Wubet Kifle
Ethiopian Economic Association
/Ethiopian Economic Policy Research
Institute/
Assistant researcher
Tel:
Fax:
To:
Trust Fund Project (PRSTF)
Ethiopian Development Research
Institue (EDRI)
P. O
1.Introduction
Many factors promote economic development,
among which population health is of critical importance. Health is an important
indicator of an individual’s well being. A central issue in this debate
concerns the extent to which childhood health status affects levels and
trajectories of some of the most central measures of socio-economic status
including education, income and wealth during one’s adult years. A number of
studies have documented the wide range of adverse economic and social
consequences of poor child health and child malnutrition.
For instance, malnutrition during infancy
and childhood substantially raises vulnerability to infection and disease and
increases the risk of premature death. Among children in developing countries,
malnutrition is an important factor contributing to illness and death.
Malnutrition during childhood can also affect growth potential and the risk of
morbidity and mortality in later years of life.[1] It is also believed to impair cognitive
achievement, labor productivity during adulthood, and lifetime earnings[2], and
lower attained height and completed grades of schooling and potential wage
earnings.[3]
This is likely to perpetuate inequities and inequalities in health and
other dimensions of household welfare (Cravioto J. and Arrieta R, 1986; WHO,
1995). Malnutrition is often cited as an important factor contributing to high
morbidity and mortality among children in developing countries (Singh 1989;
Santhanakrishnan and Ramalingam 1987; Ruzicka and Kane 1985; Serdula 1988; Katz
et al. 1989). Malnutrition during childhood can also affect growth potential
and risk of morbidity and mortality in later years of life.[4] Malnourished children are
more likely to grow into malnourished adults who face heightened risks of
disease and death (Mishra, Subrata Lahiri, and Luther, 1999). Malnutrition so,
is a major socio-economic problem in the developing countries. It is also very
complex and comprehensive strategies are required to combat it. It is therefore
important to determine its causative factors before appropriate intervention
can be implemented.
Demographic and health indicators including
nutritional status in
Past research in the field of child health
and nutrition has indicated that the relative importance of the various factors
that determine nutritional status is affected by a host of individual,
household and community level behavioral factors. Irrespective of how health
and nutritional status have been captured, the following factors have emerged
very clearly. Parental education, particularly mother’s education has a very
decisive role in terms of her child’s health.[8] Household’s
economic status was also found to have a significant impact on the well being
of the child. Birth order, apart from its biological implication, is also
important in the context of child’s health because it also captures the
experience accumulated by the mother over successive births. The employment
status of mothers, the number of living children, the level of education of the
mother and involvement/non-involvement of the mothers in the labor market are
found to significantly affect the incidence of stunting, wasting, underweight
and investments on child health.[9] There are also
significant externalities associated with access to water and sanitation at the
community level, improved living condition with access to electricity, radio
and television,[10] access to media
by the mother, and type of toilet to significantly affect child malnutrition.[11] Discrimination against
girls in feeding and health care are often cited as reasons for poorer
nutrition and higher mortality among girls than boys in many developing
countries[12]. Particularly,
in the context of our country, the social and kinship structure encourages gender
discrimination in favor of the male child.
This study aims to identify key
determinants of child nutrition outcomes in
The focus on these outcomes for children is
mainly because these outcomes, such as nutritional status are relatively less
influenced by the choices of the children themselves and potentially more
indicative of parents, households’ and community characteristics and hence may
indicate more clearly particular explanations for child health and nutrition.
The results of the study will be useful for enacting pragmatic policies and
designing effective intervention programs to improve the health and nutrition
of people, especially children who tend to be more vulnerable in many
instances.
The general objective of this study is to identify
the determinants of child nutritional status in
·
To examine the characteristics and determinants
of nutritional status of children in
·
To derive policy measures in perspective.
3.
Significance of the Study
The large prevalence of child malnutrition
among children in
4.Scope of the
Study
The study covers two household surveys
/panel data set/ from the Ethiopian Rural Household Surveys and Ethiopian Urban
Household Surveys conducted by the Department of Economics, Addis Ababa
University and the Centre for the study of African Economies of the University
of Oxford from 1994-2004.
5.Data and Analytical Framework
The
data analyzed in this paper come from the Ethiopian Rural Household Survey and
Ethiopian Urban Household Survey conducted by the Department of Economics,
National-level data on child malnutrition
in
The variables of interest are categorized
under dependent and independent variables. The dependent variables are
anthropometrics among children which are used as summary indices of nutritional
status. Anthropometric indicators are constructed using data on the children's
age, height and weight. According to WHO, the three most commonly used
anthropometric indices to measure the health and nutritional status of children
are weight-for-height (WHZ) height-for-age (
Where X is the
observed value and 
and s
are the median and the standard deviation values of the total sample
respectively.
The nutritional status variables
(weight-for-age, height-for-age and weight-for-height) which are continuous
variables were treated as dichotomous, with two values ‘well-nourished’ and
‘under-nourished’. To this end, cut-off points need to be used to estimate the
prevalence of anthropometric abnormality. The conventional cut-off point, which
is applied in the present study, is -2 standard deviation units (z-score) from
the median reference population. Children whose z-score falls below -2 standard
deviation units are classified as under-nourished (coded 1) and those above -2
standard deviation as well nourished (coded 0). The calculation of the indices
of child malnutrition involves comparison with an international reference
population as recommended by the World Health Organization (Dibley et al.
1987a; Dibley et al. 1987b). The justification for use of a reference
population is the empirical finding that well-nourished children in all
populations follow very similar growth patterns (Habicht et al. 1974; Martorell
and Habicht 1986).
The explanatory variables are composed of a
set of socioeconomic characteristics of children, their parents and the
community. The selection of control variables is guided by theoretical reasons,
availability of data and previous empirical experience. The following variables
are included: sex and age of the child, economic status of the household, birth
order (categorize as first, second, third, fourth and or higher birth order)
age of mother (grouped as below 19, 20-29, and 30 years and higher) and
educational status of the mother and father, mother’s current work status,
religion, area of residence (between urban and rural), access to different
amenities (access to pit latrine and tap water), access to information and
media (watching television/radio every week or not), community characteristics
(distance to the nearest health facility) and region.
Mother's height will capture both genetic
effects and effects resulting from family background characteristics that are
not picked up by the education variable.[16] In particular, ethnic differences, if
any, resulting from genetic or non-genetic (e.g., dietary) factors should be
picked up.[17]
Given the dichotomous nature of the
dependent variable (0, 1), estimation is based on using binary regression
models. For the analysis of the nutritional status of children, the dependent
variable was equal to 1 if the child’s z-score was below -2 standard deviation
units (under-nourished child) and equal to 0 if a z-score is above -2 standard
deviation (well nourished child). Since the analysis is concerned with children
alive at the time of the survey, addressing the issue of censoring is very
important: that is children who died before the survey will be excluded from
the analysis.
For the standard formulation of the health
demand model, we refer to Behrman and Deolalikar (1988) and Strauss and Duncan
(1995), in which a household maximizes a utility function. In this case, a
household may be assumed to choose health and nutrition Hi, of all household
members, leisure Li, consumption of goods and services Ci and tastes and
preferences of the household, 
.
U = U (Ci,
Li, Hi, ) (1)
The household maximizes this utility
function subject to two constraints; a health production function for
nutritional status and a budget constraint. The budget constraint takes the
form:
I = PcC
+WL + PY Y (2)
Where Pc and W, PY are the price vectors of
consumption goods, leisure and health inputs respectively, and I is the full
income including the value of the time endowment of the household and non-labor
income. In this framework, the reduced form function for child health is:
(3)
Where
Hinp
- health inputs such as vaccination, medical care, etc.
Cchar -Child characteristics such as age,
sex, etc.
Mchar - Mother’s characteristics such as
education, health status, age, genetics, etc.
Fchar – Father’s characteristics (the same
as mothers)
HHchr -Household characteristics such as
economic status, number of household
members,etc
COMchar - Community features like access to
health services, safe water, sanitation, etc.
are unobservable
individual health endowments.
Where the
particular functional form of the function 
( ) depends on the underlying functions characterizing
household preferences and the health production function.
Modeling nutrition outcomes in terms of flows rather than stocks results in the derived reduced forms for the stock variables that include lagged exogenous variables as argument.[18] The anthropometrics can be estimated as a short run and long-run health production functions respectively using equation (3). Different production functions (linear, Cob-Douglas, CES, trans-log, etc.) will be estimated to know the functional form that fit the data well. Fixed effect specifications and random effects models will be estimated to analyze the different determinants of child nutritional status.
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