy are likely to have different implications for child nutrition. Previous research has confirmed that son preference is widely prevalent in Nepalese society (see Karki, 1988; Leone et al., 2003), hence it is likely that there are also regional differences in the nutritional status of male and female children.
Women’s personal autonomy has been found to be influential on demographic behaviour and outcomes in many previous studies including Basu (1992) and Jejeebhoy (1991). Authors, such as Basu (1992), Dyson and Moore (1983), Mason (1984) and Bloom et al (2001), define women’s autonomy as their ability to influence decisions about themselves or close household members through control over resources and information. In traditional societies, women’s social autonomy (their rights, power and status) is often defined in the domestic sphere (Bloom et al, 2001). Using this definition of social autonomy, the current study assesses maternal socio-economic autonomy based on self-reported answers to questions in three broad areas- her knowledge and ability to make independent decisions with regard to health care both for herself and her child, her decision making power within the household and her ability to move freely to access health care.
While household decision making and freedom of movement are indicators of the level of autonomy that they enjoy in the domestic sphere, knowledge of health care and an ability to use that knowledge is also an indication of their autonomy or social status within the household.
Maternal economic status is influenced by access and control over economic resources. Hence, maternal economic status is assessed using a series of variables relating to control over economic resources. These include mother’s employment and educational status, and control over economic assets such as land and livestock.
The empirical analysis examines the influence of maternal socio-economic status on the short-term and long-term nutrition (weight-for-height and height-for-age) of pre-school age children. We estimate the regressions separately for the entire sample and for male and female children.
DATA
The data for our analysis come from the 2001 Nepal Demographic and Health Survey (NDHS). The survey was conducted by Macro International Inc. with funding from USAID, and was administered to ever-married females aged 18-49 years. It contains detailed information on household structure, labour market participation, asset ownership, health, and educational characteristics for all the household members. Furthermore, for the purposes of this study, it also contains several qualitative questions on female autonomy and decision-making power within the household, making it ideal for this analysis.
Our analysis is based on data for 4826 rural children in the 6-59 month age group[2] for whom complete information is available with regard to health, maternal and other household characteristics. In keeping with Duflo (2003) the lower bound is set at 6 months since anthropometric measures for early infants may be biased by the mother’s pre-natal practices and nutrition in early pregnancy. We further restrict our analysis to just rural children both for simplicity and also taking into account the fact that the size of our urban sample is very small (with only 500 children living in urban areas). Thus, we exclude urban households and those rural households where there are no pre-school age children, or where data are missing. According to Table 1, approximately half the children in the sample are boys, and the demographic and maternal characteristics for boys is roughly similar to that for girls for both nutritional measures.
Dependent variables
Our dependent variables are the two commonly used anthropometric measures of a child’s nutritional status- weight-for-height (a measure of wasting) and height-for-age (a measure of stunting). These measures are considered to be good indicators of short-term and long-term nutrition status of a child respectively. For these measures, z-scores are constructed and standardized by sex and age using the DHS (Demographic Health Survey) averages.
Explanatory variables
Since the main purpose of this study is to analyse the influence of maternal socio-economic status on child nutrition, and to examine if there are any gender and regional effects, we use a series of variables to measure social and economic status. Maternal social status is measured using self-reported answers to a range of questions relating to the mother’s decision making power within the household, her freedom of movement outside the household and knowledge of health care, both with regard to herself and her children. Maternal social autonomy is generally low in the sample, with some regional variations.
Respondents were asked several questions on who had the final say on a range of issues relating to household decision making, such as purchases of large household goods, visits by respondent to friends and relatives, and respondent’s health care. Based on answers to these questions, women who answered that they independently or in consultation with either their husband/partner/others made these decisions were assumed to enjoy greater autonomy, and all others were assumed to have little or no autonomy in the household decision making process.
The dataset also contains several questions on her ability to access health care for herself. We again use self-reported answers to questions relating to whether the respondent had knowledge of where to go for health care, whether it was a problem getting permission and money for transport to visit the health facility. The answers to these questions were subjective and dummies were constructed. With regard to knowledge of health care, the answer was either a ‘yes’ or a ‘no’. Similarly, with regard to getting money for transport and permission to go to a health facility, the responses were ‘not a problem’, ‘a big problem’ or ‘a small problem’. Those that reported ‘not a problem’ or ‘a small problem’ were assumed to have relatively greater autonomy with regard to freedom of movement and the others were assumed to have little or no autonomy. We similarly included an additional autonomy variable which asked the respondent whether she could independently access health care in the event of a child’s sickness.
Control over economic assets is an important means of gaining autonomy. Maternal economic status is measured by education, labour market participation, land and livestock ownership. Labour market participation and education levels have an important influence on the ability of women to control financial resources. However, labour market participation is likely to be endogenous. For example, while an increase in labour market participation increases household income, so more food and health inputs can be purchased, it also restricts the amount of time that the mother can spend with the child. Moreover, the researcher is unaware as to whether the greater labour force participation was motivated by greater female autonomy or due to poverty. Previous empirical research on the influence of maternal work on child nutrition is ambiguous (see Leslie, 1988; Glick and Sahn, 1995). However, these studies only examined whether or not the mother worked. Our dataset contains information on the types of occupation that women engage in. Using not-working as our base, we construct three categorical dummies- professionally employed, employed in agriculture or other.
Household characteristics
A shortcoming of this dataset is that it contains no information on wages, household expenditure patterns and community characteristics. However, there are other variables such as the wealth index, access to utilities (electricity and piped water) and ownership of consumer durables (bike and radio), that act as reasonable proxies for income. Hence, we construct dummies for whether the household has access to piped water, electricity and owns consumer durable goods such as a radio and a bicycle.
The wealth index in the dataset divides households into 5 wealth quintiles. Using this index, poorer households are disproportionately represented in our dataset, with less than 10% of households belonging to the highest wealth quintile whereas the lowest wealth index has a much larger 30%. This may partly be a consequence of the large rural sample. The constructed dummy variables have the advantage of providing a reasonably reliable measure of the household’s economic status, and it is not affected by the endogeneity and transitory nature of labour income.
The household’s religion and region of existence are also important considerations in the determination of child nutrition. Nearly 84% of the households in our sample are Hindu, so we construct a religion dummy, taking on a value of 1 if Hindu and 0 otherwise. With regard to the regional distribution of the sample, we note that a little less than half the sample (45%) lives in the terai (plains), and less than a quarter live in the mountains (17%).
Other household characteristics such as the ages of the mother and father, father’s occupation and education levels and female headship are also controlled.
Child characteristics
Among child characteristics we include the child’s age, gender and birth order. The gender dummy takes on a value of 1 for male children and 0 for females. For birth-order we use the absolute birth order of each child in the household to compute five dichotomous birth order dummy variables - second born, third born, fourth born, fifth to tenth born (with the first born child being the base).
Sahn and Alderman (1997) argue that the determinants of nutritional outcomes are age-dependant and attributable to biological factors, and a failure to take into account these cohort-specific influences is likely to give biased estimates. To capture age-specific effects on nutrition, we divide the sample into five age cohorts: 6-12 months (the base case), 13-24, 25-36, 37-49 and 50-60 months.
ECONOMETRIC APPROACH
The econometric approach used in this paper is based on the collective household framework due to McElroy and Horney (1981) and Chiappori (1988), where parents are assumed to have differing preferences regarding the consumption and health of their children (see Hoddinott and Haddad, 1995; Alderman et al, 1995, for comparisons of the unitary and collective household models). We assume that the household consists of two parents, mother (m) and father (f), and children of both genders, daughters (d) and sons (s) respectively. Parents care about child quality and invest in the nutritional requirements of their children to the extent that the marginal benefits of child health exceed or equal investment costs.
It is reasonable to assume that an improvement in socio-economic autonomy, i.e. higher earning ability, education or decision making power within the household, gives women greater power to allocate resources towards child health.
The health status of children, h, is determined by a biological health production technology:
[pic] (i= d, s) (1)
where I is a vector of health inputs chosen by child j’s household, H is a vector of household and parental characteristics (demographics, age, education levels etc), z is a vector of individual child characteristics (such as age, age-squared, gender), ε represents unobservable individual, household and community characteristics that affect the child’s health status.
Estimation of eq (1) would require detailed information on a large number of variables that are not available in the NDHS dataset. We therefore estimate reduced form demand functions for child health status which can be represented as follows:
[pic] (2)
where β represents the relative bargaining power of each member that may affect allocation of resources within the household. The term β includes any variable that could potentially affect a member’s (in our case mother’s) bargaining power within the household. These variables include her labour market status, education, assets (Ai), and extra environment variables (γ) such as autonomy variables relating to decision making power within the household and freedom of movement.
We estimate reduced form demand functions for both short-term (weight-for-height) and long-term (height-for-age) nutritional status, using two alternate measures: an ordinary least squares (OLS) estimation and a logit model. In both cases, we run separate regressions on the entire sample, male and female children separately using the same set of variables.
The OLS estimation allows us to take advantage of the entire range of information on both anthropometric measures. The OLS regression that is estimated is given by:
[pic] (3)
hij measures the weight-for-age and height-for-age of child i in household j; H is a vector of household characteristics including household wealth index, father’s age, education levels, access to piped water, household assets such as electricity, radio and bike. Cij is a vector of child-specific variables incorporating gender, age, and birth-order as control variables. The term G refers to geographical location, i.e. whether the household resides in the mountains, hills or terai region. Mecon is a matrix of variables that influence mother’s economic status such as mother’s occupation, education and ownership of assets such as land and livestock. The term MSautonomy is a matrix of variables denoting mother’s social status.
We considered using an ordered probit estimation since the dependent variables could be assumed to have a natural ordering - being -2 standard deviations (sd) below the median is an inferior outcome to being between -2sd below the median and the median, which in turn is an inferior outcome to having a positive standard deviation. However, when we ran the ordered probit model for each of the models above, the weight-for-age models were very poor at predicting children with either a very low z-score or a positive z-score. For the height-for-age variable, the models predicted virtually no children with a positive z-score. Thus these models were not used.
The dependent variables, child’s weight-for-height and height-for-age could however, also be considered as having two alternatives. For example, there are two possible outcomes on weight-for-height- whether the child is malnourished or stunted (i.e -2 standard deviations below the median) or not. Therefore, we additionally estimate a logit model of stunting (1 if height-for-age is more than 2 standard deviations below the DHS median and 0 otherwise). This model was therefore estimated for the three different cases considered in this paper (entire sample, male and female samples).
The logit model for weight-for-height (malnutrition) was not reported as the predicted results were inferior to those under the OLS. There is approximately 10% of the sample with a z-score at least -2sd below the median which makes prediction difficult. The models correctly predict about 2% of the children is this category. However, for the height-for-age z-scores, approximately 57% of the children in the entire sample and in the male and females samples have z-scores that are at least -2sd below the median and the models correctly predict approximately 81% correctly.
The binary choice model is derived using underlying behavioural assumptions, which imply a latent variable representation of the model. The latent variable, y* is assumed to be a linear function of the observed x’s using the following model, [pic] where X is a matrix of explanatory variables and [pic] is the error term. Under this model specification, the probability that the ith child falls into the jth category for height-for-age (stunting) is given by:
|[pic] | (4) |
where pij is the probability of being severely malnourished/stunted for the ith child in the jth household. and
[pic] (5)
The explanatory variables are the same as those used in the OLS model.
The interpretation of the coefficients is facilitated by considering the log odds ratio defined by:
|[pic] |(5) |
Thus if (jr > (kr, then an increase in the level of characteristic r increases the log odds of the child being in category j rather than k.
For interpretation, the coefficients are transformed through exponentiation, and the resulting coefficients are the odds-ratios that show the magnitude of the variable’s impact on the probability of the outcome occurring. A coefficient of 1.00 indicates no effect on the odds ratio, a coefficient greater than 1.00 indicates a positive effect on the odds ratio, and a coefficient less than 1.00 indicates a negative effect on the odds ratio.
We recognize that there is potential for unobserved heterogeneity. For example, a child’s health endowments are largely unobservable to the researcher and this could potentially lead to biased estimates due to its correlation with observed variables. This problem may be reduced by entering the health endowments of the parents. However, since the dataset contain no information on father’s health, we rely on maternal health endowments and the child’s birth-order to account for the influence of genetic factors on child health. Note that in the Demographic Health Surveys nutritional outcomes are restricted to individuals up to 17 years of age. Hence the z scores for weight-for-height and height-for age for mothers are constructed on the assumption that their height at 17 years of age reflects their final height.
Child health is affected by access to household income and resources, genetic factors and calorie intake. The child’s birth weight is however an imprecise measure in our dataset because not all children were measured at the time of birth. Since the birth weight measure used in the dataset relied on the mother’s recall of child size (large, small or average) and not a quantitative measure, we do not use this variable in our estimation.
We also do not use health inputs such as maternal use of post-natal care since in our dataset this information is restricted to children under 36 months of age. Furthermore, this variable is likely to be biased for older children since it is based on recall. Furthermore, child nutrition is influenced by calorie inputs which are unobservable to the researcher.
We further explored the possibility that the child’s birth-order may be related to mother’s age, as for example higher birth order (later born) children are born to older mothers. expand However, we found no significant difference between mother’s ages for each of the birth orders.
RESULTS
The means and descriptive statistics of the variables used in our analysis are presented in Table 1, for the entire sample and disaggregated by gender. Note that there is little variation for any one characteristic between the entire sample, males and females. Therefore, when discussing descriptive statistics we only quote the results for the entire sample. As previously mentioned, previous Nepalese studies find evidence of regional differences in female autonomy, hence we combine the social autonomy variables with regions (terai, mountain and hill) to account for the possibility of regional effects on maternal autonomy. Although maternal autonomy as defined above is typically low in the entire sample, there are observable regional differentials in our sample (Table 1). In general we find that, on all our measures of autonomy, mothers in the mountains have the lowest autonomy in the sample, relative to those living in the hills and terai. For example, the percentages of women who are able to get money for transport to get medical help in the mountains is only 14.2% while it is 28.1% and 30.4% respectively for women living in the hills and terai.
The general level of education of the mothers is also very low in this sample with substantial gender gaps in terms of both parental education levels and occupations. For example, 36% of fathers in the sample have no education (as compared to 79% for mothers), whereas nearly 35% fathers have secondary schooling (compared to 9% among mothers). Although the labour force participation is high among mothers in this sample, with only 10% reporting not working, the breakdown by occupation paints a vastly different picture. Here we observe that, while 56% of the fathers are employed in agriculture, a much higher proportion (86%) of mothers work in the agricultural sector. On the other hand while only 3% of mothers are professionally employed, the figure is more than five times that among fathers (18%).
In order to keep the discussion of the results manageable, we first discuss in detail the OLS results for both weight-for-height and height-for-age for the three models (entire sample, male and female). This is followed by a brief discussion of the logit results.
OLS estimation results
Tables 2 and 3 present the OLS estimation results for a child’s weight-for-height and height-for-age respectively. In both tables the second column reports the coefficients and standard errors for the entire sample, while the third and fourth columns report the results for the male and female children respectively. Significant differences are observed both between each of the three samples (entire sample, male and female) and also when we compare across the two anthropometrics measures.
We tested two key hypotheses (i) whether there is any gender bias in nutritional outcomes and (ii) the influence of regional effects and maternal socio-economic autonomy on nutritional outcomes of children. Our estimation results point to some interesting results which are discussed in further detail below. Three results are worth noting however: first, we find that the explanatory variables have differential effects on male and female children. In particular, while increases in household wealth improve the z-scores on both weight-for-height and height-for-age measures for the entire sample and for male children, they have no influence on female children. Second, while the influence of the interaction variables (maternal social autonomy variables interacted with region dummies) is mixed, in the mountain region where maternal autonomy is lowest, an improvement in maternal knowledge of health care has a significant and positive effect on the weight-for-height of children in the full sample, and for female children in particular. The third important result relates to age-specific effects. In the weight-for-height model for females, the z-score remains negative for all age categories, whereas for males it becomes positive and significant from when they are 3 years or older. Since weight-for-height is a measure of short-term nutritional deficiency, this suggests that the gender discrimination against a female child may be worsening as she ages. Below we discuss these results in some detail .
Interaction of maternal social autonomy variables with regions
Interestingly, while the inclusion of regional dummies shows strong regional effects, the child’s region of residence impacts differently on the two nutritional outcomes. There is a large, positive and significant effect on child’s weight-for-height from living in the mountains or hills relative to the terai, with an improvement in z scores of 0.23 (p-value = 0.04) and 0.27 (p-value = 0.00) standard deviations (sd) respectively. However, for children living in the mountains, the height-for-age z-score is significantly reduced by 0.50sd (p-value=0.00).
In anticipation of regional influences on maternal autonomy, we interact maternal social autonomy variables with regional dummies. These interaction variables provide a key test for our hypothesis that maternal autonomy varies by region, and may therefore have differential effects on the two nutritional measures. Our estimation results point to regional and maternal socio-economic autonomy characteristics having a significant and differential effect on a child’s weight-for-height and height-for-age z-score, depending on the child’s gender and region of residence.[3]
As discussed previously we consider three distinct social autonomy variables: maternal knowledge of health care facilities, constraints on maternal ability to access health care for her child and herself and variables relating to her freedom of movement. Each of these variables is interacted with the regional dummies, with the terai region serving as our base. In general, we observe very low levels of maternal autonomy in the sample, with women living in the mountain region having autonomy levels that are considerably lower than those enjoyed by mothers living in the terai and hill on all our measures of social autonomy. We examine below the manner in which regional differences in social autonomy have a differential impact on the two nutritional outcomes.
Knowledge of health care and ability to access health care for self
We assessed maternal ability to access health care for self based on responses in three areas: knowing where to go in the event of sickness, getting permission, and money for transportation to visit health care. Our estimation results indicate that maternal knowledge of health care facilities has a differential effect on male and female children, depending on whether we consider the child’s short-term (weight-for-height) or long term (height-for-age) nutrition. With regard to short-term nutrition, it is apparent that this variable has a positive and significant effect on mountain children compared with terai children, but only in the entire sample and for females. Furthermore the size of this effect is greater for female children relative to the entire sample. For the entire sample, the weight-for-height z-score of children living in the mountain region is increased by 0.39sd, but when we disaggregate by gender and run the models separately, the improvement in z-scores is considerably larger. For example, a female child living in the mountains has a weight-for-height z-score that is 0.59sd higher than the base category (terai female children). However, this variable has no influence on male children living in the mountains. These results are particularly significant when we consider that in our sample, maternal autonomy is lowest in the mountains where only 7% of mothers have the requisite knowledge of where to go for medical care (Table 1).
In the height-for-age estimations on the other hand, maternal knowledge of health care is only significant for female children living in the hills, where this variable reduces the height-for-age z-score of females in the hill region by nearly 0.26sd relative to terai female children. These results for children living in the hill region must however be treated with caution as the dummy variable for the hill region is not significant.
Again we find that, while mountain mothers are the most disadvantaged in terms of social autonomy, the autonomy levels of mothers in the hill and terai regions is roughly similar. For example, our descriptive statistics show that while 28% - 30% of hill and terai mothers easily got the money for transportation to go to health facilities, the figure for mountain mothers was less than half that figure (14.2%). The situation is similar with regard to being able to make decisions when the child is ill. Similarly, relative to terai mothers, less than half the mountain mothers get permission to go to a medical facility. These results indicate that the autonomy levels in the mountains are nearly half that in the terai.
Hence it is interesting and relevant that a small improvement in mother’s freedom of movement, has a positive and significant effect on a child’s short-term nutrition. Our empirical analysis shows that the weight-for-height z-scores of children both the mountains and the hill regions is significantly improved relative to the terai, when their mothers do not face constraints in obtaining permission to go to health facilities for self. For example, in the mountain region, relative to the base case, this variable significantly improves the z-scores for the child’s weight-for-height measure by approximately 0.39sd for the entire sample, and by a slightly smaller 0.29sd in the case of male children. Although maternal knowledge of where to go for health care was found to have no influence on male children living in the mountains, maternal ability to get permission easily only affects male but not female children from this region. However, for the hill region, this variable is highly significant and has a positive effect on weight-for-height z-scores of children in the full sample and females, whose weight-for-height is around 0.439sd better than the base category.
Next we consider the effects of constraints on maternal ability to access health care for self on her child’s long-term nutrition. First we note that none of the maternal permission variables have any impact on the height-for-age z-scores of children living in the mountains. In the case of children living in the hills, the estimated coefficient for maternal access to money for transport to health facilities in the event of sickness is marginally significant and reduces the height-for-age of children in the general sample (by 0.22sd) and for females (by 0.28sd), relative to the base category. Part of this result may be attributable to the negative (but insignificant) effect of the region dummy for hills in the height-for-age regressions.
Decision making within the household
The three maternal household decision making variables - final say on own health care, making large purchases and visits friends and family - show the degree of autonomy available to mothers in household decision making. We observe marked differences across the three regions, with mothers from the mountains faring worst in terms of each of these three variables. While only 4% and 3.3% of mountain mothers have the final say on own health care and making large purchases, the figures are much better and roughly similar for hills and terai mothers, where approximately 11% of terai and hill mothers have the final say on own health care and 11% and 13% of hill and terai mothers have the final say on large purchases. We observe that only 5.3 % have the final say in visits to their relatives, while 11.3% and 14.9% of hill and Terai mothers have the final say on visits to family.
Interestingly, maternal decision making (final say) variables only affects the height-for-age of female children living in the mountains, where maternal autonomy was the lowest in the sample. Here we find that maternal ability to have the final say in making large purchases has a negative but marginally significant in reducing a child’s height-for-age. Similarly in the mountain region, it improves the weight-for-height z-scores by 0.42sd and 0.25sd for the entire and male sample living in the mountains respectively, relative to the base case. For children living in the hill region, having a mother who has the final say on making large purchases has a large and significantly positive effect on weight-for-height z-score both for the entire sample (increases by 0.42sd) and for male children by a marginally larger 0.55sd.
Ability to access health care in the event of child’s sickness
Maternal ability to independently access health care in the event of a child’s illness has no effect on the z-scores for child’s weight-for-height in any of our models. This variable however has a significant, differential effect on the height-for-age of mountain and hill children. For example, in the entire sample of mountain children, it reduces the height-for-age z-scores by 0.16sd (p-value=0.01), whereas for a child living in the hills the height-for-age z-score increases by 0.06sd (0.71-0.11) compared with the base case. These regional differences persist for the height-for-age measure when we consider the male and female samples separately. Although the size of the effects for the male sample are similar to those found in the entire sample, hill residence has a slightly larger and positive effect on the height-for-age of female children, who are likely to be 0.10sd taller than females living in the terai. Note however that the estimated coefficient for the hill dummy variable is not significant for any of the three categories.
The influence of maternal economic status variables
Maternal economic status was assessed using a range of variables associated with maternal education, occupation and control over economic assets. We do not interact these with region dummies as the economic status of mothers albeit low, is broadly similar across the three regions in our sample. Surprisingly maternal education levels have no influence on a child’s weight-for-height in any of our models. One possible explanation for this result may be the generally low levels of mother’s education found in our sample where according to Table 1, nearly 80% of mothers are illiterate.
However, when we consider the effects of maternal education on the long-term nutrition of children, it becomes obvious that there are significant positive effects on the height-for-age z-scores. What is interesting moreover is the finding that it is the primary schooling of the mother that has a larger effect on a child’s long-term nutrition, rather than secondary schooling. Our estimation results show that in the entire and male samples, relative to a mother with no education, having a mother with either primary or secondary education has a positive and significant effect on the height-for-age of children in the entire sample and for males. For females however, only primary education is significant and a female child whose mother has completed primary schooling has a height-for-age z-score that is approximately 0.17sd above that of a female child with an uneducated mother. For male children, although both maternal primary and secondary education improve height-for-age z-scores, the effect of maternal primary education is marginally higher than for female children at 0.22sd. These results are in keeping with previous empirical results and provide a strong justification for programs to equip women with at least primary school level education.
The labour force participation of mothers in our sample is reasonably high, with only 10% of mothers reporting not working (Table 1). However, a majority of them are employed in the agricultural sector, hence it is of interest to see if being employed and, in particular, if maternal occupation exerts any influence on child nutrition. For the entire sample, our estimation results point to positive effects on both the child’s weight-for-height and height-for-age, if the mother is professionally employed rather than being unemployed. Relative to children whose mothers are not working, those whose mothers are professionally employed are significantly more likely (by 0.15sd, p-value=0.07) to have better weight-for-height outcomes. This variable however has no effect when we disaggregate by gender and run the models separately for male and female children. Interestingly, professional employment of the mother has a far larger effect on the long-term nutritional status of children, where it increases height-for-age z-scores of children in the entire sample, and in particular, for female children, for whom the height-for-age z scores increase by 0.67sd relative to the base category (p-value=0.00). Agricultural employment on the other hand, only emerges as being significant for females in the height-for-age model, where it increases the height-for-age outcomes by 0.24sd relative to the female children with non-working mothers.
We use maternal ownership and control over land and livestock either independently or in conjunction with others to assess the effect of control over economic assets. It is not possible to tell from our dataset if the land or livestock is being used for subsistence or income generation purposes. Our estimation results show that in terms of the weight-for-height measure, maternal land ownership has small but positive effect for the entire sample (z-score increases by 0.09sd, p-value=0.06), but very significant effects on male sample (0.19sd, p-value=0.00). However, land ownership has larger positive effects on height-for-age z-scores, both in the entire sample (z-score increases by 0.21sd, p-value=0.00), and for female children (0.24sd, p-value=0.02), relative to the base case.
Unexpectedly maternal ownership of livestock has a detrimental effect on the weight-for-height of male children, whose weight-for-height z-score is reduced by 0.08sd (p-value=0.05) compared to male children whose mother’s do not own livestock. Possible explanations for this rather surprising result may be to do with the time-intensive nature of livestock rearing and the fact that we do not know whether the livestock provide any income, or are used for subsistence or kept as pets. This variable however has no influence on a child’s height-for-age z score in any of our models.
Role of gender
It is interesting that while a child’s gender has no influence on weight-for-height, the positive coefficient for gender in the full model shows some improvement in height-for-age z-scores for male children. The differential effects of the various explanatory variables on the male and female sample are however noteworthy. The household wealth variables for example have no influence on the nutritional outcomes of female children in either of the measures. In contrast, wealthier children in the entire and male sample fare better on both the weight-for-height and height-for age z scores of children, than poorer children. In particular, there is a significant improvement in both short-term and long-term nutrition for children in the entire sample and for males, in the third, fourth and fifth wealth quintiles relative to the base category.
For example, a male child in the highest wealth quintile has a weight-for-height z-score (0.28sd, p-value=0.00), that is significantly higher than a male child from the lowest wealth quintile. We find similar effects in the height-for-age model where, for male children, belonging to the highest wealth quintile confers them with a height advantage of 0.27sd higher than the base category. It is rather interesting that female children are not similarly advantaged from belonging to higher wealth categories.
The influence of birth order was examined using five birth order dummies, using the child’s absolute birth order from which five dichotomous birth order dummy variables are created. This variable implicitly captures the effects of being born in a large household and also in some sense maternal endowment factors. For weight-for-height, these variables are not significant for any of the models. The long-term nutritional status of later born children on the other hand, becomes increasingly negative in all our models. However, it is interesting to note that the size of this effect is larger and much more significant for female than for male children. A male child born fifth or later in the birth order is 0.26sd (p-value=0.02) shorter than a first-born male child. A female child born fifth or later however has a height-for-age z-score 0.38sd lower than a first born child Hence a female child born fifth or higher pays a height penalty on 0.12sd relative to a similarly placed male child. It is unclear whether these effects emerge from being part of a larger household or from being female.
When we consider the effects of the different age cohorts, we observe large and negative effects in both weight-for-height and height-for-age measures. In the weight-for-height model for example, relative to the base category (6-12 months), females aged 13 - 24 months have a significant lower z score of 0.64sd on this measure. The decrease in the weight–for-height is much less for the other two age groups. For male children on the other hand, while the weight-for-height z-score is significantly decreased (by 0.38sd) for those aged between 13-24 months compared with the base, the size of the effect is substantially lower. Further, for male children over 3 years of age, the weight-for-height z-scores become positive and significant and increase for each additional age category (by at least 0.15sd for the two groups). These results have important policy implications as they indicate that the nutritional status of male children as measured by weight-for-height becomes positive for male children 3 years and older, whereas for females it continues to remain negative in each age category.
Interestingly, the results for the height-for-age measure are strikingly different from those for the weight-for-height measure. Here we find that for both male and female children, there is a very significant decrease in the z-score for children over the age of 12 months and this reduction increases with time. However, a comparison of the male and female samples shows that although negative, the decrease in height-for-age z-score in the male sample is not as great as for the female sample. In particular for female children in the last two age categories (37-48 and 49-60 months), the effect on height-for-age z-scores is significantly large and negative. The estimated coefficients show that female children in these two age groups, are over 1 standard deviations shorter than the base category. Since height-for age is our measure of long-term nutrition, these results point to the possibility of active gender discrimination.
Influence of household characteristics
The estimation results indicate a strong influence of household characteristics, in particular, access to piped water, religion and wealth are influential on both nutritional outcomes. Relative to other religions, belonging to the Hindu religion appears to have a significant and negative effect on child nutrition in all our models. Nearly 84 % of our sample is Hindu, it is therefore interesting that being Hindu exerts such a strong negative effect on children’s nutrition outcomes.
Although maternal occupation was influential in improving child nutrition outcomes, we observe no such effects with regard to paternal occupation. However, having a father who is employed in agriculture has a negative effect on male child’s height-for-age z-score (reduced by 0.10sd, p-value= 0.10). In contrast, father’s education, in particular secondary schooling, improves the weight-for-height outcomes of children in the entire and female samples, but has no influence on male children. However, it should be noted that the size of the effect is small and having a secondary educated father only marginally improves the z-score on weight-for-height by around 0.06sd in the case of the entire sample, and by 0.08sd for females. As previously noted mother’s education has no influence on children’s weight-for-height. However, having a father with secondary education is both highly significant and has a much larger positive impact on the height-for-age measures in all our models, where for female children it increases the z-score by 0.21sd relative to those whose father’s have no education (p-value=0.00).
In addition to wealth variables, we also include ownership of consumer durables (radios and bicycle) and utilities (electricity and access to piped water) as additional measures of a household’s economic status. Ownership of radios acts as a crude measure of maternal health knowledge. However, in all our models, ownership of radios has no effect on child nutrition. Access to electricity however, has a large and very significant effect on the height-for-age measure of children in the entire sample and for females, where they are 0.25sd and 0.38sd (p-values=0.00) respectively taller than the base category. In the models for weight-for-height, only the estimated coefficient for the female child is significant and it increases the weight-for-height of female children by 0.16sd (p-value=0.01).
Access to piped water is found to have a positive influence on child health outcomes (see Jalan and Ravallion, 2003). However, in our estimations, the impact of this variable is highly different and significant depending on which anthropometric measure we use. While access to piped water has a positive and significant effect on the weight-for-height measures of children in the entire and the male sample, we find that this variable has quite the opposite effect on height-for-age measures where they reduce the heights of all children and for females by up to 0.25sd relative to other forms of water.
Female headship is only ever significant in the height-for-age measure for female children, where female children from female headed households are likely to be up to 0.17sd taller than those from male headed households. Nutritional outcomes are also affected by genetic factors, and we find that while the mother’s weight-for-height has a positive and significant effect on only females, the height-for-age z scores of male children is improved by just 0.01sd (p-value=0.10).
Logit Results
As mentioned previously, we only model the child’s height-for-age, in particular the probability of being 2 standard deviations below the DHS median (which is considered a measure of stunting) for the three models (entire sample, male and female). These results are presented in Table 4, where we report the coefficients and standard errors. The second, third and fourth columns in Table 4 show the coefficients for the entire sample, the male and female sample2 respectively.
The first point to note is the significance of the gender dummy in the entire model which suggests that males are significantly less likely to be stunted (p = 0.028) and have a 15% lower odds of being stunted relative to girls (eβ = exp (-0.136)= 1.15). The effect of gender is however more apparent when we consider the male and female samples separately. Here we observe that the variables affecting the male and female samples are different. Unlike in the OLS estimation where household wealth had no influence on female children, in the logit estimation, for female children whose household wealth is in the fourth quintile, the estimated coefficient is significant, and the size of the effect is much smaller than that of male children. Whereas a male child being born in the third quintile has a 67% lower probability of being stunted, for females born in the same wealth quintile the odds of being stunted is reduced by 32%. Household wealth however, has a greater influence on the full and the male sample. For example, a male child born in the second wealth quintile has an 82% lower odds (p < 0.000), of being stunted, relative to a child born in the lowest wealth category. We see similarly reduced odds of being stunted in each higher wealth quintile relative to a child born in the lowest wealth quintile.
Maternal education emerges as being influential at reducing the odds of being stunted in all our models, although it should be noted that in the male model only mother’s primary schooling is significant, whereas in the female model although both levels of education are significant, mother’s schooling is only marginally significant (p < 0.081) for secondary. For females, however the size of the effect of mother’s education is approximately the same for both primary and secondary levels of education, where they reduce the odds of being stunted by 39% (eβ = exp (-0.33) = 0.72). For males, however, having a mother who is primary school educated reduces the probability of being stunted by a much larger 56% (eβ = exp (-0.444) = 0.641, p = 0.004).
With regard to child characteristics, in all three models children born fifth or higher in the birth order have substantially greater odds of being stunted. The size of these effects is roughly similar for the entire sample and for male children, but is marginally higher for females. A female child born fifth or higher in the birth order for example, is 1.5 times more likely to be stunted relative to a first born female child.
Similarly, relative to a child who is 6-12 months of age, each age category is significantly and positively associated with a greater likelihood of being stunted across all three models. The size of these effects however is much larger in the case of the female sample, where we observe that all things being equal, in each age category, a female child has a much greater likelihood of being stunted relative to a male child. For female children, a child who is between 13-24 months has a 3.5 times greater odds of being stunted, relative to the base category. This disadvantage in terms of age seems to persist and increase unto 3-4 years of age (OR= 7.15) and gradually drops in the 4-5 year group.
The effect of region on stunting is somewhat difficult to interpret because of the interactions with the various maternal social autonomy variables. However, it is interesting to note that in both the entire model and for females, the dummy for mountain residence is significant and positive, indicating that a child born in the mountain region has a substantially greater probability of being stunted relative to a child from the terai region. All things being equal, a female child born in the mountain region is four times more likely to be stunted relative to a female child born in the terai. We investigated if there were significant height-for-age differences between the children in the different regions and between the mothers. For female children, both the mother’s height-for-age and the children’s height-for-age were significantly smaller in the mountains and the hills compared with the terai. These results were similarly true for male children with one exception. There was no significant difference between the mother’s height-for-age for the hills and the terai.
As previously mentioned, in our sample maternal autonomy was the lowest in the mountain region relative to the other two regions. The interaction of maternal autonomy and region dummies gives us an indication of the existence of regional effects, if any, on the probability of being stunted. Here we observe that very few social autonomy variables appear to influence the probability of stunting. However, what is interesting is that maternal ability to get the money for transportation for her own health care increases the probability of being stunted marginally for a female child living in the mountains (OR= exp(1.39 – 0.58) = 2.24).
With regard to household characteristics, father’s occupation only affects the male model, where having a father who is in agriculture employment increases the probability of a male child being stunted by 35%.
DISCUSSION
Maternal socio-economic characteristics are found to have an important influence on child health in studies on child mortality. However, the influence of maternal socio-economic characteristics on the health status of surviving children has been less researched. In particular while the excess female child mortality is well-established in studies from South Asia, there is empirical ambiguity over the question of gender bias in child nutrition. This paper attempts to fill some of these gaps in the literature by focusing specifically on the relationship between maternal socio-economic status and nutritional measures of children in rural Nepal, using weight-for-height (a measure of malnutrition) and height-for-age (a measure of stunting) of pre-school age children in Nepal. In particular we explored if the differences in child nutrition outcomes exhibit any gender or regional bias attributable to mother’s socio-economic status. The estimation results point to some interesting regional and gender differences depending on the nutritional measure that we use.
Maternal autonomy and economic status is in general low in our sample. However, we observe perceptible regional differences. Among our most striking results is the finding that for females the weight-for-height z-score remains negative for all age categories, whereas for males it becomes positive from when they are 2 years or older. In other words, while the nutritional status of male children improves from age 2 onwards, whereas for females we see a consistent decline. More alarming is the result for long-term nutrition (the height-for-age model), where we find that the z-scores for female children steadily worsen with each age category, so that even if they do not start with a disadvantage, by the time they are 2 years of age, their height-for-age is over 1 standard deviation below that of a 6-12 month female child. This result has strong policy implications as it suggests that there is likely to be active discrimination against the female child.
Our maternal social autonomy variables provide mixed results. While maternal decision making variables have no effect in the height-for-age model, they only improve weight-for-age for children from the mountains (male and full sample, but not female children), when their mother has the final say on visiting relatives, and hill children where the mother has the final say on making large household purchases. Maternal education (primary) has a large and positive effect on child height-for-age measures in all samples, which is in keeping with previous research. Similarly having a mother who is professionally employed has a large and significant effect on female children and on the entire sample, but has no influence on male children.
It is also interesting to note that the wealth variable has no influence on female children, whereas in the male and general sample for both nutritional measures, there is a positive and significant effect from belonging to the three highest wealth quintiles.
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Appendix
Table 1: Descriptive statistics
| |ALL |MALES |FEMALES |
|Household characteristics |Percentage |Percentage |Percentage |
|Wealth index- 1st quintile |28.60 |28.83 |28.36 |
|2nd quintile |22.77 |22.27 |23.27 |
|3rd quintile |19.58 |19.78 |19.39 |
|4th quintile |19.71 |19.90 |19.51 |
|5th quintile |9.35 |9.22 |9.47 |
|Mountain dummy |16.99 |18.11 |15.87 |
|Hill dummy |37.69 |36.73 |38.65 |
|Terai Dummy |45.32 |45.16 |45.47 |
|Father’s occupation- technical |18.42 |18.78 |18.07 |
|Father’s occupation- agricultural |56.38 |55.80 |56.97 |
|Father’s occupation - other |25.20 |25.43 |24.97 |
|Religion-Hindu |83.82 |84.38 |83.26 |
|Female headed household |8.83 |8.77 |8.89 |
|Father’s education – no schooling |36.34 |35.56 |37.12 |
|Father’s education level- primary |27.00 |27.63 |26.37 |
|Father’s education level- secondary |34.85 |34.65 |35.06 |
|Father’s education does not know |1.80 |2.16 |1.45 |
|Piped water |35.12 |35.48 |34.77 |
|Electricity |12.35 |12.05 |12.67 |
|Radio |42.31 |42.04 |42.58 |
|Bicycle |24.49 |24.55 |24.43 |
|Mother’s characteristics | | | |
|Mother’s economic status | | | |
|Mother’s education- no schooling |79.38 |80.72 |78.05 |
|Mother’s education- primary |11.56 |10.22 |12.90 |
|Mother’s education- secondary |9.06 |9.06 |9.05 |
|Mother’s occupation – not working |9.82 |9.81 |9.84 |
|Mother’s occupation- professional |2.90 |2.41 |3.139 |
|Mother’s occupation- agricultural |86.01 |85.29 |86.77 |
|Mother’s occupation- other |1.26 |2.54 |0.00 |
|Owns land |6.13 |6.27 |5.99 |
|Owns livestock |25.57 |25.63 |25.51 |
|Maternal social status | | | |
|Medical help for self- know where to go*dummy for Mountain |6.90 |7.44 |6.37 |
|Medical help for self- getting permission to go*Mountain |4.18 |4.61 |3.76 |
|Medical help for self- getting money for transport*Mountain |14.24 |15.33 |13.14 |
|Medical help for self- know where to go*Hill |13.99 |13.29 |14.68 |
|Medical help for self- getting permission to go *Hill |7.54 |6.98 |8.10 |
Contd. on next page
|Medical help for self- getting money for transport *Hill |28.14 |27.37 |28.90 |
|Medical help for self- know where to go*Terai |8.97 |9.18 |8.76 |
|Medical help for self- getting permission to go *Terai |7.29 |7.10 |7.48 |
|Medical help for self- getting money for transport *Terai |30.36 |30.20 |30.51 |
|When child is ill can decide to get medical help*Mountain |14.44 |15.62 |13.27 |
|When child is ill can decide to get medical help*Hill |29.28 |28.54 |30.01 |
|When child is ill can decide to get medical help*Terai |34.29 |34.19 |34.39 |
|Final say on own health care*Mountain |4.00 |4.44 |3.56 |
|Final say on making large purchases*Mountain |3.34 |3.82 |2.85 |
|Final say on visits to relatives*Mountain |5.32 |5.90 |4.75 |
|Final say on own health care*Hill |11.04 |10.76 |11.33 |
|Final say on making large purchases*Hill |11.33 |11.09 |11.58 |
|Final say on visits to relatives*Hill |15.58 |15.37 |15.79 |
|Final say on own health care*Terai |10.82 |10.10 |11.53 |
|Final say on making large purchases*Terai |12.62 |11.88 |13.35 |
|Final say on visits to relatives*Terai |14.86 |14.58 |15.13 |
|Child characteristics | | | |
|Birth order- First born |20.64 |20.73 |20.54 |
|Second Born |22.56 |22.85 |22.28 |
|Third born |17.68 |17.03 |18.31 |
|Fourth born |13.94 |13.50 |14.39 |
|Fifth born |25.18 |25.88 |24.47 |
|Male |49.88 | | |
|Age-6 months |1.66 |1.50 |1.82 |
|7-12 months |10.63 |10.76 |10.50 |
|12-24 months |22.40 |21.85 |22.94 |
|25-36 months |22.34 |21.89 |22.78 |
|37-48 months |22.40 |22.89 |21.91 |
|49-60 months |20.58 |21.11 |20.05 |
| |Mean (SD) |Mean (SD) |Mean (SD) |
|Father’s age |32.38 (8.64) |32.52 (8.75) |32.25 (8.53) |
|Mother’s age |28.34 (6.40) |28.47 (6.46) |28.22 (6.34) |
|Age squared |844.34 (394.16) |855.22 (398.24) |836.50 (389.97) |
|Mother’s weight-for-height |-0.68 (8.39) |-0.57 (9.01) |-0.80 (7.73) |
|Mother’s height –for-age |-2.05 (4.49) |-1.92 (5.58) |-2.17 (3.07) |
|Child’s weight-for-age |-0.93 (0.87) |-0.95 (0.88) |-0.93 (0.86) |
|Child’s height-for-age |-2.19 (1.24) |-2.15 (1.21) |-2.22 (1.26) |
Table 2: OLS Estimation results-Dependent variable- weight-for-height Z-scores
| | |All |Male |Female |
|Determinants |Coefficient |Coefficient |Coefficient |
|Household characteristics | | | |
|Wealth index- 2nd quintile |0.01 (0.04) |0.02 (0.06) |-0.00 (0.05) |
|3rd quintile |0.08 (0.04)** |0.11 (0.06)** |0.05 (0.05) |
|4th quintile |0.10 (0.04)** |0.12 (0.06)** |0.07 (0.06) |
|5th quintile |0.20 (0.07)*** |0.28 (0.09)*** |0.15 (0.09) |
|Mountain dummy |0.23 (0.11)** |0.05 (0.17) |0.38 (0.15)*** |
|Hill dummy |0.27 (0.07)*** |0.30 (0.10)*** |0.23 (0.09)*** |
|Father’s occupation- technical |0.01 (0.04) |-0.02 (0.05) |0.04 (0.05) |
|father’s occupation- agricultural |-0.03 (0.03) |-0.05 (0.04) |-0.01 (0.04) |
|Religion-Hindu |-0.29 (0.03)*** |-0.31 (0.05)*** |-0.27 (0.05)*** |
|Female headed household |0.04 (0.05) |-0.01 (0.06) |0.09 (0.06) |
|Father’s age |0.00 (0.00)* |-0.01 (0.00)** |0.00 (0.00) |
|Father’s education level- primary |0.01 (0.03) |-0.02 (0.04) |0.05 (0.04) |
|Father’s education level- secondary |0.06 (0.03)* |0.04 (0.05) |0.08 (0.04)* |
|Father’s education- does not know |-0.11 (0.09) |-0.16 (0.11) |-0.01 (0.17) |
|Piped water |0.08 (0.03)*** |0.10 (0.04)** |0.06 (0.04) |
|Electricity |0.08 (0.05) |-0.04 (0.07) |0.16 (0.06)** |
|Radio |0.00 (0.03) |0.00 (0.04) |0.01 (0.04) |
|Bicycle |-0.06 (0.03) |-0.07 (0.04) |-0.05 (0.05) |
|Mother’s characteristics | | | |
|Mother’s age |-0.01 (0.02) |-0.03 (0.02) |0.01 (0.02) |
|Age squared |0.00 (0.00) |0.00 (0.00) |0.00 (0.00) |
|Mother’s weight-for-height |0.00 (0.00) |0.00 (0.00) |0.00 (0.00)** |
|Mother’s economic status | | | |
|Mother’s education- primary |0.01 (0.04) |0.01 (0.06) |0.01 (0.05) |
|Mother’s education- secondary |0.07 (0.05) |0.03 (0.06) |0.10 (0.07) |
|Mother’s occupation- professional |0.15 (0.08)* |0.19 (0.12) |0.13 (0.12) |
|Mother’s occupation- agricultural |0.02 (0.04) |0.02 (0.06) |0.00 (0.06) |
|Mother’s occupation- other |0.18 (0.11) |0.23 (0.17) |0.13 (0.14) |
|Owns land |0.09 (0.05)* |0.19 (0.06)*** |-0.01 (0.06) |
|Owns livestock |-0.01 (0.03) |-0.08 (0.04)** |0.06 (0.04) |
|Maternal social status | | | |
|Knowledge and ability to access health care for | | | |
|self | | | |
|Medical help for self- knowing where to |0.16 (0.07)** |0.10 (0.09) |0.21 (0.10)** |
|go*Mountain | | | |
|Medical help for self- knowing where to go*Hill |0.03 (0.05) |-0.04 (0.06) |0.08 (0.06) |
|Medical help for self- getting permission to |0.16 (0.07)** |0.24 (0.10)* |0.09 (0.10) |
|go*Mountain | | | |
|Medical help for self- getting money for |-0.09 (0.08) |-0.03 (0.12) |-0.12 (0.11) |
|transport*Mountain | | | |
|Medical help for self- getting permission to |0.12 (0.05)** |0.07 (0.08) |0.16 (0.07)** |
|go*Hill | | | |
|Medical help for self- getting the money for |-0.07 (0.05) |-0.09 (0.07) |-0.05 (0.07) |
|transport*Hill | | | |
|Ability to make independent decisions with | | | |
|regard to child’s health | | | |
|When child is ill can decide to get medical |0.01 (0.08) |0.13 (0.12) |-0.09 (0.11) |
|help*Mountain | | | |
|When child is ill can decide to get medical |0.00 (0.05) |-0.02 (0.07) |0.04 (0.07) |
|help*Hill | | | |
|Household decision making power | | | |
|Final say on own health care*Mountain |0.03 (0.09) |0.02 (0.12) |0.05 (0.13) |
|Final say on making large household |-0.07 (0.09) |-0.05 (0.11) |-0.09 (0.13) |
|purchases*Mountain | | | |
|Final say on visits to family or |0.19 (0.08)** |0.20 (0.11)* |0.18 (0.12) |
|relatives*Mountains | | | |
|Final say on own health care*Hill |-0.04 (0.06) |-0.13 (0.09) |0.02 (0.08) |
|Final say on making large household |0.15 (0.05)*** |0.25 (0.08)*** |0.08 (0.07) |
|purchases*Hill | | | |
|Final say on visits to family or relatives*Hill |-0.06 (0.05) |-0.05 (0.07) |-0.09 (0.07) |
|Child characteristics | | | |
|Birth order- Second born |-0.03 (0.04) |-0.07 (0.06) |0.02 (0.05) |
|Third born |-0.03 (0.05) |-0.02 (0.07) |-0.04 (0.06) |
|Fourth born |-0.02 (0.05) |0.05 (0.08) |-0.06 (0.07) |
|Fifth born |-0.06 (0.06) |-0.03 (0.08) |-0.08 (0.08) |
|Male |-0.02 (0.02) | | |
|Age 13-24 months |-0.51 (0.04)*** |-0.38 (0.07)*** |-0.64 (0.07)*** |
|25-36 months |-0.11 (0.04)*** |0.08 (0.06) |-0.29 (0.07)*** |
|37-48 months |-0.02 (0.04) |0.20 (0.07)*** |-0.23 (0.07)*** |
|49-60 months |0.02 (0.05) |0.15 (0.07)** |-0.11 (0.07) |
|Constant |-0.66 (0.25)*** |-0.26 (0.38) |-0.94 (0.33)*** |
|N |4826 |2407 |2419 |
Standard errors are in parantheses. *** significant at 1% level , ** significance at 5% level, * significantce at 10% level.
Table 3: OLS Estimation results- Dependent variable- Height-for age Z-scores
| |All |Male |Female |
|Determinants | | | |
|Household characteristics | | | |
|Wealth index- 2nd quintile |0.16 (0.06)*** |0.22 (0.08)*** |0.11 (0.08) |
|3rd quintile |0.18 (0.06)*** |0.29 (0.08)*** |0.07 (0.08) |
|4th quintile |0.15 (0.06)** |0.23 (0.08)*** |0.07 (0.09) |
|5th quintile |0.22 (0.09)** |0.27 (0.13)** |0.17 (0.13) |
|Mountain dummy |-0.50 (0.15)*** |-0.53 (0.23)** |-0.43 (0.20)** |
|Hill dummy |-0.11 (0.09) |-0.17 (0.12) |-0.08 (0.13) |
|Father’s occupation- technical |0.01 (0.05) |0.02 (0.07) |0.01 (0.08) |
|father’s occupation- agricultural |-0.05 (0.04) |-0.10 (0.06)* |0.01 (0.06) |
|Religion-Hindu |-0.10 (0.05)** |-0.06 (0.07) |-0.16 (0.07)** |
|Female headed household |0.09 (0.07) |0.01 (0.09) |0.17 (0.09)* |
|Father’s age |0.00 (0.00) |0.00 (0.00) |0.00 (0.00) |
|Father’s education level- primary |0.03 (0.04) |0.06 (0.06) |0.01 (0.06) |
|Father’s education level- secondary |0.18 (0.05)*** |0.15 (0.07)** |0.21 (0.07)*** |
|Father’s education does not know |-0.25 (0.13)** |-0.31 (0.16)** |-0.17 (0.20) |
|Piped water |-0.18 (0.04)*** |-0.11 (0.06)** |-0.24 (0.06)*** |
|Electricity |0.25 (0.07)*** |0.14 (0.10) |0.38 (0.09)*** |
|Radio |0.06 (0.04) |0.05 (0.06) |0.06 (0.06) |
|Bicycle |0.05 (0.05) |0.02 (0.06) |0.09 (0.08) |
|Mother’s characteristics | | | |
|Mother’s age |0.05 (0.02)* |0.06 (0.04)* |0.04 (0.03) |
|Age-squared |0.00 (0.00) |0.00 (0.00) |0.00 (0.00) |
|Mother’s height-for-age |0.01 (0.00) |0.01 (0.00)* |0.02 (0.02) |
|Mother’s economic status | | | |
|Mother’s education level- primary |0.19 (0.05)*** |0.22 (0.08)*** |0.17 (0.07)** |
|Mother’s education level- secondary |0.13 (0.07)** |0.19 (0.09)** |0.09 (0.10) |
|Mother’s occupation- professional |0.37 (0.12)*** |0.01 (0.16) |0.67 (0.17)*** |
|Mother’s occupation- agricultural |0.07 (0.06) |-0.10 (0.09) |0.24 (0.09)*** |
|Mother’s occupation- other |0.08 (0.15) |-0.11 (0.20) |0.28 (0.23) |
|Owns land |0.21 (0.07)*** |0.15 (0.09) |0.24 (0.10)** |
|Owns livestock |0.00 (0.04) |0.02 (0.05) |-0.01 (0.06) |
|Mother’s social status | | | |
|Knowledge and ability to access health care for | | | |
|self | | | |
|Medical help for self- knowing where to |-0.05 (0.10) |0.00 (0.13 ) |-0.10 (0.14) |
|go*Mountain | | | |
|Medical help for self- getting permission to |-0.10 (0.11) |-0.10 (0.15) |-0.09 (0.15) |
|go*Mountain | | | |
|Medical help for self- getting money for |0.10 (0.11) |0.06 (0.16) |0.11 (0.14) |
|transport*Mountain | | | |
|Medical help for self- knowing where to go*Hill |-0.05 (0.06) |0.10 (0.08) |-0.18 (0.08)** |
|Medical help for self- getting permission to |-0.02 (0.07) |-0.12 (0.10) |0.07 (0.09) |
|go*Hill | | | |
|Medical help for self- getting the money for |-0.11 (0.06)* |-0.01 (0.09) |-0.20 (0.09)** |
|transport*Hill | | | |
|Ability to make independent decisions with | | | |
|regard to child health | | | |
|When child is ill can decide to get medical |0.34 (0.13)*** |0.38 (0.18)** |0.30 (0.17)* |
|help*Mountain | | | |
|When child is ill can decide to get medical |0.17 (0.07)*** |0.19 (0.09)** |0.18 (0.10)* |
|help*Hill | | | |
|Household decision making power | | | |
|Final say on own health care*Mountain |0.05 (0.12) |0.08 (0.17) |-0.03 (0.17) |
|Final say on making large purchases*Mountain |-0.08 (0.13) |0.08 (0.18) |-0.30 (0.18)* |
|Final say on visits to relatives*Mountains |-0.14 (0.12) |-0.22 (0.16) |-0.01 (0.16) |
|Final say on own health care*Hill |0.01 (0.08) |0.03 (0.11) |0.01 (0.11) |
|Final say on making large purchases*Hill |-0.10 (0.07) |-0.09 (0.11) |-0.11 (0.10) |
|Final say on visits to relatives*Hill |-0.06 (0.07) |-0.06 (0.09) |-0.06 (0.10) |
|Child characteristics | | | |
|Birth order- Second born |-0.02 (0.05) |-0.01 (0.07) |-0.02 (0.08) |
|Third born |-0.07 (0.06) |0.07 (0.09) |-0.19 (0.09)** |
|Fourth born |-0.23 (0.07)*** |-0.21 (0.10)** |-0.24 (0.10)** |
|Fifth born |-0.32 (0.08)*** |-0.26 (0.11)** |-0.38 (0.12)*** |
|Male |0.09 (0.03)** | | |
|Age 13-24 months |-0.72 (0.06)*** |-0.69 (0.08)*** |-0.72 (0.08)*** |
|25-36 months |-0.76 (0.06)*** |-0.64 (0.08)*** |-0.87 (0.08)*** |
|37-48 months |-0.94 (0.06)*** |-0.84 (0.08)*** |-1.03 (0.08)*** |
|49-60 months |-0.96 (0.06)*** |-0.90 (0.08)*** |-1.01 (0.08)*** |
|Constant |-2.36 (0.36)*** |-2.53 (0.53)*** |-2.20 (0.50)*** |
|N |4826 |2407 |2419 |
Table 4: Logit estimation results- Dependent variable- height-for-weight
| | |All |Male |Female |
|Determinants | |Coeff. |Coefficient |Coefficient |
|Household characteristics | | | |
|Wealth index- 2nd quintile |-0.336 (0.101)*** |-0.596 (0.144)*** |-0.106 (0.147) |
|3rd quintile |-0.385 (0.105)*** |-0.509 (0.146)*** |-0.278 (0.155)* |
|4th quintile |-0.351 (0.116)*** |-0.485 (0.163)*** |-0.236 (0.170) |
|5th quintile |-0.409 (0.174)** |-0.524 (0.247)** |-0.350 (0.254) |
|Mountain dummy |1.037 (0.325)*** |0.603 (0.451) |1.393 (0.486)*** |
|Hill dummy |0.156 (0.179) |0.345 (0.251) |0.022 (0.260) |
|Father’s occupation- technical |0.032 (0.098) |0.124 (0.137) |-0.065 (0.143) |
|father’s occupation- agricultural |0.078 (0.078) |0.300 (0.110)*** |-0.146 (0.115) |
|Religion-Hindu |0.274 (0.088)*** |0.210 (0.125)* |0.348 (0.127)* |
|Female headed household |-0.117 (0.122) |-0.012 (0.174) |-0.175 (0.177) |
|Father’s age |0.004 (0.005) |-0.002 (0.008) |0.012 (0.008) |
|Father’s education level- primary |0.030 (0.082) |-0.054 (0.115) |0.111 (0.121) |
|Father’s education level-secondary |-0.278 (0.087)*** |-0.311 (0.122)** |-0.182 (0.126) |
|Father’s education does not know |0.460 (0.246)* |0.444 (0.320) |0.459 (0.388) |
|Piped water |0.243 (0.078)*** |0.167 (0.109) |0.339 (0.116)*** |
|Electricity |-0.317 (0.126)*** |-0.112 (0.182) |-0.541 (0.180)*** |
|Radio |-0.039 (0.077) |0.085 (0.109) |-0.154 (0.110) |
|Bicycle |-0.182 (0.094)** |-0.101 (0.125) |-0.250 (0.141)* |
|Mother’s characteristics | | | |
|Mother’s age |-0.015 (0.045) |-0.001 (0.064) |-0.028 (0.064) |
|Age-squared |0.000 (0.001) |0.000 (0.001) |-.299302D-04 (0.001) |
|Mother’s height-for-age |-0.015 (0.007)** |-0.007 (0.008) |-0.197 (0.053)*** |
|Mother’s economic status | | | |
|Mother’s education level- primary |-0.361 (0.103)*** |-0.444 (0.153)*** |-0.333 (0.143)** |
|Mother’s education level- secondary |-0.283 (0.128)** |-0.230 (0.181) |-0.327 (0.187)* |
|Mother’s occupation- professional |-0.336 (0.213) |-0.080 (0.313) |-0.473 (0.301) |
|Mother’s occupation- agricultural |-0.213 (0.112)* |0.073 (0.157) |-0.505 (0.167)*** |
|Mother’s occupation- other |-0.184 (0.294) |0.146 (0.395) |-0.555 (0.455) |
|Owns land |-0.378 (0.132)*** |-0.160 (0.186) |-0.585 (0.195)*** |
|Owns livestock |0.013 (0.074) |-0.024 (0.105) |0.051 (0.108) |
|Mother’s social status | | | |
|Knowledge and ability to access health care for | | | |
|self | | | |
|Medical help for self- knowing where to |0.046 (0.183) |-0.005 (0.245) |0.123 (0.283) |
|go*Mountain | | | |
|Medical help for self- getting permission to |-0.088 (0.198) |0.128 (0.270) |-0.359 (0.298) |
|go*Mountain | | | |
|Medical help for self- getting money for |-0.309 (0.225) |-0.008 (0.310 |-0.584 (0.342)* |
|transport*Mountain | | | |
|Medical help for self- knowing where to go*Hill |0.116 (0.121) |-0.044 (0.173) |0.239 (0.175) |
|Medical help for self- getting permission to |-0.001 (0.142) |0.147 (0.203) |-0.140 (0.205) |
|go*Hill | | | |
|Medical help for self- getting the money for |0.098 (0.127) |-0.119 (0.179) |0.301 (0.186) |
|transport*Hill | | | |
|Household decision making power | | | |
|Final say on own health care*Mountain |-0.073 (0.250) |0.116 (0.343) |-0.306 (0.372) |
|Final say on making large |0.126 (0.261) |-0.018 (0.343) |0.273 (0.412) |
|purchases*Mountain | | | |
|Final say on visits to relatives*Mountains |0.310 (0.235) |0.400 (0.320) |0.226 (0.352) |
|Final say on own health care*Hill |-0.019 (0.150) |-0.286 (0.217) |0.263 (0.214) |
|Final say on making large purchases*Hill |0.275 (0.146)* |0.305 (0.212) |0.221 (0.207) |
|Final say on visits to relatives*Hill |0.002 (0.130) |0.101 (0.187) |-0.106 (0.185) |
|Ability to make independent decisions with | | | |
|regard to child health | | | |
|When child is ill can decide to get medical |-0.642 (0.255)** |-0.620 (0.355) |-0.592 (0.375) |
|help*Mountain | | | |
|When child is ill can decide to get medical |-0.268 (0.132)** |-0.298 (0.186) |-0.270 (0.193) |
|help*Hill | | | |
|Child characteristics | | | |
|Birth order- Second born |-0.055 (0.101) |0.006 (0.142) |-0.106 (0.146) |
|Third born |0.050 (0.120) |-0.130 (0.172) |0.227 (0.173) |
|Fourth born |0.175 (0.139) |0.178 (0.200) |0.192 (0.197) |
|Fifth born |0.367 (0.149)** |0.360 (0.212)* |0.408 (0.215)* |
|Male |-0.136 (0.062)** | | |
|Age 13-24 months |1.167 (0.113)*** |1.101 (0.160)*** |1.252 (0.163)*** |
|25-36 months |1.311 (0.114)*** |1.094 (0.161)*** |1.582 (0.167)*** |
|37-48 months |1.615 (0.117)*** |1.341 (0.164)*** |1.967 (0.173)*** |
|49-60 months |1.519 (0.120)*** |1.450 (0.169)*** |1.626 (0.175)*** |
|Constant |-0.347 (0.661) |-0.653 (0.956) |-0.703 (0.946) |
|N |4826 |2407 |2419 |
-----------------------
* E-mail; d.dancer@econ.usyd.edu.au
** a.rammohan@econ.usyd.edu.au
[1] Pande (2003) is a notable exception.
[2] Our data did not contain any child who is 60 months old.
[3] As the social autonomy variables are interacted with the dummy variables for the different regions, the interpretation of the estimated coefficients must include the estimated coefficient of the regional dummy variable. For example, for the entire sample on the weight-for-age measure, consider the effect of the variable ‘mother knowing where to go to get medical help’ for a child living in the mountain region. The estimated coefficient is 0.16 for the interaction variable but, to this coefficient, must be added the estimated coefficient of the mountain region which is 0.23 giving a final z-score of 0.39.
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