Conflict Impact on Health and Nutrition Status:
A Case Study on Infants’ Anthropometric Outcomes from
the Second Congo War
SZE YEUNG LAU
Department of Economics, University of Oxford
Abstract
This paper uses three household surveys to analyse the children’s health condition with
regard to the Second Congo War. A difference-in-difference strategy is applied to exploit the
variation of the children’s health across the provinces and age. The results present that
there is no significant difference between the children from provinces with conflicts and
without conflict from 2001 to 2010. In the 2007 effect, the effect unexpectedly turns out to
be positive, which implies children exposed to the violence experienced better health
treatment. Besides, there is no change for the treatment of breastfeeding, diarrhoea and
malaria from 2001. The assistance from the international community and local government
may partially explain these phenomena.
Key words: conflict, health, nutrition, infant, children, DRC
Acknowledgements:
I am grateful to Royal Holloway, University of London, not only for enriching my profession in economic
science but also for waving beautiful memories throughout my study here. I am especially in gratitude to
Professor Michael Spagat, who has guided me into the world of economics with countless help and support. I
appreciate all the friends I met in Royal Holloway, who have brought everlasting happiness into my life.
Introduction
During the past three decades, almost every Sub-Sahara country has experienced political
conflict or civil crisis. Since the collapse of Zaire state in 1997, the new-born country,
Democratic Republic of Congo (DRC), has been involved into the continuous civil conflicts.
The country has witnessed two severe civil wars, First Congo War and Second Congo War.
The Second Congo War especially triggered a devastative wave of violence and killings
throughout the country, which resulted in detrimental access to the country’s health services
and utility supply (IRC 2007). The variation in the intensity of violence across the provinces
and the difference in ages of children exposed to the conflict provide a quasi-natural
experiment to examine a possible causality between the health outcome and the war events,
where the DRC civil wars can be treated as an exogenous shock to the household decision
and the child individual characteristic. Based on this logic, a difference-in-difference strategy
can be applied to analyse a causal relationship between conflicts and health outcomes.
Improving health condition of women and children in developing world is a global target,
which has been specifically defined as eight Millennium Development Goals since 2000
United Nations Summit. A large body of papers has focused on the children’s nutritional
status and health conditions to evaluate the warfare impact of the warfare on the whole
population health and on the subsequent economic development. For instance, civil conflict
has been widely studied to analyse the influence on economic growth (Koubi 2002),
population health (Bundervoet et al 2008), childhood schooling (Verwimp and Bavel 2013)
and household welfare (Verwimp and Burderveot 2008).
Besides, health is positively correlated with human capital accumulation, Thomas and Strauss
(2007) depict the health condition has a positive effect on human capital accumulation,
improved productivities and population living standard. Additionally, there are a number of
papers focusing on the warfare and economic conditions, especially Collier and Hoeffler
(1998) explained the economic causes of civil war and Alan Krueger (2007) use economic
roots to interpret the terrorism. However, most of the researchers make use of the macro level
explanations for the conflict. With more local household surveys conducted, nowadays an
increasing number of researchers are centering on the micro-level to explore the household
behaviours from the impact of the war. The very recent working paper from Justino, Bruck
and Verwimp (2013) introduces the in-depth research programme MICROCON, which aims
to advance knowledge in the field of conflict analysis through the construction of an
innovative micro level research institute. By applying the individual micro-level data in the
DRC, this paper also targets to provide a dynamic variation of children’s health condition
with respect to different conflict levels and economic situations.
This paper may contribute to the sectors between health and economic development. In the
literature it is generally accepted that the health condition has substantial influence on the
economic development and well-being of the population. Murdoch and Sandler (2002) use a
neoclassical growth model to empirically test the impact of civil war on steady-state income
per capita at home and neighboring countries. Verwimp and Bundervoet (2008) suggest that
temporarily famine-induced migration and illness decrease growth while good harvests, more
split-offs and higher initial levels of education increase it.
The nutritional status of children is an indicator of the overall health. Children will most
likely reach their potential physical growth only when they have access to adequate food and
they are looked after without repeated illnesses. As one of the Millennium Development
Goals towards reducing extreme poverty and hunger, United Nations has declared to reduce
by half the proportion of people suffering from hunger before 2015. The level of malnutrition
has remained concerns in the DRC for decades, 24 percent of children under five years old is
under-weight (MICS 2011). This paper attempts to quantitatively evaluate DRC’s overall
health status and propose corresponding policy to improve the living condition of the DRC.
The remainder of the article is organised as follows. Section II presents the key literatures
with main findings and conclusions. Section III states a brief review of the DRC history.
Section IV describes the data sources. Section V highlights the empirical strategies and
econometric frameworks. Section VI discusses the main results as well as the robustness
check. The final section draws the conclusions.
Literature Review
The leading literatures are divided into two major sectors. Part 1 presents the connections
between exogenous shocks and health outcomes, especially the empirical study with regard to
child health condition. Part 2 states the significance of the early life health status, and
describes the bridge between economic performance and health condition.
Part 1
A considerable amount of paper has used exogenous shocks to analyse the variation of the
household planning and behaviour. These shocks include not only natural disasters like
earthquake, droughts, fires, rainfall, floods, but man-made catastrophes like warfare, diseases,
malnutrition and famines. These natural or quasi-natural events provide opportunities to
study the causalities between exogenous shocks and health outcomes. Almond et al. (2009)
use Chernobyl fallout as the natural experiment to induce the variation in cognitive ability in
Sweden. They capture that the students born in the regions with high fallout perform worse in
second school, especially in mathematics. Damages are accentuated within families and
among children born to parents with low educations. Besides, researchers have used
anthropometric outcomes to evaluate a causal effect of conflict on health. The leading
literature is from Guerrero-Serdan (2009), which uses household surveys from 2003 Iraq War
to analyse a causal relationship between Iraq War and nutritional outcome of children.
Guerrero-Serdan uses three separate datasets to constitute a panel to discover the
anthropometric difference among the children from various ages and places; the results are
robust from several specifications. Estimates indicate that the children growing up from high
level of conflict district are 0.8 cm shorter than the children from the low level areas, besides,
she also gives empirical evidence that the malnutrition in the early childhood has negative
impact on later education, productivity and labour outcomes.
Some other papers have used micro-level analysis among African countries to study the war
effect on household decision and behaviour. Bundervoet et al. (2008) study the effect of the
civil war in Burundi on the children’s health effect. The strategy also exploits the exogenous
variation in the war’s timing across provinces and the exposure of children’s birth cohorts to
the fighting. They obtain robust results that children exposed in the conflict provinces have
0.5 standard deviations lower height-for-age z-scores those non-exposed children. It is
concluded that the poor health status of Burundian children could lead to negative welfare
effects in the long run. Similarly, Akresh et al. (2011) also use the variations across ethnicity
and cohort during Nigerian civil war (1967 to 1970) to identify the long run effects on human
health capital. As a result, the children and adolescents of all ages exposed to the war have
shown decreased adult stature, and the effects are significant among the adolescents’ cohorts.
Furthermore, they also find a reduced life expectancy and lower income for the exposed
cohort.
Furthermore, Alderman et al. (2006) identify civil war and drought in Zimbabwe to analyse
the differences in pre-school nutritional status across siblings. They apply a maternal fixed
effects-instrumental variable estimator with a long term panel dataset to show that the
children experienced the disasters are 3.4 cm shorter and 0.85 years less schooling. A better
preschooler with a higher height-for-age score are associated with more number of grades of
schooling completed. Parlow (2012) uses the experience of the Kashmir insurgency as the
exogenous shock. Parlow use three individual datasets from the National Family Health
Survey of India to analyse the effects of the Kashmir insurgency on children’s height with a
DID regression. Conclusions are drawn that children who were small at birth and children
with anemic mothers are shorter for their ages. He further adds that children who are more
affected by the insurgency are 0.9 to 1.4 standard deviations shorter compared with children
less affected by the insurgency.
Part 2
Bloom et al. (2001) has given theory and evidence of the causality between health and
economic growth. They firstly build up microeconomic theory to extend the production
functions, and then add work experience and health as the fundamental variables. The result
shows that the relationship between good health and aggregated output are robust, positive
and sizable. They also acquire that the average schooling and national output are consistent,
which means schooling has no discernible externalities. Pritchett and Summers (1996)
investigat the link between economic growth and health, as indicated by infant mortality.
Ideas are expressed in the title of the paper “Wealthier is Healthier’. That is when country
gets richer, people will have more easy access to sanitation, housing, food quality and
medical care with regard to better health. Another finding is that there is diminishing
marginal return of health from the economic growth. However, in Bhalotra’s paper (2008), he
concludes that while growth does have a significant impact on mortality risk, growth along
cannot be relied upon to achieve the goal. Conditioning on economic and demographic
variables in India economy, he interacted GDP with state dummies to obtain state-specific
growth elasticity from a panel data model.
In additional to child mortality, the nutritional intake in early life is of great significance for
the later life growth. In particular, the nutrition in the first 24 months has significant impact
on the rest of life. If the growth is deterred or slow, it is not possible to catch up in the later
life and could affect the cognitive ability from Martorell and Habicht (1986). Stein et al.
(2004) study the Dutch famine during World War II to evaluation the effect of prenatal
exposure to the famine on body proportions at birth and find that the exposure during late
pregnancy is negatively associated with child’s birth weight and body proportion.
Furthermore, Metcoff (1986) proves the health condition of the mother during the pregnancy
is also an important factor to affect the children’s later growth. Ziegler (2006) provides
evidence that infants and young children need to intake substantially more protein than the
older children. Meng and Qian (2006) find that, during the China’s Great Famine, the
children exposed on the food shortage lead to worse adult health and socioeconomic
conditions.
These above literatures have expressed a central idea that economic development and earlier
childhood health condition are closely correlated. Therefore in this paper, I am going to
analyse the children’s health outcomes during the conflicts to reflect a picture for the whole
population in the DRC.
Background: The Democratic Republic of Congo
Since 1965, the infamous dictator Mobutu Sese Seko had taken the independent Congo and
renamed it Zaire under one-party government. During the Cold War era, Zaire obtained the
support from the United States and the western world, because of Mobutu’s opposition to the
Communism. Therefore, Zaire obtained relative peace and stability. However, the state was
running under severe corruption and inefficiency. With the dissolving of USSR in the earlier
1990s, western world no longer deemed Mobutu as a necessary ally. Opponents within Zaire
demanded reforms for democracy. Soon, the new crises erupted all over the country and
Mobutu left country.
The new president Laurent-Desire Kabila renamed the country the Democratic Republic of
Congo but brought little new change. However, both Rwanda and Uganda intended to
remove Kabila as he alienated both countries. First Congo War commenced in 1996 and
finished in 1997. Only one year after, involving the similar issues, Second Congo War
started, 9 African countries and 20 armed groups participated in this so-called “African
World War”. The Second Congo War triggered a devastative wave of violence and killings
throughout the country it has been estimated that over 5.4 million people have been killed
(Coghlan et al. 2013; Robinson 2010; Bavier 2010). In 2001 Kabila was assassinated by his
body guard, his son Joseph Kabila succeeded the president position. In 2003, Joseph Kabila
called for multilateral peace talks to cease the war, Second Congo War officially ended.
However, the conflict still continues in the DRC, especially in the east regions.
The violence destroys the people’s residence, the health condition of the Congolese people
has massively deteriorated. According to the report from International Rescue Committee
(2007), more than 90 per cent people were not directly killed by the combat, but died from
the diseases like malaria, diarrhea, pneumonia and malnutrition, which were preventable and
treatable. Further, forty five percentage of the killed were children under five years old. The
overcrowded and unsanitary living condition is the main factor of the widespread of the
diseases because of the detrimental access of water, food, medicine and health services. In
2004 among all the killed, 47 per cent were children under seven, 1000 people died daily
from malnutrition and preventable diseases. (IRC 2007) However, the level of conflict has
diversified across provinces and districts in DRC, the east area being the most effected
resulting from its rich mineral resource.
This paper attempts to evaluate the nutritional differences between the children who exposed
to the Second Congo war and the children who born and grew up after the war. Based on this
logic, I expect a causality of children’s health condition within the time lines.
Data
Four independent datasets are applied in this paper.
The first dataset is from ACLED, Armed Conflict Location & Event Dataset, which is
designed for disaggregated conflict analysis and crisis mapping. ACLED datasets records
political and civil conflicts over 50 developing countries. Additionally, it covers all the
African countries from January 1997 to December 2012. The datasets contain detailed
information on the date and location of the conflict, types of events including battles, civilian
kills and protests, change of territories control and fatalities. ACLED datasets information are
obtained from diverse sources from official developing states, local media like BBC and
CNN, humanitarian agencies and research publications. The goal of ACLED is to present a
dynamic picture and comprehensive assessment of political conflicts in developing states.
For ACLED DRC datasets, it records specific event information and casualties’ number since
1997. These fatalities and conflict events information provides me a research opportunity to
validly differentiate the provinces of the DRC with different conflict level. The sorting
methodologies are mentioned in next section.
The second and the third datasets are both Multiple Indicator Cluster Surveys (MICS)
conducted by COSIT and the Ministry of Health together with Unicef. MICS has been
technically used by Unicef to monitor the situation of children and women indeveloping
countries in order to achieve the child-related global Millennium Development Goals
(MDGs). MICS investigate women between 14-44 years old and children under 5 years old,
covering indicators including nutrition, sanitation, education, child health, immunisation,
anthropometry and child labouring. Unicef help developing countries to collect and
disseminate the datasets, develop the methodologies and indicators to assess the development
in general and the situation of children and women in particular.
MICS2 and MICS4 of the DRC surveys are used in this paper. Both surveys are nationally
representatives and conducted from February to August 2001 and from February to April
2010 respectively. MICS2 covers a sample of 8704 households, of which 8622 households
are actually interviewed. MICS4 select 11490 household samples, among 11393 households
interviews were successfully held. Nutritional statuses of children are the interests of
outcomes in this paper. The interview team measured children’s weight and height according
to strict Unicef international guideline (Unicef, 2006). For children’s age, the interviewers
ask the day, month and year of birth. Besides, based on the WHO references, MICS display
direct anthropometric indicators including the calculated Z-Scores (HAZ, WAZ, WHZ).
The fourth datases is from DHS, Demographic and Health Survey. DHS is also a household
survey, which has provided over 290 surveys to advance global understanding of health and
population in developing countries and to improve health and nutrition programme. The
Measure DHS projects are funded by USAID aiming in using policy design and programme
planning and monitoring. Similar to MICS, DHS are also nationally representative, collecting
and disseminating data information on fertility, nutrition, maternal and child health, family
planning and illness. For anthropometric indicator, the interviewers measure children’s
weight and height with the same international standard as well as presenting the calculated ZScores. The DRC DHS was conducted in two phases: from January to March 2007 in capital
Kinshasa and from May to August 2007 in the other provinces. Specifically, 9995 women
and 4757 men aged 15 to 59 were interviewed.
Three household surveys, MICS2 (2001), DHS (2007) and MICS4 (2010) are used for
analysis of the children nutritional variation. Besides, these three surveys also give
information on specific family behaviour like breastfeeding, wealth index and are all
representative for urban and rural residence at provincial level. I use these variables to
identify the mechanism of the health effects from the war. Before the analysis, ACLED is
used for classifying the conflict level of the provinces.
Methodology and Identification Strategy
Theoretically, in order to acquire a precise estimation of the effect of the war on the child’s
health status, I need to observe one child exposed in two different periods, before and after
the war, and meanwhile I need also observe the same child who does not experience the war
during his grow-up. Apparently this is not feasible in reality. In addition, there is no datasets
for recording the same child in different period for the DRC case. But the household surveys
contain the information on three different timing and the different geographical intensity of
the conflict, which together constitute an identification strategy to compare the health
indicators among the children born before and after the war in provinces influenced by
different intensity of the warfare. According to the conflict fatality level I further classify the
DRC provinces into high level group and low level group. Therefore, according the cohort’s
date of birth and the place of residence, one can identify the level of conflict exposed to the
war.
i.
Nutritional Indicator (Outcome of Interest)
There are a number of anthropometric indicators to measure human nutritional outcomes. In
this paper, I use Z-Scores, which are defined by the World Health Organisation (WHO) to
measure children’s growth and malnutrition. In order to transform the cycle of poverty,
malnutrition and diseases into wealth, growth and health, in 1995 the World Health
Organisation proposed three common indicators to measure the children’s health and
indirectly assess the quality of life. The three indicators are weight-for-height, height-for-age
and weight-for-age indicators.
Weight-for-height, or WHZ, is a description of acute malnutrition, which is a direct indicator
of wasting caused by recent and rapid deduction in food supply and is also associated with
severe starvation or disease. Hence, WHZ presents the current health condition with respect
to height.
Height-for-age, or HAZ, reflects chronic malnutrition, which measures stunting due to long
term malnutrition. Stunting is a failure to reach linear growth potential as a consequence of
poor health condition or malnutrition. For instance, low food availability in long term, protein
deficiency or the suboptimal health condition of the pregnant mother can lead to chronic
malnutrition. In addition, HAZ represent the long term accumulated health condition,
therefore it expresses the overall socioeconomic condition of the country.
Weight-for-age, or WAZ, is an indicator for general malnutrition, which reflects the body
mass relative to chronological age. WAZ is influenced by both height-for-age and weight-forage. For example, underweight, or low WAZ score, can be attributed as the short term weight
reduction, low WAZ score, or long term a tall but thin child.
Generally, chronic malnutrition is significant because the earlier life stunting can remain
during the subsequent life stage, and this process is not likely for recovery. Acute
malnutrition can be cured with adequate nutritional intake as it is a short run effect. Besides
underweight (WAZ) is a reflection of both the acute malnutrition and chronic malnutrition.
According to WHO Global Database regulation, ‘the Z-score system expresses the
anthropometric value as a number of standard deviations or Z-scores below or above the
reference mean value’. Efficiently, the three household surveys used in this paper directly
provide me with the completed Z-Score values with the bar of the WHO standard. Therefore
I uses HAZ, WAZ and WHZ as the outcomes of interest in the empirical models.
ii.
Conflict Level Classification
There are eleven provinces in the Democratic Republic of Congo. In order to label a child
born in the treated place or not treated, I need to classify the DRC’s provinces into two
groups, one group with high level of conflict, and another group with low level of conflict.
As mentioned in the data section, the reports from ACLED provide me detailed information
about the conflict since 1997 for the DRC. These reports give the exact number of casualty of
the event, information source and concise descriptions of the fighting. I count the dead of the
event and the total number of the events during the year to differentiate the conflict level of
every province.
Table1: Fatalities in 2001, 2007 and 2010 in the DRC, Data Source: ACLED.
Province
Bandundu
Bas -Congo
Equateur
Kasai-Occidental
Kasai- Oriental
Katanga
Kinshasa
Maniema
North-Kivu
Orientale
South-Kivu
Sum
2001 Fatalities
1
1
129
4
28
39
26
70
280
1235
597
2410
2007 Fatalities
0
134
0
0
40
12
0
7
135
29
22
379
2010 Fatalities
0
0
250
2
18
8
2
0
390
603
372
1645
Sum
1
135
379
6
86
59
28
77
805
1867
991
4434
In order to obtain a robust result, I applied three different methods to evaluate the casualty
degree among the eleven provinces. The three household surveys were conducted in 2001,
2007 and 2009 respectively, so firstly I will list all the fatalities and the number of the
conflicts during each of the three years. The table below presents a clear picture that three
east provinces Orientale, Nord-Kivu and Sud-Kivu have a tremendous domination position,
for both the killed and the events. However, in 2010 province Equateur and in 2007 province
Bas-Congo also display high fatalities. For the investigation of these two spikes, I look into a
long run conflict result of the DRC from the data beginning 1997 to 2010. Again, the east
three provinces show large fatalities number and more fighting event. But province Maniema
give a huge death number, which is suspected. By reading the information from ACLED, it
records from British Broadcasting Company (BBC), that ‘Kabila's rebel forces are purported
to have killed (mass grave found) 25,000 Hutu refugees at the refugee camp of Tingi Tingi in
the east of Zaïre’. Therefore, for the data analysis, I can regard this event as an outlier. The
three provinces are still faced more violence. Finally I make a slightly shorter but dynamic
analysis about the conflict level analysis from the first household survey 2001 to the recent
one 2010. Now the table delivers straightforward information that the three provinces
Orientale, Nord-Kivu and Sud-Kivu belong to the high level conflict area, the rest eight
provinces belong to the low level. To a large extent, these three analysis standard give a
consistent fatalities and conflict event distributions, which add the robustness for the
classification of the conflict level.
Table 2: Events and Fatalities, 1997 to 2010, 2000 to 2010, the DRC. Data Source: ACLED
Province
Bandundu
Bas -Congo
Equateur
Kasai-Occidental
Kasai- Oriental
Katanga
Kinshasa
Maniema
North-Kivu
Orientale
South-Kivu
Sum
iii.
97-10 Events
49
80
293
35
142
351
193
170
1080
1583
992
4968
97-10 Fatalities
687
930
3203
167
1333
2025
902
25782
4822
16623
8662
65136
2000-10 Events
24
22
109
15
64
218
86
113
995
1391
775
3812
2000-10 Fatalities
65
189
1138
67
399
323
136
541
2959
13751
6341
25909
Identification Strategy
Three empirical strategies are applied for this paper. First of all, three household surveys
provide three time point across the conflicts of the DRC. When Joseph Kabila signed the
peace agreement with the multinational leaders in 2003, the Second Congo War can be
officially considered as ended. The first household survey MICS2 was conducted in 2001, so
the cohorts on this datasets were all exposed to the War. Then the cohorts in DHS (2007) and
MICS4 (2010) are all post-treated by the warfare. Based on this logic, I use this three
independent and random sample survey to constitute a sort of pseudo-panel. But the damage
of the war to the children is not via the combat but through the health condition and food
supply. Therefore I expect the child in 2007 cohort have more deteriorated nutritional status
than other groups. Besides, all of the three household surveys contain the information for the
children between 0 to 60 months old. I choose to analysis the infants’ health condition, which
are defined as 0 to 12 years old according to the medical dictionary explanation. Infant
exposed to the environment at most one year, this dramatically reduced any other unexpected
factors to influence the result. For instance, a child who is five years old in 2007 survey has
experienced the last year Congo War and growth up in both war and peace time, I suspect
this kind of case will reduce the explanatory power of the analysis. Hence, in the pseudopanel, I have 2001 as the baseline year, which represents the treatment year, and 2007 and
2010 as the post-treatment years. The control group and treatment group are divided by the
level of conflict. Therefore, any child from this panel can be easily identified as treated or not
via the birth date and the place of residence and be directly observed his/her behaviour and
response.
In terms of the second strategy, I use only the DHS 2007 household survey to compare the
young cohort 0 to 12 months old with the old cohort 36 to 48 months old between the low
and high level of conflict provinces. Both of the cohorts born belong to the post-treated
period, however, the cohorts who are 36 to 48 months old were born and grew up between
2003 and 2004, just the cease of the Second Congo War. Mass media have broadly reported
that in 2004 almost 1000 people died from the poor health condition and rare food supply due
to war’s destroy.(Time magazine; Reuters; Guardian) So I assume the cohort grew from 2003
to 2004 are mostly effected by the war and expect a positive result from the analysis.
Besides, 2001 survey and 2010 survey are both from the MICS, a UN programme, but the
2007 survey is from DHS, an American institution, Although three datasets are collected and
processed with the same international standard, I think that there is difference for
interviewing procedure, the calculation methods or measurement instrument. Moreover, the
sample observations from MICS2 and MICS4 are approximately three time more than that
from the DHS survey, all of these factors could create biases for analysing the Z-Scores.
(Wooldridge 2010) Therefore, to reduce the biases from the regression or to be regarded as
an indirect robustness check for the consistency, I deem the second strategy is necessary and
important.
As closely related to children’s health conditions, mother’s breastfeeding condition, infant’s
common illness like diarrhea and malaria are also identified. This is the third strategy, I
intend to analyse these external factors to find out the relationship between the variation of
this factors’ behaviour and the response of the infant’s health condition. From the MICS4
DRC Report, for example, among children under six months old, less than 37 per cent are
exclusively breastfed, besides only 13 per cent Congolese children receives pre-lacteal food.
This implies that the high malnutrition rate of the Congolese children can be explained by
this path.
Apart from these three identification strategies, robustness check will be presented at the last
part. I will use the cohorts who are 0 to 24 months old to substitute the infant cohort and
expect there would be little difference between the two results.
iv.
Econometric Framework
The empirical strategies estimate the treatment of the children’s health status exposed to the
conflict, therefore a difference-in-difference model can be expressed in the following
equation:
𝑌𝑖𝑡 = 𝛽1 + 𝛽2 (𝑃𝑡 ) + 𝛽3 (𝑇𝑖 ) + 𝛽4 (𝑃𝑡 ∗ 𝑇𝑖 ) + 𝜃𝑖 (1)
Y is the outcome of interest, in the first two strategies, it is the value of Z-Score. P is a
dummy variable, showing whether the child born in the post-treatment period. As the Second
Congo War ended 2003, so P appears in the 2007 and 2010 panel. T is also a dummy
variable, reflecting whether the child lives in the province with high level of conflict.
Besides, 𝛽1 is the constant term, 𝜃𝑖 is a random residual. Subscript t indicates the year of the
household survey, before or after the war, subscript i expresses the residence of the child,
high or low level of conflict.
𝛽4 is the coefficient of interaction term 𝑃𝑡 ∗ 𝑇𝑖 , representing the difference-in-difference
(DID) estimator. The central assumption for a DID estimator is that, without the treatment
effect, the cohorts from the treated areas has no significant difference from the cohorts from
the control area. So, in this case it means that if there is no war, the children from high
intensity provinces should have the same health performance to the children from low
intensity area from 2001 to the post-treatment time 2007 or 2010. In sum, 𝛽4 would be 0 in
the absence of the conflict.
However, this specification does not add any control variables for the characteristics of the
children in both treatment and control provinces. Besides, there are time invariant factors
which are possibly correlated in the same province. Therefore, with all the available dataset
information, I add individual characteristic, household characteristic and fixed effects to
control the children in high intensity and low intensity provinces have two different
characteristics in both 2001 and post-war era. The new specification is below:
𝑌𝑖𝑡 = 𝛽1 + 𝛽2 (𝑃𝑡 ) + 𝛽3 (𝑇𝑖 ) + 𝛽4 (𝑃𝑡 ∗ 𝑇𝑖 ) + 𝛽5 𝐷𝑖 + 𝛽6 𝐻𝑖 + 𝑀 + 𝑉 + 𝜀𝑖 (2)
In this new specification, D represents the individual characteristics including the gender of
the child and the residences belong to urban or rural area, both of them are dummy variables.
H is the household characteristics. I choose the child’s mother’s education level from no
education to secondary school, and the household wealth index from 1 to 5 to describe the
family’s household condition. Both are numerical values.
M and V are fixed effect, M is the month of birth of the child and V is the province of the
child lived. M and V is to account for the time-invariant effect at the provincial level.
Estimation of Results
The difference-in-difference strategy can be expressed in the following table, which records
the variation of infants’ (0 to 12 months old) height-for-age Z-Score.
Table3: Difference-In-Difference Treatment Effect of Height-For-Age Score
2001 to 2010
Height-For-Age
2001 Cohort
(n=2315)
2010 Cohort
(n=10520)
Difference
Low Intensity
Provinces
0.154
(n=1783)
-1.537
(n=7591)
1.691
High Intensity
Provinces
-0.275
(n=532)
-1.865
(n=2929)
1.59
Difference
2001 to 2007
Height-For-Age
Low Intensity
Provinces
High Intensity
Provinces
Difference
0.429
0.328
0.101
(0.098)
2001 Cohort
(n=2315)
2007 Cohort
(n=366)
Difference
0.154
(n=1783)
-1.827
(n=269)
1.981
-0.275
(n=532)
-1.21
(n=97)
0.935
0.429
-0.617
1.046***
(0.250)
Both of the panels show the difference between the low intensity provinces and high intensity
provinces and the difference between the cohorts born before the war and after the war.
Therefore the value on the south-east cell of each panel gives the DID estimator. Therefore,
from the second panel, I can interpret that cohorts born after the war and live in the high level
of conflict provinces have 1.046 standard deviation more in height-for-age scores than the
cohorts born before the war and live in the low intensity provinces. The standard error in the
parentheses reflects the result is significant. These two panels are equivalent to the
econometric model (1), which did not add any control for time-invariant provincial effects
and individual and household characteristics. So the result would be biased.
Empirical Strategy 1
(A Panel from Three Household Surveys in 2001, 2007 and 2010)
The following results present the change of infant Z-Scores from the regression of the
econometric framework (1) and framework (2).
Firstly I use height-for-age as the outcome variable. Table 4 is the effect in 2007 and Panel B
is the effect in 2010. The column one of each table applied the framework (1), which obtains
the identical result from the Table 3 for the basic DID estimate. As can be seen, the
coefficient of interest 𝛽4 is positive and significant at 1% level. Put into individual factors sex
and area into the regression, the interaction coefficient is slightly lower but still positive and
robust. The next columns add more add more individual, household and fixed effects
covariates into the regression, however, DID estimator is barely changed. Although t value of
the coefficient is not very large, remaining among 4, I have to accept a positive DID
coefficient with robustness. This result is out of my initial expectation, which implies that
from 2001 to 2007 the height-for-age score of the children from high conflict intensity
provinces are almost 1 standard deviation more than that of the children from the low
intensity provinces. In other words, even they grow up in the high conflicted places, the
infant are healthier than their non-exposed peers. Panel A from Table 5 pictures the HAZ
dynamic change from 2001 to 2010. Without the control covariates, DID estimators is
positively but insignificant. With sex and area covariates, the value still stays the same. By
looking into the all the columns, the coefficient is around 3 percentage point and is never
robust. Therefore, it is concluded that from 2001 to 2010 the height-for-age score almost has
no change whether the infant exposed to the war or not. Therefore, I conclude that from 2001
to 2007, the HAZ score goes higher for the children living with conflict, but from 2001 to
2010 the HAZ scores stay the same for both cohorts.
Table 4 : Treatment Effect in 2007 on Z-Scores of Infant in High-Violence Provinces
(Baseline: 2001 Cohort)
Panel A: HAZ
DID Estimator
1.045***
.975***
.958***
1.008***
(.250)
(.250)
(.248)
(.249)
Female
.164**
.172**
.166**
(.072)
(.071)
(.071)
Urban
.495***
.194
.118*
(.075)
(.103)
(.106)
Panel B: WAZ
DID Estimator
.701***
(.248)
.633**
(.247)
.277***
(.064)
.486***
(.067)
.623**
(.245)
.284***
(.064)
.221**
(.094)
.649**
(.249)
.271***
(.063)
.209**
(.096)
.294
(.247)
.290
(.247)
.122
(.065)
.055
(.068)
.285
(.246)
.125
(.065)
-.030
(.092)
.275
(.248)
.117
(.064)
.015
(.094)
Y
Y
Y
Y
Y
Y
Female
Urban
Panel C: WHZ
DID Estimator
Female
Urban
Controls:
Individual
Household
Fixed Effect
Robust standard errors in parentheses. Significance levels at *** 1%, ** 5%, * 10%, respectively
Panel B treats weight-for-age score as the outcome of interest to measure the acute
malnutrition of the Congolese infant. Again, Table 4 is the change from 2001 to 2007 and
Table 5 from 2001 to 2010. The result is very similar to that from HAZ regression. Even with
all the controls, the positive effect is still robust compared to a large observation, which
reflect the fact that in 2007, the infant’s WAZ is marginally higher than their older cohorts’
weight. Next, from 2001 to 2010, the WAZ score has no explanation as well with the big
standard error, indicating children were not cured the acute malnutrition across the decade.
But considering the fact that the Congolese children suffer from food shortage since 1998, it
can be accepted that the starvation and poverty still remain in the whole country until 2010.
The third table presents the result from the weight-for-height score. However, both Table 4
and Table 5 express a same value for the DID coefficient, zero. Here, the DID estimate in
Panel A is positive, in Panel B is negative, but neither of them is statistically significant at the
conventional level. But they deliver signals that from 2001 to 2007 the underweight problem
is slightly better for the infants exposed to the war, but it becoming worse in the 2010 effect.
As weight-for-height score is affected by both acute and chronic malnutrition, either stunting
or wasting can be attributed to this result.
Table 5: Treatment Effect in 2010 on Z-Scores of Infant in High-Violence Provinces
(Baseline: 2001 Cohort)
Panel A: HAZ
DID Estimator
.101
(.098)
.024
(.099)
.239***
(.032)
.496***
(.033)
-.0389
(.099)
.236***
(.032)
.203***
(.046)
-.039
(.099)
.234***
(.032)
.203***
(.046)
.0566
(.088)
-.003
(.088)
.189***
(.025)
.371***
(.025)
-.076
(.087)
.186***
(.025)
.124***
(.035)
-.070
(.087)
.183***
(.025)
.114***
(.035)
-.037
(.087)
-.046
(.087)
.080***
(.025)
.061
(.025)
-.068
(.088)
.079***
(.025)
-.017
(.034)
-.055
(.079)
.079***
(.025)
-.035
(.035)
Y
Y
Y
Y
Y
Y
Female
Urban
Panel B: WAZ
DID Estimator
Female
Urban
Panel C: WHZ
DID Estimator
Female
Urban
Controls:
Individual
Household
Fixed Effect
Robust standard errors in parentheses. Significance levels at *** 1%, ** 5%, * 10%, respectively
All of these three regressions opposed to my original assumption that in the post-war period
the children born who grew up in the provinces with more conflicts tend to have worse
nutritional outcomes. In addition, many global and local communication agencies have
reported the worsening living condition and surging mortality in the DRC especially for the
east regions.(IRC 2007; Time magazine 2006) Furthermore, the reviewed literature all
express a negative and robust effect of the war on the children’s health condition, although
none of them studied the health effect of Congo War. The results from the regressions,
therefore, are counterintuitive as a matter of fact. The reason of these results needs to be
further investigated and explained in the following section.
Empirical Strategy 2
(A Panel from Old cohort and Young cohort from DHS 2007)
Second Congo War officially ended in 2003. I suspect the cohort who born from 2003 to
2004 are mostly affected, so I compare the old cohort who are 37-48 months old with the
young aged 0-12 months, and expect a better health outcome from 2003 to 2007. Within the
same dataset, the measurement and calculation difference would be eliminated.
Table 6: Treatment Effect in 2007 on Z-Scores of Infant in High-Violence Provinces
(Baseline: 2004 Cohort)
Panel A: HAZ
DID Estimator
-.206
(.233)
-.228
(.229)
.054
(.104)
.820***
(.107)
-.225
(.228)
.053
(.104)
.692***
(.147)
-.240
(.228)
.049
(.104)
.665***
(.147)
-.069
(.172)
-.074
(.169)
.148**
(.074)
.451
(.076)
-.070
(.169)
.150**
(.074)
.262
(.102)
-.073
(.169)
.150**
(.074)
.250
(.103)
.031
(.198)
.048
(.197)
.240**
(.089)
-.078
(.092)
.050
(.197)
.243**
(.089)
-.198
(.124)
.060
(.198)
(.246)**
(.089)
-.190
(.126)
Y
Y
Y
Y
Y
Y
Female
Urban
Panel B: WAZ
DID Estimator
Female
Urban
Panel C: WHZ
DID Estimator
Female
Urban
Controls:
Individual
Household
Fixed Effect
Robust standard errors in parentheses. Significance levels at *** 1%, ** 5%, * 10%, respectively
The first column of Table 6 gives a negative but statistically insignificant DID estimator,
which represents there is no change for the children exposing to the war from 2003 to 2007.
Adding the sex and urban/rural individual covariates makes almost no difference for the DID
estimator, however, it is still negative. Finally I add all of the control variables t value still
stays small, which means the stunting issues remain the same between the children living in
east and the rest. However, the urban variable shows a positive and robust result. Although
the value goes down with more controls, it indicates that from 2003 to 2007 the children from
urban area gain significantly 0.665 SD higher than the children from the rural.
With regard to the weight-for-age estimate, the acute malnutrition, the regression result is
negative and all insignificant. When putting the control covariates, the coefficient hardly has
changed and remained insignificant. However, the robust and positive female variable
indicates that girls gain almost 0.15 SD higher WAZ than boys, which implies girls suffers
less acute malnutrition than boys. In term of the WHZ score analysis, none of the coefficients
of DID are robust as well, but they are positive. Besides, it has the same gender trend to the
WAZ result.
The Z-Score trend from 2003 to 2007 is very similar to the trend from 2001 to 2010. Both of
the estimations display an abnormal fact that the infant exposed to fighting provinces did not
experience deteriorating living condition, which demonstrate the consistency between these
two empirical strategies.
Empirical Strategy 3
Mother’s post-pregnant behaviour and children’s common illness have big impacts on
infant’s nutritional condition. Therefore, treating these factors as the anthropometric
outcomes, I run the econometric framework 2 to find any causality between the high intensity
area and the low intensity area before and after the war treatment effect.
I create dummy variables to reflect whether the child was breastfed or had illness like
diarrhoea and malaria. Like the previous model, the children are all between 0 to 12 months
old.
Table 7: Effect on Household Behaviours
Panel A: Effect in 2007
DID Estimator
Constant term
R-Square
N
Breastfeeding
-.116***
(.042)
.990***
(.002)
0.5681
2988
Diarrhoea
.018
(.035)
.734***
(.010)
0.0082
3422
Fever (Malaria)
-.025
(.042)
.391***
(.011)
0.0052
3417
Breastfeeding
.005
(.006)
.990***
(.002)
0.0014
5127
Diarrhoea
.004
(.028)
.265***
(.010)
0.0064
5138
Fever (Malaria)
-.027
(.031)
.391***
(.011)
0.0286
5136
Panel B: Effect in 2010
DID Estimator
Constant term
R-Square
N
Robust standard errors in parentheses. Significance levels at *** 1%, ** 5%, * 10%, respectively
The first column from Panel B shows that there is a positive effect from 2001 to 2010,
however, it is not statistically significant. But this suggests more mothers breastfeed their
children after the decade. Panel A depicts a different picture about this maternal behaviour.
The DID estimator is negative and statistically robust at 1% level, which means that
comparing 2001 with 2007, less infants from high conflict level provinces received mother’s
breastfeeding than infants from low conflict level provinces. This results meet the 2007 DHS
key findings that only 48 per cent infants were breastfed in the hour following birth and 18
present received foods before being breastfed.
The next two columns illustrate the common illness for children under five, diarrhoea and
malaria. Three household surveys have asked whether the child had diarrhoea or fever in the
last two weeks during their interviewing, which provide data to run a dummy regression.
However, the results indicate little effect across the timeline. For diarrhoea, two panels
present that the diarrhoea illness hardly change among 2001, 2007 and 2010. But both of the
DID estimator are positive, probably only a trivial more cases occurred in 2007 and 2010
than in 2001. Progressing into the issue of malaria, I use whether the child had fever to
indicate a malaria case. Similar to diarrhoea, two regressions are never significant. However,
the sign of the coefficient of interest are both negative, which implies fewer exposed infants
had fever, a signal for better living condition in the east regions.
Robustness Check and Further Investigation
The empirical strategy 1 comes out an unexpected result, therefore I use the children aged 0
to 24 month old to substitute the infant 0 to 12 months old. Because I consider most of the
new born infants are more effected by the prenatal condition rather than exposing to the
conflict environment. Besides, the identification strategy and econometric frame work are all
identical to empirical strategy 1.
Table 8: Robustness Check, Effect on Height-For-Age of Children Between 0 to 24 months
(Baseline: 2001 Cohort)
Panel A: 2007
DID Estimator
1.031***
(.193)
.938***
(.194)
.202***
(.055)
.556***
(.058)
.917***
(.193)
.209***
(.055)
.175**
(.081)
.167**
(.079)
.064
(.079)
.241***
(.031)
.523***
(.031)
-.001
(.079)
.238***
(.030)
.216***
(.044)
Y
Y
Y
Female
Urban
Panel B: 2010
DID Estimator
Female
Urban
Controls:
Individual
Household & FE
Robust standard errors in parentheses. Significance levels at *** 1%, ** 5%, * 10%, respectively
Regarding the height-for-age analysis, both of the panels present very similar results to the
infant regression. From 2001 to 2010, it is positive and robust without control covariates, but
never statistically significantly again with the controls. On the other hand, in terms of 2007
effect, the DID estimator is robust as before even the value is close to the empirical strategy 1
result. There is little change when adding the control factors. Hence, both the cohort who are
0 to 12 months old and the cohort who are 0 to 24 months old cast a same conclusion that
post-war children from high conflicted areas have no more health deterioration than the
children from place with peace in 2010 effect. Surprisingly, from 2001 to 2007, the children
exposing to more violence even experience healthier grow-up than the children living without
war. Therefore, geographically, the children, or the whole population, living in three east
provinces of the DRC had received a higher living standard than the people living in the rest
of the country.
When using weight-for-age score and weight-for-height score as the dependent variables, I
obtain the same results as using the infant as the observation group. At this point, I can
confirm that the conclusions drawn are robust, but I need further evidence to support this
result. The Second Congo War is named as ‘African World War’; even the International
Rescue Committee has shown that 5.4 million people have died from since 1998. They
inform that the Congo War surpass any other conflict since World War II. They also find that
the mortality rate remains highly across the country. (IRC 2007) In this way, IRC request the
government and international community to bring substantial financial investment for deal
with DRC’s health and mortality. Therefore, from Global Humanitarian Assistance, in 2010,
the DRC has received 4 billion dollar (equal to its 17 per cent gross national income)
assistance from the developed world, furthermore since 2000 the donation to the DRC is
largely increasing every year.
In 2011, the DRC was classified as a fragile country. Nevertheless, there are papers
questioning the DRC statistics from the International Rescue Committee. The Human
Security Report from Simon Fraser University has spotted the fatality number reported by the
International Rescue Committee is inappropriate. Because they find IRC only choose east
region as the sample, which cannot represent the whole population. In addition, they also
exploit the methodology error when the IRC analysing the data. Besides, comparing to DHS
2007 data, the mortality of IRC are twice more than that. Therefore, the Human Security
Report claims that both the 200 and 2001 survey from IRC should be rejected.
The Congo War has been described as an exaggerated way in the mass media. Time
magazine headline it as ‘The Deadliest War in the World’, Humanitarian New and Analysis
calls it ‘Conflict Deadliest since World War II’, Reuters and Guardian both report that
‘Congo War kills 45,000 people each month’. However, these communication agencies took
numbers from NGOs or research institutions. But issue occurs when the survey is lack of
experience and the design and implementation is inappropriate.
The high mortality reported by these international agencies has drawn world attention to the
previously ignored country, the Democratic Republic of Congo. The international community
is informed or even pressed to provide more financial or humanitarian assistance to the
Congolese people. However, if the casualty number is exaggerated, the international
community will provide excessive funding or assistance to the country. Further, some critics
have argued that some NGOs deliberately enlarge the level of the conflict to require more
funding. Now coming back to our results, it can be connected via this logic.
The east three provinces, Orientale, Nord-Kivu and Sud-Kivu has exposed to the fighting
since the beginning of the Congo war in 1998, even today there are still armed conflicts. Not
surprising, the International rescue or assistance has been focusing on these east regions.
However, more funding and assistance has distributed into the ease provinces, which left less
international or government support to the rest of the country. I propose that this is the reason
why the children from the east high conflict level provinces experienced equal or even better
nutritional treatment when they growing up.
Conclusion
This paper applies three household surveys to analyse the children’s health and nutrition
status in the Democratic Republic of Congo. A difference-in-difference identification strategy
is used to exploit the variation of the children’s health across the war effects. However, the
results from the quantitative analysis are beyond the initial expectation.
From the time span 2001 to 2010, the results are never statistically significant throughout the
three anthropometric indicators. In this way, it comes into the reality that the children’s
health statuses are rarely changed between the first decades of the 21st century. The children
born and grow up in the east regions of the DRC have a similar life standard regardless of the
explosion of the conflict. The real outthought result is from the analysis of 2001 to 2007
effects. The positive and robust estimator shoots the fact that in 2007 the children from the
east provinces with the more combat surprisingly acquired better nutritional intakes. The
assumption that an increasingly worsen living condition for the east region of the DRC is
rejected. The very likely solution to this myth is that more international rescue and
government assistance have settled down to the high conflict level places first rather than the
rest of the DRC. As the accumulated funding and support drop on the east, the rest of the
country benefited less than the east part. Therefore, although the east three provinces have
faced more conflict events and suffered more severe fatalities, the communities from outside
the region provide more assistant to compensate the damage of the war.
Furthermore, I solely use 2007 household survey to investigate the health nutritional
difference between the old cohort and the young cohort. I assume the old cohort born straight
after the ending of the war, therefore they should suffer more healthy and nutritional
unavailability. The results demonstrate that this assumption is not correct. The analysis of
height-for-age, weight-for-age and weight-for-height are never significant. This strategy
further confirms the validity of the conclusion from the previous strategy. The children born
and grow up in the province with conflict do not suffer worsening nutritional treatment.
At last I look into mother’s breastfeeding and children’s common illness to further investigate
the results from previous conclusions. Only the breastfeeding gives robust estimate, but
negative, which is not likely to explain the higher HAZ score in 2007 than in 2001. But two
common illness diarrhoea and malaria show insignificant results, no explanatory power.
However, because the data is not consistent among the three household surveys, there
probably exist biases for some indicator. Besides, the control covariates cannot capture all the
time-invariant effect, or other core individual and household characteristics like sanitation
and parent’s anthropometric characteristics, which cast biases as well.
In summary, the international community should further look into the situation of the
Democratic Republic of Congo. The results from this paper imply that the country is running
at a relatively healthier path, the international report may overestimate the trend of
deteriorating situation in the country. However, the overall living standard in the DRC is still
low, the international community may assist the DRC government for real life improvement
and help to eliminate the long-term conflict within the country.
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