ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

The Drivers of Nutritional Change in Ethiopia: 2000-2011

Authors: Mekdim DerejeDerek Headey

John Hoddinott IFPRI ESSP-IIEthiopian Economic Association Conference, July 19-21, 2012Addis Ababa

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1.Introduction• Recent years have seen a surge of interest in nutrition• Early childhood nutrition outcomes often found to be

strong predictors of school attendance and performance, labor market outcomes and overall cognitive ability

• Still, there is controversy as to which factors are the most effective drivers of nutritional change

• Nutritionists have largely focus on very nutrition-specific factors, like supplements, nutritional education, etc

• But it is also widely recognized that socioeconomic factors play a big role: income/wealth, dietary change, female education, demographic change, health services…

• Previous research has examined these factors, but mostly with just one wave of cross-sectional data

• This paper tries to understand nutritional change over time (height for age), in a very interesting context

• Ethiopia has seen solid performance in reducing malnutrition, albeit from a very low base

• But not at all clear what is driving this given rapid developments on many fronts: economic growth, road investments and other infrastructures, surge in education, big drop in fertility, and so on

• So we adopt a pseudo-panel approach to test which of these factors seem to be the main drivers of change over 2000-2010

1.Introduction

Our focus is on child stunting – the preferred indicator of chronic malnutrition - which fell 14 points in last 10 years.Note that decline was 16 points in urban areas, 12.7 in rural

2. Existing theory and evidence

Welcome Lesson list Assessment

Lesson 2.5

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UNICEF Conceptual Framework

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Nutrition outcomes have many driversUNICEF framework distinguishes between diets & disease as intermediate factorsThe factors, in turn, are influenced by deeper social and economic factors

• Previous research tends to suggest that some of the major socioeconomic drivers of chronic child nutrition (height for age, HAZ) are:

• Income/wealth • Female education (typically secondary), • Demographics (birth spacing, no. children, mother’s age)• Health factors (burden of disease, access to services• Dietary diversity of children• Infrastructure variables like water and sanitation

(although generally these are less robust)

2. Existing theory and evidence

• Many of the linkages between these factors and nutrition outcomes are complex:• Their impacts may have multiple pathways (e.g. female

educ. raises knowledge, empowerment, income)• Impacts may interact with age, location (rural/urban),

or other factors (e.g. dietary impacts depend on health)• For these reasons we disaggregate our analyses by rural

and urban, and run additional robustness tests with age-specific disaggregations

• In the future we will also test for other interaction effects

2. Existing theory and evidence

• Data used for this study is the EDHS rounds of 2000 and 2011 (2005 data had problems with nutrition component)

• Nationally representative surveys, though we drop Somali region because of sampling changes

• Contains both individual level & household variables on most of the variables of interest

• DHS does no measure income, but assets, which are commonly used to construct wealth indices

• We construct separate indices for rural and urban

3. Data

• This index often performs well, but is known to be imprecise for distinguishing welfare at lower levels

• Another data issue was dietary quality. In 2000 child diets were measured with 7-day recall, but in 2010 they switched to 24hr recall. We are still hoping to adjust for this, but diets are omitted for the present

• However, maternal BMI may proxy a bit for household dietary quality or food security

• Other explanatory variables are fairly straightforward and will be discussed below

3. Data

• Blinder-Oaxaca decomposition is a pseudo-panel technique used to look at changes over time

• We focus on stunting as dependent variable, though we have also run for height-for-age z-scores

• Breaks down predicted change into three effects. Endowment effect Coefficient Effect Interaction Effect

4. Methods

• Usually don’t expect much coefficient & interaction effect, except with quality changes (e.g. education)

• Also note we only focus on significant differences

5. Results

Table 1. Core regression resultsUrban Rural

2000 2010 2000 2010

Materna

l variable

s

Age, 0.031 0.044 0.021** 0.012Age sq. -0.001 -0.001 -0.000** 0.000Secondary educ. -0.111* -0.102* -0.168** -0.086Tertiary educ. -0.333** -0.158** -0.377*** -0.062Height -0.017*** -0.008** -0.010*** -0.012***BMI -0.005 -0.004 -0.014*** -0.012***No work -0.167*** -0.004 -0.026 -0.023No work (father) 0.383** 0.197 -0.067 -0.153*

Child

vars.

Diarrhea incidence 0.021 0.099 0.047** 0.047**Age, 0.028*** 0.020*** 0.030*** 0.028***Age sq. -0.000*** -0.000*** -0.000*** -0.000***Birth interval 0.000 -0.002*** -0.002*** -0.001

HH wealth

poor -0.590*** 0.213 -0.007 -0.013middle -0.185 -0.016 -0.035 -0.053**rich -0.269* -0.054 -0.046* -0.051**richest -0.360*** -0.054 -0.035 -0.057R-squared 0.22 0.16 0.13 0.13

• Summarizing: maternal education an important factor in urban areas, but disappears in rural areas in 2010

• Maternal nutrition highly significant, especially height• Diarrhea incidence significant in rural areas only• Wealth index significant, but not very robustAlso note that there were many variables excluded because they were never significant: medical attendance at birth, maternal literacy, improved water supply, improved toilet facilities

5. Results

• The decomposition results are somewhat disappointing because endowment changes do not explain any sizeable declines in stunting

• In other words, most of the change is linked to coefficient changes that are not easy to explain

• Age-disaggregated results to do not improve the results• We may switch to 2005-2011 comparisons since these

datasets are more similar, including diets• But this would require addressing some of the

measurement problems with stunting in the 2005 data

5. Results

• The substantial progress against stunting over last 10 years proves difficult to explain

• There is some tentative evidence that the usual wealth, education and health factors matter, but the relationships are often quite weak, and not consistent across years or the rural-urban divide

• Is the problem measurement error? (e.g. wealth)• Is it omitted variables or misspecification? (e.g. diets)• Your ideas are very welcome!

Conclusion