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The Taliban and the schooling gender gap inAfghanistan
Abdul G. Noury, New York University - Abu Dhabi; ECARES, ULBBiagio Speciale, ECARES, Université libre de Bruxelles; FNRS, Belgium
Preliminary version. February 2012
Abstract
This paper studies the e¤ects of the Taliban government (1996-2001) and insurgency (after 2001) on the schooling gender gap inAfghanistan. As soon as they came to power in 1996, the Talibanbanned girls from going to school. After they were removed frompower in 2001, they targeted several girls�schools in violent attacks.We use data from the National Risk and Vulnerability Assessment(NRVA) 2007-2008 and rely on the fact that, depending on their yearof birth, individuals were or were not in school age during the Talibangovernment. We also exploit the variation in opium poppy cultivationto solve the non-random sorting of households across districts withhigher or lower con�ict level. Being of school age while the Talibanwere in power (1996-2001) explains about 33 percent of the gendergap in the completion of 9 grades education. Moreover, the violentevents associated to the Taliban insurgency at the time of the surveyaccount for about 26 percent of the gender gap in enrollment.
1 Introduction
Promoting female education and reducing gender inequalities in schooling
rank high among the priorities for developing countries. Behrman, Foster,
1
Rosenzweig and Vashishtha (1999) �nd that increases in the schooling of
women enhance the human capital level of their children, using data describ-
ing the green revolution in India. Currie and Moretti (2003) show how higher
maternal education improves infant health, as measured by birth weight and
gestational age. Importantly, education empowers women in several dimen-
sions. Doepke and Tertilt (2009) theoretically show how the changing role of
human capital and the increase in the returns to education have shaped the
position of women in household decision making. Geddes and Lueck (2002)
empirically demonstrate how greater levels of female human capital were cru-
cial in explaining the expansion of women�s rights in the United States in the
period from 1850 to 1920. Goldin (2006) discusses the important role of ed-
ucation in the evolutionary and revolutionary phases that transformed the
economic role of women.
In this paper, we consider one of the most striking cases in which the
female�s right to education has been denied. We use data from Afghanistan,
a country that the UNDP ranked in its 2011 Human Development Report
at the 141st position of the 146 countries for which the Gender Inequality
Index was computed1. In particular, we study the schooling gender gap
1The countries that are ranked worse than Afghanistan in terms of Gender Inequality
Index are: Democratic Republic of the Congo, Mali, Niger, Chad, and Yemen. The
situation does not look better when looking at the trend over time of this index. The
available data from 2005 to 2011 show how the Gender Inequality Index was stable to
about 0.7 over this time period, suggesting that the position of women in the Afghan
2
consequences of the Taliban, a political and religious group mainly made of
rural Pashtuns educated in madrassas in Pakistan. They ruled Afghanistan
from 1996 to 2001, and regrouped as an insurgency movement after they
were ousted in 2001. The Taliban deviated from international standards of
human rights in the treatment of women. During their government, they
banned education for girls aged 8 years or over. After they were removed
from power in 2001, the Taliban targeted several girls�schools, their students
and teachers in violent attacks.
The focus of this paper is in quantifying the e¤ect of the Taliban on the
schooling gender gap. In a �rst set of regressions, we look at the e¤ects on the
outcome of interest of their government, 1996-2001. We exploit the fact that,
depending on the year of birth, individuals were or were not in school age
during the Taliban government, and we estimate how this a¤ected their prob-
ability to complete basic education. In a second set of regressions, we focus
on the Taliban insurgency after 2001, and its e¤ects on the schooling gender
gap. We rely on the di¤erences in the intensity of the con�ict across districts,
and analyze the consequences of the attacks associated to the insurgents on
the probability of enrollment at the time of the survey. We address the is-
sue of selective migration, which implies non-random sorting of households
across districts with higher or lower con�ict level depending on unobservable
characteristics, such as the head of household�s level of risk aversion. To solve
society has not improved in recent years.
3
this econometric issue, we exploit the variation in opium cultivation across
districts. Our IV estimations rely on the strong positive correlation between
opium poppy cultivation and violence associated to the insurgents, due to the
former being an important source of funding for the Taliban in recent years.
We claim that opium poppy cultivation as instrument for Taliban violence
mitigates the issue of selective migration across districts, because individuals�
location decisions are more likely to be a¤ected by insecurity concerns due
to the war rather than the amount of land devoted to poppy cultivation in a
district.
Our estimates show that the fact of being of school age while the Taliban
were in power (1996-2001) explains about 5 percentage points (that is about
33 percent) of the gender gap in the completion of basic schooling. Further-
more, at the time of the survey the violent attacks associated to the Taliban
insurgency account on average for about 5 percentage points (26 percent) of
the gender gap in enrollment.
The remainder of this paper is organized as follows. Section 2 describes
the related literature. Section 3 presents background information on the
Taliban and the status of women in Afghanistan. Section 4 provides a brief
description of the data. Section 5 presents the results of the e¤ects of the
Taliban government (1996-2001) and insurgency (after 2001) on the schooling
gender gap in Afghanistan. Section 6 concludes.
4
2 Related literature
Our paper contributes to several branches of literature. The �rst part of
this paper - which focuses on the human capital consequences of the Taliban
while they were in power - is related to the literature that explores the e¤ects
of di¤erent religions and/or political regimes on schooling or gender inequal-
ities in education. Becker and Woessmann (2009) �nd that Protestantism
led to substantially higher literacy across Prussian counties in the late nine-
teenth century. Becker and Woessmann (2010) complement their previous
work by showing that Protestantism had a positive e¤ect on school supply
and educational enrollment across Prussian counties and towns, even before
the industrialization, in 1816. The same authors highlight the gender dimen-
sion in an article published in 2008. They suggest that Protestantism was a
distinctive driving force in the advancement of female education in Prussia.
Martin Luther urged each town to have a girls�school so that girls would
learn to read the Gospel, and this helped to promote schooling for girls. Bot-
ticini and Eckstein (2005; 2007) describe and discuss the consequences of the
transformation of Judaism (200 BCE�200 CE) from a religion mainly based
on sacri�ces in the Temple into a religion whose core was the reading of the
Torah in the synagogue. They discuss how Jewish religious leaders promoted
the status of teachers and scholars, and downgraded the status of illiterate
people. They show how this transformation of Judaism in the �rst and sec-
ond centuries CE into a religion focused on literacy and education help to
5
explain the selection into urban and skilled occupations, the reduction in the
size of the Jewish population in various periods, and their diaspora all over
the world. Cooray and Potrafke (2011) study empirically whether political
institutions or culture and religion explain gender inequality in education.
Their results show no correlation between political institutions (autocratic
versus democratic regimes) and education of girls. They show association
between gender inequality in education, on the one hand, and culture and
religion, on the other hand, with discrimination against girls being especially
pronounced in Muslim dominated countries. Using cross-country data, Nor-
ton and Tomal (2009) show the existence of a negative link between female
educational attainment and the proportion of ethnoreligions, Hindu, and
Muslim adherents in a country, with similar results for the gender gap. Ku-
ran (2004) argues that traditional Islamic institutions remain a factor in the
Middle East�s economic backwardness and that de�ciencies of human capital
are rooted in applications of Islamic law. The traditional Islamic institutions
that worked well in earlier centuries became the sources of ine¢ ciency in
the modern globalized world, and this decreased the return to investment in
education in Muslim countries.
Another branch of the literature suggests that autocratically ruled so-
cieties do not tend to encourage education and investment in human capi-
tal because economic development will give rise to a middle class that will
try to build democratic institutions. As already stated in the introduction,
6
women�s education is likely to increase the stock of their children�s human
capital (Behrman et al., 1999) and, through this mechanism, promote eco-
nomic development (Bourguignon and Verdier, 2000; Glaeser, Ponzetto and
Shleifer, 2007). Baum and Lake (2003) show that democracy increases sec-
ondary education in non-poor countries.
The results in this paper related to the consequences of the Taliban insur-
gency after 2001 add to a growing literature that studies the impact of war on
human capital investment. Ichino and Winter-Ebmer (2004) study the long-
run educational cost of World War II. Akresh and de Walque (2011) analyze
the impact of Rwanda�s 1994 genocide on children�s schooling. Chamarbag-
wala and Morán (2011) examine how Guatemala�s 36-year-long civil war
a¤ected human capital accumulation. Merrouche (2011) uses data on land-
mine contamination intensity in Cambodia to evaluate the long-run impact
of Cambodia�s 30 years of war (1970�1998) on education levels and earn-
ings. Analyzing the long-run impact of bombing Vietnam on several eco-
nomic outcomes, Miguel and Roland (2011) �nd that U.S. bombing did not
have negative e¤ects on literacy through 2002. Shemyakina (2011) analyzes
the educational consequences of the 1992�1998 armed con�ict in Tajikistan.
Verwimp and Van Bavel (2011) study the human capital consequences of
the massacres (1993-1994) and the civil war (1995-2005) in Burundi. We
contribute to this literature by estimating the schooling gender gap conse-
quences of the Taliban insurgency in Afghanistan, and by employing a novel
7
instrumental variable approach that relies on the geographical distribution
of opium poppy cultivation, which is one of the main sources of funding for
the insurgents.
Finally, and more generally, this paper also adds to a recent literature
in economics and political science that uses data from Afghanistan (see,
among others, Beath, Christia and Enikopolov, 2011; Condra, Felter, Iyengar
and Shapiro, 2010; Gilligan and Noury, 2011). This literature is relatively
recent because of the di¢ culty in collecting good quality data during the
long con�ict. In the context of schooling, Burde and Linden (2009) conduct
a randomized evaluation in Afghanistan to assess the causal e¤ect of distance
on children�s schooling participation and performance. They randomly assign
some of the villages to receive community-based schools a year before the
schools were supplied to the entire sample. They �nd that the program
signi�cantly increases enrollment and test scores amongst all children and
dramatically improves the existing gender disparities.
8
3 Background information on theTaliban and
the status of women in Afghanistan
3.1 The Taliban
The Taliban is a religious and political group that ruled Afghanistan from
1996 to 20012.
Its members mostly belong to the largest ethnic group in Afghanistan, the
Pashtun. Several authors have stressed how ethnic divisions and opium pro-
duction had important in�uences on the politics of the Taliban in Afghanistan
(Johnson and Mason, 2007). Many of its members studied in madrassas (re-
ligious boarding schools) in Pakistan, which were in�uenced by the Deobandi
philosophy founded at the Dar ul-Ulummadrassa in Deoband (India) in 1866.
The Taliban movement has often been categorized as a radical Islamist group,
and several Muslim scholars criticized their interpretation of the Sharia law
(see PHR�s (2008) report; The Cairo Declaration; Final Report of the Inter-
national Conference on Population and Reproductive Health in the Muslim
World (21-24 February 1998, Al-Azhar University , Cairo); Health Promotion
through Islamic Lifestyles: The Amman Declaration, WHO, 1996)3.
2For more details on the Taliban movement, we refer the interested reader to Rashid
(2000).3See Platteau (2011) for an analysis of the relationship between Islam and politics.
He stresses that politics tends to dominate religion, and that because of the lack of a
centralized religious authority structure and the greater variability of interpretations of
9
The movement started in a period in which a provisional Islamist gov-
ernment (the Mujahideen, warriors of God) was put in place in Afghanistan
after the downfall in 1992 of Mohammad Najibullah. The latter was the
fourth president of the Soviet-backed Democratic Republic of Afghanistan.
The Taliban movement was started by Mullah Omar, an ethnic Pashtun
from the Hotak tribe of the Ghilzai (Rashid, 2000). As Matinuddin (1999)
and Rashid (2000) document, the �rst time Mullah Omar mobilized his fol-
lowers armed madrassa students was in the spring of 1994 to free teenage
girls who had been abducted and raped by a warlord in Singesar. In that
occasion, they hanged the Mujahideen commander from the barrel of a tank.
In few years after this event, the Taliban group increased its size, and in
September 1996 they seized Kabul and established the Islamic Emirate of
Afghanistan.
Their government lasted until 2001. After the September 11 attacks, the
armed forces of the US, UK, Australia, and the Afghan United Front (North-
ern Alliance) launched Operation Enduring Freedom, which had the goal of
ending the Al-Qaeda�s use of Afghanistan as a base, and the removal of the
Taliban from power. After they were ousted in 2001, the Taliban regrouped
as an insurgency movement to �ght the Nato coalition forces (ISAF, Interna-
tional Security Assistance Force) and the newly established Islamic Republic
the Islamic law, there is a risk that both the ruler and his political opponents try to outbid
each other by using the religious idiom.
10
of Afghanistan.
3.2 The status of women in Afghanistan before and
after the Taliban came to power in 1996
In their 1998 report on health and human rights in Afghanistan, the Physi-
cians for Human Rights (PHR) describe the status of women in the Afghan
society over time, and provide some key dates of their empowerment. In
1964, Afghan women were recognized the right to vote. The 1977 Constitu-
tion clearly stated in its article 27 that "women and men, without discrim-
ination have equal rights and obligations before the law". The PHR�s 1998
report also document that, by the late 1970s, female students outnumbered
male students in Kabul.
The establishment of the Islamist State of Afghanistan in 1992 implied
some slowdown in female emancipation. Women had to be modest in their
style of dress, and had to cover everything except the face and hands in
public. During the provisional Islamist government, the Mujahideen, women
could continue to work, and to study in schools and universities.
The rise to power of the Taliban movement had as a major consequence
a drastic worsening of the status of women in Afghanistan. Soon after they
conquered the capital Kabul in September 1996, the Taliban issued several
edicts that restricted women�s rights and freedom. For instance, women
were largely prohibited from working, which had catastrophic consequences
11
especially for the families who lost a male household member because of the
war. Also, women could only leave their homes if accompanied by a mahram,
i.e. a close male relative (father, brother, husband and son). When out of
their homes with a mahram, they had to wear a burqa, which covered the face
as well, and were not allowed to wear socks or shoes whose color was white,
as the Taliban �ag. Women also had restrictions in wearing shoes that made
noise while they were walking, such as shoes with high heels.
During the Taliban period, there was a policy of segregating women and
men into separate hospitals. As the PHR (1998) documents, in September
1997 the Ministry of Public Health ordered all hospitals in Kabul to suspend
medical services to women at all but one hospital, which was poorly equipped
and for female patients only.
The Taliban also introduced a ban on female presence on television and
radio, and a ban on women riding bicycles or motorcycles. These policies
were enforced by the religious police, and punishments were often carried
out publicly, as Gri¢ n (2001) documents.
When the Taliban ruled Afghanistan, they showed a particular persistence
in restricting womens�rights related to schooling and investment in human
capital. The movement led by Mullah Omar ordered the closing of many
private schools that had been educating girls. Many of these schools that
had to close were small home-based vocational training programs, which
taught girls and young women to weave carpets and sew. Schools were not
12
allowed to teach girls older than 8. Moreover, the content of the education
for these girls was limited to lessons about the Koran, the Muslim holy book
(see the New York Times, 1998)4.
After they were ousted in 2001, the Taliban burnt school buildings and
targeted civilians in violent attacks, including teachers who were killed. In
2008, when they ordered the closure of all girls�schools in the Swat district in
Pakistan, threatening to blow the schools up, the group�s leader Shah Dauran
provided as justi�cation that "female education is against Islamic teachings
and spread vulgarity in society" (see Hussain, 2008). Other examples of
activities aimed to discourage the girls� school enrollment were numerous.
For instance, the Guardian (2011) and Larson (2009) report the stories of a
head of Afghan girls�school killed by the Taliban, girls who had acid thrown
in their faces while walking to school, schools set on �re or episodes of gas
poisonings at girls�schools, in which dozens of girls fell ill.
4 Data and descriptives
We use data from the National Risk and Vulnerability Assessment (NRVA)
2007-2008. This is the third round of the NRVA survey, and provides infor-
4While they were in power, the Taliban did not publicly oppose female education,
but their o¢ cial position was that they did not have the resources to establish separate
female educational institutions with all female sta¤. See BBC News UK, 14 January 2011,
"Afghan Taliban "end" opposition to educating girls".
13
mation on a nationally representative sample for Afghanistan. The �eldwork
started in mid-August 2007 and �nished at the end of August 2008. Com-
pared to the previous two rounds of the survey (2003 and 2005), the NRVA
2007/8 shows important improvements in the questionnaire, sample design
and coverage. The 12-month period allows to account for seasonality, while
the �rst two rounds in 2003 and 2005 presented seasonally biased information.
In the Afghan context, the lenght of the �eldwork is particularly relevant be-
cause of the presence of the war. In this case, if at a certain point in time
it was dangerous to interview a primary sampling unit, the 2007/2008 round
allowed considering the primary sampling unit at a later date rather than
replacing it.
Table 1 presents descriptive statistics on schooling indicators, by gender.
In particular, the table includes information on the percentage of individu-
als who completed 9 grades education, literacy rates (% of individuals who
can read and write), percentage of individuals who had at least some formal
education (versus individuals who never attended a formal school), and per-
centage of individuals aged 6-15 who were enrolled at school at the time of
the survey. The information in the table refers to the estimation sample of
Section 5.
All the schooling indicators show the high level of education gender in-
equalities in Afghanistan. 22% of the men in the estimation sample com-
pleted basic (nine grades) education, while only about 7% of the women did.
14
Among the men, about 46% can read and write, and there is a similar per-
centage of male individuals who have attended at least some formal school.
The literacy rate for women in the estimation sample is about 16%. The
percentage of women who never had formal schooling is approximately 83%.
The gender gap in enrollment is large as well. Among the individuals aged
6-15, 56% of the boys were enrolled at school at the time of the survey, while
the enrollment rate of girls was about 38%.
In the next section, we try to assess whether the Taliban government and
insurgency contributed to this gender gap.
5 Estimation results
In this section, we present two sets of regressions. In the �rst set of regres-
sions, we explore the e¤ects on schooling of the Taliban while they were in
power in Afghanistan from 1996 to 2001. In the second set of regressions,
we quantify the e¤ect on schooling enrollment of the insurgency at the time
of the survey. In both cases, we focus on the schooling gender gap, which is
of particular interest because the Afghan society is characterized by a very
high level of women�s segregation, as Table 1 shows.
15
5.1 The e¤ects of the Taliban government (1996-2001)
on the schooling gender gap
The Taliban were in power in Afghanistan from 1996 to 2001. As soon as
they came to power, they introduced a ban on education for girls. Exposure
to the regime during school age a¤ected female education decisions through
other mechanisms as well. In the 1996-2001 period, women were also largely
prohibited from working, which in�uenced negatively their labor market ex-
perience and subsequent expectations on the returns to education. Negative
e¤ects on post-2001 female human capital investment could also be related
to the Taliban policies that might have deteriorated women�s health capital
(see Sub-Section 3.2).
The identi�cation strategy in this subsection relies on the fact that, de-
pending on the year of birth, individuals were or were not in school age during
the Taliban regime. More precisely, we estimate the following equation:
Sidt = �d + �t + �Femalei � Talibant + Femalei + "idt (1)
where Sidt is a binary schooling variable. �d are district dummies, �t are year
of birth dummies, Talibant is a dummy variable equal to 1 if the individual
was aged 6-15 while the Taliban were in power (1996-2001), and "idt is an
error term. We restrict our sample to individuals who are over 15 years (i.e.,
with year of birth�1992) at the time of the survey5.5The Taliban dummy variable (not interacted with the Female variable) does not appear
16
With regard to the variable Sidt, we consider three alternative dependent
variables. First, a dummy variable equal to 1 if the individual completed nine
grades of schooling, and zero otherwise. Second, a dummy variable equal to
1 if the individual can read and write6. Third, a dummy variable equal to 1
if the individual attended at least some formal school.
We do not observe the characteristics of the households the individuals
belonged to at the time of education. In our context, this does not represent
a source of bias for the estimated coe¢ cient. Exposure to the movement
led by Mullah Omar during school age can plausibly be assumed as random,
because it depended on the year of birth. It is unlikely that households could
foresee the Taliban regime 6-15 years before the Taliban were in power, and
act strategically in their fertility decisions.
The coe¢ cient of interest is �, which allows to quantify the e¤ect of
the exposure to the Taliban regime during school age on the gender gap
in schooling. Equation 1 provides a Di¤erence-in-Di¤erences speci�cation
if two assumptions hold. First, individuals who su¤ered the human capital
consequences of the Taliban were the girls only. Second, in the absence of the
in Equation 1 because it is a linear combination of the year of birth dummies.6As the main report of the 2007/2008 NRVA documents (Icon-Institute, 2009), this
round of the survey includes a request to the male household head and to the primary
female household member to read a sentence from a �ash card in order to check the
(self-)reported literacy. Tested and self-reported literacy were remarkably similar, which
suggests that literacy �gures of the survey are reliable.
17
Taliban government the change in the schooling indicators would have been
the same for boys and girls ("parallel trend" assumption). In this case, girls
(boys) represent the treatment (control) group. Exposure depends on the
year of birth of the individual. The estimator removes biases that could be
the result from permanent di¤erences between girls and boys in the outcomes
of interest, as well as biases from comparisons over time in the treatment
group (i.e., girls) that could be the result of trends.
In case the fact of being of school age during the Taliban government
a¤ected the outcomes of interest for boys as well, Equation 1 can still identify
the e¤ect of exposure to the Taliban on the schooling gender gap if di¤erences
in education are not driven by other time-varying factors correlated with the
1996-2001 regime. To check whether this represents an issue, we adopt three
di¤erent strategies7. First, we run the same speci�cation as in Equation 1,
and control for district-speci�c time trends as well. The estimated equation
can now be written as:7During their government, the Taliban ruled about 95% of the Afghan territory. To
further check the robustness of our results, an alternative strategy would be to use infor-
mation on the districts that were not ruled by the Taliban with a Di¤erence-in-Di¤erence-
in-Di¤erences speci�cation. In our context, we can not use this empirical strategy because
we only observe the district of residence at the time of the survey, but not at the time of
exposure to the Taliban. The two do not necessarily coincide because of migration across
districts.
18
Sidt = �d + �t + �dt + �Femalei � Talibant + Femalei + "idt (2)
where �dt is a district-speci�c time trend.
Second, we present estimates using time-windows of di¤erent lenght. We
restrict our sample to individuals who were born after 1975. With a long
time-window, it would be more likely that other events might confound the
e¤ects of the Taliban government on the schooling gender gap. Our results
are robust to the use of di¤erent subsamples of individuals who di¤er in their
year of birth and, consequently, in their level of exposure during school age
to the movement led by Mullah Omar.
Third, we present results from placebo regressions using additional data
on older individuals. To illustrate this robustness check, let "cohort 1" denote
the individuals with year of birth such that they were exposed to the Taliban
regime during school age. In the main regressions, we compare the schooling
outcomes of men and women from �cohort 1�, with the outcomes of men and
women who were not exposed to the Taliban during school age because of
their year of birth. Let "cohort 0" denote the latter group. In the placebo
regressions, we consider individuals from a previous cohort of birth, "cohort -
1", and compare schooling outcomes of men and women from this group with
similar schooling indicators that refer to men and women from "cohort 0".
Individuals in both cohorts -1 and 0 were not exposed to the Taliban during
school age. These placebo regressions aim to represent a false experiment.
19
Estimates do not show any gender di¤erences in schooling outcomes between
cohorts -1 and 0. This provides additional support that the results in our
main regressions are not driven by other time-varying factors correlated with
the 1996-2001 regime.
Results from the estimation of Equation 1 (i.e., without adding district-
speci�c time trends) using the Linear Probability Model are presented in
Table 28. Regressions in columns 1, 4 and 7 compare individuals whose year
of birth is 1976�t�1980 (i.e., individuals who were not aged 6-15 while the
Taliban were in power in Afghanistan) with individuals whose year of birth
was 1981�t�1992 (i.e., individuals who had at least some exposure to the
Taliban while they were aged 6-15, and who were over 15 at the time of the
survey). In columns 2, 5 and 8, the second group of individuals have year of
birth such that 1981�t�1986, as robustness check to further restrict the time-
window. In columns 3, 6 and 9, we compare individuals with 1976�t�1980,
i.e. who had no exposure at all with the Taliban regime while aged 6-15,
with individuals who were aged 6-15 during the whole period the Taliban
were in power (i.e., 1986�t�1990).
The results show that, conditional on district and year of birth dummies,
the fact of being of school age while the Taliban were in power explains about
5 percentage points of the gender gap related to the completion of nine grades
8Results from Probit and LPM estimation are qualitatively similar. We use sampling
weights in all regressions.
20
education. This is a particularly large e¤ect, especially considering that only
22% of men and 7% of women in the sample have completed basic (9 grades)
schooling. The 1996-2001 government accounts for about 33 percent of the
gender gap in this dimension.
The exposure to the Taliban during schooling a¤ected the ability of read-
ing and writing as well (see columns 4-6 of Table 2). More precisely, it
contributed to about 3 percentage points of the gender gap in literacy. The
latter is very large even for the subsample of individuals who were not ex-
posed to the Taliban regime because of their year of birth. Conditional on
district and year of birth dummies, women in this subsample are 26 per-
centage points less likely to be literate than men (see the coe¢ cient on the
Female variable, i.e. b ).The results in columns 7-9 of Table 2 show that women who were exposed
to the 1996-2001 regime are about 29 percentage points less likely to have
attended (at least some) formal school than men from similar cohorts of
birth. Our estimates suggest that about 5 percentage points of the gender
gap in this dimension is due to the Taliban government, 1996-2001.
Table 3 presents results when adding district-speci�c time trends, that
is from the estimation of Equation 2. Results are very similar to the ones
already presented in Table 2. These estimates provide additional support
that the coe¢ cient of interest identi�es the e¤ect of the Taliban government
on the schooling gender gap in Afghanistan, rather than other time-varying
21
factors correlated with the 1996-2001 regime.
Finally, in Table A1 we present results from the placebo regressions where
we compare the individuals with year of birth 1971�t�1975, with the individ-
uals who were born between 1976 and 1980. All the people belonging to this
sample were not exposed to the Taliban during school age. The interaction
term between Female and the dummy variable equal to 1 if the individual
was born between 1976 and 1980 does not have a statistically signi�cant ef-
fect on the 3 schooling indicators of interest. This false experiment provides
additional support to the robustness of the results presented in Table 2.
5.2 The e¤ects of theTaliban insurgency on the school-
ing gender gap
After the Taliban were removed from power in late 2001, they regrouped as
an insurgency movement to �ght the International Security Assistance Force
and the newly established Islamic Republic of Afghanistan. In this sub-
section, we aim to assess whether insecurity concerns related to the current
war are a¤ecting the schooling gender gap in Afghanistan.
To answer this research question, we use data on violent con�icts, which
were publicly released by WikiLeaks.org in July 2010. The data are of high
quality, compiled from soldiers��eld reports and include each event related
to the Afghan con�ict between 2004 and the end of 20099. In particular, we
9See Gilligan and Noury (2011) for a paper that analyzes the determinants of local
22
consider the so-called "red events" in the WikiLeaks dataset, which include
violent events involving insurgents (attacks, direct or indirect �re and impro-
vised explosive devices attacks, both where those devices were detonated and
where they were found and disarmed by the authorities). We denote this vari-
able as V iolence_Insurgents. It is aggregated at the district level. Figure
1 presents the geographic distribution of violent events involving insurgents
for the year of the survey (2008).
We estimate the following equation:
Eidt = �d+�t+ �Femalei � V iolence_Insurgentsd+ Femalei+ �X+ "idt
(3)
where E is a binary variable equal to 1 if an individual is enrolled in school
at the time of the survey. We only consider boys and girls aged 6-15 in
the sample. All regressions include district dummies (�d) and year of birth
dummies (�t). Because in all speci�cations we condition on district dummies,
the V iolence_Insurgents variable only appears in Equation 3 interacted
with the Female variable.
X is a vector of control variables, which includes the characteristics of the
violence in Afghanistan, using the WikiLeaks data. See also O�Loughlin et al. (2010), who
report that the WikiLeaks data and the Armed Con�ict Location Event Data (ACLED)
are positively correlated, with the latter being a fraction of the former data. O�Loughlin
et al. (2010) also report that this correlation is particularly high when considering the
geographic distribution of violent events.
23
head of household (age, education, civil status and gender), log of household
size, log of number of household members aged 6-15, a dummy equal to 1 if
the household lives in a rural area, and log of total household income.
In the speci�cations related to Equation 3, the variable V iolence_Insurgentsd
varies at the district level. A potential issue in these regressions is selection
into districts due to violence. Observable and unobservable characteristics of
the household might imply selective migration from one district to another,
therefore a non-random assignment of individuals into geographical areas10.
In these regressions, a potential omitted variable is the head of household�s
risk aversion11. This variable is unobserved, and its omission might bias the
estimated coe¢ cients because households might select into more or less dan-
gerous districts depending on their degree of risk aversion. The omission of
this unobservable variable can represent an issue because risk aversion is also
an important determinant of the education decisions.
To solve the econometric problem due to unobserved risk aversion and
selective migration depending on the level of district violence, we adopt dif-
ferent strategies. First, we include in the regression a comprehensive set of
control variables. We condition on several characteristics of the head of the
household: education, gender, civil status and age. There is literature show-
10Internal migration (within Afghanistan) accounts for about 51% of the total migration
observed in the 5 years before the 2008 NRVA survey.11See also Jaeger et al. (2010), who show that risk aversion a¤ects the probability of
migrating between labor markets in Germany.
24
ing that gender, age and education are related to the degree of risk aversion
(see, among others, Barsky et al., 1997; Guiso and Paiella, 2008; Borghans
et al., 2009).
Second, we use an instrumental variable approach that exploits the high
positive correlation between opium poppy cultivation and violence of the in-
surgents across districts. Gilligan and Noury (2011) explain the rationale for
this positive correlation. Insurgents need money to �nance their activities,
for instance to buy weapons and pay soldiers. To generate this money, they
need to "tax" the population, in a more or less coercive way. Opium produc-
tion is one of the best targets for the extortionary activities of the insurgents,
because it is a highly pro�table activity, and its illicit nature implies that
those who are taxed can not complain to the authorities12.
Figure 2 depicts the geographical distribution of opium poppy cultivation
for the year 2008. Data represent estimated hectares of land, and come from
the United Nations O¢ ce on Drugs and Crime (UNODC) annual Afghanistan
Opium Survey. In the �gure, white districts have no hectares of land devoted
to opium poppy cultivation. Yellow (red) districts have less (more) than 250
hectares of land producing opium poppy. Visual inspection of Figures 1
and 2 provides a �rst hint of the positive association between violent events
12Our paper is not the �rst to present a link between drugs production and the �nancing
of rebellion activity. In addition to Gilligan and Noury (2011), see Angrist and Kugler
(2008) who show a positive e¤ect of coca production on con�icts in Colombia.
25
involving insurgents and opium production13.
We use the interaction term between the female dummy variable and
the hectares of land devoted to opium poppy cultivation in the district of
residence (Femalei � Opium_Productiond) as an instrument for Femalei �
V iolence_Insurgentsd. Our identi�cation strategy mitigates the omitted
variable problem because individuals are more likely to select into districts
depending on the level of con�icts than on the amount of poppy cultiva-
tion in a district. Internal migration therefore implies that the individuals�
risk aversion is more likely to be correlated with the district violence associ-
ated to the Taliban insurgency than with the amount of opium production
in a district. Our instrument needs to ful�ll two conditions. First, it has
to be strongly correlated with the endogenous regressor. The value of the
�rst stage F-stat in Table 4 is equal to about 179, which con�rms that the
interaction term Femalei � Opium_Productiond is a strong instrument for
Femalei � V iolence_Insurgentsd14. Second, conditional on other explana-
tory variables, the instrument needs to only in�uence schooling enrollment
through its impact on the interaction term Femalei�V iolence_Insurgentsd.13The estimated total hectares of land devoted to opium poppy cultivation in
Afghanistan for the year 2008 are 157000. According to the UNODC annual World Drug
Report, Afghanistan currently produces about 90% of the world�s opium production.14It is worth to stress that reverse causality in the �rst stage is irrelevant to consistency
of the 2SLS estimator. Lind, Moene and Willumsen (2011) argue that con�icts cause
opium production in Afghanistan.
26
A possible threat to validity is represented by the fact that opium production
can increase household income and relax credit constraints. To control for
this mechanism, we add the log of total household income in the speci�cations
of columns 3 and 4 of Table 4.
Another threat to validity comes from the potential selection into dis-
tricts that may concern the households involved in opium production. These
households might di¤er from the other households, for instance in the level
of their head�s risk aversion. If some districts have a better soil and more
favorable conditions than other districts for the cultivation of opium poppy,
then there is a potential association between opium production and the level
of risk aversion of the district population. This association depends on selec-
tive migration, and represents a threat to the validity of opium production
as instrumental variable. To solve this potential econometric issue, and in
addition of always including a vector of control variables related to the char-
acteristics of the head of household (age, education, civil status and gender),
we exploit the information we have in the NRVA survey on the most impor-
tant sources of household income. In Table A1, we report results from the
LPM and IV-LPM estimation of Equation 3. These auxiliary regressions pro-
vide a validity check of our instrumental variable. We gradually exclude from
the estimation sample the households that report either the production and
sale of opium, or opium wage labor as their main sources of income. More
in particular, in columns 1 and 2 we consider the whole sample. In columns
27
3 and 4, we exclude from the estimation sample the households that report
either the production and sale of opium, or opium wage labor as their �rst
source of income. The estimation sample in columns 5 and 6 does not include
the households for which opium is among the two most important sources of
income, etc. The coe¢ cient of interest of the LPM estimates is very simi-
lar across all the speci�cations where we do not correct for endogeneity (see
odd columns). The comparison of the LPM and IV-LPM estimates instead
shows that selective migration across districts can represent an issue for our
instrument when we also consider in the sample the households with either
the production and sale of opium, or opium wage labor among their two most
important sources of income. On the contrary, estimation results of columns
6, 8 and 10 are very similar.
Following the validity checks of our instrument in Table A1, Table 4
presents both LPM and IV-LPM results from the estimation of Equation 3
excluding from the sample the 572 households for which poppy cultivation is
among the two most important sources of income. We use sampling weights
in all regressions. In Table 4, columns 3 and 4 di¤er from columns 1 and 2,
because they include the log of total household income as additional control
variable. The estimates in this table show that, if we consider the districts in
which there is no violence related to the Taliban insurgency then, conditional
on background characteristics, girls are about 18 percentage points less likely
to be enrolled in school than boys at the time of the survey (see estimated
28
coe¢ cients b in Equation 3). At the mean value of V iolence_Insurgentsd(=100), that is 0.286 (about 29 violent events associated to insurgents per
district), our estimates suggest that the gender gap in enrollment is about
5 percentage points higher because of the Taliban insurgency (23 percentage
points versus 18 percentage points in districts with no violence)15. Using the
information on enrollment by gender from Table 1, our empirical �ndings
imply that the violent events associated to the insurgents at the time of the
survey account for about 26 percent of the gender gap in enrollment.
With regard to the control variables in Table 4, all of them have a statis-
tically signi�cant e¤ect on enrollment, except the dummy equal to 1 if the
head of household is female. Moreover, all the included control variables have
the expected sign (negative for the coe¢ cients of the variables "log of number
of household members", "log of number of household members aged 6-15",
"rural", "age of the head of household", and positive for the coe¢ cients of
the variables "education of the head of household", "head of household is
married" and "log of total household income").
6 Concluding remarks
This paper studies the schooling gender gap consequences of the Taliban in
Afghanistan. We �nd that the fact of being of school age while the Tal-
15In 2008, there were more than 100 violent events in 6% of the districts, with a maxi-
mum value of 559 events observed in the district of Panjwayee.
29
iban were in power (1996-2001) explains about 33 percent of the gender gap
in the probability of completing nine grades education. Our estimates also
show that the gender gap in enrollment at the time of the survey is on aver-
age about 5 percentage points higher because of the Taliban insurgency (23
percentage points versus 18 percentage points in districts with no violence).
References
[1] Akresh, R. and D. de Walque (2011) "Armed con�ict and schooling:
evidence from the 1994 Rwandan Genocide", IZA Discussion Paper No.
3516.
[2] Angrist, J. D. and A. D. Kugler (2008) "Rural windfall or new resource
curse? Coca, income and civil con�ict in Colombia," Review of Eco-
nomics and Statistics, 90, pp. 191-215.
[3] Barsky, R. B., Juster, F.T., Kimball, M. S. and M .D. Shapiro (1997)
�Preference Parameters and Behavioral Heterogeneity: an Experimental
Approach in the Health and Retirement Study�, Quarterly Journal of
Economics, 112 ( 2), pp. 537-579.
[4] Baum, M. A. and D. A. Lake (2003) "The Political Economy of Growth:
Democracy and Human Capital", American Journal of Political Science,
Vol. 47, No. 2, pp. 333�347.
30
[5] Beath, A., Christia, F. and R. Enikopolov (2011) "Winning hearts and
minds through development aid: evidence from a �eld experiment in
Afghanistan", mimeo.
[6] Becker, S. O. and L. Woessmann (2008) "Luther and the Girls: Religious
Denomination and the Female Education Gap in Nineteenth-century
Prussia", Scandinavian Journal of Economics 110 (4), pp. 777�805.
[7] Becker, S. O. and L. Woessmann (2009) "Was Weber Wrong? A Hu-
man Capital Theory of Protestant Economic History", The Quarterly
Journal of Economics, May, pp. 531-596.
[8] Becker, S. O. and L. Woessmann (2010) "The e¤ect of Protestantism
on education before the industrialization: Evidence from 1816 Prussia",
Economics Letters 107, pp. 224�228.
[9] Behrman, J., A. D. Foster, M. R. Rosenzweig and P. Vashishtha (1999)
�Women�s School, Home Teaching and Economic Growth�, Journal of
Political Economy, 107, pp. 682�714.
[10] Borghans, L., Golsteyn, B., Heckman, J. and H. Meijers (2009) �Gender
Di¤erences in Risk Aversion and Ambiguity Aversion�, Journal of the
European Economic Association, 7 (2-3), pp. 649-658.
31
[11] Botticini, M. and Z. Eckstein (2005) "Jewish Occupational Selection:
Education, Restrictions, or Minorities?", Journal of Economic History
65, pp. 922�948.
[12] Botticini, M. and Z. Eckstein (2007) "From Farmers to Merchants, Con-
versions and Diaspora: Human Capital and Jewish History", Journal of
the European Economic Association 5, pp. 885�926.
[13] Bourguignon, F. and T. Verdier (2000) "Oligarchy, democracy, inequal-
ity and growth", Journal of Development Economics 62, pp. 285�313.
[14] Burde, D. and L. L. Linden (2009) "The E¤ect of Proximity on
School Enrollment: Evidence from a Randomized Controlled Trial in
Afghanistan", mimeo.
[15] Chamarbagwala, R. and H. E. Morán (2011) "The human capital conse-
quences of civil war: evidence fromGuatemala", Journal of Development
Economics 94, pp. 41�61.
[16] Condra, L., Felter, J., Iyengar, R. and J. N. Shapiro (2010), "The E¤ect
of Civilian Casualties in Afghanistan and Iraq", NBER Working Paper
16152.
[17] Cooray, A. and N. Potrafke (2011) "Gender inequality in education:
Political institutions or culture and religion?", European Journal of Po-
litical Economy, vol. 27(2), pp. 268-280.
32
[18] Currie, J. and E. Moretti (2003) "Mother�s Education and the Inter-
generational Transmission of Human Capital: Evidence from College
Openings", Quarterly Journal of Economics 118, pp. 1495�1532.
[19] Doepke, M. and M. Tertilt (2009) "Women�s Liberation: What�s in it
for Men?", Quarterly Journal of Economics, November 2009, Vol. 124,
No. 4, pp. 1541-1591.
[20] Geddes, R. and D. Lueck (2002) "The Gains from Self-Ownership and
the Expansion of Women�s Rights", The American Economic Review,
Vol. 92, No. 4 (Sep., 2002), pp. 1079-1092.
[21] Gilligan, M. J. and A. Noury (2011) "Explaining Local Violence in
Afghanistan", mimeo.
[22] Glaeser, E. L., Ponzetto, G. A. M. and A. Shleifer (2007) "Why does
democracy need education?", Journal of Economic Growth 12, pp. 77�
99.
[23] Goldin, C. (2006) "The Quiet Revolution that Transformed Women�s
Employment, Education, and Family", American Economic Review 96,
pp. 1�21.
[24] Gri¢ n, G. (2001) "Reaping the Whirlwind: The Taliban movement in
Afghanistan", London: Pluto Press.
[25] The Guardian (2011) "Taliban kill head of Afghan girls�school".
33
[26] Guiso, L. and M. Paiella (2008) �Risk Aversion, Wealth and Background
Risk�, Journal of the European Economic Association, 6 (6), pp. 1109-
1150.
[27] Hussain, Z. (2008) �Taleban Threaten to Blow up Girls�Schools If They
Refuse to Close,�The Times 2008.
[28] Ichino, A. and R. Winter-Ebmer (2004) "The long-run educational cost
of World War II", Journal of Labor Economics 22 (1), 57�86.
[29] Icon-Institute (2009) "National Risk and Vulnerability Assessment
2007/2008: A Pro�le of Afghanistan. Main report"
[30] Jaeger, D. A., Dohmen, T., Falk, A., Hu¤man, D., Sunde, U. and H.
Bonin (2010) �Direct Evidence on Risk Attitudes and Migration�, Re-
view of Economics and Statistics, 92 (3), pp. 684�689.
[31] Johnson, T. H. andM. C. Mason (2007) "Understanding the Taliban and
insurgency in Afghanistan", Orbis, Volume 51, Issue 1, Winter 2007, pp.
71-89.
[32] Kuran, T. (2004) "Why the Middle East Is Economically Underdevel-
oped: Historical Mechanisms of Institutional Stagnation", The Journal
of Economic Perspectives, Vol. 18, No. 3, pp. 71-90.
34
[33] Larson, A. (2009) "Toward an Afghan Democracy? Exploring Percep-
tions of Democratisation in Afghanistan" Discussion paper, Afghanistan
Research and Evaluation.
[34] Lind, J. T., Moene, K. O. and F. Willumsen (2011) "Opium for the
masses? Con�ict-induced narcotics production in Afghanistan", mimeo.
[35] Matinuddin, K., (1999) "The Taliban Phenomenon, Afghanistan 1994�
1997", Oxford University Press, (1999), pp. 25�26.
[36] Merrouche, O. (2011) "The Long Term Educational Cost of War: Evi-
dence from Landmine Contamination in Cambodia", Journal of Devel-
opment Studies, 47(3), pp. 399-416.
[37] Miguel, E. and G. Roland (2011) "The long-run impact of bombing
Vietnam", Journal of Development Economics 96, pp. 1�15.
[38] The New York Times (1998) "100 Girls�Schools In Afghan Capital Are
Ordered Shut".
[39] Norton, S. W. and Tomal, A. (2009) "Religion and Female Educational
Attainment", Journal of Money, Credit and Banking, Vol. 41, No. 5, pp.
961-986.
[40] Physicians for Human Rights (1998) "The Taliban�s War on Women: a
Health and Human Rights Crisis in Afghanistan".
35
[41] Platteau, J.-P. (2011) "Political Instrumentalization of Islam and the
Risk of Obscurantist Deadlock", World Development Vol. 39, No. 2, pp.
243�260.
[42] Rashid, A. (2000) "Taliban: Militant Islam, Oil and Fundamentalism in
Central Asia", New Haven: Yale University Press.
[43] Shemyakina, O. (2011) "The e¤ect of armed con�ict on accumulation of
schooling: Results from Tajikistan", Journal of Development Economics
95, pp. 186�200.
[44] Verwimp, P. and J. Van Bavel (2011) "Schooling, Violent Con�ict and
Gender in Burundi", HiCN Working Paper 101.
36
Tabl
e 1:
Des
crip
tive
stat
istic
sM
ale
Fem
ale
com
plet
ed 9
gra
des
of s
choo
ling
0.22
0.06
7[0
.414
][0
.251
]ca
n re
ad a
nd w
rite
0.45
70.
157
[0.4
98]
[0.3
64]
at le
ast s
ome
form
al s
choo
l0.
463
0.16
8[0
.498
][0
.373
]cu
rren
tly e
nrol
led
at s
choo
l (ag
ed 6
15)
0.55
90.
378
[0.4
96]
[0.4
85]
Stan
dard
err
ors
in b
rack
ets.
Des
crip
tive
stat
istic
s on
ind
ivid
uals
who
hav
eco
mpl
eted
bas
ic e
duca
tion
(9 g
rade
s of
sch
oolin
g),
can
read
and
wri
te,
and
have
att
ende
d (a
t le
ast
som
e) f
orm
al s
choo
l ref
er t
o in
divi
dual
s w
ith y
ear
ofbi
rth>
=197
6 an
d <=
1992
(see
est
imat
ion
sam
ple
of t
he r
egre
ssio
ns p
rese
nted
in S
ectio
n 5)
.
37
Tabl
e 2:
The
Talib
ango
vern
men
t (19
962
001)
and
the
gend
er g
ap in
sch
oolin
g/lit
erac
y in
Afg
hani
stan
.D
epen
dent
var
iabl
es: C
OLU
MN
S 1
3: D
umm
y eq
ual t
o 1
if th
e in
divi
dual
has
com
plet
ed b
asic
edu
catio
n (9
gra
des
of s
choo
ling)
.CO
LUM
NS
46:
Dum
my
equa
l to
1 if
the
indi
vidu
al c
an re
ad a
nd w
rite
.CO
LUM
NS
79:
Dum
my
equa
l to
1 if
the
indi
vidu
al a
tten
ded
(at l
east
som
e) fo
rmal
sch
ool.
Dep
ende
nt v
aria
ble:
com
plet
ed 9
gra
des
of s
choo
ling
can
read
and
wri
teat
leas
t som
e fo
rmal
sch
ool
12
34
56
78
9Fe
mal
e*Ta
liban
0.0
51**
*0
.033
***
0.0
67**
*0
.032
***
0.0
21*
0.0
35**
*0
.053
***
0.0
33**
*0
.062
***
[0.0
08]
[0.0
09]
[0.0
09]
[0.0
10]
[0.0
11]
[0.0
11]
[0.0
10]
[0.0
11]
[0.0
11]
Fem
ale
0.1
08**
*0
.107
***
0.1
08**
*0
.262
***
0.2
60**
*0
.263
***
0.2
42**
*0
.240
***
0.2
42**
*[0
.007
][0
.007
][0
.007
][0
.008
][0
.008
][0
.009
][0
.008
][0
.008
][0
.008
]
Dis
tric
t dum
mie
sye
sye
sye
sye
sye
sye
sye
sye
sye
sYe
ar o
f bir
th d
umm
ies
yes
yes
yes
yes
yes
yes
yes
yes
yes
Obs
erva
tions
3981
921
727
2356
539
741
2169
023
513
3974
021
688
2351
2R
squa
red
0.21
0.2
0.22
0.28
0.25
0.28
0.34
0.32
0.33
Robu
st s
tand
ard
erro
rs in
bra
cket
s. *
sig
nific
ant
at 1
0%; *
* si
gnifi
cant
at
5%; *
** s
igni
fican
t at
1%
. "Ta
liban
"is
a du
mm
y eq
ual t
o 1
if th
e in
divi
dual
was
aged
61
5 w
hile
the
Talib
anw
ere
in p
ower
(19
962
001)
. Es
timat
ion
sam
ple
in c
olum
ns 1
47
: in
divi
dual
s w
ith 1
976<
=yea
r of
bir
th<=
1992
. Es
timat
ion
sam
ple
in c
olum
ns 2
58
: in
divi
dual
s w
ith 1
976<
=yea
r of
bir
th<=
1986
.Est
imat
ion
sam
ple
in c
olum
ns 3
69
: in
divi
dual
s w
ho w
here
age
d 6
15 d
urin
g th
ew
hole
per
iod
the
Talib
anw
ere
in p
ower
(i.e
., 19
86<=
year
of
birt
h<=1
990)
and
ind
ivid
uals
who
had
no
expo
sure
to
the
Talib
andu
ring
the
age
61
5(1
976<
=yea
r of
bir
th<=
1980
).
38
Tabl
e 3:
The
Talib
ango
vern
men
t (19
962
001)
and
the
gend
er g
ap in
sch
oolin
g/lit
erac
y in
Afg
hani
stan
. With
dis
tric
tsp
ecifi
c tim
e tr
ends
.D
epen
dent
vari
able
s: C
OLU
MN
S 1
3: D
umm
y eq
ual t
o 1
if th
e in
divi
dual
has
com
plet
ed b
asic
edu
catio
n (9
gra
des
of s
choo
ling)
.CO
LUM
NS
46:
Dum
my
equa
l to
1 if
the
indi
vidu
al c
an re
ad a
nd w
rite
.CO
LUM
NS
79:
Dum
my
equa
l to
1 if
the
indi
vidu
al a
tten
ded
(at l
east
som
e) fo
rmal
sch
ool.
Dep
ende
nt v
aria
ble:
com
plet
ed 9
gra
des
of s
choo
ling
can
read
and
wri
teat
leas
t som
e fo
rmal
sch
ool
12
34
56
78
9Fe
mal
e*Ta
liban
0.0
52**
*0
.034
***
0.0
69**
*0
.036
***
0.0
22*
0.0
38**
*0
.058
***
0.0
34**
*0
.067
***
[0.0
08]
[0.0
09]
[0.0
1][0
.01]
[0.0
12]
[0.0
12]
[0.0
1][0
.011
][0
.011
]Fe
mal
e0
.107
***
0.1
07**
*0
.107
***
0.2
6***
0.2
61**
*0
.261
***
0.2
4***
0.2
41**
*0
.241
***
[0.0
07]
[0.0
07]
[0.0
07]
[0.0
09]
[0.0
09]
[0 .0
09]
[0.0
08]
[0.0
08]
[0.0
08]
Dis
tric
t dum
mie
sye
sye
sye
sye
sye
sye
sye
sye
sye
sYe
ar o
f bir
th d
umm
ies
yes
yes
yes
yes
yes
yes
yes
yes
yes
Dis
tric
tsp
ecifi
c tim
e tr
ends
yes
yes
yes
yes
yes
yes
yes
yes
yes
Obs
erva
tions
3981
921
727
2356
539
741
2169
023
513
3974
021
688
2351
2R
squa
red
0.22
0.21
0.24
0.3
0.27
0.29
0.36
0.33
0.35
Robu
st s
tand
ard
erro
rs in
bra
cket
s. *
sig
nific
ant
at 1
0%; *
* si
gnifi
cant
at
5%; *
** s
igni
fican
t at
1%
. "Ta
liban
"is
a du
mm
y eq
ual t
o 1
if th
e in
divi
dual
was
aged
61
5 w
hile
the
Talib
anw
ere
in p
ower
(19
962
001)
. Es
timat
ion
sam
ple
in c
olum
ns 1
47
: in
divi
dual
s w
ith 1
976<
=yea
r of
bir
th<=
1992
. Es
timat
ion
sam
ple
in c
olum
ns 2
58
: in
divi
dual
s w
ith 1
976<
=yea
r of
bir
th<=
1986
.Est
imat
ion
sam
ple
in c
olum
ns 3
69
: in
divi
dual
s w
ho w
here
age
d 6
15 d
urin
g th
ew
hole
per
iod
the
Talib
anw
ere
in p
ower
(i.e
., 19
86<=
year
of
birt
h<=1
990)
and
ind
ivid
uals
who
had
no
expo
sure
to
the
Talib
andu
ring
the
age
61
5(1
976<
=yea
r of
bir
th<=
1980
).
39
Tabl
e 4:
The
Talib
anin
surg
ency
and
the
scho
olin
g ge
nder
gap
in A
fgha
nist
an.
Dep
ende
nt V
aria
ble:
Dum
my
equa
l to
1 if
the
indi
vidu
al (a
ged
615
) is
enro
lled
in s
choo
l at t
he ti
me
of th
e su
rvey
.Es
timat
orLP
MIV
LPM
LPM
IVL
PMfe
mal
e*nu
mbe
r of
vio
lent
eve
nts
rela
ted
to in
surg
ents
(/10
0)0
.106
***
0.1
63**
*0
.106
***
0.1
63**
*[0
.01]
[ 0.0
48]
[0.0
1][0
.048
]fe
mal
e0
.196
***
0.1
81**
*0
.196
***
0.1
81**
*[0
.006
][0
.014
][0
.006
][0
.014
]lo
g of
num
ber o
f hou
seho
ld m
embe
rs0
.01
0.0
10
.03*
**0
.03*
**[0
.01]
[0.0
1][0
.011
][0
.011
]lo
g of
num
ber o
f hou
seho
ld m
embe
rs a
ged
615
0.0
67**
*0
.068
***
0.0
63**
*0
.064
***
[0.0
07]
[ 0.0
07]
[0.0
07]
[0.0
07]
rura
l0
.145
***
0.1
45**
*0
.144
***
0.1
45**
*[0
.012
][0
.011
][0
.012
][0
.011
]ed
ucat
ion
of th
e he
ad o
f hou
seho
ld0.
023*
**0.
024*
**0.
022*
**0.
022*
**[0
.002
][0
.002
][0
.002
][0
.002
]ag
e of
the
head
of h
ouse
hold
0.0
004*
0.0
004*
0.0
004*
*0
.000
4**
[0 .0
002]
[0.0
002]
[0 .0
002]
[0 .0
002]
head
of h
ouse
hold
is m
arri
ed0.
025*
*0.
025*
*0.
025*
*0.
025*
*[0
.012
][0
.012
][ 0
.012
][0
.012
]he
ad o
f hou
seho
ld is
fem
ale
0.00
40.
004
0.00
60.
006
[ 0.0
21]
[0.0
21]
[ 0.0
21]
[0.0
21]
log
of to
tal h
ouse
hold
inco
me
0.02
2***
0.02
2***
[0 .0
04]
[0.0
04]
dist
rict
dum
mie
sye
sye
sye
sye
sye
ar o
f bir
th d
umm
ies
yes
yes
yes
yes
Firs
t sta
ge F
sta
t17
9.2
179.
21O
bser
vatio
ns36
317
3631
736
317
3631
7R
squa
red
0.25
90.
259
0.26
0.26
Robu
st s
tand
ard
erro
rs in
bra
cket
s. *
sig
nific
ant
at 1
0%;
** s
igni
fican
t at
5%
; **
* si
gnifi
cant
at
1%.
The
endo
geno
usri
ght
hand
sid
e va
riab
le is
"fe
mal
e*nu
mbe
r of
vio
lent
eve
nts
rela
ted
to in
surg
ents
(/1
00)"
. The
inst
rum
enta
l var
iabl
e is
"fem
ale*
opiu
m_p
rodu
ctio
n". B
oth
the
num
ber
of v
iole
nt e
vent
s re
late
d to
insu
rgen
ts a
nd o
pium
pro
duct
ion
vary
at
the
dist
rict
leve
l.
40
Figu
re 1
: Geo
grap
hica
l dist
ribut
ion
of v
iole
nt e
vent
s ass
ocia
ted
to th
eTa
liban
insu
rgen
cy in
200
8.
41
Figu
re 2
: Geo
grap
hica
l dist
ribut
ion
of o
pium
pop
py c
ultiv
atio
n in
200
8.
Whi
te d
istr
icts
: est
imat
ed h
ecta
res
of la
nd d
evot
ed t
o op
ium
popp
y cu
ltiva
tion=
0.Ye
llow
di
stric
ts:
estim
ated
he
ctar
es
of
land
de
vote
d to
op
ium
po
ppy
culti
vatio
n<=2
50. R
ed d
istr
icts
: est
imat
ed h
ecta
res
of la
nd d
evot
ed to
opi
um p
oppy
culti
vatio
n>25
0. D
ata
sour
ce: U
nite
d N
atio
ns O
ffic
e on
Dru
gs a
nd C
rime
(UN
OD
C)an
nual
Afgh
anO
pium
Surv
ey.
42
Tabl
e A
1: T
he g
ende
r gap
in s
choo
ling/
liter
acy
in A
fgha
nist
an. F
alse
exp
erim
ent.
Dep
ende
nt v
aria
bles
: CO
LUM
N 1
: Dum
my
equa
l to
1 if
the
indi
vidu
al h
as c
ompl
eted
bas
ic e
duca
tion
(9 g
rade
s of
sch
oolin
g).
COLU
MN
2:D
umm
y eq
ual t
o 1
if th
e in
divi
dual
can
rea
d an
d w
rite
.CO
LUM
N 3
: Dum
my
equa
l to
1 if
the
indi
vidu
al a
tten
ded
(at
leas
t som
e) fo
rmal
sch
ool.
Dep
ende
nt v
aria
ble:
com
plet
ed 9
gra
des
of s
choo
ling
can
read
and
wri
teat
leas
t som
e fo
rmal
sch
ool
12
3Fe
mal
e*Co
hort
of b
irth
197
619
800.
011
00
.013
[0.0
10]
[0.0
13]
[0.0
12]
Fem
ale
0.1
16**
*0
.260
***
0.2
27**
*[0
.007
][0
.010
][0
.009
]
Dis
tric
t dum
mie
sye
sye
sye
sYe
ar o
f bir
th d
umm
ies
yes
yes
yes
Obs
erva
tions
1619
716
171
1617
0R
squa
red
0.2
0.25
0.29
Robu
st s
tand
ard
erro
rs i
n br
acke
ts.
* si
gnifi
cant
at
10%
; **
sig
nific
ant
at 5
%;
***
sign
ifica
nt a
t 1%
. Es
timat
ion
sam
ple:
ind
ivid
uals
with
1971
<=ye
ar o
f bir
th<=
1980
.
43
Tabl
e A
2: T
heTa
liban
insu
rgen
cy a
nd th
e sc
hool
ing
gend
er g
ap in
Afg
hani
stan
.
Dep
ende
nt V
aria
ble:
Dum
my
equa
l to
1 if
the
indi
vidu
al (a
ged
615
) is
enro
lled
in s
choo
l at t
he ti
me
of th
e su
rvey
.
Estim
atio
n sa
mpl
eA
BC
DE
Estim
ator
LPM
IV
LPM
LPM
IVL
PMLP
MIV
LPM
LPM
IVL
PMLP
MIV
LPM
fem
ale*
num
ber o
f vio
lent
eve
nts
rela
ted
to in
surg
ents
(/10
0)0
.089
***
0.0
140
.1**
*0
.082
**0
.106
***
0.1
63**
*0
.107
***
0.1
61**
*0
.107
***
0.1
62**
*
[0.0
09]
[0.0
2][0
.01]
[0.0
35]
[0.0
1][0
.048
][0
.01]
[0.0
48]
[0.0
1][0
.048
]
Oth
er re
gres
sors
(fem
ale,
log
num
ber o
f hou
seho
ld m
embe
rs,
log
num
ber
of h
ouse
hold
mem
bers
age
d 6
15, r
ural
, edu
catio
nye
sye
sye
sye
sye
sye
sye
sye
sye
sye
sof
the
head
of h
ouse
hold
, age
of t
he h
ead
of h
ouse
hold
, hea
d
of h
ouse
hold
is m
arri
ed, h
ead
of h
ouse
hold
is fe
mal
e an
d lo
g
of to
tal h
ouse
hold
inco
me)
dist
rict
dum
mie
sye
sye
sye
sye
sye
sye
sye
sye
sye
sye
s
year
of b
irth
dum
mie
sye
sye
sye
sye
sye
sye
sye
sye
sye
sye
s
Firs
t sta
ge F
sta
t45
5.75
257.
4517
9.21
178.
6417
6.09
Obs
erva
tions
3688
936
889
3647
836
478
3631
736
317
3626
636
266
3625
236
252
Rsq
uare
d0.
260.
260.
260.
260.
260.
260.
260.
260.
260.
26Ro
bust
sta
ndar
d er
rors
in b
rack
ets.
* s
igni
fican
t at
10%
; **
sig
nific
ant
at 5
%;
***
sign
ifica
nt a
t 1%
. Th
e en
doge
nous
rig
hth
and
side
var
iabl
e is
"fe
mal
e*nu
mbe
r of
vio
lent
eve
nts
rela
ted
to in
surg
ents
(/10
0)".
The
inst
rum
enta
l var
iabl
e is
"fe
mal
e*op
ium
_pro
duct
ion"
. Bot
h th
e nu
mbe
r of
vio
lent
eve
nts
rela
ted
to in
surg
ents
and
opiu
m p
rodu
ctio
n va
ry a
t th
e di
stri
ct le
vel.
Estim
atio
n sa
mpl
es: A
=who
lesa
mpl
e; B
=Exc
ludi
ng h
ouse
hold
s w
ith e
ither
the
prod
uctio
n an
d sa
le o
f opi
um, o
r op
ium
wag
e la
bor
as 1
st m
ost
impo
rtan
t sou
rce
ofin
com
e; C
=Exc
ludi
ng h
ouse
hold
s w
ith e
ither
the
prod
uctio
n an
d sa
le o
fop
ium
, or
opi
um w
age
labo
r am
ong
the
two
mos
t im
port
ant
sour
ces
of i
ncom
e; D
=Exc
ludi
ng h
ouse
hold
s w
ith e
ither
the
pro
duct
ion
and
sale
of
opiu
m,
or o
pium
wag
e la
bor
amon
g th
e th
ree
mos
tim
port
ant s
ourc
es o
f inc
ome;
E=E
xclu
ding
hou
seho
lds
with
eith
er th
e pr
oduc
tion
and
sale
of o
pium
, or
opiu
m w
age
labo
r am
ong
the
four
mos
t im
port
ant s
ourc
es o
f inc
ome.
44