WPS6468
Policy Research Working Paper 6468
Education and Civil Conflict in Nepal
Christine Valente
The World Bank
Development Economics Vice Presidency
Partnerships, Capacity Building Unit
May 2013
Policy Research Working Paper 6468
Abstract
Between 1996 and 2006, Nepal experienced violent civil which often targeted school children, have the reverse
conflict as a consequence of a Maoist insurgency, which effect. Male schooling tended to increase more rapidly
many argue also brought about an increase in female in areas where the fighting was more intense, but the
empowerment. This paper exploits variations in exposure estimates are smaller in magnitude and more sensitive to
to conflict by birth cohort, survey date, and district specification than estimates for females. Similar results are
to estimate the impact of the insurgency on education obtained across different specifications, and robustness
outcomes. Overall conflict intensity, measured by checks indicate that these findings are not due to selective
conflict casualties, is associated with an increase in female migration.
educational attainment, whereas abductions by Maoists,
This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org.
The author may be contacted at christine.valente@bristol.ac.uk.
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names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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Education and Civil Conflict in Nepal
Christine Valente 1
Keywords: Civil conflict, Education, Gender, Nepal.
JEL Codes: I20, J12, O12
Sector Board: EDU
1
Christine Valente is a Lecturer (Assistant Professor) at the University of Bristol, United Kingdom;
her email address is christine.valente@bristol.ac.uk.
Between 1996 and 2006, Nepal experienced violent civil conflict as a consequence of a Maoist
insurgency. This paper investigates the impact of being exposed to this insurgency at a young age
on education outcomes.
This study makes both an empirical and a methodological contribution to the growing literature
on the impact of civil conflict on human capital formation. First, this paper extends our
understanding of the impact of civil conflict on education to include a conflict of moderate
intensity. With just over 13,000 casualties and less than 1 percent of the population forcibly
displaced, the level of violence considered in this study was much lower than in conflict episodes
considered in previous studies. 1 Second, two alternative identification strategies are employed to
increase confidence in the reliability of the estimates: the first relies on variation in conflict
exposure across birth cohorts and geographic areas in a single survey, as is standard in the literature
(e.g., Akresh and de Walque 2008; Shemyakina 2011; Chamarbagwala and Morán 2011), and the
second relies on variation in exposure to conflict among school-aged individuals between household
surveys and geographic areas.
Educational attainment is generally expected to be adversely affected by exposure to armed civil
conflict. Direct youth enrollment in the military, limited mobility, and the destruction of schools
may all negatively affect the ability of children to attend school. Increased poverty may drive
parents to remove children from school (to avoid direct costs) and put them to work (to avoid
opportunity costs). Political instability and reduced life expectancy may decrease expected returns
to education and, in turn, reduce investments in human capital. Moreover, the schooling of girls is
often more sensitive to worsening economic conditions than that of boys. A conflict environment
may also hinder the functioning of education programs by weakening government institutions and
imposing logistical and staff security challenges on local and international NGOs.
However, the general expectation that schooling is disrupted in conflict areas may not be well
founded in the particular case of Nepal. National trends do not indicate an increase in poverty
coinciding with the conflict but rather a steady decline in poverty (World Bank 2005). Despite
2
difficulties with public service provision, basic health and education services have been maintained
(Armon et al. 2004). In addition, the insurgency may have had a positive effect on schooling
outcomes despite the fighting through both intended and unintended consequences of the Maoist
presence. The insurgents have been reported to police teacher absenteeism (Hart 2001; see also
Collins 2006 and Devkota and van Teijlingen 2010, for a similar argument regarding health
workers) and have explicitly opposed caste- and ethnicity-biased traditions; these actions may have
directly benefited both male and female education. In addition, the insurgents have publicly
opposed gender inequality, including gender inequality in access to schooling. For instance, it has
been reported that “the Maoists have taken a strong stand on this issue – insisting that girls of
school age attend the local facilities, even to the point of holding parents accountable and liable to
punishment for the non-attendance of their daughters� (Hart 2001, p. 32). Although the egalitarian
rhetoric has not been followed completely in practice, a number of women were directly involved in
combat, and there is anecdotal evidence of improved conditions for women in areas controlled by
the Maoists, such as decreases in polygamy, domestic violence, and alcoholism (Lama-Tamang et
al. 2003; Manchanda 2004; Geiser 2005; Aguirre and Pietropaoli 2008; Ariño 2008). The diffusion
of the egalitarian Maoist ideology may also have increased the aspirations of young girls for their
own education and the aspirations of parents for their daughters’ education. One unintended aspect
of the insurgency may have contributed to improving schooling outcomes, especially for girls:
female labor force participation increased (Menon and Rodgers 2011), and there is evidence that
when women have more control over household expenditure investments in children increase,
especially for girls (e.g., Thomas 1990; Duflo 2003). Thus, in the case of Nepal, contrary to most
episodes of violent conflict, the direction of the effect of the Maoist insurgency on schooling
outcomes seems unclear a priori.
Nonetheless, one particular aspect of the Nepalese conflict is likely to have been unambiguously
detrimental to education: the common insurgent practice of abducting civilians. Parents may have
been deterred from sending their children to school out of fear that they would be abducted by the
3
insurgents (Human Rights Watch 2004). Quoting figures from the Informal Sector Service Center
(INSEC), UNESCO (2010) reports that between 2002 and 2006, the Maoists abducted 10,621
teachers and 21,998 students (p.8). According to additional data provided by INSEC, the total
number of abductions by Maoist forces during the conflict amounted to more than 85,000. Although
most abductees were seemingly returned unharmed after a few days of intensive indoctrination
(Becker 2009; Macours 2011), a number of youths joined the Maoist fighters (in 2003, an estimated
30 percent of Maoist forces were aged 14–18 years). The indoctrination sessions held during
abductions are likely to have played a part in their recruitment.
In this paper, I exploit differences in the intensity of violence experienced by individuals born at
different times, surveyed at different times, and in different districts to shed light on the ways in
which experiencing the insurgency at a young age affected educational outcomes.
Individual data from the 2001 and 2006 Demographic and Health Surveys (DHS) of Nepal are
merged with detailed conflict data collected by INSEC, namely, the number of conflict fatalities,
school destructions, and abductions by Maoists at the district level.
I find that overall conflict intensity, as measured by conflict casualties, was associated with an
increase in female educational attainment, whereas abductions by Maoists, which often targeted
school children, had the reverse effect. Male schooling also tended to increase more rapidly in areas
where the fighting was more intense, but the estimates are smaller in magnitude and more sensitive
to specification. Similar results are obtained across different identification strategies, and robustness
checks indicate that these findings are not due to selective migration.
In the next section, I review the existing evidence on the impact of armed conflict on education
outside Nepal with an emphasis on male-female differences. I then present the Nepalese conflict in
section II, the data in section III, the empirical strategy in section IV, and the estimation results in
section V. Section VI concludes.
4
I. <>Literature Review
A number of cross-country analyses suggest that political instability has large negative effects
on growth but that recovery to equilibrium levels tends to be rapid (see Blattman and Miguel 2010,
for a review). At the microeconomic level, the results of an emerging body of literature on the
impact of war-related destruction or civil conflict on educational attainment show that violent
conflict often leads to worse educational outcomes, but estimates vary substantially by conflict,
gender, and educational level. Overall, girls in postprimary education appear to experience the
worst effects.
In war-torn Germany and Austria, school-aged individuals exposed to war received fewer years
of education (Ichino and Winter-Ebmer 2004; Akbulut-Yuksel 2009). In Guatemala, where the
worst period of the Guatemalan civil war saw nearly 200,000 deaths, Chamarbagwala and Morán
(2011) find that individuals who were of schooling age in departments that were more affected by
the war completed fewer years of schooling and that this effect was much more marked for girls. In
Bosnia and Herzegovina, Swee (2009) estimates that cohorts of children exposed to greater conflict
intensity at the municipal level were less likely to complete secondary schooling, but primary
schooling attainment was unaffected. Shemyakina (2011a) finds that girls (but not boys) who were
of schooling age during the Tajik civil war were less likely to complete mandatory schooling in
areas severely affected by conflict events. Rodriguez and Sanchez (2009) find that in Colombia,
children aged 12 years and older who were exposed to more violence at the municipal level were
more likely to drop out of school and enter the labor market. León (2012) finds that individuals who
were born and raised in Peruvian districts that were more affected by conflict-related violence
completed fewer years of education. Three recent papers, one by Akresh and de Walque (2008) and
two by Annan, Blattman, and colleagues (Annan et al. 2009; Blattman and Annan 2010), illustrate
the marked heterogeneity in findings on the impact of civil conflict across demographic groups and
across conflicts. Akresh and de Walque (2008) estimate that cohorts of children exposed to the
extremely violent Rwandan genocide, which killed 10 percent of the country’s population,
5
completed 18.3 percent fewer years of education. However, contrary to results from Guatemala, for
example (Chamarbagwala and Morán 2011), they find that due to the nature of the conflict,
nonpoor, male individuals were more negatively affected. Studying the effect of forced recruitment
into the Ugandan Lord’s Liberation Army, Annan et al. (2009) and Blattman and Annan (2010) find
dramatically different effects for men and for women in the opposite direction of those obtained by,
for example, Shemyakina (2011a) and Chamarbagwala and Morán (2011). The abducted men in
their sample, who were abducted, on average, for just over 15 months, experienced much worse
educational attainment and labor market outcomes as well as poorer psychological health (Blattman
and Annan 2010). However, these authors find no such effects for female abductees, which they
attribute to the lack of opportunities for women in general (Annan et al. 2009).
II. <>Conflict in Nepal
Nepal was an absolute monarchy until 1990. Despite multiparty democratic elections in 1991, a
Maoist insurgency broke out in February 1996 in the Rolpa district and ended in 2006. The
insurgency was initially concentrated in a few Communist strongholds in Western Nepal, but by the
end of the war, conflict-related casualties were recorded in 73 of the 75 Nepalese districts. The
Maoist presence varied from sporadic attacks to the organization of local governments and law
courts. Over the course of the conflict, Maoists attacked government targets, such as army barracks,
police posts, and local government buildings (Do and Iyer 2010). They were also reported to
terrorize, loot, abduct, and physically assault civilians (Bohara et al. 2006). However, government
security forces also killed civilians and were accused of using children for spying, torturing,
displacing, and summarily convicting civilians (Bohara et al. 2006).
The principal objective of the insurgents was the creation of a constituent assembly to draft a
new constitution. Other important stated aims were land redistribution and equality for all castes,
language groups, and women.
6
A crucial moment in the conflict was the Maoists’ abandonment of a short-lived cease fire in
November 2001. From that point, the government’s response intensified dramatically, involving the
Royal Nepal Army and leading to an escalation of violence (see figure S1 in the supplemental
appendix, available at http://wber.oxfordjournals.org/). Building on opposition to King Gyanendra’s
authoritative reaction to the prolonged conflict, the Maoists joined forces with some of the
country’s major political parties, leading to the signing of a peace agreement in November 2006 and
the creation of an interim government led by a power-sharing coalition that included the Maoists.
The intensity of conflict varied substantially across the districts of Nepal, as illustrated in figure
1, which depicts the distribution of districts between the three terciles of conflict deaths per 1,000
inhabitants. One specific characteristic of the Nepalese conflict that is likely to be particularly
relevant for an analysis of educational outcomes is the insurgents’ practice of abducting civilians,
particularly school children, en masse for short periods of intensive indoctrination. As illustrated by
figure S2, there is a positive correlation between the number of abductions by Maoists and the
intensity of fighting as measured by conflict-related casualties, but the relationship is not
systematic. Hence, it is possible to consider the effect of abductions over and above that of overall
conflict intensity. 2 Districts with the highest proportion of abductees among the population are
found in the middle tercile, which may be due to a lesser need for indoctrination in Maoist
strongholds. 3 Districts in the top quartile of the distribution of abductions per 1,000 inhabitants that
are not also in the top quartile of the distribution of casualties per 1,000 inhabitants tend to be found
at the far western or eastern borders, close to districts characterized by intense fighting (i.e.,
numerous casualties).
Several arguments have been advanced to explain the district variation in the intensity of the
insurgency, including geography (Murshed and Gates 2005; Bohara et al. 2006; Do and Iyer 2010),
poverty (Murshed and Gates 2004; Do and Iyer 2010), a lack of political participation (Bohara et al.
2006), and intergroup inequality (Murshed and Gates 2005; Macours 2011). Determinants of district
conflict intensity are therefore likely to be correlated with the explained variables of interest, which
7
could give rise to omitted variable bias. As long as the omitted variables in question are constant
over time, the inclusion of district fixed effects will suffice to remove any bias. If there are time-
varying omitted variables correlated with both conflict intensity and the explained variables of
interest, the inclusion of district fixed effects will not remove all potential biases, and additional
steps must be taken to shed light on the causal impact of the insurgency. In section IV, I test for the
presence of such time-varying omitted variables and discuss how I address potential threats to
identification.
Despite the civil conflict, Nepal has experienced steady growth in real gross GDP (5 percent per
year between 1995/96 and 2003/04), an additional increase in disposable income due to substantial
flows of remittances from abroad (representing 12.4 percent of the GDP), a steady decrease in
poverty over the period (from 42 percent in 1995/96 to 31 percent in 2003/04), and an improvement
in human development indicators, such as primary school enrollment (up from 57 percent to 73
percent) and child mortality, which decreased by 5 percent per year (World Bank 2005; Macours
2011).
However, the positive outlook for Nepal as a whole may mask unequal progress due to
heterogeneous conflict intensity across districts. Indeed, national trends may hide a slower decrease
in poverty, or even an increase in poverty, in more conflict-affected areas. In this paper, I exploit
variation in the intensity of exposure to violent conflict by birth cohort, survey year, and district to
investigate differential changes in primary educational attainment and completed years of education
across districts that experienced varying degrees of violence.
III. <>Data
DHS have been conducted in a number of developing countries as part of the Measure DHS
project, a reputable USAID-funded project. The second and third DHS in Nepal were conducted in
2001 and 2006, respectively, and are nationally representative repeated cross-sections. The timing
8
of these surveys is particularly useful because the surveys either preceded or followed the bulk of
the fighting. 4
For each DHS, a household survey collected the usual individual demographic and education
data as well as household-level socioeconomic information. More detailed information was then
collected from all women and a subset of men of reproductive age (if ever married, in the case of
the 2001 survey). The data used in this analysis come from the household survey as well as
migration information from the detailed interviews with women in the 2006 DHS. 5
Summary statistics by district conflict intensity and specification subsample can be found in
table S1.
The data used to measure conflict intensity are taken from electronic files provided by INSEC,
an independent, well-regarded human rights NGO based in Kathmandu with reporters in each of the
75 Nepalese districts who monitor human rights violations. The INSEC data files contain the
number of conflict-related deaths per month per district of Nepal between February 1996 and
December 2006 as well as the total number of school destructions and abductions by Maoists at the
district level, which are used to construct most measures of exposure to conflict used in this paper.
Data from INSEC have been extensively used in the media, international agencies, and government
reports and in a number of academic studies, including those by Bohara et al. (2006) and Do and
Iyer (2010). However, conflict deaths and school destructions are easier to monitor than abductions,
and there are some surprising figures in the abduction data provided by INSEC, such as only 284
abductions by Maoists in Rolpa during the entire conflict. A degree of measurement error is likely
to affect any conflict event data. If uncorrelated with the actual number of conflict events, this
classical measurement error would lead to attenuation bias. However, the measurement error would
have to be both inversely related to the true number of conflict events and very severe for it to lead
to a reversal of the sign of the estimated effect of conflict. Such a result appears implausible given
the degree of consensus on INSEC conflict data. Furthermore, findings using the number of
casualties are consistent with the simple difference-in-difference calculations in table 1, in which
9
the conflict event counts are collapsed into binary indicators. These are more blunt indicators of
conflict intensity, but they are also less prone to measurement error.
In addition, this study uses two indicators of Maoist control over a given district by 2003 based
on classifications reported by Hattlebak (2007). I consider, in turn, two alternative definitions of
Maoist control. I first categorize as under Maoist control any district that is categorized as such by
both Maoists and the government (Definition 1). I then apply what Hattlebak (2007) considers a
more reliable classification, the government classification (Definition 2).
IV. <>Empirical Strategy
I exploit the fact that surveyed individuals to have been exposed to varying degrees of conflict
intensity according to their district of residence, year of birth, and whether they were surveyed in
2001 or in 2006.
The baseline estimation strategy is similar to that in much of the literature estimating the impact
of civil conflict on individual outcomes reviewed in section I. The strategy exploits differences in
exposure to conflict by birth year cohort and district of residence for individuals surveyed at the end
of the conflict in 2006.
To check the robustness of the baseline results, I use a second identification strategy in which
the source of identification is the change in the intensity of conflict within the district between 2001
and 2006, just before and just after the escalation of the conflict. By 2006, individuals born in 1991
and 1996, for example, would have been exposed to the same total amount of conflict before or
during their schooling careers (albeit at different times in their lives). Hence, a comparison of the
schooling outcomes of individuals born in 1991 and 1996 in the 2006 DHS would not be
particularly informative. However, individuals born in 1991 who were observed at age 10 years in
the 2001 survey experienced much less conflict by the time their education data were collected in
2001 than individuals born in 1996 and observed at age 10 years in the 2006 survey. Therefore,
comparing their education outcomes at age 10 years is informative. 6 This second approach allows
10
me to use variation in conflict intensity over time that would be discarded in the traditional
approach. In addition, the second approach provides the opportunity for useful checks of the
robustness of my findings for potential migration and mortality biases.
I consider two outcome variables: a binary indicator for primary schooling completion and the
number of years of education completed. Less than 45 percent (20 percent) of the male (female)
adult population surveyed in the 2006 Nepal DHS had completed primary education, so primary
schooling completion is a relevant cutoff in the present context. Given the recent occurrence of the
conflict, a focus on primary education also has an advantage in that many individuals whose
primary schooling careers coincided with the conflict period are old enough to have completed their
primary education; hence, their long-term primary schooling outcomes are observed. Finally, given
the high prevalence of voluntary migration in Nepal, it is important to test the robustness of my
findings to migration bias. I do this by comparing the schooling attainment of children under 15
years of age surveyed in 2001 and 2006 in a given district. This age group is appropriate as long as I
focus on primary education. As of 2004, 97 percent of Nepali migrants were men aged 15–44 years
who typically left their wives and children behind (Lokshin and Glinskaya 2009). By focusing on
children under 15 at the time of the survey, the individual is thus both unlikely to have migrated
himself and unlikely to have accompanied a migrant parent. The DHS did not collect detailed
migration data, but it does provide data on the date of arrival at the current location for women of
reproductive age. Before the age of 15 years, the overwhelming majority of children are still living
with their mothers; hence, I can further test the robustness of my findings for migration bias by
restricting the sample to children whose mothers had not migrated since the beginning of the
conflict.
<>Specification 1: Exploiting Differences in Exposure to Conflict between Birth Year
Cohorts within Districts
11
Similar to previous studies on the impact of conflict on educational attainment, I first use data
from the postconflict DHS (2006) and exploit variations in exposure to conflict by birth year cohort
and district. In its simplest form, the estimating equation can be written as follows:
í µí±¡ í µí±¡ í µí±¡ (1)
í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— í µí±— + í µí¼€í µí±–í µí±— ,
= í µí¼¹í µí²‹ + í µí¼¶í µí²• + í µí»½ í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±¡
where í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— is, in turn, a dummy variable equal to one if individual i in district j born in year t
has completed primary education and zero otherwise or the number of years of education completed
by this individual; í µí¼¹í µí²‹ represents district fixed effects; í µí¼¶í µí²• represents birth year dummies; and
í µí±¡
í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— is the interaction between a dummy equal to one when the individual belongs to the
treated cohort and the number of conflict casualties (per 1,000 inhabitants) in district j during 1996–
2006. In the baseline regressions, I define the treated cohort as those aged 5 to 9 years at the
beginning of the conflict in 1996, whereas the control cohort includes individuals aged 16 to 19
years at the beginning of the conflict. This choice is discussed in the preliminary analysis at the end
of this section.
Under the assumption that there is no correlation between the number of district casualties and
unobserved factors varying with district and birth cohort, β is the causal effect of a one-unit increase
í µí±¡
in í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— on the primary completion rate or on the number of years of education completed by
í µí±¡
exposed cohorts. A one-unit increase in í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— roughly corresponds to one standard deviation
in the district-level distribution of casualties (0.98). Another way of appraising the magnitude of β
í µí±¡
is to consider a one-unit increase in í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— as a move from the district with the least conflict to
the 53rd district (out of 75) in order of conflict intensity or from the 53rd district to the 69th district
in order of conflict intensity—a very large increase in conflict intensity.
There are five “developmental regions� in Nepal, which are relatively homogeneous in terms of
their level of development (see figure 1). In equation (1), I implicitly restrict birth cohort effects
12
(í µí¼¶í µí²• ) to be identical across development regions. To reduce the potential for unobserved cohort-
district varying factors to bias the estimate of the effect of conflict exposure, in the main set of
results, I report estimates of equation (1R) in which the birth year intercepts are allowed to vary by
development region (í µí¼¶í µí±¹ 7
í µí²• ). Here, the effect of conflict is identified by using the difference in
exposure to conflict by district and birth year cohort, net of birth year trends common to all districts
within a given development region:
í µí±¡ í µí±¡ í µí±¡ (1R)
í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— = í µí¼¹í µí²‹ + í µí¼¶í µí±¹ í µí±— + í µí¼€í µí±–í µí±—.
í µí²• + í µí»½ í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
In addition, I estimate variants of equation (1) in which I add regressors capturing specific
aspects of the conflict that are likely to have affected schooling outcomes, namely, the number of
school destructions and abductions by Maoist forces (per 1,000 inhabitants) during the conflict. I
also consider whether Maoist control over the district had an effect on primary attainment by
í µí±¡
estimating variants of equation (1) in which I replace í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— with an indicator variable that
switches on when the district was under Maoist control at the height of the conflict.
It would be desirable to control for the socioeconomic status of the household in which an
individual was raised. However, household characteristics are not included as regressors in this
specification because these are only observed at the time of the survey in 2006, 10 years after the
beginning of exposure to conflict, and may therefore be caused by the conflict. In addition, at the
time of the survey, individuals in the control group were 25–28 years old, and members of the
treated group were 14–18 years old. Therefore, it is difficult to imagine household characteristics
that would not depend on the individual’s education level at the time of the survey. In the second
identification strategy described below, I observe school-aged children in their household; hence, I
can control for household characteristics.
13
<>Specification 2: Comparison of Outcomes in 2001 and 2006 for a Given Age at
Interview
In equation (1), I only use data from the postconflict DHS (2006) and exploit variations in
exposure to conflict according to birth year cohort and district. However, there is a comparable
survey for 2001, just before the conflict escalated, which allows me to estimate the impact of the
conflict using an alternative identification strategy based on variation in conflict exposure by survey
date and district. The idea is to exploit the fact that a child aged, for example, 10 years in 2001 will
have experienced much less conflict during his lifetime than another child aged 10 years in 2006 in
the same district, and the difference in conflict exposure between these two children will also vary
across districts. Finding results similar to those obtained using the traditional identification strategy
in equation (1) would bolster confidence in the reliability of my estimates. More specifically, I
estimate the following:
í µí± í µí± í µí± â€² í µí± (2)
í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— = í µí¼½í µí²‹ + í µí½€í µí²” + í µí»¾ í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— + í µí±‹í µí±–í µí±— í µí¼‘ + í µí¼‡í µí±–í µí±—
í µí± = 2001, 2006
í µí±
í µí±
í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— = � í µí±—í µí±¦ ,
í µí°¶í µí±‚í µí±?í µí°¹
í µí±¦=í µí±?í µí±–í µí±Ÿí µí±¡â„Ží µí±¦í µí±–
í µí±
where í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— is the primary education completion dummy or the number of completed years of
education of individual i in district j observed in survey year s, í µí¼½í µí²‹ represents district fixed effects,
í µí½€í µí²” is a survey dummy equal to one for DHS 2006 and zero for DHS 2001, í µí°¶í µí±‚í µí±?í µí°¹
í µí±—í µí±¦ is the number of
í µí±
conflict deaths per 1,000 inhabitants that occurred in district j in year y, and í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— is the
number of conflict deaths per 1,000 inhabitants in district j that occurred between the individual’s
birth year and survey year s in which the individual is interviewed, which I calculate from yearly
í µí±
district death counts. When í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— is the primary education indicator, the sample comprises
14
children aged 10–18 years, who may have completed primary education at the time of the survey.
í µí±
When í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— is the number of years of education completed, the sample comprises children aged
5–14 years, who are of primary school age at the time of the survey. Under the assumption that
there is no correlation between the cumulative number of district casualties between 1996 and year
s and unobserved district-survey-varying factors, í µí»¾ is the causal effect of a one-unit increase in
í µí±
í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— on the rate of primary schooling completion (the number of years of education
completed) by the 10- to 18-year-old (5- to 14-year old) group. The magnitude of í µí»¾ can be directly
í µí±¡ í µí±
compared to that of β because both í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— and í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— are expressed in district casualties
per 1,000 inhabitants.
í µí±
í µí±‹í µí±–í µí±— is a set of controls that contains age at interview dummies and their interaction with the
survey dummy in all specifications, thus allowing the educational attainment of each age group to
vary independently between the two surveys. 8 These covariates are included to control for potential
differences in the 2001–2006 change in the district-level age composition of the 10- to 18-year-old
or 5- to 14-year-old group that, if correlated with conflict intensity, could bias the results. In some
í µí±
variants, í µí±‹í µí±–í µí±— also includes household characteristics at the time of the survey (rural location and
education of the household head). These characteristics are only measured at the time of the survey
and could potentially be caused by past conflict. However, finding similar results when these
observable household characteristics are included would suggest that potential changes in the
composition of households due to mortality or migration do not drive my findings.
To further reduce the potential for unobserved time-varying factors to bias the estimate of the
effect of conflict exposure, in the main set of results, I report estimates of equation (2R) in which
the coefficients of the survey dummy, the age at survey intercepts, and their interactions are allowed
to vary by development region. Here, the effect of conflict exposure is identified using the within-
district change in conflict exposure at age x between 2001 and 2006, net of 2001–2006 changes in
educational attainment at age x common to all districts in a given development region:
15
í µí± í µí± í µí± í µí±… ′ í µí± (2R)
í µí°¸í µí°·í µí±ˆí µí°¶í µí±–í µí±— = í µí¼½í µí²‹ + í µí½€í µí±¹
í µí²” + í µí»¾ í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— + í µí±‹í µí±–í µí±— í µí¼‘ + í µí¼‡í µí±–í µí±— .
This second identification strategy has three important advantages compared to the traditional
approach based on variation in exposure by birth cohort and district for individuals observed at a
single point in time. First, it has the advantage of comparing cohorts that are born only five years
apart but have experienced very different degrees of conflict (i.e., a 10-year-old in 2001 in district j
was exposed to much less conflict than a 10-year-old in 2006 in district j but was born only five
years earlier), which reduces concerns regarding potential confounders, including differential
migration patterns. Second, when the sample is restricted to children aged 14 years and under, the
concern regarding selection bias due to voluntary migration decreases because most migrants are
men aged 15–44 years, who typically leave their wives and children behind (Lokshin and Glinskaya
2009). Third, I can further test the robustness of my findings to migration bias by excluding from
the sample children surveyed in 2006 whose mothers moved to their current location after 1996. 9
All specifications are estimated using linear district fixed-effects panel data models. All models
allow for error terms to be correlated in an arbitrary fashion within a district to avoid overrejection
of the null hypothesis of zero treatment effect due to serial correlation, following Bertrand et al.
(2004).
<>Preliminary Analysis
An inspection of the data shows that although the legally mandated age for beginning schooling
is six years old and there are five years of primary schooling, a sizeable proportion of children are
enrolled in primary school before age six (70.1 percent at five years) and until age 14 (16.1 percent)
in the 2006 DHS, with numbers subsequently decreasing sharply (7.9 percent at 15 years and 2.85
percent at 16 years). 10 Therefore, an analysis of the long-term effect of conflict on primary
schooling completion should consider children aged at least 14 years at the time of the survey, and
control cohorts should have been at least 15 years at the beginning of the conflict (and preferably
16
slightly older). When estimating variants of equation (1), I therefore define the treated cohort as
comprising individuals aged five to nine years at the beginning of the conflict in 1996, such that all
treated cohorts are exposed to the conflict during most of their potential primary schooling careers
and the youngest exposed cohort is observed at age 14 in the 2006 DHS. In the main regressions,
the control cohorts include individuals aged 16 to 19 years at the beginning of the conflict—that is,
individuals who were born too early to have their primary education affected by the conflict but are
as close, and therefore as comparable, to the treated cohorts as possible. I also provide a robustness
check in which the control group comprises individuals aged 18–25 years at the start of the conflict.
In the baseline specification, I exclude cohorts aged 10 to 15 years in 1996 because the treatment
status of these cohorts is less clear. Many of these individuals could have been enrolled in primary
schooling during the conflict, but they were not exposed to conflict during most of their primary
schooling careers (see figures S3a and S3b).
Panel A of table 1 illustrates the basic identification strategy used in the baseline specification.
This panel shows the difference in the increase in primary schooling between cohorts exposed (row
(1)) and not exposed (row (2)) to conflict during their primary schooling careers in districts
experiencing above-median conflict casualties (columns (1) and (4)) compared to districts
experiencing below-median conflict casualties (columns (2) and (5)). Women born too early to be
affected by the conflict during their primary schooling years have a much lower rate of primary
schooling in high-conflict districts compared to women in low-conflict districts. In contrast, primary
schooling completion is slightly higher in high-conflict districts compared to low-conflict districts
among the cohort of women who were entering primary school around the beginning of the conflict,
resulting in an additional increase in female primary schooling of 19 percentage points between the
two cohorts in high-conflict areas compared to low-conflict areas. A qualitatively similar but less
dramatic effect is observed among men.
To shed light on the direction of the potential biases due to differential preconflict trends, I
conduct several control or “placebo� experiments in which conflict exposure is artificially assigned
17
to cohorts who were too old to be affected by the conflict. In panel B1, I compare the change in
primary schooling attainment between cohorts aged 16–19 years and cohorts aged 26–29 years at
the beginning of the conflict in above-median versus below-median conflict intensity districts. The
difference-in-difference is negative, and for females, it is statistically significant. This finding
indicates that rates of primary schooling completion were improving more slowly in areas where
more conflict occurred in 1996–2006 when comparing cohorts that were potentially enrolled in
primary school (i.e., aged 5–14 years) in the 1972–1984 period and cohorts that were potentially
enrolled in the 1982–1994 period. If this trend had continued during the conflict period, the
estimates presented in this paper would be a lower bound of the true effect of conflict; that is, the
positive coefficient of the conflict variable would be an overly conservative estimate, especially for
females.
The ideal placebo experiment would be based on the actual cohorts involved in the experiment
of interest in the absence of conflict. Such a test is clearly not feasible. However, it is possible to
conduct an additional placebo experiment based on cohorts born immediately before the period
relevant to the experiment of interest to check for differences in trends as close as possible to the
period of interest. The results of this additional placebo experiment comparing cohorts aged 16–19
years at the beginning of the conflict with those aged 20–23 years are shown in panel B2. During
this period immediately preceding the conflict, I cannot reject the hypothesis that the evolution of
primary schooling was parallel in districts with above- and below-median conflict casualties for
both males and females.
In panel C, the experiment is conducted by replacing the below- and above-median casualty
categories with below- and above-median Maoist abduction categories. The results in panel C show
that primary schooling has progressed more rapidly in districts with above-median Maoist
abductions. However, two-thirds of districts classified as high (low) conflict based on the median
number of casualties are also classified as high (low) conflict based on the median number of
abductions. Therefore, these simple two-by-two calculations may capture the effect of overall
18
conflict intensity, the effect of Maoist abductions, or both. In the regressions that follow, I
disentangle the effect of overall conflict intensity and Maoist abductions by including both conflict
variables.
Panels D1 and D2 show results for tests that replicate the placebo tests for panels B1 and B2,
where the definition of high- and low-conflict districts based on above- and below-median conflict
casualties is replaced with the definition based on above- and below-median district abductions.
Females experienced similar preconflict primary education trends in high- and low-abduction
districts. Male cohorts found in high-abduction districts experienced slower progress in preconflict
primary education relative to low-abduction districts. If the same trends continued for cohorts
considered in the experiment of interest, then the effect of exposure to Maoist abductions would
tend to be biased downward for males (i.e., to be more negative) but not for females. On the
contrary, in the regression analysis, I find that after controlling for district casualties, the education
of females suffered from Maoist abductions, but that the education of males did not. Therefore, the
difference in male trends observed in panels D1 and D2 does not drive my conclusions.
V. <>Results
The preliminary analysis in section IV suggested that primary schooling completion rates tended
to increase more rapidly during armed conflict in areas that experienced a high intensity of conflict,
especially for girls. In tables 2 to 4, I present estimates of the impact of exposure to conflict on
educational outcomes to determine whether this striking conclusion of the preliminary analysis is
confirmed when using more detailed information on the intensity of conflict, controlling for
unobserved heterogeneity between individual districts and between regions over time and using
different identification strategies.
Table 2 reports findings on the impact of conflict exposure on primary schooling completion.
The first two columns present estimates of the long-term effect of conflict intensity on primary
schooling completion using the baseline specification (equation (1R)). The last four columns
19
indicate the robustness of these findings through comparison of the change in primary completion
rates for the 10- to 18-year-old group for districts with varying degrees of conflict intensification
between the 2001 and 2006 DHS (equation (2R)). The last two columns include controls for rural
location and the educational attainment of the household head.
The results in the first column indicate that areas with more fighting witnessed a larger increase
in female primary education attainment. Casting this result in terms of the distribution of conflict
violence, an increase in violence of one standard deviation of the district-level distribution of
casualties during the conflict (0.98 casualties per 1,000 inhabitants) increases female primary
schooling attainment by 5.6 percentage points. This is roughly the effect of a move from the 5th to
the 75th percentile of the district-level conflict distribution of total casualties. The sign and order of
magnitude of this effect is confirmed when comparing cohorts born only five years apart but
exposed to very different levels of conflict using equation (2R). Across all specifications in table 2,
conflict exposure does not appear to significantly affect male primary schooling completion,
although the gender difference in the conflict effect is only statistically significant in columns (3) to
(6).
These results are robust to including controls for household characteristics, suggesting that the
results are not driven by a change in household composition due to, for example, selective mortality
or migration (columns (5) and (6)). Table S2 presents specifications similar to those in table 2 but
replaces the indicator for primary schooling completion with years of education completed. Similar
results are obtained, indicating that an increase in violence of one standard deviation increases
female educational attainment by 0.6 years.
Table S3 presents three different specifications to further check the robustness of the
baseline results in the first two columns of table 2 to the following changes in specification:
restricting birth year fixed effects to be identical across the five development regions of Nepal,
changing the control cohort, and replacing the number of casualties with its natural logarithm. The
results in table S3 confirm that primary education progressed more rapidly during the conflict in
20
districts experiencing more casualties and that this effect was more robust across specifications for
females.
Next, I investigate whether specific aspects of the conflict had different effects on primary
schooling completion (table 3). First, I use INSEC data on the total number of school destructions
per district to test whether these destructions had a negative effect on primary schooling completion
despite the overall positive impact of the insurgency (columns (1) and (2)). For both genders, I find
a statistically insignificant effect, which is likely because a district-level analysis lacks the power to
identify the effect of school destructions. School destructions were a rare and isolated aspect of the
conflict 11 that could be expected to have had a large effect on schooling at a disaggregated level but
not at the district level. Second, I use INSEC data on the total number of abductions by Maoists per
district to test whether a larger number of abductions, often targeting school children, had an
adverse effect on schooling. The results in columns (3) and (4) indicate that abductions had a
negative effect on female primary schooling. An increase in the number of abductions (per 1,000
inhabitants) by one standard deviation of the district-level distribution (16.82) decreases female
primary schooling attainment by 3.7 percentage points. In other words, the effect of a move from
the 5th to the 75th percentile of the district-level distribution of total abductions yields a 1.6
percentage point decline in female primary completion. Third, I test whether primary schooling
completion improved more in districts controlled by Maoists where the insurgents were better able
to affect schooling provision according to their ideology (columns (5) to (8)). There is no clear-cut
definition of insurgent control, with discrepancies between the classifications used by the People’s
Army and the government (Hatlebakk 2007). Therefore, I use two alternative classifications. The
choice of definition affects the magnitude and significance of estimates, but the overall message is
that primary schooling has tended to become more prevalent over time for both genders in areas
controlled by the Maoists.
Tables S4 and S5 replicate the analysis in table 3 with birth year fixed effects restricted to be
identical across development regions of Nepal (tables S4 and S5) and the control cohort replaced
21
with individuals aged 18–24 years at the beginning of the conflict (table S5). The same conclusions
apply as those drawn from the set of preferred results in table 3.
In table 4, I turn to the estimated effect of an increase in conflict intensity between 2001 and
2006 on completed years of education of children of primary schooling age (5–14 years) at the time
they were surveyed (as per equation (2R)).
Column (1) of table 4 indicates that an increase in casualties since birth by one standard
deviation increases the completed years of education by just over one-quarter of a year for girls
aged 5 to 14 years in 2006 compared to girls from the same district who were the same age when
surveyed in 2001, before the conflict escalated. For boys, the coefficient of interest is less than half
the magnitude of that for girls and is significantly different from the estimated conflict effect for
girls (at the 10 percent significance level). The estimates are very similar when restricting the 2006
sample to children whose mothers had not moved since 1996 (columns (3) and (4)), which confirms
that changes in composition due to migration patterns are not driving these findings. In table S6, I
repeat the analysis in table 4 but restrict the age intercepts and survey year dummy to be identical
across Nepal’s development regions. The results for the female sample are almost identical, but
estimates for the male sample are now nearly as large as those for the female sample and are
statistically significant. Echoing the findings for primary schooling completion, overall, these
results confirm that primary education progressed more rapidly during the conflict in districts that
experienced more casualties, and this effect is more robust across specifications for females.
In conclusion, the results presented in this section provide no support for the hypothesis that the
Nepalese civil conflict had a negative effect on schooling overall. There is a robust positive effect
of the intensity of the insurgency on female educational attainment, but there is less of an effect for
male educational attainment. There is also evidence of a decrease in female primary schooling
completion where insurgents were more prone to abductions, holding overall conflict intensity
constant.
22
VI. <>Concluding Remarks
Despite experiencing a substantial civil conflict between 1996 and 2006, Nepal has surprisingly
enjoyed one of the best periods in its history in terms of economic growth and poverty reduction. At
present, however, little is known about whether this period of development at the aggregate level
hides disparities at a more disaggregated level due to the wide variation in conflict intensity across
the country.
In this paper, I exploit variation in exposure to conflict by birth cohort, survey date, and district
to estimate the impact of conflict intensity on schooling outcomes.
I find no support for the hypothesis that civil-conflict-related violence, as measured by the
number of conflict casualties, had a negative effect on the quantity of schooling attained by children
of either gender. On the contrary, there is robust evidence that female primary schooling attainment
increased in districts that experienced more conflict deaths relative to districts with fewer conflict
deaths. This result holds irrespective of whether one compares (within a given district) (i) the
completion of primary education for cohorts exposed and not exposed to the conflict and observed
at the end of the conflict or (ii) years of education completed by a given age for cohorts observed
before (2001) and after (2006) a sharp escalation of the conflict and that were therefore exposed to
very different degrees of conflict. It is also robust to a number of changes in specifications. In
particular, robustness checks indicate that changes in household composition due to conflict-
induced migration patterns do not drive this finding.
However, one aspect of the Nepalese civil conflict that is particularly relevant to schooling
outcomes had adverse consequences on female primary schooling: the widespread insurgent
practice of abducting civilians, many of whom were school children.
The findings reported in this paper echo the positive changes observed for Nepal as a whole
during the conflict period in terms of economic growth, education, and child health. The present
analysis shows that the progress in education observed at the country level does not hide a slower
increase in districts where more fighting occurred, but the insurgent practice of abducting civilians
23
adversely affected female educational outcomes. The estimates presented in this paper are in line
with the existing qualitative literature on the Nepalese civil conflict, which consistently reports
mixed conclusions with respect to the impact of the conflict on education and female empowerment
(e.g., Hart 2001; Lama-Tamang 2003; Manchanda 2004; Pettigrew and Schneiderman 2004; Geiser
2005; Aguirre and Pietropaoli 2008; Ariño 2008; Falch 2010).
Education, particularly female educational attainment, appears to have benefited from the
societal changes induced directly or indirectly by the insurgency more than it was adversely
affected by the loss of income and other disruptions caused by the conflict. Data limitations prevent
a more detailed analysis of the channels through which the conflict affected education beyond the
distinction between the effect of conflict as a whole and that of abductions. However, potential
mechanisms suggested by the existing anthropological and peace studies literature include Maoist
efforts to remove barriers to schooling for all children from the lower castes and to reduce teacher
absenteeism (e.g., Hart 2001; Lama-Tamang 2003), which could have benefited both male and
female education; the Maoist influence in encouraging or coercing parents to send girls to school
(Hart 2001); and the Maoists’ effect on female empowerment. Although the exact figure is
contested, a substantial share of the guerillas in the Maoist ranks was female. Many more females
were involved in the Maoist movement without direct participation in combat, such as by
disseminating propaganda (Lama-Tamang et al. 2003; Pettigrew and Schneiderman 2004), and even
larger numbers may have been influenced by the Maoist discourse on gender equality. In addition,
there is anecdotal evidence of an improvement in the condition of women in areas controlled by the
Maoists, such as decreases in polygamy, domestic violence, and alcoholism, as well as greater
support for women to divorce their husbands (Lama-Tamang et al. 2003; Manchanda 2004; Geiser
2005; Ariño 2008). Although the insurgents’ rhetoric was often in contrast with their actual practice
(Pettigrew and Schneiderman 2004), the presence of females in their ranks and the propaganda
promoting female autonomy may have increased female bargaining power within the household as
well as female aspirations. According to Hart (2001), “girls and women are strongly encouraged to
24
gain an education and to participate in society generally and in activities connected to the ‘People’s
War’ in particular. This directly challenges their traditional role and apparently stimulates girls to
consider leading lives beyond marriage and the home (Hart 2001, p.35)�. Furthermore, an
unintended consequence of the conflict has been that women have adopted roles typically reserved
for men. Women’s involvement in the labor market increased as a consequence (Menon and
Rodgers 2011). The rise in female labor market participation may have increased returns to female
schooling and motivated girls to obtain more education and parents to invest more in their
daughters’ education. Increased female earnings are also likely to improve the ability of mothers to
influence the way household resources are spent. Moreover, there is evidence that when women
have more control over household expenditures, such as because their own earnings make up a
larger share of the household’s income, investments in children increase; this is especially the case
for girls (e.g., Thomas 1990; Duflo 2003), although this may not be the case in all contexts
(Quisumbing and Maluccio 2003; Gitter and Barham 2008). Finally, the nature of the occupations
of women outside the home also changed. In many areas, women were reported to take on
leadership roles in local institutions, including schools (Pettigrew and Schneiderman 2004). This
improvement in female representation in local institutions may have contributed to increased
education, especially for girls.
Data limitations prevent rigorous tests of the role played by these different potential channels in
explaining the finding that education, particularly female education, increased more in areas where
the fighting was more intense. 12 Future research aiming to disentangle the role of each of the
channels through which the insurgency may have improved educational outcomes would be
valuable.
An issue beyond the scope of this paper is the important question of the effect of civil conflict
on the quality of education, which is potentially large (for a review, see Shemyakina and Valente
2011). Data limitations have thus far precluded quantitative research on the impact of conflict on
the quality of schooling, but there is growing evidence that cognitive skills, rather than completed
25
years of education, matter for individual earnings and economic growth (e.g., Hanushek and
Woessmann 2008). Therefore, even where the number of years of education completed is not
adversely affected by civil conflict, such conflict may have deleterious effects on human capital if
the quality of learning deteriorates.
From an international perspective, this paper contributes to unpacking the complexity that lies
behind the generic term civil conflict. The idiosyncrasies of each conflict highlight the need for
additional research on the impacts of different conflicts to shed light on the range of potential
effects rather than a focus on extreme, but thankfully rare, instances.
From a policy perspective, the present findings call for measures that aim to protect school
children and teachers from being directly targeted by combatants. As shown in this paper, even
where primary education systems appear very resilient to surrounding violence, direct targeting of
schools, however mild (e.g., brief abductions of pupils and teachers for indoctrination purposes),
has adverse effects on schooling, especially for girls.
NOTES
The author thanks INSEC for sharing their conflict data and Martha Ainsworth, Quy-Toan Do,
Helge Holterman, Steve McIntosh, Gudrun Østby, Kati Schindler, Olga Shemyakina, Helen
Simpson, Sarah Smith, Frank Windmeijer, Hassan Zaman, the World Bank’s Nepal Country
Director's office, and participants at two Gender and Conflict Research Workshops at the World
Bank (Washington) and Peace Research Institute Oslo for their useful comments. The author also
thanks three anonymous referees for their valuable comments and suggestions. This work was
26
supported by the World Bank-Norway Trust Fund. The views expressed in this paper are those of
the author alone and do not necessarily reflect those of the funding agencies.
1
The estimated number of individuals forcibly displaced in Nepal is approximately 200,000
(USAID, 2007). Studies reviewed in this paper have considered the impact of conflict on education
in Tajikistan (between 50,000 and 100,000 deaths and 10 percent of the country’s population
internally displaced in the two worst years of conflict, Shemyakina, 2011b), Guatemala (200,000
deaths during the worst conflict period, Chamarbagwala and Morán, 2011), Rwanda (nearly 10
percent of the population killed, Akresh and de Walque, 2008), and Peru (just under 70,000 deaths,
León 2012).
2
The correlation coefficient between total conflict deaths and abductions per 1,000 inhabitants is
0.14.
3
Hutt (2005) also links abductions to the weakness of support for the Maoists: “The Maoists know
that much of their support is hollow and based on fear. Maoist cadres have taken to mounting
temporary abductions of large numbers of school teachers and students, who are taken to remote
locations and subjected to political indoctrination sessions� (Hutt, 2005, p.86).
4
In 2001, six out of 257 sampling units had to be dropped from the sample for security reasons
(Ministry of Health et al., 2002, p.6).
5
In the 2001 survey, children listed on the household roster cannot be matched to their mothers.
6
The variation in exposure between surveys also varies substantially between districts. For
instance, in Mahottari, there were 0.14 additional deaths per 1,000 inhabitants between the two
DHS, whereas in Jumla, there were close to three additional deaths per 1,000 inhabitants during the
same period.
7
Note that region dummies are subsumed under the district fixed effects, but the interactions
between regions and birth year dummies are not.
27
8
When estimating equation (2) without any of the controls included, is positive and statistically
significant for both females and males in the completed years of education regression and positive
and statistically significant for females only in the primary education completion regression (full
results are available upon request).
9
The education data used in this paper come from the DHS household datasets. Information on date
of arrival at the present location was only collected in individual interviews with women aged 15–
49 years. The same exclusion could not be implemented for the 2001 DHS because individuals
listed on the household roster cannot be matched to their mothers.
10
In the 2001 DHS, 44.4 percent of five-year-olds, 24.6 percent of 14-year-olds, and 13.6 percent of
15-year-olds were enrolled in primary schooling.
11
According to the data provided by INSEC, 76 schools were destroyed.
12
See the appendix for some insights based on self-reported measures of female empowerment
available in the DHS.
28
REFERENCES
Aguirre, Daniel, and Irene Pietropaoli. 2008. “Gender Equality, Development and Transitional
Justice: The Case of Nepal.� The International Journal of Transitional Justice 2: 356-377.
Akbulut-Yuksel, Mevlude 2009. “Children of war: the long-run effects of large-scale physical
destruction and warfare on children.� IZA Working Paper No.4407. IZA, Bonn.
Akresh, Richard, and Damien de Walque. 2008. "Armed Conflict and Schooling: Evidence from the
1994 Rwandan Genocide.� World Bank Policy Research Paper No.4606. World Bank,
Washington.
Annan, Jeannie, Chris Blattman, Dyan Mazurana, and Khristopher Carlson. 2009. “Women and
Girls at War: ‘Wives’.� Mothers, and Fighters in the Lord’s Resistance Army, HICN
Working paper 63.
Ariño, Maria Villellas. 2008. “Nepal: A Gender View of the Armed Conflict and Peace Process.�
Quaderns de Construcció de Pau No.4, Escola de Cultura de Pau, Barcelona.
Armon, Jeremy, Chris Berry, Debi Duncan, Rebecca Calder, Susan Clapham, and Mark Harvey.
2004. “Service Delivery in Difficult Environments: the case of Nepal.� DFID, Policy
Division, Asia Policy Division, Nepal Country Office.
Becker, Jo. 2009. “Child Recruitment in Burma, Sri Lanka, and Nepal.� In Child Soldiers in the Age
of Fractured States, eds. S. Gates and S. Reich, 108–120. University of Pittsburg Press,
Pittsburg.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We Trust
Differences-in-Differences Estimates?� Quarterly Journal of Economics 1191: 249–275.
Blattman, Chris, and Jeannie Annan. 2010. “The Consequences of Child Soldiering.� Review of
Economics and Statistics 92 (4): 882–898.
Blattman, Chris, and Edward Miguel. 2010. “Civil War.� Journal of Economic Literature 48 (1): 3–
57.
29
Bohara, Alok, Neil Mitchell, and Mani Nepal. 2006. “Opportunity, Democracy, and the Exchange
of Political Violence: A Subnational Analysis of Conflict in Nepal.� Journal of Conflict
Resolution 50 (1): 108–128.
Central Bureau of Statistics [Nepal]. 2009. Nepal Statistical Yearbook 2009.
http://www.cbs.gov.np/Year_Book_2009/images/Final_Chapters/chapter1/1.3.pdf.
Chamarbagwala, Rubiana, and HilcÃas E. Morán. 2011. “The Human Capital Consequences of Civil
War: Evidence from Guatemala.� Journal of Development Economics 94: 41–61.
Collins Sophia. 2006. “Assessing the Health Implications of Nepal's Ceasefire.� Lancet 368: 907–
908.
Devkota, Bhimsen, and Edwin R. van Teijlingen. 2010. “Understand Effects of Armed Conflict on
Health Outcomes: The case of Nepal.� Conflict and Health 4: 20.
Do, Quy-Toan, and Lakshmi Iyer. 2010. “Geography, Poverty and Conflict in Nepal.� Journal of
Peace Research 47 (6): 735–748.
Duflo, Esther 2003. “Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold
Allocation in South Africa.� World Bank Economic Review 17 (1): 1–25.
Falch, Ashild. 2010. “Women’s Political Participation and Influence in Post-Conflict Burundi and
Nepal.� Peace Research Institute Oslo Working Paper, Oslo.
Geiser, Alexandra. 2005. “Social exclusion and conflict transformation in Nepal: women, Dalit and
ethnic groups.� Working Paper 5/2005, Swisspeace, Bern.
Gitter, Seth R., and Bradford L. Barham. 2008. “Women's Power, Conditional Cash Transfers, and
Schooling in Nicaragua.� World Bank Economic Review 22 (2): 271–290.
Hanushek, Eric A., and Ludger Woessmann. 2008. “The Role of Cognitive Skills in Economic
Development.� Journal of Economic Literature 46: 607–668.
Hart, Jason. 2001. “Conflict in Nepal and its Impact on Children.� A Discussion Document
Prepared for UNICEF Regional Office South Asia, Refugee Studies Centre, Oxford.
30
Hatlebakk, Magnus. 2007. “LSMS Data Quality in Maoist Influenced Areas of Nepal.� No 6, CMI
Working Papers, CMI (Chr. Michelsen Institute), Bergen.
Human Rights Watch. 2004. “Between a Rock and a Hard Place: Civilians Struggle to Survive in
Nepal’s Civil War.� http://www.hrw.org/en/reports/2004/10/06/between-rock-and-hard-
place?print, accessed 10th Feb 2011.
Hutt, Michael. 2004. “Nepal and Bhutan in 2004: Two Kings, Two Futures.� Asian Survey 45 (1):
83–87.
Ichino, Andrea, and Rudolf Winter-Ebmer. 2004. “The Long-Run Educational Cost of World War
II.� Journal of Labor Economics 22 (1): 57–86.
INSEC. 2009. Electronic data files. Informal Sector Service, Kathmandu.
Lama-Tamang, Mukta S., Sumitra M. Gurung, Dharma Swarnakar, and Sita Rana Magar. 2003.
“Social Change in Conflict Areas: Assessment Report.� Prepared for UK Department for
International Development (DFID) Nepal. Kathmandu.
León, Gianmarco. 2012. “Civil Conflict and Human Capital Accumulation: The Long-Term Effects
of Political Violence in Perú.� Journal of Human Resources 47 (4): 992–1022.
Lokshin, Michael M., and Elena Glinskaya. 2009. “The Effect of Male Migration on Employment
Patterns of Women in Nepal.� World Bank Economic Review 23 (3): 481–507.
Macours, Karen. 2011. Increasing Inequality and Civil Conflict in Nepal.� Oxford Economic Papers
63 (1): 1–26
Manchanda, Rita. 2004. “Maoist Insurgency in Nepal. Radicalizing Gendered Narratives.� Cultural
Dynamics 162 (3): 237–258.
Menon, Nidhiya, and Yana Rodgers. 2011. “War and Women’s Work: Evidence from the Conflict
in Nepal.� World Bank Policy Research Working Paper No. 5745, World Bank,
Washington.
31
Ministry of Health [Nepal], New ERA, and ORC Macro. 2002. Nepal Demographic and Health
Survey 2001. Calverton, Maryland, USA: Family Health Division, Ministry of Health; New
ERA; and ORC Macro.
Murshed, S. Mansoob, and Scott Gates. 2005. “Spatial-Horizontal Inequality and the Maoist
Insurgency in Nepal.� Review of Development Economics 9(1): 121–134.
Pettigrew, Judith, and Sara Schneiderman. 2004. “Women and the Maobaadi: Ideology and Agency
in Nepal’s Maoist Movement.� Himal South Asia 17 (1): 19–29.
Quisumbing, Agnes R., and John A. Maluccio. 2003. “Resources at Marriage and Intrahousehold
Allocation: Evidence from Bangladesh, Ethiopia, Indonesia, and South Africa.� Oxford
Bulletin of Economics and Statistics 65: 283–327.
RodrÃguez, Catherine, and Fabio Sánchez. 2009. “Armed Conflict Exposure, Human Capital
Investments and Child Labor: Evidence from Colombia.� HiCN Working Paper No. 68.
Shemyakina, Olga 2011a. “The Effect of Armed Conflict on Accumulation of Schooling: Results
from Tajikistan.� Journal of Development Economics 95 (2): 186–200.
Shemyakina, Olga 2011b. “Labor Market, Education and Armed Conflict in Tajikistan.� World
Bank Policy Research Working Paper No. 5738, World Bank, Washington.
Shemyakina, Olga, and Christine Valente. 2011. “Education and Violent Conflict in South Asia:
Challenges and Solutions.� Background paper prepared for the World Bank Regional Study
on “Improving the Quality of Learning in South Asia.� Mimeograph, University of Bristol,
Bristol.
Swee, Eik Leong. 2009. “On War and Schooling Attainment: The Case of Bosnia and
Herzegovina.� HiCN Working Paper No. 57.
Thomas, Duncan. 1990. “Intra-Household Resource Allocation: An Inferential Approach.� Journal
of Human Resources 25 (4): 635–664.
UNESCO. 2010. Education under Attack. United Nations Educations Scientific and Cultural
Organization, Paris.
32
USAID. 2007. Nepal – Humanitarian Assistance Fact Sheet No. 1, June.
http://pdf.usaid.gov/pdf_docs/PNADJ147.pdf. Accessed 5th May 2010.
Wallensteen, Peter, and Margareta Sollenberg. 2001. “Armed Conflict 1989–2000.� Journal of
Peace Research 38 (5): 629–644
World Bank. 2005. “Nepal - Resilience amidst Conflict: An Assessment of Poverty in Nepal, 1995–
96 and 2003–04.� Report No. 34834-NP. World Bank, Washington.
33
Figure 1. Conflict Intensity across Districts of Nepal. Author’s calculations are based on casualties
recorded in INSEC (2009) and district population figures from the 1991 population census (Central
Bureau of Statistics, 2009). District terciles are defined by the distribution of total district casualties
per 1,000 inhabitants.
34
TABLE 1. Preliminary Difference-in-Difference Calculations, Completion of Primary Schooling
Female Male
Primary Education Rate by
Number of Number of
Casualties in Casualties in
District District
(1) (2) (3) (4) (5) (6)
High Low Difference High Low Difference
Panel A: Binary DiD Experiment
(1) Age 5 to 9 in 1996 0.67 0.64 0.03 0.80 0.79 0.01
(2) Age 16 to 19 in 1996 0.27 0.43 −0.16 0.63 0.70 −0.07
Difference 0.40 0.21 0.19 *** 0.17 0.09 0.08 *
(0.045) (0.038)
Panel B1: Placebo DiD Experiment 1
(1) Age 16 to 19 in 1996 0.27 0.43 −0.16 0.63 0.70 −0.07
(2) Age 26 to 29 in 1996 0.13 0.20 −0.07 0.50 0.53 −0.03
Difference 0.14 0.23 −0.09 ** 0.13 0.17 −0.04
(0.045) (0.047)
Panel B2: Placebo DiD Experiment 2
(1) Age 16 to 19 in 1996 0.27 0.43 −0.16 0.63 0.70 −0.07
(2) Age 20 to 23 in 1996 0.15 0.32 −0.17 0.58 0.62 −0.04
Difference 0.12 0.11 0.01 0.05 -
0.08 −0.03
(0.040) (0.047)
Number of Number of
Abductions in Abductions in
District District
High Low Difference High Low Difference
Panel C: Binary DiD Experiment
(1) Age 5 to 9 in 1996 0.70 0.62 0.08 0.82 0.77 0.05
(2) Age 16 to 19 in 1996 0.33 0.37 −0.04 0.61 0.70 −0.09
Difference 0.37 0.25 0.12 ** 0.21 0.07 0.14 ***
(0.053) (.038)
Panel D1: Placebo DiD Experiment 1
(1) Age 16 to 19 in 1996 0.33 0.37 −0.04 0.61 0.70 −0.09
(2) Age 26 to 29 in 1996 0.15 0.19 −0.04 0.53 0.52 0.01
Difference 0.18 0.18 0.00 0.08 0.18 −0.10 **
(0.051) (0.049)
Panel D2: Placebo DiD Experiment 2
(1) Age 16 to 19 in 1996 0.33 0.37 −0.04 0.61 0.70 −0.09
(2) Age 20 to 23 in 1996 0.22 0.27 −0.05 0.62 0.59 0.03
Difference 0.11 0.10 0.01 −0.01 -
0.11 −0.12 ***
(0.041) (0.045)
Source: INSEC 2009, Central Bureau of Statistics 2009. Education data are based on Nepal DHS 2006.
Note: District casualties are expressed per 1,000 inhabitants. “High� and “low� refer to above-median or below-median
district totals per 1,000 inhabitants. Standard errors clustered at the district level are in parentheses. All first differences
(i.e., row (1) – row (2) for a given conflict category) are statistically significant, except for values marked with -. DiD
indicates difference-in-difference.
* p < 0.10
** p < 0.05
*** p < 0.01
35
TABLE 2. Impact of Conflict Intensity Measured by Casualties on Primary Schooling Completion
(1) (2) (3) (4) (5) (6)
Primary Primary
Primary Primary Primary Primary
Explained Variable and Education - Education -
Education - Education - Education - Education -
Sample Female 10– Female 10–
Female Male Male 10–18 Male 10–18
18 18
Specification Eq. (1R) Eq. (1R) Eq. (2R) Eq. (2R) Eq. (2R) Eq. (2R)
=1 if 5–9 in 1996 × 0.0555** 0.0241
District casualties during
1996–2006
í µí±¡
(í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— ) (0.0272) (0.0251)
District casualties before 0.0764* 0.0036 0.0811** 0.0094
í µí±
survey (í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— ) (0.0431) (0.0368) (0.0368) (0.0338)
=1 if rural −0.1094*** −0.0622***
(0.0205) (0.0154)
=1 if head has primary 0.0543*** 0.0684***
education (0.0133) (0.0115)
=1 if head has secondary 0.2127*** 0.2046***
education (0.0120) (0.0139)
=1 if head has higher 0.3249*** 0.2597***
education (0.0251) (0.0253)
Panel variable District District District District District District
Included dummies:
Year of birth Yes Yes No No No No
Region × Year of birth Yes Yes No No No No
DHS 2006 No No Yes Yes Yes Yes
Age at interview No No Yes Yes Yes Yes
Region × DHS 2006 No No Yes Yes Yes Yes
DHS 2006 x Age at
interview No No Yes Yes Yes Yes
Region × Age at interview No No Yes Yes Yes Yes
DHS 2006 × Region × Age
at interview No No Yes Yes Yes Yes
Observations 3,823 3,055 9,595 9,267 9,584 9,255
a a a
No. of clusters 75 75 69 69 69 69a
R-squared 0.1106 0.0368 0.2077 0.3021 0.2602 0.3372
p value male vs. femaleb 0.345 0.074 0.044
Source: INSEC 2009, Central Bureau of Statistics 2009, Nepal DHS 2001, and Nepal DHS 2006.
Note: All specifications are estimated using the panel fixed-effects estimator and include a constant. District casualties
are expressed per 1,000 inhabitants. Columns (1) and (2): Sample only includes individuals surveyed in the Nepal DHS
2006 and aged 5–9 years (treatment group) or 16–19 years (control group) at the beginning of the conflict in 1996.
Columns (3) to (6): Sample only includes individuals surveyed in Nepal DHS 2001 or 2006 aged 10 to 18 years at the
time of the survey.
a
DHS data collection was somewhat affected by the conflict in 2001. Hence, contrary to DHS 2006, four districts were
not covered: Dolpa, Jajarkot, Rolpa, and Rukhum. The small districts of Manang and Mustang were not surveyed, but
these districts did not experience any casualties during the conflict.
b
p value of an F test of equality between the reported treatment effects for males and females.
Standard errors clustered at the district level are in parentheses.
* p < 0.10
** p < 0.05
*** p < 0.01
36
TABLE 3. Impact of Alternative Conflict Variables on Primary Schooling Completion
(1) (2) (3) (4) (5) (6) (7) (8)
School School Maoist Maoist Maoist Control Maoist Control Maoist Control Maoist Control
Destructions Destructions Abductions Abductions Female – Male – Female – Male –
Female Male Female Male Definition 1 Definition 1 Definition 2 Definition 2
Primary Primary Primary Primary Primary Primary Primary Primary
Explained Variable
Education Education Education Education Education Education Education Education
Specification Eq. (1R) Eq. (1 R) Eq. (1 R) Eq. (1R) Eq. (1R) Eq. (1R) Eq. (1R) Eq. (1R)
=1 if 5–9 in 1996 × District 0.0539* 0.0301 0.0638** 0.0231
casualties during 1996–2006
í µí±¡
(í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— )
(0.0302) (0.0291) (0.0275) (0.0259)
=1 if 5–9 in 1996 × District schools 0.4176 −1.7848
destroyed 2002–2006 (3.1298) (2.3395)
=1 if 5–9 in 1996 × Maoist −0.0022*** 0.0002
Abductions during 1996–2006 (0.0005) (0.0010)
=1 if 5–9 in 1996 × District 0.0916 0.2009***
controlled by Maoists (Definition 1) (0.0590) (0.0529)
=1 if 5–9 in 1996 × District 0.1456*** 0.0923*
controlled by Maoists (Definition 2) (0.0457) (0.0506)
Panel variable District District District District District District District District
Included dummies:
Year of birth Yes Yes Yes Yes Yes Yes Yes Yes
Region × Year of birth Yes Yes Yes Yes Yes Yes Yes Yes
Observations 3,823 3,055 3,823 3,055 3,823 3,055 3,823 3,055
No. of clusters 75 75 75 75 75 75 75 75
R-squared 0.1106 0.0370 0.1118 0.0368 0.1105 0.0415 0.1130 0.0382
a
p value male vs. female 0.466 0.0134 0.208 0.456
Source: INSEC 2009, Central Bureau of Statistics 2009, Nepal DHS 2006,
Note: All specifications are estimated using the panel fixed-effects estimator and include a constant. School destructions and abductions by Maoists are expressed per 1,000
inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2006 and aged 5–9 years (treatment group) or 16–19 years (control group) at the beginning of the conflict in
1996. Definition of a district controlled by Maoists based on matches between People’s Army and government classifications as of 2003 (Definition 1) or government classification
(Definition 2), according to Hattlebak (2007). ap value of an F test of equality between the reported treatment effects for males and females. Standard errors clustered at the district
level are in parentheses.
* p < 0.10,** p < 0.05, *** p < 0.01
37
TABLE 4. Impact of Conflict Intensity on Completed Years of Education, 5- to 14-year-olds
(1) (2) (3) (4)
Years of Years of
Years of Years of Education Education
Explained Variable and Sample Education Education Female 5–14 – Male 5–14 –
Female 5–14 Male 5–14 Mother here Mother here
since 1996 since 1996
Specification Eq. (2R) Eq. (2R) Eq. (2R) Eq. (2R)
District casualties before survey 0.2795** 0.1224 0.2633** 0.0755
í µí±
(í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— ) (0.1147) (0.1207) (0.1172) (0.1391)
=1 if rural −0.3916*** −0.1559** −0.4703*** −0.1770**
(0.0788) (0.0664) (0.0914) (0.0703)
0.2210*** 0.2301*** 0.2051*** 0.2381***
=1 if head has primary education
(0.0428) (0.0361) (0.0453) (0.0374)
0.8263*** 0.7064*** 0.8619*** 0.7242***
=1 if head has secondary education
(0.0431) (0.0386) (0.0509) (0.0388)
=1 if head has higher education 1.1291*** 1.0828*** 1.3829*** 1.2016***
(0.1035) (0.0846) (0.1137) (0.1124)
Panel variable District District District District
Included dummies:
DHS 2006 Yes Yes Yes Yes
Age at interview Yes Yes Yes Yes
Region × DHS 2006 Yes Yes Yes Yes
DHS 2006 × Age at interview Yes Yes Yes Yes
Region × Age at interview Yes Yes Yes Yes
DHS 2006 × Region × Age at
interview Yes Yes Yes Yes
Observations 11,793 12,116 9,772 9,959
No. of clusters 69a 69a 69a 69a
R-Squared 0.5062 0.6077 0.4909 0.5996
b
p value male vs. female 0.079 0.065
Source: INSEC 2009, Central Bureau of Statistics 2009, Nepal DHS 2001, and Nepal DHS 2006.
Note: All specifications are estimated using the panel fixed-effects estimator and include a constant. District casualties
are expressed per 1,000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2001 and 2006 and
aged 5–14 years at the time of the survey. In columns (3) and (4), the 2006 sample is restricted to individuals whose
mothers were interviewed individually and whose mothers reported having lived in their current place of residence as
of 1996. The same exclusion could not be implemented for the 2001 DHS because individuals listed on the household
roster cannot be matched to their mothers.
a
DHS data collection was somewhat affected by the conflict in 2001. Hence, contrary to DHS 2006, four districts were
not covered: Dolpa, Jajarkot, Rolpa, and Rukhum. The small districts of Manang and Mustang were not surveyed, but
these districts did not experience any casualties during the conflict.
b
value of an F test of equality between the reported treatment effects for males and females.
Standard errors clustered at the district level are in parentheses.
* p < 0.10
** p < 0.05
*** p < 0.01
38
Appendix
Effect of Conflict Intensity on Self-Reported Measures of Female Empowerment
The 2001 and 2006 DHS surveys both asked women interviewed individually about who had the
final say in their household with respect to decisions regarding (i) their own healthcare, (ii) making
large household purchases, (iii) making household purchases for daily needs, and (iv) visits to
family or relatives. In addition, women interviewed individually in the 2001 and 2006 DHS who
reported having worked in the previous 12 months were asked who decided how the money they
earned was spent. When estimating the effect of conflict intensity on the probability that women
report being either the sole- or joint decision maker for each of the four items listed above using
Equation (2), no statistically significant change was observed. However, using the same estimation
strategy, I find that women became significantly more likely to report control over the way their
earnings were spent where the conflict intensified more between 2001 and 2006. 1 The information
conveyed by these self-reported measures of female bargaining power in the household is limited,
and gives mixed support to the idea that women gained more control over household finances
during the conflict in areas more affected by the insurgency.
1
Results available upon request.
Appendix Tables
Table S1: Summary Statistics
Conflict Casualties Tercile Low Intensity Medium Intensity High Intensity
(1) (2) (3) (4) (5) (6)
Male Female Male Female Male Female
Panel A – Sample used to estimate Eq. (1R) in the primary schooling completion analysis (Tables 1-3)
=1 if primary education 0.769 0.562 0.743 0.545 0.742 0.511
District casualties per 1000 0.265 0.270 0.647 0.642 1.564 1.549
during 1996-2006 (0.098) (0.101) (0.160) (0.161) (0.958) (0.972)
School destructions per 1000 0.003 0.003 0.005 0.005 0.006 0.006
during 1996-2006 (0.005) (0.005) (0.010) (0.010) (0.008) (0.008)
Maoist abductions per 1000 1.135 1.301 11.890 11.411 5.131 4.904
during 1996-2006 (2.515) (2.454) (27.159) (27.138) (5.482) (5.264)
=1 if district controlled by
0.042 0.058 0.123 0.129 0.304 0.321
Maoists (Definition 1)
=1 if district controlled by
0.102 0.122 0.357 0.337 0.541 0.554
Maoists (Definition 2)
Age at interview 19.983 20.376 19.020 19.659 18.873 19.928
(5.286) (5.327) (5.018) (5.098) (5.032) (5.220)
=1 if DHS 2006 1 1 1 1 1 1
Observations 1264 1454 904 1122 887 1247
Panel B - Sample used to estimate Eq. (2R) in the completed years of education analysis (Table 4)
Years of education 1.928 1.604 1.925 1.639 1.894 1.620
(2.207) (2.148) (2.140) (2.065) (2.150) (2.035)
District casualties before survey 0.133 0.135 0.312 0.321 0.687 0.693
í µí±
per 1000 (í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— ) (0.151) (0.153) (0.327) (0.329) (0.711) (0.697)
Age at interview 9.289 9.305 9.350 9.353 9.389 9.406
(2.792) (2.857) (2.810) (2.801) (2.850) (2.810)
=1 if DHS 2006 0.463 0.460 0.463 0.475 0.466 0.473
=1 if rural 0.757 0.767 0.866 0.873 0.845 0.842
Education of the household head:
=1 if no education 0.483 0.495 0.529 0.521 0.553 0.560
=1 if primary education 0.225 0.227 0.262 0.273 0.235 0.233
=1 if secondary education 0.239 0.223 0.176 0.180 0.178 0.170
=1 if higher education 0.053 0.055 0.032 0.026 0.034 0.037
Observations 4714 4449 3808 3852 3594 3492
Author’s calculations based on DHS 2001 and 2006 and INSEC (2009). District population as of the 1991 Population
Census based on Central Bureau of Statistics (2009). Standard deviations in parentheses. Definition of district
controlled by Maoist based either on matches between People’s Army and government classifications as of 2003
(Definition 1) or based on government classification (Definition 2).
Table S2: Impact of Conflict Intensity Measured by Casualties on Completed Years of Education
(1) (2) (3) (4) (5) (6)
Years of Years of
Years of Years of Years of Years of
Explained Variable and Education - Education -
Education - Education - Education - Education -
Sample Female Female
Female Male Male 10-18 Male 10-18
10-18 10-18
Specification Eq. (1R) Eq. (1R) Eq. (2R) Eq. (2R) Eq. (2R) Eq. (2R)
0.6161*** 0.2849
=1 if 5-9 in 1996 x district
casualties during 1996-2006
í µí±¡
(í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— ) (0.2162) (0.2444)
District casualties before 0.4379 -0.0141 0.4766** 0.0350
í µí±
survey (í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— ) (0.2667) (0.2190) (0.2139) (0.1844)
=1 if rural -0.8859*** -0.6072***
(0.1491) (0.1043)
0.4221*** 0.4676***
=1 if Head has primary
Education (0.0810) (0.0699)
1.7433*** 1.4665***
=1 if Head has secondary
Education (0.0822) (0.0909)
2.5281*** 2.1129***
=1 if Head has higher
Education (0.2092) (0.1730)
Panel variable District District District District District District
Included dummies:
Year of Birth Yes Yes No No No No
Region x Year of Birth Yes Yes No No No No
DHS 2006 No No Yes Yes Yes Yes
Age at Interview No No Yes Yes Yes Yes
Region x DHS 2006 No No Yes Yes Yes Yes
DHS 2006 x Age at
Interview No No Yes Yes Yes Yes
Region x Age at Interview No No Yes Yes Yes Yes
DHS 2006 x Region x Age
at Interview No No Yes Yes Yes Yes
Observations 3823 3055 9595 9267 9584 9255
No. of Clusters 75 75 69a 69a 69a 69a
R-Squared 0.0982 0.0434 0.1961 0.3394 0.2882 0.4042
All specifications are estimated using the panel fixed-effects estimator and include a constant. District casualties are
expressed per 1000 inhabitants (INSEC 2009, Central Bureau of Statistics 2009). Columns (1) and (2): Sample only
includes individuals surveyed in the Nepal DHS 2006 and age 5-9 (treatment group) or 16-19 years old (control group)
at the start of conflict in 1996. Columns (3) to (6): Sample only includes individuals surveyed in Nepal DHS 2001 or
2006, age 10 to 18 at the time of the survey. aDHS data collection was somewhat affected by the conflict in 2001, and
so contrary to DHS 2006 four districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small
districts of Manang and Mustang were not surveyed either, but these districts did not experience any casualty during
the conflict. Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.
Table S3. Impact of Conflict Intensity Measured by Casualties on Primary Schooling Completion –
Alternative Specifications
(1) (2) (3) (4) (5) (6)
Basic DiD Basic DiD Alternative Alternative Log Log
Female Male Control Control Casualties Casualties
Cohort Cohort Male Female Male
Female
Explained Variable Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed.
Specification Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1)
=1 if 5-9 in 1996 0.0756*** 0.0387*
x district casualties during 1996-
2006 (0.0184) (0.0217)
=1 if 5-9 in 1996 0.0637*** 0.0171
x district casualties during 1996-
2006, alternative control group (0.0187) (0.0171)
=1 if 5-9 in 1996
x log(district casualties during 0.0954*** 0.0521**
1996-2006) (0.0246) (0.0222)
Panel Variable District District District District District District
Year of Birth dummies Yes Yes Yes Yes Yes Yes
Observations 3823 3055 4433 3620 3808 3043
No. of Clusters 75 75 75 75 73 73
R-Squared 0.0998 0.0249 0.1722 0.0378 0.1028 0.0262
All specifications are estimated using the panel fixed-effects estimator and include a constant. District casualties are
expressed per 1000 inhabitants (INSEC 2009, Central Bureau of Statistics 2009). All columns except Columns (3) and
(4): Sample includes only individuals surveyed in the Nepal DHS 2006 and age 5-9 (treatment group) or 16-19 years
old (control group) at the start of conflict in 1996. Columns (3) and (4): Sample includes only individuals surveyed in
the Nepal DHS 2006 and age 5-9 (treatment group) or 18-24 years old (control group) at the start of conflict in 1996.
The samples in Columns (5) and (6) are slightly smaller compared to Columns (1) and (2) because the natural
logarithm of conflict casualties is undefined for observations from the small districts of Manang and Mustang, where
there were no conflict casualties. Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05,
***p<0.01.
Table S4: Impact of Alternative Conflict Variables on Primary Schooling Completion – Basic Difference-in-Differences Specification
(1) (2) (3) (4) (5) (6) (7) (8)
School School Maoist Maoist Maoist Control Maoist Control Maoist Control Maoist Control
Destructions Destructions Abductions Abductions Female – Male – Female – Male –
Female Male Female Male Definition 1 Definition 1 Definition 2 Definition 2
Explained Variable Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed.
Specification Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1)
=1 if 5-9 in 1996 x district 0.0710*** 0.0423* 0.0806*** 0.0364*
casualties during 1996-2006
í µí±¡
(í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— ) (0.0219) (0.0238) (0.0186) (0.0218)
=1 if 5-9 in 1996 x district 1.3545 -1.2498
schools destroyed 2002-2006 (4.5298) (2.4198)
=1 if 5-9 in 1996 x Maoist -0.0021*** 0.0008
Abductions during 1996-2006 (0.0007) (0.0013)
=1 if 5-9 in 1996 x District 0.0794 0.1781***
controlled by Maoist
(Definition 1) (0.0572) (0.0546)
=1 if 5-9 in 1996 x District 0.1036** 0.0641
controlled by Maoist
(Definition 2) (0.0477) (0.0475)
Panel Variable District District District District District District District District
Year of Birth dummies Yes Yes Yes Yes Yes Yes Yes Yes
Observations 3823 3055 3823 3055 3823 3055 3823 3055
No. of Clusters 75 75 75 75 75 75 75 75
R-Squared 0.0999 0.0250 0.1010 0.0250 0.0971 0.0282 0.0987 0.0249
All specifications are estimated using the panel fixed-effects estimator and include a constant. School destructions and abductions by Maoists are expressed per 1000 inhabitants
(INSEC 2009, Central Bureau of Statistics 2009). Sample only includes individuals surveyed in the Nepal DHS 2006 and age 5-9 (treatment group) or 16-19 years old (control group)
at the start of conflict in 1996. Definition of district controlled by Maoist based either on matches between People’s Army and government classifications as of 2003 (Definition 1) or
based on government classification (Definition 2), according to Hattlebak (2007). Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.
Table S5: Impact of Alternative Conflict Variables on Primary Schooling Attainment - Basic Difference-in-Differences Specification and Alternative Control Cohort
(1) (2) (3) (4) (5) (6) (7) (8)
School School Maoist Maoist Maoist Control Maoist Control Maoist Control Maoist Control
Destructions Destructions Abductions Abductions Female – Male – Female – Male –
Female Male Female Male Definition 1 Definition 1 Definition 2 Definition 2
Explained Variable Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed. Primary Ed.
Specification Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1) Eq. (1)
=1 if 5-9 in 1996 x district 0.0482** 0.0291 0.0668*** 0.0167
casualties during 1996-2006
í µí±¡
(í µí±‡í µí±‚í µí±‡í µí°¶í µí±‚í µí±?í µí°¹
í µí±— ) (0.0212) (0.0213) (0.0190) (0.0172)
=1 if 5-9 in 1996 x district 3.8806 -3.0837
schools destroyed 2002-2006 (3.3306) (2.6280)
=1 if 5-9 in 1996 x Maoist -0.0013** 0.0001
Abductions during 1996-2006 (0.0006) (0.0007)
=1 if 5-9 in 1996 x District 0.0997* 0.1101**
controlled by Maoist
(Definition 1) (0.0596) (0.0491)
=1 if 5-9 in 1996 x District 0.1348*** 0.0479
controlled by Maoist
(Definition 2) (0.0463) (0.0425)
Panel Variable District District District District District District District District
Year of Birth dummies Yes Yes Yes Yes Yes Yes Yes Yes
Observations 4433 3620 4433 3620 4433 3620 4433 3620
No. of Clusters 75 75 75 75 75 75 75 75
R-Squared 0.1729 0.0384 0.1726 0.0378 0.1706 0.0394 0.1736 0.0382
All specifications are estimated using the panel fixed-effects estimator and include a constant. School destructions and abductions by Maoists are expressed per 1000 inhabitants
(INSEC 2009, Central Bureau of Statistics 2009). Sample only includes individuals surveyed in the Nepal DHS 2006 and age 5-9 (treatment group) or 18-24 years old (control group)
at the start of conflict in 1996. Definition of district controlled by Maoist based either on matches between People’s Army and government classifications as of 2003 (Definition 1) or
based on government classification (Definition 2), according to Hattlebak (2007). Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.
Table S6: Impact of Conflict Intensity on Completed Years of Education, 5-14 year-olds – Basic
Specification
(1) (2) (3) (4)
Years of Years of
Years of Years of Education Education
Explained Variable and Sample Education Education Female 5-14 – Male 5-14 –
Female 5-14 Male 5-14 Mother here Mother here
since 1996 since 1996
Specification Eq. (2) Eq. (2) Eq. (2) Eq. (2)
District casualties before survey
í µí±
(í µí°¶í µí±‚í µí±?í µí°¹í µí°¸í µí±‹í µí±ƒí µí±–í µí±— ) 0.2623*** 0.2565*** 0.2059** 0.1920**
(0.0882) (0.0947) (0.0914) (0.0886)
=1 if Rural -0.3728*** -0.1617** -0.4514*** -0.1757**
(0.0827) (0.0661) (0.0939) (0.0705)
=1 if Head has primary education 0.2246*** 0.2311*** 0.2082*** 0.2435***
(0.0438) (0.0364) (0.0459) (0.0379)
=1 if Head has secondary education 0.8231*** 0.7134*** 0.8650*** 0.7365***
(0.0437) (0.0391) (0.0513) (0.0389)
=1 if Head has higher education 1.1095*** 1.0926*** 1.3758*** 1.2198***
(0.1047) (0.0847) (0.1132) (0.1110)
Panel variable District District District District
Included dummies:
DHS 2006 Yes Yes Yes Yes
Age at Interview Yes Yes Yes Yes
Observations 11793 12116 9772 9959
No. of Clusters 69a 69a 69a 69a
R-Squared 0.4858 0.5971 0.4722 0.5890
All specifications are estimated using the panel fixed-effects estimator and include a constant. District casualties are
expressed per 1000 inhabitants (INSEC 2009, Central Bureau of Statistics 2009). Sample only includes individuals
surveyed in the Nepal DHS 2001 and 2006 and age 5-14 at the time of the survey. In Columns (3) and (4), the 2006
sample is restricted to individuals whose mothers were interviewed individually and whose mothers report already
living in their current place of residence as of 1996. The same exclusion could not be implemented for the 2001 DHS
as individuals listed in the household roster cannot be matched to their mothers. a DHS data collection was somewhat
affected by the conflict in 2001, and so contrary to DHS 2006 four districts were not covered, namely Dolpa, Jajarkot,
Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either, but these districts did not
experience any casualty during the conflict. Standard errors clustered at the district level are in parentheses. *p<0.10,
**p<0.05, ***p<0.01.
Appendix Figures
Figure S1
Source: Author’s calculations based on INSEC (2009).
Figure S2
Source: Deaths and abductions are taken from INSEC (2009). District population figures are based on the 1991 population
census (Central Bureau of Statistics 2009). Taplejung, which had 129.3 abductions per 1000 inhabitants, is excluded for
readability.
Figure S3a
Source: Author’s calculations based on the Nepal Demographic and Health Survey 2006, casualties recorded in INSEC (2009)
and district population figures from the 1991 population census (Central Bureau of Statistics, 2009). Conflict-intensity terciles
are based on the district distribution of conflict-related deaths per 1000 inhabitants from 1996 to 2006.
Figure S3b
Source: Author’s calculations based on the Nepal Demographic and Health Survey 2006, casualties recorded in INSEC (2009)
and district population figures from the 1991 population census (Central Bureau of Statistics 2009). Conflict-intensity terciles
are based on the district distribution of conflict-related deaths per 1000 inhabitants from 1996 to 2006.