Maternal Education and Child Nutrition: Evidence from the 2000 and 2005 Ethiopian Demographic and Health Surveys

I used the 2000 and 2005 Ethiopian Demographic and Health Surveys to analyze the effect of maternal education and its pathways on chronic and acute malnutrition in Ethiopia. The pathways examined in this study are socioeconomic status, maternal health-seeking behavior, maternal knowledge of health and family planning and reproductive behavior. I find that maternal education works through all except health-seeking behavior. I also find that maternal education and its pathways are more relevant and robust in explaining chronic than acute malnutrition. Socioeconomic status is the most important factor linking maternal education and child nutritional status. Although girls’ education is a high policy priority, it may take time before its direct and indirect impacts substantially improve child health outcomes. Faster results would require direct interventions on key elements of socioeconomic status

Maternal Education, Linkages and Child Nutrition in the Long and Short-run: Evidence from the Ethiopia Demographic and Health Surveys

We used the 2000 and 2005 Ethiopian Demographic and Health Surveys to analyze the effect of maternal education and its pathways on chronic (long-run) and acute (short-run) malnutrition inEthiopia. The pathways examined in this study are socioeconomic status, maternal health-seeking behavior, maternal knowledge of health and family planning and reproductive behavior. We find that maternal education works through all except health-seeking behavior. We also find that maternal education and its pathways are more relevant and robust in explaining chronic than acute malnutrition. Socioeconomic status is the most important factor linking maternal education and child nutritional status. Although girls’ education is a high policy priority, it may take time before its direct and indirect impacts to materialize and substantially improve child health outcomes. Faster results would require direct interventions on key elements of socioeconomic status

Identifying the most deprived in rural Ethiopia and Uganda: A simple measure of socio-economic deprivation

The Extreme Deprivation Index uses easily verifiable answers to ten questions about the ownership of the most basic non-food wage goods - things that poor people in a variety of rural contexts want to have because they make a real difference to the quality of their lives. Using this Index, we define rural Ethiopians and Ugandans who lack access to a few basic consumer goods as 'most deprived': they are at risk of failing to achieve adequate education and nutrition; becoming pregnant as a teenager; remaining dependent on manual agricultural wage labour and failing to find to a decent job. As in other African countries, they have derived relatively little benefit from donor and government policies claiming to reduce poverty. They may continue to be ignored if the impact of policy on the bottom 10 per cent can be obscured by fashionably complex indices of poverty. We emphasize the practical and political relevance of the simple un-weighted Deprivation Index: if interventions currently promoted by political leaders and aid officials can easily be shown to offer few or no benefits to the poorest rural people, then pressures to introduce new policies may intensify, or at least become less easy to ignore

Accounting for drinking water quality in measuring multidimensional poverty in Ethiopia.

The Multidimensional Poverty Index is used increasingly to measure poverty in developing countries. The index is constructed using selected indicators that cover health, education, and living standards dimensions. The accuracy of this tool, however, depends on how each indicator is measured. This study explores the effect of accounting for water quality in multidimensional poverty measurement. Access to drinking water is traditionally measured by water source types. The study uses a more comprehensive measure, access to safely managed drinking water services, which are free from E. coli contamination, available when needed and accessible on premises in line with Sustainable Development Goal target 6.1. The study finds that the new measure increases national multidimensional headcount poverty by 5-13 percentage points, which would mean that 5-13 million more people are multidimensionally poor. It also increases the poverty level in urban areas to a greater extent than in rural areas. The finding is robust to changes in water contamination risk levels and Multidimensional Poverty Index aggregation approaches and weighting structures

OLS regression of z-scores.

<p>OLS regression of z-scores.</p

Concentration of stunted children (number of stunted children per km2).

<p>The figure shows the number of stunted children per km2 in each woreda.</p

Small area estimation of child undernutrition in Ethiopian woredas

<div><p>Reducing child undernutrition is a key social policy objective of the Ethiopian government. Despite substantial reduction over the last decade and a half, child undernutrition is still high; with 48 percent of children either stunted, underweight or wasted, undernutrition remains an important child health challenge. The existing literature highlights that targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part due to lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 Demographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even compared to household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished within each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number and concentration of undernourished children point to vastly different locations. There is also limited correlation between share of children underweight and stunted across woredas, indicating that different locations face different challenges.</p></div

OLS regression of z-scores.

<p>OLS regression of z-scores.</p

Share of children stunted in each woreda.

<p>The figure shows share of children stunted.</p

Spatial correlation between share, number and concentration of underweight children.

<p>Spatial correlation between share, number and concentration of underweight children.</p