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77
Pak. j.
life soc. Sci. (201
3
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11
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8
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ISSN: 2221
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7630
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ISSN: 1727
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4915
Pakistan Journal of Life and Social Sciences
www.pjlss.edu.pk
RESEARCH ARTICLE
Maternal Education and Child Nutritional Status in Bangladesh: Evidence
from Demographic and Health Survey Data
Sofi a An war1 , *, Samia N asre en1, Zahra B atoo l2and Zakir Husain1
1Depa rtme nt o f Ec onomics, Go vern ment Col lege University, Faisalab ad, Pak ista n
2Depa rtme nt o f So ciology, Univer sity of Agri culture, Faisalabad, Pa kist an
ARTICLE INFO
ABSTRACT
Received:
Accepted:
Online:
Jan 18, 2013
Mar 11, 2013
Mar 17, 2013
The objective of present study is to determine the effect of maternal education on
child nutritional status in Bangladesh. The study has used 2007 Bangladesh
Demographic and Health Survey (BDHS) data for the analysis. The study has
employed various pathway measures linking maternal education and child
nutritional status in Bangladesh. Logistic regression results explain that maternal
education has significant effect on child better nutritional status. Socioeconomic
status and attitude towards modern health care services are the most important
pathways linking the both. Health knowledge and reproductive behavior describe
some of the effect of mother’s education on child nutrition. Only women
empowerment appears to be the weakest pathway in our analysis.
Keywords
Bangladesh
Child nutrition
Health knowledge
Maternal education
Socioeconomic status
*Corresponding Author:
Sofia_ageconomist@yahoo.com
INTRODUCTION
Socioeconomic development and quality of life of
masses; living in any country is determined through
child health and infant mortality rate. Children are the
hope for this world. Development is not obtained only
with physical capital in form of bricks, wheels,
computers rather the actual capital is human itself. A
healthy, intelligent and honest human force is the ardent
energy to utilize the bricks and run the wheels. This is
the reason that every developing country is striving
hard to convert its population into human capital. It is
one of the Millennium Development Goals (MDGs) to
reduce infant mortality rate by two-third till 2015.
Nourished children not only perform better in education
rather grow into healthier adults, become active labour
force and hence are able to give their own children a
better life. In Bangladesh, under-five mortality rate is
estimated as 65 deaths per 1000 child birth are reported
Neonatal deaths are estimated at 57 percent of under-
five death rate (BDHS, 2007).
Whatever are the cultural and economic setups; at
household level mothers are considered the main
catalyst in better nutrition, health and education of
children. Higher years of mother’s schooling are
considered to lower the child mortality rate as education
provides information relating to child nutrition, their
proper treatment during illness and information relating
to vaccination. According to World Bank (1993),
mother education is necessary for improving child
health status and lowering infant mortality rate. Various
studies on health confirm the significant association
between motherly learning and child health and use of
health facilities than father education (Mondal et al.,
2009; Frost et al., 2005; Young et al., 1983). Maternal
education can improve child health and reduce infant
mortality through various ways like women
empowerment, enhanced use of modern health care
services, small family size, better health knowledge,
and increased family income (Glewwe, 1999; Castro
and Juarez, 1995; Caldwell and Caldwell, 1993;
Schultz, 1993; Cleland, 1990; Mason, 1984). Caldwell
(1979) was the first to conclude that children of
educated mothers have better health than uneducated
mothers. Some studies on cross-country comparison
demonstrated negative relationship between mother’s
years of education and children death rate (Bicego and
Boerma, 1991; Mensch et al., 1985; Ware, 1984).
Glowwe (1999) explained that health knowledge is the
most significant factor explaining the relationship
Anwar et al
78
between mothers’ education and child health. However,
there are some studies that failed to explain strong
causal relationship between maternal education and
child health (Kunstadter, 1995; Desai and Alva, 1995;
Cleland and Ginneken, 1988).
Socioeconomic status is one of the most important
pathways explaining the link between maternal
education and child nutritional status (Frost et al., 2005;
Desai and Alva 1998; Caldwell, 1994). Education and
socioeconomic status not only improves mothers’
knowledge relating to health but also change their
attitude and behavior, which in turn positively affect the
nutritional status of their children (Cleland, 1990).
Children health depends upon educational attainment of
mothers and their capacity to purchase those goods and
services which are necessary for maintaining better
health status of their children (Frost et al., 2005; Defo,
1997; Cleland and Ginneken, 1988). Higher years of
education provide woman with more opportunities to
find a better job and increase their family income
(Barrette and Brown, 1996). Moreover, educated
women prefer to marry highly educated men, with
sound earnings (Cleland and Ginneken, 1988).
According to Desai and Alva (1998) introduction of
socioeconomic variables in the model reduce the
strength of relationship between mother education and
child health. On the contrary, Frost et al. (2005)
considered the socioeconomic status as the most
important variable explaining the relationship between
mothers’ education and child nutritional status.
Similarly, Cleland and Ginneken (1988) found that
nearly half of the bearing of mothers’ education on
child nutrition is explained by socioeconomic status.
Attainment of formal education makes women
knowledgeable relating to wide range of health issues
like causes and symptoms of diseases, precautionary
measures, proper nutrition during disease and greater
exposure to health related messages and suggestions
through different sources like mass media (Benta et al.,
2011; Casterline, 2001; Defo 1997; Streatfield et al.,
1990; Cleland and Ginneken 1988). The relationship
between knowledge and child health implies that
knowledge about conditions and cognitive measures for
maintaining better health directly change previous
behavior relating to health. According to Defo (1997)
health knowledge can reduce the risk of infectious
disease through improved hygiene, nutritious food and
greater access to health products. Further, education
leads to better income generating activities enabling the
provision of improved housing. Enjoying these
facilities in household environment means that theses
family members are less unhygienic on average (Frost
et al., 2005).
Education not only has direct and significant effect
rather can shift negative attitude, traditional practices
and beliefs relating to health towards the adoption of
modern ideas and medicated practices (Benta, et al.,
2011; Frost et al., 2005; Defo 1997; Barrette and Brown
1996).Therefore, it is believed that educated mothers
are less likely to believe in the supernatural reasoning
of their child disease rather use modern medicine and
preventive measures for the cure of disease (Heaton et
al., 2005). Educated mothers’ can take early decision
regarding the health problem of their children (Frost et
al., 2005; Jejeebhoy, 1995) and their family size and
birth interval (Benta et al., 2011; Levin et al., 1994).
Women greater control on family income can improve
the nutritional status of children particularly female
child (Saraswathi, 1992) and children survival rate
(Kishor, 1995).
The relationship between maternal age and child health
status is also found in many studies. Benta et al. (2011)
found that child health increases with mother age.
However, Sommerfelt and Stewart (1994) found no
systematic relationship between child health and
mothers’ age. An inverse relationship existed between
years of education, fertility and early age marriage
(Cleland and Ginneken, 1988). Early age marriage is
the main cause for increase in infant mortality rate
(Tagoe-Dark, 1995).
The present study signifies the impact of maternal
education on child nutritional status (assessed by
stunting/ height for age) in Bangladesh using
Demographic and Health Survey (DHS, 2007) data.
According to BDHS (2007) stunting is the result of
inadequate nutrition to children that affect child
population over a long period of time. In Bangladesh,
national estimates reveal that 43 percent children are
stunted while 16 percent are severely stunted. This
study provides the answer of the question;Whether
maternal education significantly affects child nutritional
status in Bangladesh?
MATERIALS AND METHODS
Bangladesh Demography and Health Survey (BDHS,
2007) collected separate estimates of major indicators
for each of the six divisions of the country including
Barisal, Chittagong, Dhaka, Khulna Rajshahi and
Sylhet. The present study is limited to the sample set of
ever married women having at least one alive child. The
purpose of present study is to describe the effect of
maternal education on child nutritional status through
various parameters such as socio-economic status,
health knowledge, attitude towards the utilization of
health care services, women empowerment and
reproductive variables. Height for age is an
anthropometric index that is used as a proxy to measure
the child nutritional status. According to WHO
International Growth Conference, “the children who are
below 2, standard deviations on the height for age
growth curve are classified as stunted” (Benta et al.,
Maternal education and child nutritional status in Bangladesh
79
2011; Dibley et al., 1987). This variable is treated as
dependent variables in our analysis in the form of
binary variable. Child’s height for age =1 for those
children who are below negative 2 standard deviation of
the median population and “0” otherwise following the
Heaton et al. (2005).
Mothers education is the main explanatory variable and
is classified into three categories: illiterate/ no
education, primary, secondary and above. Using
various measures, indices are constructed to
operationalize the effect of selected pathways on child
health. Before constructing these indices, different
variables are selected that properly measure each
pathway. Then, a factor analysis is performed to
determine how well each set of variables factored
together, omitting obvious outliers. Table 1 presents
scores of factor analysis and Cronbach’s alpha for each
of the selected variables to show that every index is
formulated from correlated variables. After this, the
indices are constructed in simple additive form.
Socioeconomic status is measured by two additive
indices: household wealth and household environment.
Both indices range from 1 to 5. Household
environment index is measured by the availability of
following four basic facilities: (1) piped drinking water
(2) flush toilet facility (3) non-dirt floor and (4)
electricity. Household wealth index is measured by the
availability of following four durables: (1) radio (2)
television (3) refrigerator and (4) telephone. Various
proxy variables are used to measure knowledge, attitude
and empowerment because their direct measures are not
available in survey data. Health knowledge index is
constructed based on the knowledge about four
indicators; (1) oral rehydration therapy (2) AIDS (3)
tuberculosis and (4) modern methods of contraception.
Women empowerment is measured through two proxy
variables. Reproductive variables included are (1)
mother’s age (2) birth interval and (3) birth
order/parity. Interval between two births is given in
months. Birth order/parity is further classified into four
categories ;(1)1st birth (2)2nd-3rd birth (3) 4th-6th birth (4)
above 6th birth. Mother’s age is given in complete year.
Source of information is measured by the access to (1)
newspaper (2) radio (3) television. Access to each of
the sources is coded by a dummy variable. In addition
to these variables, division of residence, rural/urban
disparity and husband education are treated as control
variables.
Analytical framework
In our analysis, predicted variable: child height for age
has two categories. So, our model was estimated by
following binary logistic regression equation.
(
1
Pr( 1) Pr( _ )
ln ln ( ) ..............(1)
Pr( 0) Pr( )
J
j ij
j
Y Y chld stunt
Y Z
Y Y Nostunt
Where;
α is the constant and β is the slope coefficient of the
estimated parameter, P/1-P is the odd ratio of the
occurring of an event given the value of the predictor
variable. Here a flow chart diagram is also added to
explain the relationship of variables.
Fig. 1: Relationship between maternal education
and child nutritional status
The estimated coefficients are interpreted on the basis
of their significance level. Finally, exponential log of
estimated coefficients is taken to find out odd ratios
(Bronte and Dejong, 2005). Odd ratios explain the
effect of explanatory and control variables on the
probability of child stunting.
RESULTS AND DISCUSSION
It was alarming to know that approximately 65 percent
of children were stunted in Bangladesh (Table 2).
Maternal education attainment level was not
satisfactory in Bangladesh as 71 percent of mothers
were either illiterate or having less than primary
education. Socioeconomic status of women; measured
by household wealth index and environmental index,
indicated that wealth status of women was not
satisfactory in Bangladesh. The average score of wealth
index was less than 2.5 out of total score of 5 and
environmental index was less than 3.5 out of total score
of 5 showing a better sign. Results demonstrated that
mother’s knowledge relating to health was not
satisfactory, showing an average score of 2.78 out of a
total score of 6. Average score for health care
utilization index was 2.72 out of total score of 6.
Women empowerment index showed a moderate score
of 2.11 out of a total score of 4.
Reproductive variables show that approximately 34
percent of children were born within the birth interval
of 0 to 24 months, 47 percent within the 25 to 45
months and only 19 percent of children were having
birth interval greater than 45 months. The average age
of mothers at the time of 1st birth interval was
approximately 17 years. Father’s education attainment
Anwar et al
80
Table 1: Results of Factor Analysis of Variables
included in Indices
Indices/Variables Factor
analysis Reliability
Analysis (α)
Household Wealth Index
Own a TV 0.772
Own a Radio 0.464 0.789
Own a refrigerator 0.574
Own a telephone 0.684
Household Environment Index
(better environment conditions)
Electricity available in house 0.736
Piped water provided in house 0.651 0.668
Flush toilet avail be in house 0.485
Non
-dirt floor type 0.624
Knowledge index
(improved
knowledge)
heard of oral rehydration therapy 0.447
heard of modern method of
contraception 0.642 0.752
Has heard of tuberculosis 0.588
Has heard of HIV/AIDs 0.707
Health care utilization
( Healthy
lifestyle)
Received pre-natal care from
doctor 0.636
Doctor attended birth 0.825 0.724
Received tetanus injection before
birth 0.537
Has used modern method of
contraception 0.489
Women empowerment
Decision for spending money in
household 0.621 0.710
Decision relating to family
planning 0.697
level was also low. About 36 percent of father’s were
illiterate, 45 percent were having primary education and
only 19 percent of men reported secondary and higher
level of education. About 70 percent of women were
living in rural areas. Majority of women lived in
Chittagong division (22 percent) followed by Dhaka
(21 percent). Surprisingly, most commonly used source
of information in Bangladesh was television, as 45
percent of women were watching television to get
information relating to child health.
The results of logistic regression are reported in Table
3. Model 1 shows that educated mothers significantly
decrease the likelihood of having a stunted child by 14
percent (1- exponent of the log odds) and 60 percent
respectively as compared to illiterate mothers. In model
2, when place of residence and division of residence
was added in model; educated mothers were again less
likely to have stunted child meaning have lower.
Table 2: Descriptive Statistics of households (N=5382)
Stunted =1 65.4%
Not stunted = 0
34.6%
Mother’s education
No education
34.3%
Primary 36.6%
Secondary and higher 29.1%
Socio economic status Range, Mean, SD
Household Wealth Index 1-5, 2.34, 1.106
Household Environment Index 1-5, 3.26, 1.210
Knowledge index 1-6, 2.78, 1.66
Health care utilization Index 1-6 2.72, 1.776
Women empowerment 1-4, 2.11, 1.735
Reproductive Variables
Mother age at 1
st
birth 17.39, 2.936
Birth order
1
st
birth 18.1%
2-3 birth 25.4%
4-6 birth 39.3%
Above 6 birth 17.2%
Birth interval
0-24 months 33.8 %
25-45 months 46.8 %
Above 45 months 19.5 %
Control Variables
Partner’s education ,
Illiterate(no. education) 35.9 %
Primary 44.7 %
Secondary and higher 19.5 %
Place & type of residence
Rural 70.5 %
Urban 29.5 %
Division of residence
Barisal 15.1 %
Chittagong 22.6 %
Dhaka 21.1 %
Khulna 08.7 %
Rajshahi 14.0 %
Sylhet 18.5 %
Source of Information
Listen to radio 20.8 %
Watch TV 44.8 %
Read News Paper 15.9 %
In model 3 on addition of mother age and partner
educational level, maternal education again confirmed to
have less probability of stunted or poor nutrition child
but education of child’s father (partner education)
indicated higher probability for having stunted child.
This explains for the more promising effect of mother
education on child health and nutrition than his father
education.
However, after controlling for wealth index, household
environmental index, health knowledge index, health
care utilization index and women empowerment index
Maternal education and child nutritional status in Bangladesh
81
Table 3: Effects of Maternal Education and Intervening Mechanisms on Likelihood of Stunting of children in
households of Bangladesh
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
Maternal Education (uneducated , reference category)
Primary education 0.862**
(-0.15) 0.883***
(-0.12) 0.875
(-0.13) 0.877
(-0.13) 0.902
(-0.10) 0.920
(-0.08) 0.911
(-0.09) 1.226
(0.20) 1.215
(0.19)
Secondary /above 0.403*
(-0.90) 0.426*
(-0.85) 0.574*
(-0.55) 0.575*
(-0.55) 0.628*
(-0.46) 0.669*
(-0.40) 0.664*
(-0.41) 0.612**
(-0.49) 0.652*
(-0.43)
Place of residence (Rural, reference category)
Urban 0.758*
(-0.27) 0.750*
(-0.29) 0.752*
(-0.28) 0.859***
(-0.15) 0.876
(-0.13) 0.886
(-0.12) 0.986
(-0.014) 0.989
(-0.011)
Region of residence (Barisal, reference category)
Chittagong 0.981
(-0.02) 0.841
(-0.17) 0.839
(-0.17) 0.905
(-0.09) 0.899
(-0.10) 0.893
(-0.11) 0.773
(-0.25) 0.775
(-0.25)
Dhaka 0.822**
(-0.19) 0.717**
(-0.33) 0.715**
(-0.33) 0.791***
(-0.23) 0.815***
(-0.20) 0.80***
(-0.22) 0.570
(-0.562) 0.572
(-0.55)
Khulna 0.592*
(-0.52) 0.516*
(-0.66) 0.514*
(-0.66) 0.549*
(-0.59) 0.573*
(-0.56) 0.573*
(-0.55) 0.082*
(-2.501) 0.089*
(-2.42)
Rajshahi 0.682*
(-0.38) 0.626*
(-0.46) 0.624*
(-0.47) 0.659*
(-0.14) 0.668*
(-0.40) 0.664*
(-0.41) 0.48***
(-0733) 0.489***
(-0.71)
Sylhet 0.802**
(-0.22) 0.683*
(-0.38) 0.681*
(-0.38) 0.74**
(-0.30) 0.73**
(-0.31) 0.71**
(-0.43) 0.410**
(-0.891) 0.414**
(-0.88)
Mother’s Age at 1
st
birth 0.999
(-0.001) 0.999
(-0.001) 1.004
(0.004) 1.008
(0.008) 1.009
(0.009) 0.993
(-0.007) 0.994
(-0.006)
Partner’s education (uneducated , reference category)
Primary education 1.500*
(0.40) 1.551*
(0.44) 1.322*
(0.28) 1.222*
(0.200) 1.204*
(0.18) 1.207*
(0.19) 1.211**
(0.19)
Secondary education 1.591*
(0.46) 1.791*
(058) 1.699*
(0.53) 1.647*
(0.49) 1.663*
(0.509) 1.665
(0.509) 1.666
(051)
Socioeconomic Stutus
Wealth index 0.993*
(-0.007) 0.971*
(-0.03) 0.929*
(-0.07) 0.982*
(-0.02) 0.975*
(-0.025) 0.99*
(-0.01)
Better Environment
index 0.883*
(-0.12) 0.844*
(-0.17) 0.772*
(-0.26) 0.793*
(-0.23) 0.789**
(-0.24)
Improved Knowledge
index 0.850***
(-0.16) 0.871
(-0.14) 0.890
(-0.11) 0.898
(-0.10)
Healthy life style
(Health care Index) 0.923*
(-0.08) 0.922*
(-0.08) 0.981*
(-0.02) 0.983*
(-0.02)
Women
empowerment 0.930
(-0.04) 0.921
(-0.08) 0.925
(-0.07)
Birth order (Above 6 birth , reference category )
1
st
birth 0.472**
(-0.75) 0.491**
(-0.71)
2-3 birth 0.741***
(-0.30) 0.778***
(-0.25)
4-6 birth 1.197
(0.18) 1.185
(0.17)
Birth interval (Above 45 months, reference category)
0-24 months 1.46**
(0.38) 1.48**
(0.39)
25-45 months 0.901
(-0.10) 0.902
(-0.10)
Source of Information (No,reference)
Listen to radio 1.215
(0.19)
Watch TV 0.663***
(-0.41)
Read News Paper 0.438
(-0.82)
-2 Log likelihood 6739.90 6686.83 6126.37 6127.18 6109.99 6100.51 6095.12 5890.98 5862.54
Note: coefficient values are reported below the odd ratios. *P<0.001, ** P<0.05, *** P<0.01.
Anwar et al
82
(model 4 to 7) secondary education has significant
effect on child nutritional status while primary
education of mothers has less effect on child health. In
model 8, when birth parity and child birth interval are
controlled for and source of information was included
(model 9) as a proxy for awareness, secondary school
and above level of mother education decreases the
likelihood of stunting by approximately 35 percent
compared to those mother having no educational status.
However, the effect of mothers’ primary education and
child nutritional status got weaker and insignificant
after controlling for source of information (model 9).
Household wealth index and better environment index
are main contributing factors in explaining child health
status. These results support the findings of Benta et al.
(2011), Frost et al. (2005) and Desi and Alva (1998).
Socio-economic status is the primary pathway
explaining the effect of maternal education on child
nutritional status, but modern health care utilization
services and health knowledge also explain the effect of
maternal education on child health. In reproductive
behavior, birth order and birth interval has some
influence on child health status. Watching television is
connected with less likelihood of children being
stunted. This means that watching television is an
important source for broadcasting public health
campaign in Bangladesh. On the basis of our result we
can conclude that maternal education not only improve
socioeconomic condition but also transform behavior
from traditional and fatalistic view of health care to the
acceptance and utilization of modern health care
services. Still in our final model, maternal secondary
education has significant influence on child nutritional
status.
We find insignificant effect of mothers’ empowerment
on child health status. This result supports the findings
of Benta et al. (2011). The reason for insignificance
effect of women empowerment on child health status is
the lack of direct measures of this variable in BHDS
dataset. Further this variable requires other household
conditions like better socioeconomic conditions, wealth
and better education of child father to facilitate these
facilities as being traditional developing country this is
important in south Asian culture. This result is totally in
contradiction to Gupta (1990) who pointed out those
personality characteristics and abilities of mother
influence child health status are independent of
education and wealth status. She also said that mothers
with strong will and decision making power in
household matters have children with better health
status. Simon et al. (2002) concluded that mothers
having greater power in household matters can better
utilize the resources to meet the nutritional needs of
their children.
Conclusions
In this study, we used the Bangladesh Demography and
Health Survey (BDHS) data to estimate the effect of
maternal education on child nutritional status (measured
by stunting/ height for age) through various pathways
like socioeconomic status, health knowledge, and
attitude toward modern health care services, women
autonomy and reproductive behavior. Results estimated
by applying Binary Logistic regression models
explained that socioeconomic status, attitude toward
modern health care and health knowledge are important
pathways in explaining the effect of maternal education
on child health. In reproductive behavior birth interval
(25-45 months) has significant effect on child health
status. Estimated results overall suggest that maternal
education can influence child nutritional status by
improving health knowledge, reproductive behavior and
using modern health care services.
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