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).

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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