This paper employed a spatial multi-level model to analyze child nutrition data from Nepal. Four levels corresponding to child, household, cluster and district were used. Data from various sources such as NDHS, NLSS, NDVI, rainfall, agriculture production and storage, transportation, and health infrastructure were combined for the analysis. I
estimated eight different models that included variables at different levels (child, mother, household, cluster, and district). Spatial lags for some of the district-level variables were created and incorporated in the regressions. As a robustness test, I dropped the urban sample and repeated the regressions at the district level with and without agriculture related variables. I also dropped the rural sample and agriculture variables, and repeat the regression at the district level.
The first objective of this study was to identify factors or variables that are strongly correlated with the child nutrition outcomes in Nepal. I identified a number of variables influencing WHZ and HAZ. Variables that are statistically significant at five percent level of significance or greater were considered as strongly correlated with child nutrition outcomes. The independent variables that are (a) highly robust, (b) strongly correlated with the long-term nutrition outcomes (HAZ), and (c) important from a policy
perspective are twin status, mother’s employment status, ethnicity, mother smoking, mother’s education, total children ever born in the family, place of delivery, use of smoke-producing fuels, percentage of households with a bank account in a cluster, food deficit status of a district, bridge density, and presence of pediatrician in a district
hospital. The independent variables that are (a) highly robust, (b) strongly correlated with the short-term nutrition outcomes (WHZ), and (c) important from a policy perspective are
season of birth (autumn), twin status, presence of diarrhea and fever, mothers working on farm, husband away from home, electricity facility, percentage of households with refrigerator, mean crop yield, district food deficit, percentage of households producing eggs, public food storage capacity, roads, vacant posts of doctors, health facilities that are equivalent of zonal hospital, and number of private hospitals in a district. Especially in this critical period of aftermath of earthquakes, all of these results provide insights into potential policies to reduce current suffering in form of acute (short-term) malnutrition and improve long-term child nutrition outcomes in Nepal.
The second objective of this study was to assess what factors might account for the observed improvements in average outcomes between 2006 and 2011. The strong statistical evidence of improvements in Z-scores over time is largely explained by changes occurring in higher level variables, underscoring the importance of changes occurring at the cluster and district level.
I provide some policy implications based on the results that may be especially
important for targeting nutrition intervention programs. Since children born as twins have lower HAZ and WHZ (by 0.73 and 0.48), child nutrition programs targeted for twin children may help to improve nutrition outcomes. A one percent increase in the number of vaccines given to a child can increase HAZ by 11%, encouraging vaccination efforts to reach all children. Kids suffering from diarrhea and fever have lower WHZ, underscoring that efforts to prevent and treat diarrhea will help to improve short-term child nutrition outcomes. The mother-level variables were found to be very important for child nutrition outcomes. For example, a one percent increase in mother’s years of education is likely to increase HAZ by 8% and WHZ by 6%. Government programs that promote mother’s
education encourage mothers to quit smoking, provide incentives to deliver babies at hospitals will help to improve child nutrition outcomes. An increase of one percent of the number of children in a household is associated with a 6% decrease in the HAZ of her children. Given such a statistics, awareness programs such as importance of family planning and importance of female education will create greater consciousness for mothers and may help to improve child nutrition outcomes. Child nutrition programs can be targeted to children from Unprivileged and Brahmin families. As Brahmin families avoid meat and meat products, programs like substitution of animal protein with plant protein may be very important to improve child nutrition outcomes in Brahmin
communities. Programs that lead to substitution of smoke-producing fuels to smokeless fuels, encouraging savings through bank accounts, building sanitation facilities, and reducing poverty helps to improve child nutrition outcomes. Higher rainfall is found to improve WHZ at the cluster level. This finding likely points to the importance of water availability for agriculture in short-term child nutrition and supports the expansion of irrigation facilities in the country.
Child nutrition outcomes in food deficit districts were found to be lower than in food surplus districts. Thus if government launches agricultural programs in food-deficit districts that lead to food surplus districts, child nutrition outcomes are likely to be improved. Some of these agricultural programs that helps to boost total agricultural production and productivity can be through constructing irrigation facilities, distribution of improved seeds and good quality fertilizers in a timely manner, promoting use of farm machinery equipment, and stabilizing output prices. Districts with a higher percentage of households producing eggs have higher WHZ. Programs that lead to small scale egg and
poultry production may be helpful in improving short-term child nutrition outcomes. Investments in public food storage facilities are likely to support child nutrition not only locally, but also in neighboring districts. The lowest HAZ is mainly from the
mountainous and hilly districts while the lowest WHZ is mainly from the Terai district. Any public interventions related to the improvement of the long-term nutrition outcomes and short-term nutrition outcomes can be prioritize to the hilly and mountainous districts, and the Terai districts, respectively. Random intercept plots at the district-level identify the districts with the lowest average HAZ and WHZ. In terms of launching nutrition intervention programs, more priority may be given to those districts that have the worst child nutrition outcomes.
Based on these findings, higher densities of roads and bridges in a district can help to improve the short- and long-term child nutrition outcomes, respectively, emphasizing the importance of transport investments. The mean distance to a hospital in minutes by foot is negatively correlated with short-term child nutrition outcomes, underscoring the importance of quick access to hospitals. Government policies to create the pediatrician position in government hospitals in all district of the country can help to improve long- term child nutrition outcomes. Similarly government polices to quickly fill empty post of doctors will help to improve short-term child nutrition outcomes. If the government builds more zonal hospitals or health facilities equivalent to zonal hospitals, and encourages construction of private hospitals, short-term child nutrition outcomes are likely to improve, underscoring the importance of health infrastructure in supporting child health and nutrition.
Although I tried my best in terms of selecting model and including all relevant variables, this study fails to include some of the important variables especially at the child level. Variables such as the type and amount of food consumed by a child, feeding interval, and birth spacing are not available or available for only a small portion of the children sampled. It was hard to find suitable instruments to make causal statement for many variables; thus limiting us from casual interpretation of these coefficients. Due to the unavailability of the GPS coordinates at the household level, I was not able to account for spatial dependency occurring at the child level. Furthermore, I was also not able to take account of spatial error correlation due to the complexities owed by introducing spatial error component in the multi-level model. These remains future work.