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Global Health Action
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/zgha20
Wealth- and education-related inequalities in
minimum dietary diversity among Indonesian
infants and young children: a decomposition
analysis
Bunga A. Paramashanti, Michael J. Dibley, Ashraful Alam & Tanvir M. Huda
To cite this article: Bunga A. Paramashanti, Michael J. Dibley, Ashraful Alam & Tanvir M. Huda
(2022) Wealth- and education-related inequalities in minimum dietary diversity among Indonesian
infants and young children: a decomposition analysis, Global Health Action, 15:1, 2040152, DOI:
10.1080/16549716.2022.2040152
To link to this article: https://doi.org/10.1080/16549716.2022.2040152
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 07 Apr 2022.
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ORIGINAL ARTICLE
Wealth- and education-related inequalities in minimum dietary diversity
among Indonesian infants and young children: a decomposition analysis
Bunga A. Paramashanti
a,b
, Michael J. Dibley
a
, Ashraful Alam
a
and Tanvir M. Huda
a
a
Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia;
b
Department of Nutrition,
Faculty of Health Sciences, Universitas Alma Ata, Yogyakarta, Indonesia
ABSTRACT
Background: Over the last two decades, Indonesia has experienced remarkable economic
growth. However, the percentage of infants and young children meeting the minimum
dietary diversity (MDD) criteria has stagnated. Despite the growing body of evidence of the
association between MDD and socioeconomic factors, there is little information about socio-
economic inequalities in MDD in Indonesia.
Objectives: The current study seeks to quantify the wealth- and education-related inequalities in
MDD among infants and young children in Indonesia and determine the contribution of different
factors to these disparities.
Methods: We included a total of 5038 children aged 6–23 months of the 2017 Indonesia
Demographic and Health Survey. We measured wealth- and education-related inequalities using
the concentration curve and Wagstaff normalised concentration index. Using a concentration index
decomposition analysis, we then examined factors contributing to wealth- and education-related
inequalities in MDD.
Results: The concentration indices by household wealth and maternal education were 0.220
(p < 0.001) and 0.192 (p < 0.001), respectively, indicating more concentration of inequalities
among the advantaged population. The decomposition analysis revealed that household wealth
(29.8%), antenatal care (ANC) visits (16.6%), paternal occupation (15.1%), and maternal education
(11.8%) explained the pro-rich inequalities in MDD in Indonesia. Maternal education (26.1%),
household wealth (19.1%), ANC visits (14.9%), and paternal occupation (10.9%) made the most
considerable contribution to education-related inequalities in MDD.
Conclusions: There is substantial wealth- and education-related inequalities in MDD. Our
findings suggest an urgent need to address the underlying causes of not reaching dietary
diversity by promoting infant and young child feeding equity in Indonesia.
ARTICLE HISTORY
Received 26 October 2021
Accepted 6 February 2022
RESPONSIBLE EDITOR
Jennifer Stewart Williams
KEYWORDS
Minimum dietary diversity;
socioeconomic disparities;
concentration index;
decomposition analysis;
determinants; Indonesia
Background
Malnutrition is a predominant public health issue among
children. Globally, an estimated 22% or 149 million chil-
dren under five are affected by stunting. Wasting remains
to threaten the lives of an estimated 7% or 45 million
children under five. Overweight affects an estimated 6%
or 39 million children under five [1]. In Indonesia, child
malnutrition rates remain alarming. The 2018 Indonesia
Basic Health Research (Riskesdas), the most recent
nationally representative survey, has reported a stunting
prevalence at 31%, wasting at 10%, underweight at 18%,
and overweight at 8% [2]. These rates indicate that
Indonesia is making slow progress and are off track in
meeting the Global Nutrition Targets [3].
Eating a variety of food in addition to breastmilk
help infants and young children achieve optimum
growth, health, and development [4,5]. A diversified
diet also reflects the quality and quantity of food intake,
food security, and micronutrient adequacy of children
[6–8]. Children who consume a diversified diet are
more likely to have a reduced risk of stunting [9–11].
Moreover, children from low- and middle-income
countries (LMICs) could avoid more than 11 million
stunting cases if 90% or more of infants and young
children received food from different groups to meet
the MDD criteria [12]. Minimum dietary diversity is
also associated with a decreased risk of anaemia [13,14]
and developmental delays [15,16]. Overall, MDD may
have long term effects on adult human capital, health,
and economic productivity [17].
The World Health Organization (WHO)/United
Nations International Children’s Emergency Fund
(UNICEF) has recommended infants and young chil-
dren meet a minimum dietary diversity (MDD), con-
suming foods and beverages from at least five out of
eight food groups during the previous day, starting
from six months. These food groups include 1)
grains, roots, tubers, 2) pulses, 3) vitamin A-rich
fruits and vegetables, 4) other fruits and vegetables, 5)
dairy products, 6) flesh foods, 7) eggs, and 8) breast-
milk [4,18]. This food group method is a relatively
simple and easy measurement used in survey settings
CONTACT Bunga A. Paramashanti bpar8840@uni.sydney.edu.au; bunga@almaata.ac.id Sydney School of Public Health, The University of
Sydney, Edward Ford Building A27, Fisher Road, Sydney, New South Wales 2006, Australia.
GLOBAL HEALTH ACTION
2022, VOL. 15, 2040152
https://doi.org/10.1080/16549716.2022.2040152
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
[7]. Additionally, MDD has been used as one of the
process indicators to monitor the effectiveness of
various breastfeeding and complementary feeding
interventions in the Global Nutrition Monitoring
Framework [4].
Existing studies in LMICs has extensively examined
factors affecting MDD at the child, maternal, household,
and community levels. These studies have found that
maternal education and household economic status are
among the factors that are consistently associated with
MDD. Children of higher economic status are at greater
odds of receiving foods from diverse foods [19–23].
Mothers with higher education are more likely to have
children who consume a more varied diet than those with
lower education [19–21,23–25]. Other factors, such as
maternal employment [26], paternal education [20,27],
antenatal care (ANC) [20,25] and residency [21,26], are
often related to increasing dietary diversity but have
shown mixed results across studies. Overall, these find-
ings suggest that socioeconomically disadvantaged chil-
dren are more likely not to reach MDD.
Dietary diversity increases with economic improve-
ments [12,28]; however, Indonesia may not be the case.
Over the last 22 years, Indonesia experienced a substan-
tial economic change. The poverty rate was halved from
24% in 1999 to 11.3% in 2004. The annual economic
growth averaged 6% between 2005 and 2015 [29,30].
Yet, despite the overall economic improvement of the
country, dietary diversity among children has stag-
nated. A nationally representative analysis study
showed that the consumption of a minimum of five
out of eight groups in Indonesia was 53.1% in 2007,
51.7% in 2012, and 53.7% in 2017 [23]. One important
reason might be the persistent income inequality, which
could have worsened the unequal access to nutrition,
clean water, sanitation, and health services [29].
Socioeconomic inequalities pose a significant chal-
lenge to optimal feeding practices [28]. However, very
few studies have examined the extent of socioeconomic
disparities related to dietary diversity and the factors
contributing to the inequality [31,32]. Moreover, no
study has examined socioeconomic inequalities in diet-
ary diversity in the Indonesian context. While earlier
research has extensively estimated odds ratios to analyse
the relationship between socioeconomic status and diet-
ary diversity [19–25], the concentration index may better
assess inequalities across the whole population (e.g. in
a cumulative share of individuals ranked by household
economic status). Furthermore, the concentration index
can also be decomposed into a range of explanatory
variables that influence socioeconomic-related inequal-
ities [33]. Understanding socioeconomic inequalities in
MDD may assist policymakers and public health profes-
sionals to target specific groups of the population at risk
to improve child dietary diversity and reduce the burden
of not meeting MDD on child well-being. Therefore, this
paper aims to fill in the gaps in the existing literature by
quantifying the extent of wealth- and educational-related
inequalities in MDD and examining the contribution of
explanatory variables to wealth- and educational-related
inequalities among infants and young children in
Indonesia.
Methods
Data source
We used data from the 2017 Indonesia Demographic
and Health Survey (IDHS), nationally representative of
the 34 provinces. Provinces are the largest subdivisions in
Indonesia, followed by districts/municipalities, subdis-
tricts, and urban/rural villages in the lower administra-
tive units. The survey used a two-stage stratified
sampling design. First, primary sampling units or census
blocks (CB) were selected by probability proportional to
size, where the size is the number of households listed in
the 2010 population census. The CB was stratified by
rural and urban areas with implicit stratification in each
stratum by sorting the CB by the wealth index category.
Second, 25 households were selected systematically from
each CB. All women aged 15–49 were eligible for indivi-
dual interviews in these households. The 2017 DHS
report provides detailed information on the question-
naires and sampling procedures [34].
Outcome variable
The study outcome, minimum dietary diversity (MDD),
assesses the percentage of children 6–23 months of age
who have consumed at least five out of eight food groups
in the past 24 hours. The food groups include 1) grains,
roots, and tubers; 2) legumes and nuts; 3) dairy pro-
ducts; 4) flesh foods; 5) eggs; 6) vitamin A-rich fruits
and vegetables; 7) other fruits and vegetables; 8) breast-
milk [18]. We coded the answers as either ‘1 = yes, con-
sumed’ or ‘0 = no, not consumed’ [34].
Socioeconomic status
We used two indicators of socioeconomic inequalities:
household wealth and maternal education. The wealth
index was computed based on household assets using
principal component analysis, and the key household
assets variables included ownership of infrastructures
and amenities. Briefly, the principal component analysis
estimates a cumulative wealth score for each household
based on its asset [35]. We divided these scores into five
quintiles, from the lowest 20% representing the poorest
group to the highest 20% representing the richest group.
We grouped maternal education into four categories:
none or not completed primary school, completed pri-
mary school, completed secondary school, and com-
pleted higher education.
2B. A. PARAMASHANTI ET AL.
Contributory factors to socioeconomic inequality
in dietary diversity
We selected the contributory factors to socioeco-
nomic inequality in dietary diversity based on our
study on MDD determinants in Indonesia. For the
present study analysis, we only included significant
variables in relation to MDD found in our previous
research [23]. These variables included child’s age (6–
11 months, 12–17 months, 18–23 months), mother’s
education (none or incomplete primary school, com-
pleted primary school, completed secondary school,
completed higher education), mother’s access to
media (none, at least one media), mother’s occupa-
tion (not working, agricultural, non-agricultural),
father’s occupation (not working, agricultural, non-
agricultural), number of ANC visits in the last preg-
nancy (<4 visits, ≥4 visits), household wealth (poor-
est, poorer, middle, richer, richest), area of residence
(rural, urban), and regions (Java and Bali, Sumatera,
Kalimantan, Sulawesi, Eastern Indonesia).
Data analysis
To assess the socioeconomic inequality in dietary diver-
sity, we calculated the concentration index [36], which is
a widely used measure of socioeconomic inequality, and
is written as:
C¼2
μcov h;rð Þ;
where h is the health variable in which inequality is
measured, μ is its mean, cov denotes the covariance,
and r is the individual’s fractional rank in the distribution
of socioeconomic position [33]. The value of the con-
centration index ranges from −1 to +1. A negative value
indicates a disproportionate concentration of MDD
Table 1. Characteristics of the study population and proportions of minimum dietary diversity (weighted n = 5038).
Variable frequencies Proportion of MDD
Variables n % (95% CI) n % (95% CI) p
Child factors
Child’s age <0.001
6–11 months 1639 32.5 (30.9–34.2) 553 33.7 (30.9–36.6)
12–17 months 1785 35.4 (33.7–37.2) 1088 60.9 (57.7–64.1)
18–23 months 1615 32.1 (30.4–33.7) 1012 62.7 (59.6–65.7)
Maternal factors
Mother’s education <0.001
None or incompleted primary school 293 5.8 (5.0–6.7) 104 34.2 (28.2–40.8)
Completed primary school 2340 46.5 (44.5–48.4) 1106 47.3 (44.6–50.0)
Completed secondary school 1543 30.6 (29.0–32.3) 873 56.5 (53.5–59.5)
Completed higher education 862 17.1 (15.7–18.6) 573 66.5 (62.7–70.0)
Mother’s occupation <0.001
Agricultural 357 7.1 (6.2–8.1) 130 36.3 (30.8–42.3)
Non-agricultural 1876 37.3 (35.6–57.4) 1095 58.4 (55.6–61.0)
Not working 2797 55.6 (53.8–57.4) 1422 50.8 (48.3–53.4)
Mother’s access to media at least once a week 0.001
None 719 14.3 (13.1–15.6) 326 45.3 (40.8–49.9)
Any media 4319 85.7 (84.4–86.9) 2326 53.9 (51.9–55.8)
Paternal factors
Father’s education <0.001
None or incompleted primary school 332 6.7 (5.9–7.7) 134 40.3 (34.5–46.3)
Completed primary school 2145 43.5 (41.5–45.5) 1044 48.7 (45.9–51.5)
Completed secondary school 1724 35.0 (33.1–36.9) 943 54.7 (51.6–57.8)
Completed higher education 729 14.8 (13.5–16.2) 487 66.7 (62.5–70.6)
Father’s occupation <0.001
Agricultural or not working 1080 21.9 (20.4–23.6) 458 42.4 (38.9–46.0)
Non-agricultural 3841 78.1 (76.4–79.6) 2144 55.8 (53.8–57.9)
Health care, household, and community factors
Number of antenatal care visits <0.001
<4 429 8.7 (7.7–9.8) 180 41.8 (36.6–47.2)
≥4 4499 91.3 (90.2–92.3) 2452 54.5 (52.6–56.4)
Household wealth <0.001
Poorest 1010 20.1 (18.6–21.6) 404 39.9 (36.4–43.6)
Poorer 1032 20.5 (19.0–22.0) 523 50.7 (47.0–54.3)
Middle 1123 22.3 (20.8–23.9) 639 56.8 (53.2–60.4)
Richer 995 19.7 (18.2–21.3) 602 60.5 (56.4–64.5)
Richest 878 17.4 (15.9–19.2) 486 55.3 (51.0–59.5)
Living residency <0.001
Urban 2487 49.4 (47.7–51.0) 1422 57.2 (54.7–59.7)
Rural 2551 50.6 (49.0–52.3) 1230 48.2 (45.6–50.8)
Region <0.001
Java and Bali 2850 56.5 (55.0–58.1) 1566 55.0 (52.2–57.7)
Sumatera 1133 22.5 (21.2–23.8) 620 54.8 (51.3–58.1)
Kalimantan 297 5.9 (5.4–6.5) 160 53.5 (48.0–59.1)
Sulawesi 356 7.1 (6.5–7.7) 158 44.1 (39.5–48.7)
Eastern Indonesia 402 8.0 (7.4–8.6) 150 37.2 (33.4–56.0)
n and %: weighted count and proportion, respectively.
p: p-value based on the chi-square test.
GLOBAL HEALTH ACTION 3
among the disadvantaged groups, whereas a positive
value indicates a disproportionate concentration of
MDD among the advantaged groups. Zero value means
the absence of wealth- and education-related inequalities.
However, our outcome is a binary variable, the bounds of
the concentration index do not extend to −1 and +1, but
equal to μ1 and 1μ. Therefore, we normalised the
concentration index by dividing its value by its bound as
proposed by Wagstaff et al. [37,38]:
Cnorm ¼C
1μ
We also plotted the concentration curves to display the
cumulative proportion of the MDD (y-axis) against the
cumulative proportion of the children sorted by their
household wealth and maternal education on the x-axis,
beginning with the most disadvantaged and ending with
the most advantaged groups. The curve that lies above the
line of equality indicates that MDD is concentrated
among the disadvantaged groups. Conversely, the curve
below the equality line suggests that MDD is more con-
centrated among the advantaged groups. The farther the
curve deviates from the line of equality, the greater the
degree of inequality [33].
To ascertain the factors contributing to the observed
socioeconomic inequalities in dietary diversity, we
decomposed the concentration index to measure the
explanatory variables’ contribution to wealth- and educa-
tion-related inequalities in MDD. For a linear additive
relationship between MDD (y) and a set of determinants
Xk
ð Þ, such as
y¼aþXkβkXkþε;
allows the concentration index for y to be written as:
C¼Xkβk�
Xk=μ
�CkþGCε=μ;
where μ is the mean of y, �
Xk is the mean of Xk, Ck is the
concentration index for Xk (defined analogously to C),
βk�
Xk=μ is the elasticity of MDD with explanatory vari-
ables, and GCε=μ is the generalised concentration index
for the error term (ε). A negative contribution revealed
that an independent variable operated towards the pro-
poor distribution of MDD. In contrast, a positive con-
tribution indicated that an independent variable worked
towards the pro-rich distribution of MDD [33]. In this
study, we applied Wagstaff ’s correction [37,38] into the
equation:
Cnorm ¼Pkβk�
Xk=μ
�Ck
1μþGCε=μ
1μ
As the outcome’s binary nature, we used
a Generalised Linear Model (GLM) with a binomial
family and probit link to decompose MDD inequality
[39]. In addition, our analysis demonstrated the low
level of multicollinearity with a mean of variance
inflation factor (VIF) of 1.38. We also performed
interaction tests among possible dependent variables
(i.e. household wealth, maternal education, father
occupation, ANC visits, residency, region), but statis-
tically not significant. We used Stata version 17.0
(StataCorp, College Station, TX) for statistical analy-
sis, with the significance level determined at p < 0.05.
We applied the ‘svy’ commands throughout the ana-
lyses to adjust the survey design of the IDHS by
including sampling weight, strata, and cluster.
Results
Characteristics of the study participants and
prevalence of minimum dietary diversity
Table 1 presents the background characteristics of
the study participants and the percentage of chil-
dren who met the MDD criteria. We included
a total of 5038 children aged 6–23 months in the
analysis. The overall prevalence of MDD among
children 6–23 months was 52.6% (95% CI: 45.6–
49.2). The prevalence meeting standards for MDD
was higher among children aged 18–23 months
(62.7%) and those whose mothers and fathers
attained at least a higher educational degree
(66.5% and 66.7%, respectively). In addition, we
found a wide gap in the proportion of MDD
among children across different household wealth
categories, with 39.9% in the lowest quintile and
55.3% in the highest quintile. The prevalence of
MDD was exceptionally high among children who
resided in urban areas (57.2%). Minimum dietary
diversity also displayed a remarkable regional dif-
ference, ranging from 37.2% in Eastern Indonesia
to 55.0% in Java and Bali.
Socioeconomic inequality in minimum dietary
diversity
The normalised concentration indices (C
norm
) for
MDD among infants and young children aged 6–
23 months, ranked by household wealth and maternal
education, are estimated at 0.220 and 0.192, respec-
tively (see Table 2). The positive values of C
norm
suggest that children from wealthier households and
educated mothers had a more diverse meal. Figure 1
Table 2. Wagstaff normalised concentration index of mini-
mum dietary diversity by household wealth index and mater-
nal education.
Wealth Education
Index value SE pIndex value SE P
C 0.104 0.009 <0.001 0.091 0.009 <0.001
C
norm
0.220 0.020 <0.001 0.192 0.019 <0.001
C: concentration index; C
norm
: Wagstaff normalized concentration index;
SE: standard error; p: p-value.
4B. A. PARAMASHANTI ET AL.
depicts the concentration curves for MDD among
infants and young children aged 6–23 months,
ranked by household wealth and maternal education.
As illustrated, concentration curves lie below the 45-
degree line, confirming that the proportion of MDD
is higher in children with wealthier households and
highly educated mothers.
Contribution of the determinants to wealth- and
education-related inequality in minimum dietary
diversity
Table 3 summarizes the decomposition analysis results
of wealth-and education-related inequality in MDD
among children aged 6–23 months in Indonesia. Each
column shows the elasticity of MDD, the concentration
Table 3. Decomposition of wealth- and education-related inequalities in minimum dietary diversity among Indonesian infants
and young children.
Wealth Education
Variables Elasticity C
norm
Absolute
contribution
Relative
contribution (%) Elasticity C
norm
Absolute
contribution
Relative
contribution (%)
Child factors
Child’s age
6–11 months
12–17 months 0.189 −0.018 −0.003 −1.4 0.189 −0.022 −0.004 −2.2
18–23 months 0.185 −0.006 −0.001 −0.4 0.185 −0.021 −0.004 −2.0
Subtotal −0.024 −0.004 −1.9 −0.043 −0.008 −4.2
Maternal factors
Mother’s education
None or incomplete primary
Completed primary 0.064 −0.413 −0.027 −11.2 0.064 −0.781 −0.050 −26.2
Completed secondary 0.076 0.256 0.019 8.2 0.076 0.507 0.039 20.0
Completed tertiary 0.062 0.566 0.035 14.8 0.062 0.997 0.062 32.3
Subtotal 0.408 0.028 11.8 0.722 0.050 26.1
Mother’s occupation
Agricultural
Non-agricultural 0.046 0.332 0.015 6.4 0.046 0.371 0.017 8.9
Not working 0.062 −0.151 −0.009 −3.9 0.062 −0.236 −0.015 −7.6
Subtotal 0.181 0.006 2.5 0.135 0.002 1.2
Mother’s access to media at least
once a week
None
Any media 0.004 0.343 0.002 0.6 0.004 0.164 0.001 0.4
Subtotal 0.343 0.002 0.6 0.164 0.001 0.4
Paternal factors
Father’s education
None or incomplete primary
Completed primary −0.010 −0.425 0.004 1.7 −0.010 −0.496 0.005 2.5
Completed secondary −0.023 0.263 −0.006 −2.5 −0.023 0.303 −0.007 −3.6
Completed tertiary 0.007 0.623 0.004 1.9 0.007 0.738 0.005 2.7
Subtotal 0.462 0.003 1.1 0.545 0.003 1.7
Father’s occupation
Agricultural or not working
Non-agricultural 0.069 0.521 0.036 15.1 0.069 0.305 0.021 10.9
Subtotal 0.521 0.036 15.1 0.305 0.021 10.9
Health care, household, and
community factors
Number of antenatal care visits
<4
≥4 0.093 0.426 0.039 16.6 0.093 0.308 0.029 14.9
Subtotal 0.391 0.036 16.6 0.308 0.029 14.9
Household wealth
Poorest
Poorer 0.014 −0.511 −0.007 −3.0 0.014 −0.238 −0.003 −1.8
Middle 0.033 −0.017 −0.001 −0.2 0.033 −0.050 −0.002 −0.9
Richer 0.036 0.500 0.018 7.5 0.036 0.195 0.007 3.6
Richest 0.061 1.000 0.061 25.7 0.061 0.568 0.035 18.1
Subtotal 0.972 0.071 29.8 0.476 0.037 19.1
Residency
Urban
Rural 0.008 −0.533 −0.004 −1.7 0.008 −0.290 −0.002 −1.1
Subtotal −0.533 −0.004 −1.7 −0.290 −0.002 −1.1
Region
Java and Bali
Sumatera 0.017 −0.111 −0.002 −0.8 0.017 0.091 0.002 0.8
Kalimantan 0.005 −0.111 −0.001 −0.2 0.005 −0.066 0.000 −0.2
Sulawesi −0.007 −0.271 0.002 0.8 −0.007 −0.033 0.000 0.1
Eastern Indonesia −0.013 −0.563 0.007 3.1 −0.013 −0.065 0.001 0.4
Subtotal −1.056 0.007 2.9 −0.072 0.002 1.2
Total 0.180 76.8 0.135 70.2
Residual 0.040 23.2 0.043 29.8
C
norm
: Wagstaff normalised concentration index.
GLOBAL HEALTH ACTION 5
index, and the absolute and the percentage contribu-
tions of each contributor to the MDD concentration
index. The elasticity shows how sensitive MDD is to
each contributor. We found that MDD is mainly
responsive to the child’s age, mother’s education,
father’s occupation, and ANC visits.
The C
norm
represents the degree of inequality in
MDD for each contributor. As indicated by negative
concentration indices, children of mothers with
primary education (−0.413), fathers with primary edu-
cation (−0.425), poorer households (−0.511), rural areas
(−0.533), Sulawesi (−0.271) and Eastern Indonesia
(−0.563) were highly concentrated among the poorer
population. Similarly, children of mothers with primary
education (−0.781), unemployed mothers (−0.236),
fathers with primary education (−0.496), poorer house-
holds (−0.238), and rural areas (−0.290) were concen-
trated among the less educated population.
Figure 1. (a) Concentration curves of minimum dietary diversity ranked by household wealth index and (b) level of maternal
education.
Figure 2. (a) Concentration curves of minimum dietary diversity ranked by household wealth index and (b) level of maternal
education.
6B. A. PARAMASHANTI ET AL.
Table 3 shows the contributions of explanatory
variables to wealth- and education-related inequal-
ities in MDD. Mother’s education, father’s occupa-
tion, ANC visits, and household wealth explain most
of the wealth- and education-related inequalities in
MDD. The large elasticities of MDD for these con-
tributors are responsible for their considerable con-
tribution to MDD concentration indices. Conversely,
there is a notable degree of wealth- and education-
related inequalities in the father’s education and resi-
dency, but there is a minor sensitivity of MDD to
variation in these contributors, thus making a small
contribution to MDD concentration indices.
Furthermore, considering that each contribution is
the product of the sensitivity of MDD for that factor
and the degree of wealth- and education-related
inequalities in that factor, the positive or negative
value of the contributor comes from the positive or
negative elasticity or concentration index. For exam-
ple, the contributions of being 12–17 months and 18–
23 months old, having mothers with primary school,
having unemployed mothers, having fathers with sec-
ondary school, belonging to poorer and middle eco-
nomic status, residing in rural areas and residing in
Kalimantan are negative. The negative contributions
are derived from the negative elasticity or concentra-
tion index of these factors.
Figure 2 depicts the percentage contribution of the
explanatory variables to wealth- and education-related
inequalities. For wealth-related inequality in MDD, the
largest contributor was household wealth (29.8%), fol-
lowed by ANC visits (16.6%), paternal occupation
(15.1%), and maternal education (11.8%). Similarly,
the largest contributions toward education-related
inequality in MDD included maternal education
(26.1%), household wealth (19.1%), ANC visits
(14.9%), and paternal occupation (10.9%). On the
other hand, the child’s age, maternal employment,
maternal access to media, paternal education, residency,
and geographical regions showed minimal or no con-
tribution to wealth- and education-related inequality in
MDD. Overall, these variables explained nearly 76.8%
and 70.2% of the wealth- and education-related inequal-
ities in MDD.
Discussion
This study is the first to examine the extent of wealth-
and education-related inequalities in MDD among
infants and young children and decomposed them
into contributing factors in Indonesia. The study
found that the proportion of children who had met
the WHO’s minimum dietary diversity criteria was
more concentrated among children from wealthier
households and those born to mothers with higher
educational attainment. Household wealth, mother’s
education, father’s occupation, and ANC visits
mainly contributed to the pro-rich and pro-
educated socioeconomic inequalities in MDD.
Our result of pro-rich wealth-related inequalities
in MDD was in line with previous studies [28,32].
Although no study has assessed education-related
inequality in MDD, the distribution of infant and
young child feeding indicators was higher among
mothers with higher education in several studies
[40,41]. However, these findings do not imply that
eating a diversified diet does not occur among chil-
dren from poorer families and less-educated
mothers. Instead, it revealed MDD is disproportio-
nately concentrated among the richer and educated
population.
We found that household wealth was the predo-
minant contributor to the wealth- (29.8%) and edu-
cation-related (19.1%) inequalities in MDD.
Similarly, earlier studies in Ethiopia [31] and
Zimbabwe [32] have shown that household eco-
nomic status was the main factor explaining socio-
economic disparities in MDD. Since dietary
diversity is associated with the availability, access
and utilisation of food, wealthier households are
more likely to have enough resources to consume
varied and nutritious food [42]. They have greater
affordability to purchase non-staple foods, leading
to improved dietary diversity [41,43]. At the same
time, they also have better access to health care and
information [44], thus applying the recommended
feeding practices. Interventions that improve food
purchasing power, such as income-generating stra-
tegies (e.g. homestead food production) and cash
transfers, would help reduce the economic barriers
to accessing a diversified diet [45–47]. In addition,
infant and young child feeding promotions should
be made available to all mothers and their children,
especially those with lower economic status.
Of all the mother’s factors, maternal education is
the most significant contributor to the wealth-
(11.8%) and education-related (26.1%) inequalities
in MDD. Although there has been no study exam-
ining the contribution of maternal education in the
MDD inequalities, several studies have highlighted
the contribution of this factor in explaining the
disparities in child undernutrition [48–50]. The
role of maternal education in improving child diet-
ary diversity could be due to higher dietary knowl-
edge [24,43,51] and better health literacy, dietary
information-seeking behaviour, understanding, and
critical thinking skills related to nutritional infor-
mation [52]. Between 2002 and 2017, senior high
school enrollment in Indonesia rose considerably
from 50% to 71%. However, there was a 25% dis-
crepancy in school enrollment between the poorest
and wealthiest quintiles in the latest year. Moreover,
29% of these students did not complete their studies
for various reasons, including insufficient funds,
GLOBAL HEALTH ACTION 7
participation in the labour force, distance, marriage,
and taking care of households for girls [53]. Thus,
there is a need to narrow the gap in formal educa-
tion participation across economic status, geogra-
phical regions, and gender [54], especially at the
secondary and higher degree levels, for a long
term investment in child nutrition. Governments
should commit to encouraging school participation,
for example, providing pro-poor incentives (e.g.
cash transfers, food-for-education), decentralizing
education to the district/municipality level, and
developing alternative learning programs (e.g. non-
formal education) [55]. Such initiatives should be
designed to include people from marginalized com-
munities, regardless of gender or ethnicity.
We also found that ANC visits had a distinct con-
tribution to wealth- (16.6%) and education-related
(14.9%) inequalities in MDD. Counselling received
from the health practitioners during the visit, followed
by appropriate practice, may lead to feeding children
with a diversified diet [56]. In Indonesia, 96% of
pregnant women had access to ANC services in 2018.
However, only 74% met at least four ANC visits, ran-
ging from 44% in Papua to 90% in Yogyakarta and
58% among women without formal education to 83%
among women with a higher degree [2]. Increasing
maternal awareness about ANC service by targeting
the most vulnerable community is vital [50]. While the
National Health Insurance (NHI) covers the ANC
service fee, there is also a need to expand the NHI
coverage to reduce sociodemographic inequalities in
access to maternal and health services [57].
Our study also revealed that paternal occupation
explained wealth- (15.1%) and education-related
(10.9%) inequalities in MDD. Household head
employment was associated with dietary diversity as
it could determine the earnings [42]. However,
Indonesian agriculture jobs dominated by small-
scale farmers remain struggling with low incomes
[58]. Such income disparities may increase the risk
of food insecurity, making it difficult for them to
afford healthy diets [59]. In addition to increasing
crop production, a study in Bangladesh suggested
that farmers could cover their household food
expenses by seeking off-farm income. Thus, there is
a need for policy support in agricultural development
(e.g. best agronomic practices, access to information
and credit, infrastructure investment) and off-farm
income generating for smallholder farmers to achieve
food security and lift them out of poverty [60]. In
addition, there is much to learn from Tanzania,
where nutrition-sensitive agriculture and agroecology
interventions among food-insecure smallholder farm-
ers have improved sustainable agricultural practices
and women’ empowerment in income allocation,
which could enhance household food security and
children’s dietary diversity [61].
The development of nutrition education to
improve a diversified diet in Indonesia began with
the ‘Healthy Four Perfect Five’ (Empat Sehat Lima
Sempurna) campaign. However, although this slogan
encouraged people to eat various food groups (sta-
ples, plant- and animal-protein source food, fruits,
vegetables), the value of milk as the ‘perfect’ food has
been exaggerated. Milk mainly was not locally pro-
duced and costly, making it available only for the
rich [62]. The most updated guideline, Guide for
Balanced Nutrition, also encourages the population
to eat a diversified diet by carrying a message of ‘be
grateful and enjoy various food’. Nevertheless, this
guideline is less socialized and implemented.
Perhaps because of its simplicity, some industries
and communities continue to use the old ‘Healthy
Four Perfect Five’ [59]. Although the newest guide-
line has been developed for all populations across all
ages, including children five years [63], some recom-
mendations should follow the global indicators for
infant and young child feeding practices, including
minimum dietary diversity. Practical and straight-
forward messages may help communities adopt
new nutritional information [64]. Health practi-
tioners should adequately promote the nutrition
guidelines by including locally available food [65]
and pricing information [66] during all contact
with mothers and young children, such as antenatal
and postnatal care. Nutrition counselling and educa-
tion should occur in multiple settings, involve local
human resources, and reach out to mothers regard-
less of their socioeconomic backgrounds to ease
disparities.
Strengths and limitations
To our knowledge, this is the first study in Indonesia to
measure both wealth- and education-related inequal-
ities in MDD and to decompose the inequality by a set
of contributing factors. The study used a nationally
representative sample to generalise the findings to chil-
dren aged 6–23 months in Indonesia. The use of the
WHO’s most updated MDD indicator is helpful for
ongoing monitoring and comparing with international
guidelines [18]. However, MDD is constructed based
on the single 24-hour food recall during the survey,
thus not reflecting the actual feeding patterns [67].
While the decomposition analysis enables us to under-
stand various factors contributing to the inequality in
MDD, we could not draw a causal inference [33]. This
issue also occurs when using cross-sectional data.
Conclusions
The present study provided evidence on substantial
wealth- and education-related inequalities in the
MDD proportion among infants and young children
8B. A. PARAMASHANTI ET AL.
in Indonesia. The overall findings of this study urge the
need for multisectoral approaches to addressing the
underlying causes of socioeconomic inequalities in
MDD. We should prioritise children of poorer house-
holds and less educated mothers. Improving access and
the quality of prenatal and postnatal health care is
beneficial for delivering health-facility-based nutrition
education. Nutrition-sensitive agriculture interventions
may improve diet diversity through food production
and income-generating. While there is a national
recommendation on Balanced Nutrition Guideline,
there is no evidence of whether the promotion of this
guideline benefits infant and young child feeding and
this issue requires further research. Finally, examining
the changes of inequalities in MDD over time is vital
for improving child nutrition outcomes in Indonesia.
Acknowledgments
The lead author (BAP) conducted this research and pub-
lication as part of her doctoral study, for which she highly
acknowledged the Indonesia Endowment Fund for
Education (LPDP) for providing a scholarship.
Author contributions
BAP, TMH, and MJD designed the study. BAP conducted
the data analysis and interpreted the data under the gui-
dance of TMH. BAP drafted the initial manuscript under
the supervision of TMH and MJD. TMH, MJD, and AA
read and revised the manuscript. All authors approved the
final manuscript draft.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Ethics and consent
The ICF International Review Board (IRB) ethically
approved the 2017 IDHS to ensure compliance with the
US Department of Health and Human Services require-
ments for the ‘Protection of Human Subjects’ (45 CFR 46)
and the host country’s IRB, with written informed consent
obtained from all participants during the survey. DHS
granted the first author permission to use the data.
Funding information
This publication was funded by the Indonesia Endowment
Fund for Education (LPDP). LPDP did not have a role in
the design, analysis, and writing of this article.
Paper context
While earlier studies have documented evidence on the deter-
minants of minimum dietary diversity, very few have evalu-
ated the magnitude and drivers of socioeconomic disparities in
minimum dietary diversity. This paper provides evidence on
wealth- and education-related inequalities in minimum
dietary diversity among infants and young children in
Indonesia. Our findings support calls for greater equality of
wealth, education, and health care to close gaps in child
nutrition.
ORCID
Bunga A. Paramashanti http://orcid.org/0000-0001-
6066-2039
Michael J. Dibley http://orcid.org/0000-0002-1554-5180
Ashraful Alam http://orcid.org/0000-0001-7034-1095
Tanvir M. Huda http://orcid.org/0000-0002-8996-4361
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