2015 CSAE conference, Oxford
Long-term effects of violent conflict on second-generation
health outcomes: evidence from Liberia
Soazic Elise Wang Sonne and Eleonora Nillesen1
1United
Nations University-Maastricht Economics and social Research Institute and training center on Innovation and
Technology
(UNU-MERIT)
Preliminary Draft: October 2014
Abstract
A recent body of literature has investigated the consequences of conflict on health outcomes and
generally finds a negative impact of exposure to conflict on various health indicators. This paper
aims to examine the long-term effect of households’ exposure to the 1989-2003 Liberian civil war
on second-generation children’s anthropometric outcomes, born after the war. We use village-level
data on violent attacks and detailed information about the household’s migration history to
accurately measure exposure to violence during war. Results are robust to including co-variates,
village, and birth cohort fixed effects. By exploiting geographical and temporal variation, we find a
significant and negative effect of the residency in conflict affected areas combined with the number
of years at least one parent (the household head) spent under the war when attending primary
school, on children short term health outcomes (measured by the Weight for Height Z score).
Keywords: WHZ, children’s health, conflict, Liberia
JEL Codes: I12, J13, O12, O15
1
1. Introduction
A country going through conflict experiences a huge destruction of physical and human capital, with
negative implications in terms of growth and development (e.g. Guidolin and La Ferrara, 2007;
Collier and Hoeffler, 1998). A relatively recent strand of the literature has focused on the
microeconomic impacts of conflict on health, particularly for young children. This focus is well
placed, as a person’s health status during childhood is a strong predictor of future welfare, including
educational attainment, socio-economic status, and life expectancy (see e.g Almond, 2006;
Yamauchi, 2006; Alderman et al., 2006; Maccini and Yang, 2006). Most of these studies
concentrated on assessing the effect of children’s exposure to conflict on their long and short-term
health status. For instance, Bundervoet et al., (2009) found an average impact of 0.35 to 0.53
Standard Deviations of children’s exposure to the Burundian civil war. Akresh et al.(2012) also
found that children exposed to the Ethiopian-Eritrean war are shorter by almost 0.42 standard
deviations in comparison with their peers of the same population. Maccini and Yang (2009); Minoiu
and Shemyakina (2012) and Barre and Domingues( 2012) report similar findings.
Yet these studies all focus on the direct impact of children’s exposure to conflict on their health
outcomes. By contrast, little is known on second-generation impacts; do negative impacts sustain
and also affect the next generation of children, even though they were born after the ending of the
war? Or may we observe (some) convergence to outcomes of those that were not, or less exposed?
This paper aims to shed on these questions focusing on the empirical estimation of the intergenerational effect of households’ exposure to war on their children’s anthropometric outcomes. We
hereby contribute to the growing literature on the micro-economic impacts of civil war violence,
using unique micro-level data from Liberia.
The remainder of the paper is organized as follow: section 2 outlines a framework that describes
how conflict may impact on next generations not directly exposed to the violence. It also presents
an historical overview and a course of the Liberian civil war. Section 3 discusses the data and
presents descriptive statistics of our key variables. Section 4 presents the empirical framework as
well as the main results we obtained. The main implications of the paper and potential caveats are
discussed in section 5.
2
2. Literature review and historical background
2.1) Previous studies
Little is known about long-term consequences of violent conflict (Blattman and Miguel, 2010).
Moreover, to the best of our knowledge, there is no empirical evidence on the potential harmful
effect of violence on future generations. Devakumar et al. (2014) propose a theoretical framework
to explain the different channels through which the exposure to conflict in one generation could
propagate harmful effects to future generations. They suggest a taxonomy of routes through which
conflict could spread across future generations, distinguishing between direct and indirect channels.
Indirect channels may for example include the displacement of people or the depletion of natural
resources that impacts parent’s livelihoods and their children. The destruction of social
infrastructure, the instauration of a culture and climate of violence as well as the redirection of all
resources to war are other channels that may have a negative impact on parent’s health and
therefore, the health status of their offspring.
In addition to these indirect channels and drawing upon the common saying that ‘’a war may end
but its effect do not’’, Devakumar et al. (2014) put a special emphasis on four specific (direct)
features of conflict, namely: violence, mental health’s issues, infection and malnutrition. For each of
these key dimensions they suggest how it could affect the parents’ and therefore, children’s health
status later on.
i)
Violence
During conflict, people experience different form of violence, which could impact on their health as
well as having future adverse effects on their children. Among others, these are domestic violence,
mental illness or physical trauma, trafficking and; specific to women, prostitution and rape. These
impacts are known to affect children’s health in various ways, including low birth weight, perinatal
morbidity and mortality (See Devakumar et al. 2014; UNICEF, 2004)
3
ii)
Mental health
Looking at the (often) large physical or human losses that households and families experience during
the war, scholars have studied different type of mental health as main consequences of conflict.
These are Post-Traumatic Stress Disorder (PTSD) and psychosomatic disorders, depression and
anxiety. The intergenerational transmissions of such mental health’s impairment have been well
studied outside the economics domain (see e.g Bombay et al. (2009); Yehuda & Bierer, (2008)). Main
intergeneration impacts have found to be lower growth of the child, poor educational attainment as
well as larger infection rates.
iii)
Infection
It has also been shown that war could lead to an increase of infectious disease’ transmission as well
as a rise in maternal mortality and obstetric complications. These harmful effects could later on lead
to fetal or infant mortality, congenital malformations but also impaired the cognitive development of
the child.
iv)
Malnutrition
During conflict, the destruction of productive and farm assets as well as the depletion of core
natural resources useful for agriculture may have an adverse effect on the level of food security.
People may go through different kind of malnutrition such as micronutrient deficiencies, obstetric
complications and could even lead to maternal mortality. This could have permanent deleterious
effects on their next generation such as: low birth weight, congenital abnormalities and low
educational attainment. It could also impair the growth and development of the child.
Overall the schematic diagram below presents a general overview of the different effects conflict
could have on second-generation (children’s) health outcomes. Our focus is on the long-term effect
(the red arrow below).
4
Figure 1: Immediate, long term and intergenerational effects of conflict on children’s health
Intergenerational
effect
Indirect
determinants of
Health
nd
2 Generation
Immediate effect
Conflict
2nd Generation
Long term effect
Parent’s health status
1st Generation
Indirect
determinants of
health
st
1 Generation
Children Health status
2nd Generation
Conflict
1st Generation
1ST GENERATION
2ND GENERATION
Source: Devakumar et al. (2014)
2.2) The Liberian case study: a course of the civil war
Geographically located in Western Africa, the republic of Liberia, enters several years after its
independence in a conflict that lasted for 14 years : from 1989 to 2003 with a short break from 1997
to 19991. The civil war was mainly between the president Charles Taylor and the political party
LURD, (‘Liberians United for Reconciliation and Democracy’) supported by the US and Guinean
leaders. This first political conflict ends up in a social conflict by pitching ethnic groups against
ethnic groups and brothers against brothers. The civil war has had a huge negative impact on human
capital claiming the lives of more than 250 000 people, equivalent to 10% of the Liberian population
1
The first Liberian civil war has taken place from 1989 to 1996 while the second one was from 1999 till 2003.
5
at that time, along with one million displaced people and 75 000 refugees (Rincon, 2010). The first
Liberian civil war (1989 -1996) has been known has one of the bloodiest conflicts in Africa, leaving
more than 200 000 people dead.
The war had a devastating impact on the already low level of economic development of the country:
GDP levels fell by some 90 % during the first war period, specifically from 1987 and 1995, and
Liberia was among the 10 countries with the lowest score on the UN Human Development Index.
In addition to important impacts on human capital, the war resulted in a severe economic, social and
institutional crisis (e.g. Fearon et al., 2009; Sawyer, 2005). Institutional and economic weaknesses
have been stepped up by the massive brain drain of highly skilled professional Liberians who fled
their country during the war. In the health sector for instance, Liberia was only left with about 100
doctors in 2003 at the end of the conflict, compared to 2000 medical practitioners that were listed
before the conflict. Furthermore, the legacy in term of poverty incidence was important with almost
two thirds of Liberian living below the poverty when Ellen Johnson Sirleaf became president in
2005.
3. Data
3.1) Data sources, sample and questionnaire design
We use micro-level survey data that was collected in 2010 by researchers from the Development
Economics Group (DEC) of Wageningen University (The Netherlands) as part of a large impact
assessment project in collaboration with ZOA, a Dutch NGO implementing agricultural livelihoods
programs in rural areas to improve food security, community cohesion and farmer’s income. The
research was undertaken in three counties, closest to the Liberian capital Monrovia: Montserrado,
Margibi and Bong county. Baseline data collection took place in March- April 2010. Two surveys
were implemented: a community questionnaire that relied on interviews with the local chief and one
or two elders of the village and a household questionnaire for which the head of household or the
spouse was invited.
The community questionnaire entailed questions on demography and infrastructure, war history,
conflicts and shocks, land, assets collective action. This information was collected from about 225
community leaders (the chief or his assistant, a village elder, or women’s and youth leader) in nearly
60 communities. Household and individual data were mostly collected on economic and
6
demographic characteristics, income and labor, land tenure and agriculture, expenditure and
consumption, cooperation and trust, conflicts and war history. Anthropometric information of all
children less than five years of age was obtained using official instruments from UNICEF (scales
and wooden boards to measure weight and height).
Data was collected from 1200 households in 60 communities, in three districts. We obtained
anthropometric measures of 695 children under five years of age. Households were selected using a
two-stage clustered random sampling procedure.
Communities were considered eligible for selection if they satisfied three criteria: i) the selected
community had not previously received a ZOA intervention; ii) the community had at least 20
households; and iii) communities were at least 5 km away from the next treatment community to
avert potential spillover effects. In the second step of the selection process, some 20 to 30
households were randomly chosen to participate in the survey.
3.2) Descriptive statistics
Table 1 presents an overview of the key dependent and independent variables relevant for our
analysis. These are anthropometric variables characterizing the short and long run health status of
the household as well as variables measuring people’s economic and social status. Variables on the
geographical and time variation of the conflict were drawn from the community survey.
Exposure to violence was primarily measured using geographical information, that is we knew from
the community survey whether a village had been attacked or not. We thus construct a dummy
variable that takes the value of 1 if the household lived in the village that got attacked during the war
and 0 otherwise. Note that we also have detailed information about the households’ migration
history (with about just 45 % of people who declared that they were actually born in the area they
were currently residing in 2010), which enables us to accurately measure exposure to violence.
Stemming from our descriptive statistics, about 60 % of the communities in our sample got
attacked. Also, the average age of children in our sample is around 27 months (about 02 years old)
whereas the one of the household head is almost 40. Since al but 1 percent of household heads with
children younger than five in our sample were born after the war, we have (almost) no time variation
(i.e. every head we consider had been exposed to the full period of war). As a robustness, we
therefore consider household head’s exposure to violence during primary school age, as this is
7
arguably the most important period that predicts outcomes later in life (also see Islam et al. 2014).
We therefore use the number of years household heads were exposed to the war during their
primary school age, as an additional source of (plausibly) exogenous variation over time.
On average, we find that household heads spent about 0.34 (almost 04 months) of their primary
school age under conflict, with quite a lot of variation looking at the standard deviation which is a
bit more than one (1.32). It is also worth mentionning that the average years of schooling of
household heads is around 2.6.
Considering children’s anthropometric indicators, we do find that there is a relatively strong
correlation between the different indicators with 36.59% of children of our sample who are
stunted.(Height for Age Z score less than -2). Also, they seem to be a significant WHZ score’s mean
difference between children living in conflict affected areas or no.
[insert Table 1 about here]
4. Empirical strategy
4.1) Specification of the econometric model
We are interested in assessing whether a household head’s exposure to violence has an impact on his
or her children’s anthropometric outcomes (all children were born after the war). We estimate the
following model (also see Domingues and Barre (2012):
𝑊𝐻𝑍𝑖,𝑗 =𝛽𝐶𝑊 ( 𝐶𝐻𝐼𝐿𝐷𝑊𝐴𝑅𝑖,𝑗 ∗ 𝐶𝐻𝐼𝐿𝐷𝑃𝐴𝑅𝐸𝑁𝑇𝑆𝐶𝐻𝑂𝑂𝐿 𝑊𝐴𝑅𝑖,𝑗 ) + 𝑋𝑖𝑗 + 𝜎𝑗 + 𝜀𝑖,𝑗
(1)
Where:
-- 𝑊𝐻𝑍𝑖,𝑗 is the Weight for Height Z-score for a child i living in the household j as computed in
the children database:
-- 𝐶𝐻𝐼𝐿𝐷𝑊𝐴𝑅𝑖,𝑗 is a dummy variable indicating whether the household head (parent) of the child 𝑖
of the household 𝑗 lived in a conflict affected area or no.
-- 𝐶𝐻𝐼𝐿𝐷𝑃𝐴𝑅𝐸𝑁𝑇𝑆𝐶𝐻𝑂𝑂𝐿 𝑊𝐴𝑅𝑖,𝑗 is a continuous variable indicating the number of years that the
parent of the child i living in household j has spent under war during his primary school age.
--σj is a child’s year of birth fixed effects.
βCW captures the combined effect of the time that parents spent under the conflict during their
primary school age while residing in a conflict affected area or no.
8
Xij is a set of controls variables of the parents of the child 𝑖 living in household 𝑗. These are socio
economic and demographic characteristics of the household head that could also explain the Weight
for Height Z score.
εi,j is a random error term
4.2) Results
Table 6 presents the results of a parsimonious model. In column [1] we present the results of a the
OLS regression of Weight for Height Z score on exposure to conflict. No controls, except for year
of birth and district fixed effects are included here. Exposure to violence (thus the child’s parent was
living in a community that got attacked during the war) has no significant impact on weight for
height scores of the second generation. Yet when we consider variation in exposure during the
household head’s primary school age column [2] shows a strong negative and significant effect on
the Weight for Height Z score of second-generation children. However, the coefficient of the
interaction term is non significant at all (column [3]). In column [4] we add a dummy variable for
girls and a triple interaction between violence exposure and gender to see if there is gender-effect
but this variable is not significant even if the previous interaction term which was not significant
before becomes significant with a negative coefficient.
Table 7 presents the full model where we include relevant demographic and socio-economic covariates. The type of religion seems to matter: if a household head’s practices animism, this has a
strong negative and significant effect on the second generation children’s health outcome as shown
in column [1]. This result doesn’t change when controlling for the marital status and the ethnicity of
the head of the household in the regression as shown in column [3] and [4].
Finally, column [5] reveals a gender-differential impact when controls are considered. Indeed, the
interaction between time variation in parent’s exposure to conflict, residence of the head of the
household in a conflict-affected areas during the war, and the child female dummy shows a
significant negative effect on the Weight for Height Z score children health outcomes.
Overall, these results tends to confirm the hypothesis that parents’ exposure to conflict tends to
have long-lasting impacts that spill over to their children. Second-generation children with at least
one its parents exposed to violence during the war have a 7% lower weight for height score than
second-generation children whose parent was not exposed.
9
4.3) Discussion: potential caveats
This is to the best of our knowledge the first empirical study that tackles the issue of secondgeneration health impacts of civil war victims of violence. The results are consistent with other
studies that investigate the direct impact of civil war violence on health outcomes: exposure to
violence has strong negative impacts on children’s health. Yet outcomes should be interpreted with
some caveats in mind. We only have data on survivors or in other words, data do not include
information on individuals/ parents who died during the conflict. If these individuals were more
likely to have children that have a low Weight to Height Z score, then our negative effects found
would likely to be underestimated. Also, we only have the migration history for the household head;
hence we can only accurately measure exposure to violence of one of the parents, not both.
06. Conclusion
In this paper, we investigate the long-term effect of the Liberian civil war on second-generation
health outcomes. We found that children with at least one of their parents (the household head)
living in areas that were attacked during the war, and being exposed to violence during his or her
primary school age tends to have lower anthropometric outcomes than their peers. We also find this
effect to be more pronounced for second-generation girls than boys. Our results suggest that the
impact of civil war violence can be persistent and continues to have a profound effect on people’s
lives that only started long after the shootings stopped.
10
06. References
Akresh, Richard, Tom Bundervoet, and Phillip Verwimp. 2009. “Health and Civil War in
Burundi.” Journal of Human Resources 44, No. 2:536-63
Asadul Islam, Chandarany Ouch, Russell Smyth and Liang Choon Wang (2014). The long-term
effects of civil conflicts on education, earnings and fertility: evidence from Cambodia. Department of
Economics ISSN, 1441-5429, Discussion Paper 36/14
Barre, T., & Domingues, P. (2012). The health consequences of the Mozambican civil war: an
Anthropometric Approach. University of Paris 1 Pantheon Sorbonne, Erudite, University of Paris Est
Blattman, C. and E. Miguel (2010). Civil war. Journal of Economic Literature 88(48), 3–57
Caldwell, J. C. (2004). Social upheaval and fertility decline. Journal of Family History,
29(4), 382-406.
Fearon, J. D., Humphreys, M. and J. M. Weinstein (2009). Can Development Aid Contribute to
Social Cohesion After Civil War? Evidence from a Field Experiment in Post-Conflict Liberia.
American Economic Review, Papers and Proceedings 99: 287–91
Leroy, Jef L (2011). zscore06: Stata command for the calculation of anthropometric z-scores using
the 2006 WHO child growth standards http://www.ifpri.org/staffprofile/jef-leroy
Maccini, S., & Yang, D. (2009). Under the Weather: Health, Schooling, and Economic
Consequences of Early-Life Rainfall. American Economic Review, 99, 1006–1026.
Minoiu, C., & Shemyakina, O. (2012). Child Health and Conflict in Côte d'Ivoire. American
Economic Review: Papers and Proceedings, 102, 294-299
Rincon, J. M. (2010). Ex-combatants, Returnees, Land and Conflict in Liberia. DIIS Working Paper
Sawyer, A. (2005). Social capital, survival strategies and their potential for post-war governance in
Liberia. UNU-Wider Research Paper No. 200515.
11
ANNEX
Table 1: Descriptive statistics
Variables
Number of
Observation
Mean
Std. Dev.
Children Anthropometrics variables
haz06
658
-1.3098
2.7139
waz06
whz06
bmiz06
649
640
649
-.515069
.2994375
.67767
1.7081
2.130175
3.62857
Child_age
697
27.66643
16.43743
Female_child
697
.5121951
.5002102
Households/parents status variables
hh_size
Household Head gender
684
689
6.239766
0.470247
2.747201
0.499477
Household head is educated
673
0.325409
0.468876
Household Head_age
682
40.02786
12.87067
Household Head is single married
689
0.545718
0.498267
Household Head is literate
686
5.759475
Household head number of years of
689
2.608128
education
Household head is animist
679
.0014728
Household head ethnicity_Kpelle
679
0.017673
Household head is born here
685
0.4554745
Parents primary school violence
exposure
679
0.017673
Parents violence exposure variables
Conflict area household head
552
.5978261
exposure
Primary school years violence
697
.3428981
exposure
0.933804
4.129832
.0383765
0.131857
0.4983774
0.131857
.4907814
1.324529
Source: Liberia baseline survey, 2010
12
Figure 2: Distribution of under five years old children z-Scores in Liberia, Baseline survey 2010
Source: Liberia baseline survey, 2010
13
Figure 3: Scatter plot of the relationship between the WHZ , the HAZ and the BMI Zscore
Source: Liberia baseline survey, 2010
Source: Liberia baseline survey, 2010
14
Table 2: Test of difference of WHZ score mean per conflict affected household
Group
Obs.
Mean
Std dev
[95% Conf.
Interval]
Not Attacked
206
0.132864
1.542188
-0.07898
0.344712
Attacked
312
0.384904
1.601719
0.206481
0.563327
Diff (Mean ‘Not
attacked’, Mean
‘Attacked)
Ha: diff != 0
Pr(|T| > |t|) =
0.0759
Source: Liberia baseline survey, 2010
Table 3: Descriptive statistics of the health status of children, stunted or no
Group
Obs.
Percentage
Not stunted
(HAZ score less than -2)
442
63.41
Stunted
(HAZ score greater than -2)
255
36.59
Source: Liberia baseline survey, 2010
Table 4: Mean difference of the HAZ score between boy and girl children
HAZ score
Mean
Std Dev
Obs
Boy
0.388235
0.488067
340
Girl
0.344538
0.475884
357
Source: Liberia baseline survey, 2010
Table 5: Mean difference of the WHZ score between boy and girl children
WHZ score
Mean
Std Dev
Obs
Boy
0.3009
1.890936
311
Girl
0.298055
2.336812
329
Source: Liberia baseline survey, 2010
Table 6: Impact of parents’ exposure to conflict on child’s health. Baseline regressions without controls
15
DEPENDANT VARIABLE
(WHZ Score)
[1]
Conflict area
.2516889
[.1723368]
Parents’ primary school years
exposure
[2]
[3]
[4]
.2072615
[.1687246]
-.1461797***
[.0449799]
-.1517038 **
[.068129]
.0576927
[.0760238]
Conflict area* Parents’ primary
school years exposure
-.0728319*
[.0406998]
Parent conflict area*years
uderconflict-primary Conflict
area* Female child
-.0028186
[.061779 ]
Female Child
-.0055994
[.1637452 ]
Year of Birth Fixed effects
Yes
Yes
Yes
Yes
R-squared
0.0327
0.0219
0.0417
0.0279
Number of Obs
518
640
518
518
Notes: Robust standard errors in [ ], clustered at the village level.
* significant at 10%; ** significant at 5%; *** significant at 1%
Data Sources: Liberia baseline survey, 2010
16
Table 7: Impact of parents’ exposure to conflicts on child’s health. Baseline regressions with controls and year of birth fixed effects
DEPENDANT VARIABLE
(WHZ Score)
Conflict area* primary school years exposure
[1]
[2]
[3]
[4]
-.065416 *
[.0358621]
-.0778508*
[.0392294]
-.0799055*
[.0413571]
-.0696576*
[.0407713]
Female child
-.0994327
[.1590639]
-.0752361*
[.0431]
Conflict area* primary school years exposure*Female child
HH head_Age
[5]
.004361
[.0061789]
-.0036743
[.0171359]
3.271274***
[.1580829]
.002393
[.0058546]
-.0028248
[.0164132 ]
3.236788***
[.1763068]
3.458644***
[.3908778]
3.546501***
[.4367399]
HH Head_Muslim
-.3924438
[.4180476]
-.4244003
[.4054327]
-.4820799
[.4451805]
-.4720514
[.4318699]
HH Head_Protestant
-.0110051
[.2089666]
-.0053637
[.2106817]
-.0721449
[.1983357]
-.0563994
[.1981274]
HH Head_Catholic
-.394505
[.3720658]
-.3988534
[.3499392]
HH head number of years of education
HH Head_Animism
HH head_Married
HH head_Partner
.0016927
[.0058311]
-.0108326
[.0163168 ]
.9465587
[.9289314]
.8806725
[ .9410636 ]
-.3019934
[.2708883]
1.045043
[.941165 ]
.9622763
[.9469264 ]
-.2805922
[.2748965 ]
1.08208
[.9471212]
1.00345
[.9568796]
17
HH head_Separated
HH head_Divorced
1.089912
[ .9610528]
.3236203
[1.166314]
1.163159
[.9724059]
.4945639
[1.224615]
1.102205
[1.004091]
.5017704
[1.231358]
0.0254
501
-1.133556
[1.138472]
-.4877546
[1.149689]
-1.271442
[1.105558 ]
-.7861479
[1.037801]
-.7927668
[1.080112 ]
.8805665
[1.131615 ]
-.8446161
[1.079979]
-1.342397
[1.11405]
-1.802767
[1.080112 ]
-.6000256
[1.139689]
-1.480966
[1.569161]
0.0448
501
-1.135242
[1.122258 ]
-.5101749
[1.133628]
-1.248164
[1.080911]
-.7684628
[1.0167]
-.7720579
[ 1.054882]
.9178476
[1.106571]
-.823755
[1.055153]
-1.863144
[1.155405]
-1.732342
[1.034006]
-.5895502
[1.120029]
-1.475775
[1.537041]
0.0457
501
HH ethnicity_bassa
HH ethnicity_gbandi
HH ethnicity_gio_dan
HH ethnicity_gola
HH ethnicity_grebo
HH ethnicity_kissi
HH ethnicity_kpelle
HH ethnicity_krahn
HH ethnicity_kru
HH ethnicity_loma
HH ethnicity _mandinga
R-squared
Number of Obs
0.0300
507
0.0147
501
Notes: Robust standard errors in [ ], clustered at the village level. * significant at 10%; ** significant at 5%; *** significant at 1% Sources: Liberia BS
18
19