WPS8664
Policy Research Working Paper 8664
Malnutrition Gap as a New Measure
of Child Malnutrition
A Global Application
Juan Feng
Shamma Alam
Patrick Hoang-Vu Eozenou
Development Economics
Development Data Group
December 2018
Policy Research Working Paper 8664
Abstract
“Leaving no one behind” is an overarching principle of the measures are moving in the same direction, in many other
Sustainable Development Goals. Many countries are prior- cases, they are moving in opposite directions. Moreover,
itizing resources for those who are furthest behind. Existing employing the new measures, the study can identify coun-
malnutrition indicators—underweight, stunting, wasting, tries that have low levels of headcount for a malnutrition
overweight, and severe wasting—are headcount ratios. They measure but comparatively high severity of malnutrition
do not capture how far behind malnourished children are according to the gap measures, and vice versa. This suggests
relative to the World Health Organization growth standards. that these new malnutrition measures provide additional
To understand the severity of malnutrition, this study devel- information on the severity of malnutrition that is not
ops a new malnutrition measurement, using the method possible to be known from headcount measures. These
originally developed for estimating poverty. This study esti- new measures of the severity of malnutrition can there-
mates the prevalence, gap, and gap squared for stunting, fore improve the monitoring of child malnutrition across
wasting, overweight, and underweight, using data from 94 countries, and consequently help countries to achieve their
developing countries over 20 years. The results show that Sustainable Development Goals.
although in most cases the headcount measures and gap
This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World
Bank to provide open access to its research and make a contribution to development policy discussions around the world.
Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be
contacted at juan.feng@fao.org.
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Produced by the Research Support Team
Malnutrition Gap as a New Measure of Child Malnutrition: A Global Application
Juan Feng*, Shamma Alam†, and Patrick Hoang-Vu Eozenou‡
Keywords: new measurement; malnutrition; gap; severity; global dataset
Disclaimer: The views expressed in this paper are those of the authors and do not necessarily reflect the official
views of the Food and Agriculture Organization of the United Nations or the World Bank.
*
Corresponding author, Food and Agriculture Organization of the United Nations, juan.feng@fao.org
†
Dickinson College, alams@dickinson.edu
‡
The World Bank, peozenou@worldbank.org
The authors gratefully acknowledge the funding from the Knowledge for Change Program (KCP).
1. Introduction
A renewed aspiration from the Millennium Development Goals (MDGs), the second
Sustainable Development Goal (SDG) calls for achieving, by 2025, the internationally agreed
targets for reduction of stunting and wasting in children under 5 years of age.1 “Leaving no one
behind” is an overarching principle of the newly adopted SDGs. The UN 2016 SDGs Report states,
“In committing to the realization of the 2030 Agenda for Sustainable Development, Member
States recognized that the dignity of the individual is fundamental and that the Agenda’s
Goals and targets should be met for all nations and people and for all segments of society.
Furthermore, they endeavored to reach first those who are furthest behind.” (UNSD 2016,
p. 48)
The World Health Organization (WHO) defines child malnutrition as growth measures more
than 2 standard deviations (SD) below the median WHO growth standards. In addition, the WHO
defines severe acute child malnutrition as weight for height below -3SD from the median WHO
growth standards (WHO and UNICEF 2009). Existing child malnutrition indicators include
prevalence of underweight (weight for age below -2SD), stunting (height for age below -2SD),
wasting (weight for height below -2SD), overweight (weight for height above 2SD), and severe
wasting (weight for height below -3SD).
These prevalence indicators are headcount measures and do not vary with the distance
between individual Z-scores (number of SD) and the WHO reference lines. And thus, such
headcount measures fail to identify malnourished children furthest away from the reference line,
i.e. the inequality in malnutrition present among the malnourished population. As the SDGs require
1
United Nations. Sustainable Development Goals. (http://www.un.org/sustainabledevelopment/sustainable-
development-goals).
2
more granular data to monitor progress, it has motivated us to develop new indictors to provide
supplemental, yet critical, evidence to the conventional indicators.
There have only been a limited number of studies that attempt to develop a measure of
severity of child malnutrition. McDonald et al. (2014) proposes a measure of malnutrition based
on the notion of multiple anthropometric deficits. For example, a child is considered to be severely
malnourished if she/he is both stunted and underweight. However, this measure is still a headcount
measure, and it compounds the information when people want to know how stunted and how
underweight a child is separately.
In contrast, studies by Shekar et al. (2015) and Jolliffe (2004 & 2004) adopt the techniques
used for measuring poverty to measure nutrition outcomes. Specifically, they put the Foster, Greer
and Thorbecke (1984, hereafter referred to as FGT) class of poverty indicators in the context of
malnutrition. Shekar et al. (2015) estimated FGT(0) as the stunting prevalence (similar to the
poverty headcount measure) and FGT(1) as the stunting gap (similar to the poverty gap) in Mali
from 2001 to 2013. Similarly, Jolliffe (2004 & 2004) uses FGT to calculate the overweight gap
and gap-squared to understand the overweight problem in the U.S. They demonstrated that the
stunting gap and overweight gap, analogous to the poverty gap, can provide further information in
addition to the stunting prevalence in nutrition diagnostics and policy recommendations.
This paper aims to provide supplementary, but critical, information to the conventional
headcount measures of malnutrition. Specifically, following Shekar et al. (2015), this paper will
adopt the techniques used for measuring the depth and severity of poverty to measure the severity
of malnutrition. More specifically, we develop the following eight measures of malnutrition in this
study: (i) stunting gap, (ii) stunting gap squared, (iii) wasting gap, (iv) wasting gap squared, (v)
3
overweight gap, (vi) overweight gap squared, (vii) underweight gap, and (viii) underweight gap
squared.
This study makes two important contributions to the research literature. First, while the
stunting gap measure has been developed by Shekar et al. (2015), the other seven measures of
malnutrition are developed for the first time in this study. Hence, in addition to the conventional
headcount indicators, these proposed indicators can provide useful information about a country’s
malnutrition status, especially on the depth and severity of malnutrition, which can consequently
improve evidence-based decision-making. Second, we employ over 20 years of malnutrition data
from 94 developing countries to calculate the new measures. Employing the new measures, we are
able to identify countries that have low levels of headcount for a malnutrition measure, but
comparatively high severity of malnutrition according to the gap and gap-squared measures, and
vice versa. This allows us to identify cases where headcount measures may be providing an
incomplete description of a certain country’s malnutrition status.
From a policy perspective, it is important to distinguish between malnutrition measures based
on the headcount and measures of depth and severity in malnutrition. As countries are in particular
committed to reach first those who are furthest behind in order to realize the SDGs 2030 agenda,
high-quality data are needed for monitoring the progress of these individuals and providing
evidence for effective policy making.
We proceed as follows. Section 2 discusses the methodology for the new measures. Section 3
describes the data used in the empirical application. Section 4 presents the results. Section 5
concludes.
2. Methodology
4
While we adopt the method of Shekar et al. (2015), it is not the only application of the FGT
poverty indicators in non-monetary indicators. Nguyen and Wodon (2012, 2015) applied the same
approach to the estimation of child marriage. Apart from estimating the incidence of child marriage
(the share of girls marrying before age 18), they also estimated the “child marriage gap,” which
accounts for how early a girl marries.
We intend to generalize the method for all of the aforementioned existing malnutrition
indicators and produce the gap estimate for every country with data, using a standardized data set
of growth Z-scores calculated from Demographic and Health Surveys (DHS) and Multiple
Indicator Cluster Surveys (MICS). We will also extend the calculation to the squared malnutrition
gap, i.e., FGT(2). Foster, Greer, and Thorbecke (1984) showed that the FGT class of poverty
indicators have a number of attractive axiomatic properties such as additive decomposability and
subgroup consistency.
Analog to the poverty gap, the malnutrition gap is defined as the average shortfall of
children’s Z-scores of an anthropometric measure from the reference line (counting zero shortfall
for non-malnourished children) as a proportion of the reference line. It measures how far off a
child is from the WHO growth standards. Taking stunting as an example, the national average
stunting gap (Gap) can be expressed as
∑ (1)
where N denotes the total number of children under 5 years of age in a given population, M denotes
the number of stunted children, and denotes individual Z-scores of stunting and 2 in this
equation. Implicitly in this equation, the shortfall for non-stunted children is zero when 2.
Subsequently, the national average squared stunting gap (SqGap) can be expressed as
5
∑ . (2)
The squared malnutrition gap takes into account not only the distance between the malnourished
children and the reference line (the malnutrition gap), but also the inequality among the
malnourished children. That is, a higher weight is placed on those who are further away from the
reference line.
3. Data
We reanalyzed all DHS and MICS, phases 3 onwards, and calculated individual Z-scores
for all children with available anthropometric data according to the WHO standard approach. As
of today, we obtained estimates for 168 DHS from 1993 to 2014 and 70 MICS from 2005 to 2014.
These surveys combined cover 94 countries. Annex 1 lists all the surveys included in this data set.
The recalculation of Z-scores was based on the WHO child growth standards and
prevalence estimates were generated following standard analysis as per available Stata macro
(http://www.who.int/childgrowth/software/en/). The recalculated Z-scores may generate slightly
different prevalence estimates from those published by DHS and MICS reports, mainly due to the
use of the WHO standard approach, which (i) uses all valid Z-scores for each child, and (ii) imputes
the missing day of birth as 15.
Each of our surveys is representative for the data collection areas, and most are nationally
representative for the country. Therefore, we use survey weights in our analysis to ensure that we
have a representative estimation for the country or the areas where data were collected.
4. Results
6
4.1 Comparison of Changes in Malnutrition Headcount and Malnutrition Gap
We applied the class of FGT measures to each of the malnutrition indicators and produced
results for all eight measures mentioned in section 1. Given the space limitation, we limit our
discussions on the results to the primary malnutrition indicator – stunting. For interested audiences,
the whole data set is available upon request.
One of the motivations to develop these new measures is to obtain insight that is not offered
by the headcount measures. For example, if the malnutrition gap of a country increases
significantly over time, but the malnutrition headcount does not, it would indicate that malnutrition
severity in a country is increasing over time, a fact that is not captured by the headcount measure.
This is why we examine whether the headcount measure and gap measure change in a similar
manner over time for each country.
To understand these changes, we measure the change in malnutrition headcount and the
change in malnutrition gap over each consecutive survey rounds for each country. We identify
whether the headcount measure and gap measure increase significantly, decrease significantly, or
face no significant change over time. If there is a significant change in one measure (headcount or
gap), but no significant change or a significant change in the opposite direction for the other
measure, then we categorize those two differing changes as “headcount and gap moving in
different directions.” In contrast, if both the headcount and gap remain statistically unchanged, or
increase or decrease statistically significantly, then we categorize them as “headcount and gap
moving in the same direction.”
Figure 1 presents four examples of countries where the headcount and gap are moving in the
same direction and four examples of countries where the headcount and gap are moving in different
directions. For the cases of the headcount and gap moving in the same direction, we can observe
7
that the headcount and gap track each other closely for all four countries: Colombia, Côte d’Ivoire,
Kazakhstan and Niger. In contrast, for countries where the headcount and gap are moving in
different directions, we can observe the opposite movements in each measure. For example, the
stunting headcount remained the same for Chad from 1996 to 2004 (44%). However, in that same
period, the stunting gap in Chad increased from 29% to 32%. Similarly for Mozambique, the
stunting headcount increased by 2 percentage points between 1997 and 2003 and decreased by 1
percentage point between 2008 and 2011. In contrast, the stunting gap moved in the opposite
direction: it decreased by 3 percentage points between 1997 and 2003 and increased by 2
percentage points between 2008 and 2011.
Figure 1a Examples of countries with gap and headcount moving in same direction
Colombia Cote d'Ivoire
20% 40%
35%
15%
30%
25%
10%
20%
5% 15%
10%
0% 1993 1998 2003 2008
1999 2001 2003 2005 2007 2009 2011
Headcount Gap
Headcount
8
Kazakhstan Niger
20% 55%
16%
45%
12%
35%
8%
25%
4% 1998 2000 2002 2004 2006 2008 2010 2012
1998 2002 2006 2010
Headcount Gap
Headcount
Figure 1b Examples of countries with gap and headcount moving in different directions
Chad Nigeria
42%
44%
40%
37%
36%
32%
32%
28%
27%
1996 2004
2003 2004 2005 2006 2007 2008
Headcount Headcount
9
Mozambique Madagascar
50%
45%
50%
40%
35%
40%
30%
25%
30%
20% 1997 2003
1996 2001 2006 2011 Headcount Gap
Headcount
Source: Authors’ calculations using DHS and MICS.
These results may imply the effects of different policy interventions. Overall, the fact that in
most cases the headcount and gap are moving in the same direction (and improving) is supportive
of the fact that the policies in place have been effective for all the children below the reference
line, not just those closest to it. For example, countries like Colombia that have lowered both the
stunting headcount and stunting gap may have targeted their interventions to all children and
brought them above, or closer to, the reference line (Figure 1a). Likewise, a country for which the
gap is improving but not the headcount may have targeted its interventions to the most
malnourished, and the effects of this targeting are not showing up yet on the headcount (Figure
1b). By contrast, if a country wants to prioritize its efforts and investments towards those children
who are the closest to the reference line, this would be consistent with an improving headcount
without necessarily seeing improvements in the gap measures (Figure 1b).
Figure 1 only provides a handful of examples. To have a comprehensive understanding of
the differences in trends in the headcount and gap measures across all developing countries, it is
10
important to understand the proportion of countries that have headcount and gap measures moving
in different directions. In Table 1, we present the results of our analysis where we summarize the
number and percentage of countries that had the headcount and gap either (i) moving in the same
direction, or (ii) moving in different directions.
Given there is a strong long-term decreasing trend in malnutrition across most countries, to
understand whether the gap measure is changing differently than headcount measures, it is
important to measure changes in malnutrition over relatively short periods of time. For a fair
comparison of changes in malnutrition over time across countries, we want to compare all
countries over the same time period. Therefore, we chose the following three overlapping time
ranges: years 1993 to 2005, 2000 to 2009, and 2005 to 2014. We chose overlapping time ranges
to ensure that we have sufficient number of countries with consecutive survey rounds in each of
the three time ranges; otherwise, we would be unable to measure changes over time for certain
periods. As repeat surveys occur within 5 years of a prior round for most countries, we have an
overlap of 5 years between the three time ranges to ensure that we cover the greatest number of
consecutive survey rounds.
As shown in Table 1, we find that between 1993 and 2005, 29 percent of cases represented
the stunting headcount and gap moving in different directions than each other. Similarly, for the
periods 2000-2009 and 2005-2014, we find that for 17 percent of cases, the stunting headcount
and gap are moving in different directions. We observe similar percentages, 21 percent, 17 percent
and 17 percent respectively, over the three periods for the underweight measures. In contrast, we
observe lower percentages, 6 percent, 11 percent, and 5 percent respectively, for the overweight
measures. However, overall, these results suggest that the stunting gap provides additional
11
important information on the severity of malnutrition that is not always represented by headcount
measures.
Table 1 Changes in headcount and gap for child stunting, underweight and overweight
Changes from 1993 to Changes from 2000- Changes from 2005 to
2005 2009 2014
Number Number Number
of % of of % of of % of
countries countries countries countries countries countries
Stunting
Headcount and gap
moving in same direction 24 71% 29 83% 49 83%
Headcount and gap
moving in different
direction 10 29% 6 17% 10 17%
Underweight
Headcount and gap
moving in same direction 27 79% 29 83% 49 83%
Headcount and gap
moving in different
direction 7 21% 6 17% 10 17%
Overweight
Headcount and gap
moving in same direction 32 91% 31 89% 56 95%
Headcount and gap
moving in different
direction 2 6% 4 11% 3 5%
Source: Authors’ calculations using DHS and MICS.
4.2: Comparison of Severity for Countries with Similar Headcounts
In addition to understanding changes in malnutrition over time, it would also be useful to
examine how the malnutrition gap and gap-squared vary for countries with similar headcount rates.
If the malnutrition gap and gap-squared measures are substantially different for countries that have
similar headcount rates, it would indicate that the gap measures are capturing information
regarding severity that is not captured in the headcount measures. This is why we compare the gap
and gap-squared measures for countries that have similar headcount rates, i.e. headcount rates
within one percentage point of another country.
12
Ideally, we would want to compare the malnutrition status across countries for each year.
However, as we do not have malnutrition data for enough countries for each year, we instead create
four time periods: 1993-2000, 2001-2005, 2006-2010, and 2011-2014. Both Figure 2 and Table 2
illustrate how diverse the stunting gaps can be for countries with similar stunting prevalence. In
Figure 2, for example, Pakistan, Timor-Leste and Burundi have a similar headcount ratio (57.7%,
56.4% and 57.4% respectively) in 2009-2010, but their gaps are various (45.2%, 37.6% and 32.5%
respectively).
In Table 2, we provide examples of countries whose stunting headcount is within 1
percentage point of another country, but their gap and gap-squared measures are statistically
significantly different from the other (tested using a t-test). Each group of countries is shaded or
unshaded for ease of visualization in the table. For example, the first two countries in the list are
Ethiopia and Nepal. Ethiopia has a stunting headcount rate of 57 percent and Nepal has 56 percent.
However, their stunting gap and stunting gap-squared are substantially different, which is
expressed in the t-test results in the last column, which examines whether the difference in the gap
between the countries is statistically significant. The stunting gap and gap-squared for Ethiopia are
both 38 percent, but for Nepal they are 31 and 28 percent, respectively. Similarly, the next set of
countries, India, Bangladesh, and Tanzania, have similar stunting headcount rates: 50, 50, and 49
percent, respectively. However, India’s gap and gap squared measures are substantially different
from the other two countries. India’s stunting gap rate is 33 percent compared to 28 percent for
Bangladesh and Tanzania. And the stunting gap squared in India is 35 percent, which is
substantially higher than 24 percent for Bangladesh and Tanzania. This shows that India has more
severe malnutrition even though a similar fraction of its population suffers from stunting as in
Bangladesh and Tanzania. The rest of Table 2 provides similar differences between the headcount
13
and gap measures, which suggests that the gap measures are capturing important differences in
malnutrition status that the headcount measure does not, which points to the importance of these
new measures.
Figure 2 Comparison of stunting head count ratios and stunting gaps
50%
Pakistan 2010
45%
40%
Timor‐Leste 2009
35%
Burundi 2010
30%
Gap (%)
25%
20%
15%
10%
5%
0%
0% 10% 20% 30% 40% 50% 60% 70%
Headcount (%)
1993‐2000 2001‐2005 2006‐2010 2011‐2014
Source: Authors’ calculations using DHS and MICS.
14
Table 2: List of countries/surveys with similar headcount rates but different gap measures
Stunting Stunting Stunting t-stat
Country Year Source gap squared for gap
headcount gap
Years: 1993-2000
Ethiopia 2000 DHS 57% 38% 38%
Nepal 1996 DHS 56% 31% 28% 7.8
India 1998 DHS 50% 33% 35%
Bangladesh 1999 DHS 50% 28% 24% 9.4
Tanzania 1996 DHS 49% 28% 24% 9.3
Nigeria 1999 DHS 47% 38% 43%
Rwanda 2000 DHS 48% 28% 25% 7.0
Tanzania 1999 DHS 48% 26% 21% 7.9
Niger 1998 DHS 46% 29% 29% 5.6
Uganda 2000 DHS 44% 24% 21%
Burkina Faso 1998 DHS 44% 28% 28% 5.2
Chad 1996 DHS 44% 29% 29% 6.3
Mozambique 1997 DHS 44% 30% 31% 6.5
Uzbekistan 1996 DHS 34% 23% 24%
Bolivia 1994 DHS 34% 18% 16% 3.5
Zimbabwe 1999 DHS 33% 17% 15% 4.0
Egypt, Arab Rep. 1995 DHS 33% 20% 20%
Bolivia 1998 DHS 33% 16% 13% 7.4
Kyrgyzstan 1997 DHS 32% 13% 10% 6.9
Years: 2001‐2005
Malawi 2004 DHS 51% 32% 30%
Bangladesh 2004 DHS 50% 26% 21% 8.1
Mozambique 2003 DHS 46% 27% 26%
Sierra Leone 2005 MICS 46% 31% 33% 5.0
Chad 2004 DHS 44% 32% 34%
Tanzania 2004 DHS 44% 22% 17% 12.8
Lesotho 2004 DHS 44% 24% 21% 7.0
Cambodia 2005 DHS 42% 21% 17%
Nigeria 2003 DHS 42% 28% 30% 8.6
Mali 2001 DHS 42% 27% 27% 8.4
Congo (Brazzaville) 2005 DHS 30% 18% 16%
Honduras 2005 DHS 30% 13% 9% 8.3
15
Stunting Stunting Stunting t-stat
Country Year Source gap squared for gap
headcount gap
Years: 2006‐2010
Pakistan (Balochistan) 2010 MICS 57% 45% 51%
Burundi 2010 DHS 57% 32% 28% 12.3
Timor Leste 2009 DHS 56% 38% 38% 8.0
Guinea‐Bissau 2006 MICS 46% 31% 32%
Malawi 2010 DHS 46% 25% 21% 6.8
Zambia 2007 DHS 45% 25% 22% 6.7
Congo, Democratic Republic 2007 DHS 44% 29% 31%
Rwanda 2010 DHS 44% 21% 17% 8.6
Bangladesh 2007 DHS 43% 20% 16%
Sierra Leone 2010 MICS 43% 28% 29% 11.7
Central African Republic 2006 MICS 43% 29% 30% 13.1
Tanzania 2010 DHS 42% 20% 16%
Benin 2006 DHS 42% 27% 29% 13.2
Congo, Democratic Republic 2010 MICS 43% 26% 24% 9.7
Nigeria 2007 MICS 41% 32% 37%
Tanzania 2010 DHS 42% 20% 16% 19.8
Central African Republic 2010 MICS 40% 21% 18% 18.4
Cambodia 2010 DHS 40% 18% 14% 15.3
Tanzania 2010 DHS 42% 20% 16% 19.8
Somalia 2006 MICS 41% 29% 31% 12.3
Cambodia 2010 DHS 40% 18% 14%
Chad 2010 MICS 39% 26% 27% 13.4
Côte D’Ivoire 2006 MICS 39% 23% 21% 7.3
Nigeria 2008 DHS 39% 27% 29% 15.3
Nigeria 2008 DHS 39% 27% 29%
Lesotho 2009 DHS 39% 19% 15% 10.6
Uganda 2006 DHS 38% 19% 16% 10.6
Sierra Leone 2008 DHS 35% 24% 26% 10.3
Kenya 2008 DHS 35% 18% 15% 7.3
Zimbabwe 2009 MICS 35% 15% 12% 10.3
Burkina Faso 2010 DHS 34% 18% 16% 6.9
Djibouti 2006 MICS 32% 25% 30%
Zimbabwe 2010 DHS 32% 14% 10% 9.1
South Sudan 2010 MICS 30% 20% 21%
Eswatini 2010 MICS 31% 13% 9% 9.9
Togo 2010 MICS 30% 12% 9% 11.3
São Tomé and Príncipe 2008 DHS 29% 15% 14%
Peru 2007 DHS 28% 10% 6% 5.0
Egypt, Arab Rep. 2008 DHS 28% 17% 16%
Peru 2007 DHS 28% 10% 6% 10.7
16
Stunting Stunting Stunting t-stat
Country Year Source gap squared for gap
headcount gap
Peru 2008 DHS 27% 10% 6%
Syrian Arab Republic 2006 MICS 27% 17% 18% 14.6
Years: 2011‐2013
Benin 2011 DHS 45% 37% 45%
Ethiopia 2011 DHS 44% 25% 23% 17.9
Lao PDR 2011 MICS 44% 23% 20% 21.6
Congo, Democratic Republic 2013 DHS 42% 26% 25%
Bangladesh 2011 DHS 41% 19% 16% 11.3
Source: Authors’ calculations using DHS and MICS.
4.3: Regional Analysis
Until now we have focused on headcount and gap measures only at the country level.
Extending this analysis to the regional level may provide further insight on malnutrition across the
world. Therefore, we examine the regional averages of malnutrition. As survey data are not
available every year for most countries, only a few countries in a particular region have a survey
in a given year, with some regions having no survey conducted in certain years.
There are two common practices for calculating regional averages in such cases: (1) modeling
methods and (2) aggregating over a range of years. An example of modeling methods closely
related to this study is the UNICEF-WHO-World Bank joint child malnutrition estimates (JME)
(UNICEF, WHO, and the World Bank 2018). The JME adopts linear mixed-effect models
allowing for random effects at the country level and for heterogeneous covariance structures. One
model is fitted for each region or country group for calculating its aggregated number. Such
modeling methods are beyond the scope of this study. For simplicity, we chose to calculate
regional averages for a range of years following the exercise in Nguyen and Wodon (2015). Thus,
we create regional averages for five-year periods: 1993-1997, 1998-2002, 2003-2007, and 2008-
2012. We use these five-year periods so that the middle years of these ranges,1995, 2000, 2005,
17
and 2010, coincide with the years in which under-five population data are compiled for each
country in the sample.
We create regional averages of stunting measures weighted by the under-five population of
each country of the middle of each reference period. Following the World Bank regional
classification, we divide the countries in our sample into six regions: East Asia and Pacific (EAP),
Europe and Central Asia (ECA), Latin America & Caribbean (LAC), Middle East & North Africa
(MNA), South Asia (SAS), and Sub-Saharan Africa (SSA). These regional averages are presented
in Figure 3 using two graphs that show (i) headcount rates for the different regions, and (ii) gap
rates for the regions.
Figure 3: Regional trends of stunting headcount and stunting gap
Stunting headcount
70%
60%
50%
40%
30%
20%
10%
0%
1995 2000 2005 2010
East Asia and Pacific Europe and Central Asia
Latin America & Carribean Middle East and North Africa
South Asia Sub‐Saharan Africa
18
Stunting gap
40%
35%
30%
25%
20%
15%
10%
5%
0%
1995 2000 2005 2010
East Asia and Pacific Europe and Central Asia
Latin America & Carribean Middle East and North Africa
South Asia Sub‐Saharan Africa
Source: Authors’ calculations using DHS and MICS. Results need to be interpreted with caution
as the population coverage for some regions and years are below 50%.
A comparison of malnutrition across regions suggests that the stunting gap is telling a slightly
different story from the stunting headcount for some regions. While the trends of the headcount
and gap are the same for each particular region, the ranks of some regions differ depending on the
type of measurement being used, i.e. headcount or gap rates. For example, according to the
headcount measures in 2005 and 2010, EAP has a greater level of malnutrition than MNA. In
contrast, according to the gap measures for the same period, MNA has a greater level of
malnutrition than EAP.
Similarly, according to the headcount measures, MNA and LAC have similar headcount rates
in 2005, about 24% each, which is significantly higher than that of ECA (17%). However, in terms
of the gap measure, MNA has a significantly higher gap rate (13%) than LAC (10%), and LAC is
actually closer to ECA (8%). Similarly, for 2010, the headcount measure suggests a sizeable
difference between LAC (19%) and ECA (15%). However, the gap measures suggest that both are
19
around 7 percent. This shows the importance of the gap measure, providing us further insight in
addition to the headcount measures.
4.4 Income-group Analysis
Next we conduct our analysis by different income groups as defined by the World Bank: low
income, lower-middle income, and upper-middle income. The results are presented in Figure 4.
Similar to our regional analysis, we find that the trend of the stunting gap can reveal a different
story than the trend of the stunting headcount. Specifically, the stunting headcount of lower-
middle-income countries as a whole was slightly lower than that of low-income countries until
sometime between 2000 and 2005. However, the trend of the stunting gap of lower-middle-income
countries during this period of time was higher than that of low-income countries. In the reference
year of 2000, the difference amounted to 2 percentage points.
Figure 4: Trends of stunting headcount and gap by income groups
Stunting headcount
60%
50%
40%
30%
20%
10%
0%
1995 2000 2005 2010
Low income Lower middle income Upper middle income
20
Stunting gap
35%
30%
25%
20%
15%
10%
5%
0%
1995 2000 2005 2010
Low income Lower middle income Upper middle income
Source: Authors’ calculations using DHS and MICS. Results need to be interpreted with caution
as the population coverage for some regions and years are below 50%.
4.5 Population Coverage
Next, we examine the population coverage in our analysis, i.e. the percentage of population in
low- and middle-income countries that we cover through the nationally representative surveys in
our analysis in each of the time ranges. We use data from WHO on the number of children below
the age of 5 for all low- and middle-income countries in five-year intervals: 1995, 2000, 2005, and
2010. Thus, for each of survey year for a particular country, we use the closest population data
available from WHO. Hence, we use population data from: 1995 for surveys conducted in years
1993-1997; 2000 for surveys in years 1998-2002; 2005 for surveys in years 2003-2007; and 2010
for surveys in years 2008-2012.
Table 3: Percentage of low and middle income country
population covered in the malnutrition aggregations
Years Percentage of population
1993-1997 18%
21
1998-2002 47%
2003-2007 57%
2008-2012 38%
Table 3 presents the population coverage for each five-year time period. We find that for the
initial surveys from 1993 to 1997, the population coverage of low- and middle-income countries
in our analysis was 18%. However, we see population coverage increase over the years to 47%,
57%, and 38% for the periods 1998-2002, 2003-2007, and 2008-2012, respectively. This shows
that population coverage improved, likely because the number of surveys across countries
increased over the years. While we may not have sufficient data for precise aggregation at this
point, as more surveys are conducted in the future, we will have greater population coverage and
greater precision in future analysis.
Table 4: Percentage of regional population covered in the malnutrition aggregations
Region: 1995 2000 2005 2010
East Asia and Pacific 0% 1% 5% 7%
Europe and Central Asia 40% 31% 57% 16%
Latin America & Caribbean 54% 22% 19% 20%
MENA 26% 26% 57% 43%
South Asia 12% 85% 85% 26%
Africa 32% 65% 86% 86%
Similarly, in Table 4 we present the population coverage for the regional aggregations in
Figure 3. As we can see, while South Asia and Africa are well-represented in several of the time-
windows, the coverage for the other regions are generally well below 50%. This demonstrates that
the regional coverage estimates need to be interpreted with caution. However, it is important to
note that the purpose of this exercise is not to create regional aggregates with sufficient coverage.
It is instead to show how these new indicators and new estimates can be used for analysis.
22
5. Conclusions
This paper develops a new method of measuring malnutrition across the world. The current
key measures of malnutrition, such as stunting and wasting, are based on headcount measures, i.e.
the proportion of children who are suffering from malnutrition. However, a potential drawback of
these headcount measures is that they do not inform us about the depth and severity of malnutrition.
It is possible that a country with a low headcount rate for a particular malnutrition measure also
has a high severity of malnutrition compared to countries with a similar headcount rate, and vice
versa. Therefore, it is important to develop a measure of the severity of malnutrition.
To develop a measure of the severity of malnutrition, this study adopts a particular technique
used in the development literature, specifically the Foster, Greer and Thorbecke (1984) class of
poverty indicators, in the context of child malnutrition. Employing this new technique, we develop
eight new measures of malnutrition in this study: (i) stunting gap, (ii) stunting gap squared, (iii)
wasting gap, (iv) wasting gap squared, (v) overweight gap, (vi) overweight gap squared, (vii)
underweight gap, and (viii) underweight gap squared. We employ over 20 years of malnutrition
data from 95 developing countries to calculate these measures of severity.
Due to space limitations, this paper presents the results on stunting only, although all results
have been calculated. It is of our interest to explore all our results in our future studies to
understand if the additional information provided by the gap and gap squared measures is more
useful for one malnutrition indicator than for another.
The malnutrition gap as a new measure enables us to monitor the development progress of
those furthest away from the reference line, serving the principle of SDGs. Employing the new
measures, we are also able to identify countries that have low levels of headcount for a malnutrition
23
measure, but have comparatively high severity of malnutrition according to the gap measures, and
vice versa. This allows us to identify numerous cases where headcount measures may be providing
a misleading description of a certain country’s malnutrition status. Additionally, through regional
and income-group analysis, we identify differences in the headcount and gap measurements. This
study is extremely important from a policy perspective because comparing countries with similar
headcount measures could hide important differences in the depth of malnutrition as reflected by
differences in the malnutrition gap.
24
References
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25
Annex 1
Surveys with anthropometric measurements for children under 5 years of age
COUNTRY SURVEYS
Afghanistan MICS 2010
Albania MICS 2005 / DHS 2008
Armenia DHS 2000 / DHS 2005 / DHS 2010
Azerbaijan DHS 2006
Bangladesh DHS 1996 / DHS 1999 / DHS 2004 / DHS
2007 / DHS 2011
Barbados MICS 2012
Belarus MICS 2005
Belize MICS 2006 / MICS 2011
Benin DHS 1996 / DHS 2001 / DHS 2006 / DHS
2011
Bhutan MICS 2010
Bolivia DHS 1994 / DHS 1998 / DHS 2003 / DHS
2008
Bosnia and Herzegovina MICS 2006 / MICS 2011
Brazil DHS 1996
Burkina Faso DHS 1998 / DHS 2003 / MICS 2006 / DHS
2010
Burundi DHS 2010
Central African Republic DHS 1994 / MICS 2006 / MICS 2010
Cambodia DHS 2000 / DHS 2005 / DHS 2010
Cameroon DHS 1998 / DHS 2004 / MICS 2006 / DHS
2011
Chad DHS 1996 / DHS 2004 / MICS 2010
Colombia DHS 1995 / DHS 2000 / DHS 2005 / DHS
2010
Comoros DHS 1996 / DHS 2012
Congo Brazzaville DHS 2005 / DHS 2011
Congo Democratic Republic DHS 2007 / MICS 2010 / DHS 2013
Côte d’Ivoire DHS 1994 / DHS 1998 / MICS 2006 / DHS
2011
Djibouti MICS 2006
Dominican Republic DHS 1996 / DHS 2002 / DHS 2007 / DHS
2013
Egypt, Arab Rep. DHS 1995 / DHS 2000 / DHS 2005 / DHS
2008
Ethiopia DHS 2000 / DHS 2005 / DHS 2011
Gabon DHS 2000 / DHS 2012
Gambia, The MICS 2005 / DHS 2013
Georgia MICS 2005
26
Ghana DHS 1993 / DHS 1998 / DHS 2003 / MICS
2006 / DHS 2008 / MICS 2011
COUNTRY SURVEYS
Guatemala DHS 1995 / DHS 1998
Guinea DHS 1999 / DHS 2005 / DHS 2012
Guinea Bissau MICS 2006
Guyana MICS 2006 / DHS 2009
Haiti DHS 1994 / DHS 2000 / DHS 2005 / DHS
2012
Honduras DHS 2005 / DHS 2011
India DHS 1998 / DHS 2005
Iraq MICS 2006 / MICS 2011
Jordan DHS 1997 / DHS 2002 / DHS 2007 / DHS
2012
Kazakhstan DHS 1995 / DHS 1999 / MICS 2006 /
MICS 2010
Kenya DHS 1993 / DHS 1998 / DHS 2003 / DHS
2008
Kyrgyzstan DHS 1997 / MICS 2005 / DHS 2012
Lao PDR MICS 2006 / MICS 2011
Lesotho DHS 2004 / DHS 2009
Liberia DHS 2007 / DHS 2013
Macedonia MICS 2005 / MICS 2011
Madagascar DHS 1997 / DHS 2003
Malawi DHS 2000 / DHS 2004 / MICS 2006 / DHS
2010
Maldives DHS 2009
Mali DHS 1995 / DHS 2001 / DHS 2006 / DHS
2012
Mauritania MICS 2007 / MICS 2011
Moldova DHS 2005 / MICS 2012
Mongolia MICS 2005 / MICS 2010
Montenegro MICS 2005 / MICS 2013
Morocco DHS 2003
Mozambique DHS 1997 / DHS 2003 / MICS 2008 / DHS
2011
Namibia DHS 2000 / DHS 2006 / DHS 2013
Nepal DHS 1996 / DHS 2001 / DHS 2006 / DHS
2011
Nicaragua DHS 1997 / DHS 2001
Niger DHS 1998 / DHS 2006 / DHS 2012
Nigeria DHS 1999 / DHS 2003 / MICS 2007 / DHS
2008 / MICS 2011 / DHS 2013
Pakistan DHS 2012
Pakistan (Baluchistan) MICS 2010*
27
Pakistan (Punjab) MICS 2011*
Palestinians in Lebanon MICS 2011*
COUNTRY SURVEYS
Peru DHS 1996 / DHS 2000 / DHS 2005 / DHS
2007 / DHS 2008 / DHS 2009 /
DHS 2010 / DHS 2011 / DHS 2012
Rwanda DHS 2000 / DHS 2005 / DHS 2010
São Tomé and Príncipe DHS 2008
Senegal DHS 2005 / DHS 2010 / DHS 2012 / DHS
2014
Serbia MICS 2005 / MICS 2010 / MICS 2014
Sierra Leone MICS 2005 / DHS 2008 / MICS 2010 /
DHS 2013
Somalia MICS 2006
St. Lucia MICS 2012
West Bank and Gaza MICS 2010
Sudan (North) MICS 2010
Sudan (South) MICS 2010
Suriname MICS 2006 / MICS 2010
Eswatini DHS 2006 / MICS 2010
Syrian Arab Republic MICS 2006
Tajikistan MICS 2005 / DHS 2012
Tanzania DHS 1996 / DHS 1999 / DHS 2004 / DHS
2010
Thailand MICS 2005
Timor-Leste DHS 2009
Togo DHS 1998 / MICS 2006 / MICS 2010 /
DHS 2013
Tunisia MICS 2011
Turkey DHS 1993 / DHS 1998 / DHS 2003
Uganda DHS 1995 / DHS 2000 / DHS 2006 / DHS
2011
Uzbekistan DHS 1996 / MICS 2006
Vanuatu MICS 2007
Vietnam MICS 2010
Zambia DHS 1996 / DHS 2001 / DHS 2007
Zimbabwe DHS 1994 / DHS 1999 / DHS 2005 / MICS
2009 / DHS 2010 / MICS 2014
*Subnational sample
28