Gender Discrimination in Education - Ricerca sull'intelligenza artificiale specializzata al MIT

Gender Discrimination in Education
Expenditure in Nepal: Evidence
from Living Standards Surveys
Shaleen Khanal∗

There is a signiﬁcant amount of literature on the role of parental gender
preferences in determining the level of education expenditure for children. In
this study, I examine the effects of such preferences on parents’ education
expenditure in Nepal. Using longitudinal data from three Nepal Living
Standards Surveys, I apply several decomposition methods to determine the
level of bias that parents display in spending on their children’s education. I ﬁnd
that parents indeed spend more on boys than girls in both rural and urban areas
in Nepal. I also ﬁnd that this bias is reﬂected in the higher enrollment levels of
boys than girls in private schools.

Keywords: decomposition, education expenditure, gender discrimination,
household decisions, Nepal Living Standards Surveys
JEL codes: H52, I24

IO. introduzione

Nepal has made remarkable progress in achieving a degree of gender parity
in the ﬁeld of education. Net enrollment rates have achieved parity at all levels of
schooling, reﬂecting the government’s success in ensuring the equal participation
of girls in schools. Tuttavia, while improvements in enrollment rates are a positive
ﬁrst step, this does not imply gender parity in the education sector. Various forms of
discrimination—such as the reproduction of discriminatory norms in the process of
socialization and in the classroom (per esempio., a curriculum that favors traditional gender
roles), encouragement for continuing traditional course selection (Collins 2009),
and at times outright discriminating behavior—have been observed in schools
(Hickey and Stratton 2007, Bandyopadhyay and Subhramaniam 2008). Al
household level as well, girls are expected to spend more time on chores rather
than on education (Mason and Khandker 1996, Levison and Moe 1998); are more
likely to drop out of school (Sabates et al. 2010); and are less likely to continue their

∗Shaleen Khanal: Research Ofﬁcer, South Asia Watch on Trade, Economics and Environment, Nepal. E-mail:
shaleenkhanal@gmail.com. I would like to express my sincere gratitude to Sweta Khanal and Ashmita Poudel for
their valuable inputs. I would also like to thank the managing editor and the anonymous referee for helpful comments
and suggestions. The usual disclaimer applies.

Asian Development Review, vol. 35, NO. 1, pag. 155–174
https://doi.org/10.1162/adev_a_00109

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156 Asian Development Review

education at higher levels. Another form of household discrimination, which forms
the topic of this study, is differential treatment in education expenditure in which
parents spend more on boys’ education than they do on girls’ education.

Gender parity is a basic precondition for a just and equitable society.
Arguments for gender equality also go beyond reasons of justice and equality.
Empowering women is crucial for the socioeconomic development of any country.
Studies report that higher levels of education in women lead to higher economic
growth (Coulombe and Tremblay 2006); reductions in child and infant mortality
rates (Cochrane 1982, LeVine 1987); and better outcomes for all children in the
family (Schultz 1961; Alderman and King 1998; Strauss, Mwabu, and Beegle
2000). Yet, despite governments promoting the participation of women in schooling
and education, societies continue to observe disparities in women’s access to
education and the labor force. The feminist movement attributes this phenomenon
A (io) the existing sexual division of labor that assigns women to domestic tasks;
E (ii) men’s control over women’s sexuality, which includes strict supervision of
movements outside the home and limits on societal interactions (Stromquist 1992).
Economic models explain that such disparities arise out of differential parental
preferences (assuming parents to be rational economic agents) due to differences
in children’s cognitive endowment, birth order, E (more importantly) variations
in expected returns on investment between boys and girls (Behrman, Pollak, E
Taubman 1982; Lehmann, Nuevo-Chiquero, and Vidal-Fernandez 2012).

In Nepal, societal norms dictate that women after a certain age are married
away. Additionally, patriarchy is pervasive in Nepal’s legal and socioeconomic
environments, a fact substantiated by the widespread inequality observed in
legal outcomes (Nowack 2015); wealth (Bhadra and Shah 2007); employment
opportunities (ADB 2010, Bhadra and Shah 2007); and education (UNESCO 2015).
The incentives for parents to pay for girls’ education are lower compared with boys
not only because women are likely to face unequal opportunities in the labor force,
but also because boys are expected to look after their parents and the family estate
when the parents grow old.

While much has been written on gender discrimination in education in Nepal,
very little empirical work has been done to analyze the extent of the discrimination.
This paper tries to ﬁll that gap by examining the nature and extent of one form
of discrimination—inequality in household expenditure—faced by women in the
education sector by comparing expenditure on education for girls versus boys, E
then decomposing the observed gap in expenditure into explained and unexplained
components. The paper is organized as follows. Section II presents the motivation
behind the research, including an identiﬁcation of the research gap that this paper
addresses. The methodology and the data set used in this study are described in
section III. Section IV details the major results and the ﬁndings. Section V consists
of conclusions and policy recommendations.

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Gender Discrimination in Education Expenditure in Nepal 157

II. Motivation

The right to an education is a fundamental human right. Yet, women in the
developing world are underrepresented at all levels of education (Vedere, Per esempio,
Annex 1 of the Global Campaign for Education 2012). While progress has been
made globally in improving the net enrollment ratio at primary levels, a noticeable
decline is observed in girls’ participation at higher levels of education (Globale
Campaign for Education 2012). Inequality is not only observed in terms of ability to
participate in schooling, but also in terms of quality of schooling.1 The participation
of girls is also found to be lower in private schools compared with public schools
in developing economies (Harma 2011; Maitra, Pal, and Sharma 2011; Woodhead,
Frost, and James 2013; Sahoo 2014).

As was mentioned earlier, one of the reasons behind the ineffective inclusion
of girls in educational opportunities is the unequal investment made by parents in
their male and female children’s education. The prevalence of unequal returns to
education in terms of wages and work opportunities in the labor market implies that
parents are likely to invest more in boys’ education than in girls’ (Garg and Morduch
1998 as cited in Sahoo 2014, Leclercq 2001). Results are further skewed in favor of
boys if women are expected to leave their parents’ home after they get married while
men are expected to remain at home to eventually take care of their elderly parents.2
Various studies have found differential treatment resulting from parents’ investment
decisions. Per esempio, Burgess and Zhuang (2000) and Gong, van Soest, E
Zhang (2005) ﬁnd signiﬁcant bias in favor of boys in education expenditure in the
People’s Republic of China. Allo stesso modo, in India, Kingdon (2005) and Saha (2013)
ﬁnd evidence of differential education expenditure between boys and girls in certain
stati. Similar ﬁndings were presented in the cases of Pakistan (Aslam and Kingdon
2008), Paraguay (Masterson 2012), and Bangladesh (Shonchoy and Rabbani
2015).

Considering the cultural and socioeconomic similarities between many of the
above-mentioned countries and Nepal, and the existence of widespread patriarchy
in Nepal, we can expect to ﬁnd signiﬁcant levels of gender bias in education
expenditure patterns among Nepalese households. Unequal access to and outcomes
in education with respect to gender are characteristic features of the Nepalese
education system. School enrollment has long skewed in favor of boys (World Bank
2014). More recently, there has been a drive to make education (along with other
social services) equitable and inclusive. The Constitution of Nepal 2015 has made

1Discrimination against girls is also pervasive in a school environment. Tuttavia, the focus of analysis in this

study concerns parental expenditure choices that are biased in favor of boys.

2In the Indian subcontinent, men are expected to live with their parents and look after them in their old
age, while women are expected to live with their husbands. This practice contributes signiﬁcantly to the unequal
treatment of women and girls in terms of human capital development, marriage, and other critical life decisions
including inheritance.

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158 Asian Development Review

the right to an education an inalienable right for all (Government of Nepal 2015).
Gender equality and social inclusion guidelines have been formulated across all
government sectors to make policies, strategies, and outcomes gender sensitive.
The Education for All initiative and the School Sector Reform Plan prioritize equal
participation for girls at all levels of education (Ministry of Education and Sports
2003). As a consequence, net enrollment ratios have risen for all children and are
now comparable for both boys and girls at primary and secondary schools (National
Planning Commission 2013). Yet, the participation of boys in private education
and higher education remains higher when compared with girls (Department of
Education 2015). Therefore, while the gender gap in terms of school enrollment
at primary and secondary levels has almost disappeared, instances of gender
discrimination can still be observed among Nepalese households both in terms of
education quality and expenditure.3

Decomposing such discrimination can provide policy makers with valuable
insights into understanding and minimizing the extent of such bias and incentivizing
households to achieve better education outcomes for girls. Tuttavia, studies
on gender discrimination and education in Nepal are scarce. Most reports on
discrimination typically analyze participation rates and do not consider other forms
of discrimination (Vedere, Per esempio, Unterhalter 2006, Herz 2006, and Huxley
2009).

Similar patterns can be observed in academic studies. One of the earliest
studies in the ﬁeld incorporating historical data was conducted by Stash and
Hannum (2001), who ﬁnd evidence of a signiﬁcant gender gap in primary school
participation rates. Using data from the 1991 Nepal Fertility, Family Planning, E
Health Survey, they ﬁnd that the educational attainment of head of households
and rural–urban households bore no effect on school participation rates for girls.
Therefore, they conclude that traditional indicators of development had little impact
on discriminatory educational outcomes. LeVine’s (2006) ethnographic study of
Nepal examines the determinants of school attendance of girls and the reasons
behind their dropping out of school. The study ﬁnds that since the 1990s, profound
socioeconomic transformations have led to a more equitable attitude of parents
toward their children’s education, although girls were still less likely to complete
their education or attain higher education because of marriage. A recent study by
Devkota and Upadhyay (2015) examines inequality in education outcomes owing to
various household factors like income, sex, ethnicity, and location of the household

3Private schools are generally considered to provide higher quality education in Nepal than public schools.
They are more expensive to attend, spend more on children’s education per student, have lower rates of teacher
absenteeism, have better school management systems, and exercise more stringent grade promotion systems. As a
consequence, private schools produce better results in School Leaving Certiﬁcate exams. In 2012, the success rate
of private school students taking School Leaving Certiﬁcate exams was 93.1% compared with only 28.2% for public
school students (Sharma 2012). Parents prefer private schools provided they can afford them. Therefore, the higher
rate of participation of boys in private schools is indicative of discriminatory expenditure decisions at the household
level.

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Gender Discrimination in Education Expenditure in Nepal 159

and the school. They ﬁnd that while men in Nepal were likely to attain a higher level
of education, their advantage had signiﬁcantly declined between 1996 E 2004.

Some studies have looked at the effects of migration on education outcomes
in Nepal. Bontch-Osmolovski (2009) studies the role of migration in education and
ﬁnds signiﬁcant positive effects of parental migration on their children’s enrollment
in school. Tuttavia, the author ﬁnds no signiﬁcant difference, on average, del
effect of migration by the gender of the child, which is contrary to Nepal (2016),
who ﬁnds higher levels of school enrollment, greater incidence of private schooling,
and shorter working hours for boys in migrant households when compared with
girls. Bansak and Chezum (2009) also ﬁnd that remittances positively affect school
attendance, with a greater positive impact among boys than girls.

The aforementioned studies rely primarily on enrollment and school
participation rates as the basis of analysis of gender discrimination, assuming
parental decisions only affect the participation of children at school and ignore other
forms of discrimination between boys and girls already enrolled in schools. Questo
discussion becomes even more pertinent given rising enrollment and participation
rates for both boys and girls at the primary and secondary school levels. Considering
the clear evidence of unequal expenditure in favor of boys’ education in comparable
societies, there is a need to investigate whether this trend exists in Nepal as well.
Vogel and Korinek (2012) were the ﬁrst to evaluate the expenditure allocation
decisions of households on education in Nepal. Their study examines how
remittance income is allocated in terms of schooling expenditure for boys and
girls within the same family. They ﬁnd that households that receive substantial
remittances tend to increase education spending for boys but not for girls. Therefore,
in girls’
more remittances do not necessarily result
formazione scolastica. Tuttavia, the study primarily limits itself to remittance-based households
and does not take nonmigrating households into consideration.

in increased investment

This paper aims to build on the ﬁndings of Vogel and Korinek (2012) by
looking at the education expenditure allocation decisions of Nepalese households.
It focuses on the extent of discrimination practiced against girls in terms of
expenditure patterns on education and examines the possible reasons behind
such inequality. Using the Blinder–Oaxaca decomposition method (along with
decomposition using quantile regressions),
the study examines the extent of
explained differences and unexplained differences (proxied as discrimination) In
education expenditure for families across Nepal.

III. Data and Methodology

Data for the study comes from the three rounds of the Nepal Living
Standards Survey (NLSS) conducted in 1995–1996, 2003–2004, and 2010–2011.4

4Henceforth, NLSS I, NLSS II, and NLSS III will imply surveys conducted in 1995–1996, 2003–2004, E

2010–2011, rispettivamente.

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160 Asian Development Review

The surveys follow the methodology developed by the World Bank in its Living
Standards Measurement Study and collect information from all over Nepal on
wide-ranging variables including, among others, poverty; income, wealth, E
expenditure sources; household composition; and migration. The latest survey
collected data from 5,988 households (in addition to 1,032 households used for
the panel sample) from 71 districts (499 primary sampling units) across Nepal
over a 12-month period. For the study, I use samples from both rural and urban
households from all three geographical regions surveyed in the study. Due to a
lack of observations among students of higher studies and for schools under other
systems of education, I have conﬁned the samples for the regression analysis to
include students until the 10th standard of their schooling and who have studied
in either community schools or private schools.5 To arrive at total education
expenditure per student, I have calculated total school fees of individual children
by adding the costs of uniforms, text books, transportation, private tuition, E
other fees, and then deducting the monetary value of any scholarships. Fees are
presented on a nominal basis and have not been converted to real terms. The sample
for education expenditure per child was trimmed by the top 0.1% and the bottom
0.1% to remove potential outliers.

Two methods have been popularly used to disaggregate biases in education
expenditure in popular research. The ﬁrst methodology makes use of Engel Curves,
which observes household-level expenditure data and analyzes the relationship
between changes in household gender composition and patterns of expenditure.
In the absence of individual-level data on expenditure patterns,
this method
can provide valuable insights into inferring the level of bias from the overall
household expenditure data (Aslam and Kingdon 2008). Tuttavia, the validity of
this methodology has also been challenged (Kingdon 2005).

Where individual-level data are available, the use of decomposition provides
far more useful results. First used by Blinder (1973) and Oaxaca (1973), this method
decomposes the expenditure gap into an endowment gap and a coefﬁcient gap.
The endowment gap explains differences in expenditure based on differences in
endowments and the coefﬁcient gap is the discrimination coefﬁcient (Madheswaran
and Attewell 2007). While the Blinder–Oaxaca decomposition is popularly used
to decompose bias in wage gaps in the labor market, the methodology is as
effective in understanding the bias in education expenditure as well, and has been

5The education system in Nepal is classiﬁed into primary (1st–5th grade), lower secondary (6th–8th grade),
secondary (9th–10th grade), higher secondary (11th–12th grade), and tertiary levels. Classiﬁcation is made based on
national level examinations and students are required to attend. All students must clear the School Leaving Certiﬁcate
examinations in 10th grade to qualify for higher-level studies in which students can choose boards and areas of
interesse. School Leaving Certiﬁcate examinations are traditionally considered the entry gate for higher education
in Nepal. The government has prioritized the elimination of gender disparity in education through the secondary
level under the Education for All Initiative (Ministry of Education and Sports 2003). The NLSS classiﬁes primary
and secondary schools into four categories: (io) community or government-owned schools, (ii) institutional or private
schools, (iii) technical schools, E (iv) religious schools. As can be observed from Table 1, the share of students
studying in the latter two categories is extremely small.

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Gender Discrimination in Education Expenditure in Nepal 161

used in studies analyzing decomposition of education expenditure. Here, I use the
Blinder–Oaxaca decomposition method to disaggregate bias in the expenditure gap
that can be explained by differences of endowments and the unexplained gap.

The basic equation can be represented as

log (Exp)i jt

= αi jt + β1 poor jt + β2rural jt + β3ethnii jt + β4Income jt
+ β5Schooltypeit + β6Currentclassi jt + β7distschooli jt
+ β8birthorderi jt + β9Motheredui jt + β10Fatheredui jt
+ β11H H size jt + β12Femalei j + εi jt

(1)

where Expi jt is the expenditure by household j on child i in year t. Femalei j is the
dummy variable where Femalei j has a value of 1 if the child is a girl and 0 if the
child is a boy. Allo stesso modo, Femalei j, poor jt, rural jt, and ethnii jt are dummy variables
for families that are poor, live in rural areas, or belong to upper castes, rispettivamente,
in year t.6 Income jt is the total income of the household in thousands of Nepalese
rupees (NRs). Schooltypeit is a dummy variable where 0 equals government school
E 1 equals private school. Currentclassi jt is a vector of grade levels ranging from
1st until 10th grade. Distschooli jt represents the distance from the child’s house
to the school (measured in kilometers for NLSS III and in hours for NLSS I and
NLSS II). Birthorderi j is a categorical variable that quantiﬁes the order of the
child’s birth in the family where a value of 1 represents the ﬁrstborn child, 2 È
the second child, and so on. Motheredui j and Fatheredui j represent the level of
the parents’ education with a value of 10 signifying completion of 10th grade.
Additionally, H H size jt describes the total size of the household of the student under
consideration. For the ordinary least squares (OLS) regression, I have included
Femalei j as a dummy variable where a value of 1 implies a girl student and 0 implies
a boy.

I use the Blinder–Oaxaca decomposition where the gross education

expenditure differential for years t can be deﬁned as

Gt = Expmt − Exp f t

Exp f t

(2)

where Expm and Exp f
represent education expenditure on boys and girls,
rispettivamente. In the absence of any discrimination, the differences in expenditure
could be explained only by the household-related variables where

Qt =

− Exp0
f t

Exp0
mt
Exp0
f t

(3)

6For the purpose of this study, Brahmin (hills and terai) and Chettris (hills and terai) are considered to be

members of the upper castes.

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162 Asian Development Review

The discrimination coefﬁcient Dt can therefore be understood as

(cid:2)
Expmt/Exp f t

(cid:3)

−

(cid:4)

(cid:5)

Exp0
mt
(cid:5)

/Exp0
f t

(cid:4)
Exp0
mt

/Exp0
f t

Dt =

(4)

The logarithmic transformation of gross differential ln(Gt + 1) can therefore be
equated as

ln (Gt + 1) = ln (Qt + 1) + ln (Dt + 1)

(5)

Following equation (1),

ln (Expmt ) =

(cid:2)

Exp f t

(cid:3)

(cid:6)

βmtXmt,

β f tX f t,

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(cid:7)

Dove
elaborated in equation (1):

βX represents a vector of determinants of education expenditure as

ln (Gt + 1) = ln (Expmt ) − ln

(cid:2)

Exp f t

(cid:6)

(cid:3)

βmtXmt −

(cid:6)

β f tX f t

(6)

Then, the explained and unexplained expenditure gaps can be divided into

(cid:2)

Expmt

(cid:3)

− ln

(cid:3)

(cid:2)
Exp f t

(cid:2)
X mt − X f t

(cid:3)

(cid:4)

(cid:5)

ˆβmt + X f t

ˆβmt − ˆβ f t

= E + D

(7)

where the ﬁrst term E is considered the difference in endowment and D represents
the difference in expenditure between girls and boys with identical endowments,
which can be interpreted as the bias (Madheswaran and Attewell 2007).

While the Blinder–Oaxaca decomposition method is very popular, it tends
to ignore what is referred to as the common support problem in which chances
of misspeciﬁcation can arise because characteristic features of two cohorts being
examined are generally ignored while computing the outcomes. In such cases,
nonparametric decomposition methods like Black et al. (2008) and Ñopo (2008)
have been used to simulate results for subsamples with comparable characteristics
(Fortin, Lemieux, and Firpo 2010). Here, I also employ the Ñopo (2008)
nonparametric estimation where the difference in education expenditure is given
by
(cid:2)

(cid:3)

Expmt

− ln

(cid:2)
Exp f t

= Dxt + Dmt + D f t + D0t

(8)

where Dx represents differences in expenditure due to uneven distribution of gender-
speciﬁc characteristics across the two gender cohorts; Dm represents differences
in expenditure due to differences in endowment between males and females, E
the possibility of extent of an increase in expenditure provided that females have

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Gender Discrimination in Education Expenditure in Nepal 163

Tavolo 1. Summary Statistics of Education Enrollment and Fees across School Categories

Boys

Girls

School Category

1995–1996 2003–2004 2010–2011 1995–1996 2003–2004 2010–2011

Enrollment (%)

Community or government
Institutional or private
Technical or vocational
Gurukul–madrasa–gumba
Other
Total students

86.61
11.39
–
0.73
1.27

77.07
21.59
0.21
–

1.13

71.80
27.32
0.26
0.55
0.07

87.37
10.88
0.61
0.09
1.05

78.50
19.78
0.26
–

1.45

77.89
21.06
0.07
0.90
0.07

1,650

2,835

2,720

1,140

2,270

2,673

Community or government
Institutional or private
Technical or vocational
Gurukul–madrasa–gumba
Other
Total expenditure

911.57
6,522.88
–
1257.67
391.43
1,546.82

Expenditure (NRs)

1,290.12
10,151.52
12,309.17
–

715.63
3,219.89

2,867.21
16,450.57
23,820.71
857
9,558
6,864.54

869.98
7,148.99
2,100

383.71
183.75
1,543.83

1,137.01
10,459.88
1317.5
–

237.18
2,968.45

2,454.53
18,264.95
14,015

1,982.3
2,739.33
5,978.48

Note: NLSS I does not contain the gurukul–madrasa–gumba category but instead includes a category for community
schools. Allo stesso modo, NLSS II only categorizes government schools, private schools, technical schools, and other
schools.
Fonte: Author’s calculation based on Nepal Living Standards Surveys.

represents differences in the characteristics of males
male characteristics; D f
and females, and the potential decline in male expenditure if they had female
endowments; and D0 represents unexplained discrimination.

Considering the possibility of differential effects of various control variables
I also use the quantile decomposition
across the expenditure distribution,
methodology of Melly (2005) to evaluate levels of discrimination across various
points in the distribution of the education expenditure. The methodology goes
beyond the mean and decomposes differences in education expenditure between
the two groups (girls and boys) at different quantiles of the variable of interest.

IV. Findings

Analysis of the descriptive summary of the variables suggests the existence
of a discrepancy in spending between boys and girls, with the total expenditure
pattern showing that education expenditure on boys is slightly greater than that on
girls (Tavolo 1). While there is not much difference in the fees paid among various
school categories,7 in fact expenditure in private schools is higher in the case of

7Since the proportion of schools other than government schools and private schools is less than 2%, the focus
in the remainder of this paper will be on community (public) and institutional (private) schools. Policy documents,
including the Education for All Initiative and the annual Flash Report of the Department of Education, also focus on
these two school structures. Therefore, leaving out religious schools and vocational schools will not detract from the
analytical discussion (Ministry of Sports and Education 2003, Department of Education 2015).

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164 Asian Development Review

Tavolo 2. Summary Statistics on Enrollment and Fees across School Categories in Rural
Areas

Boys

Girls

School Category

1995–1996 2003–2004 2010–2011 1995–1996 2003–2004 2010–2011

Community or government
Institutional or private
Technical or vocational
Gurukul–madrasa–gumba
Other
Total students

Enrollment (%)

93.39
4.30
–
0.80
1.51

88.59
9.95
0.05
–
1.40

80.74
18.33
0.18
0.71
0.04

1,256

1,999

2,258

Expenditure (NRs)

95.44
2.34
0.86
0.12
1.23

811

89.42
8.41
0.19
–
1.98

87.13
11.45
0.05
1.24
0.14

1,569

2,175

Community or government
Institutional or private
Technical or vocational
Gurukul–madrasa–gumba
Other
Total expenditure

746.36
3,507.80
–
783.7
418.95
860.43

976.22
5,648.92
1,500
–
378.21
1,433.28

2,477.87

10,906.9
18,136.25
711.63

3,024
4,038.86

612.34
2,956.37
2,100

383.71
181.5
661.80

844.42
4,476.68
1,000
–
248.29
1,138.53

2,056.99
10,984.11
4830
1,982.3
2,739.33
3,080.28

girls,8 the representation of boys in private schools is much higher than that of girls.9
Worryingly, the overall difference in expenditure between boys and girls increased
over the course of the three surveys. The mean of actual expenditure shows that
while the difference in expenditure per student was only NRs3 in 1995–1996, esso aveva
risen to NRs886 by 2010–2011. Since mean expenditure in private schools is almost
8 times the mean expenditure in government schools, the faster rate of private school
enrollment among boys when compared with girls over the last 15 years has proved
to be the major source of expenditure bias and discrimination against girls.

The rural–urban classiﬁcation of enrollment and expenditure echoes the
ﬁndings of the national aggregate (Tables 2 E 3). While in absolute terms the
amount of expenditure on education (for both girls and boys) is higher in urban
areas, the share of girls’ fees to boys’ fees is signiﬁcantly lower in rural areas (0.76)
than in urban areas (0.93), suggesting a higher degree of discrimination among rural
populations.10 However, over time while the inequality in terms of expenditure has

8The declassiﬁcation of expenditure, which is not shown in Table 1, reveals that parents spend more for girls’
transportation and other costs compared with boys’ in private schools, leading to higher expenditure per student for
girls among private schools. It is not clear why this is the case. An examination of school distances and modes of
transportation do not provide an answer.

9See footnote 3.
10After accounting for all categories of schools, differences in expenditure in rural areas could be observed in
terms of textbook and supplies, private tuition fees, and other fees not described in the NLSS. This suggests corrective
measures require not only making schools more attractive for girls but a more thorough approach of changing parental
mindsets by discouraging patriarchy and promoting equality of girls at the household level.

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Gender Discrimination in Education Expenditure in Nepal 165

Tavolo 3. Summary Statistics on Enrollment and Fees across School Categories in Urban
Areas

Boys

Girls

School Category

1995–1996 2003–2004 2010–2011 1995–1996 2003–2004 2010–2011

Enrollment (%)

Community or government
Institutional or private
Technical or vocational
Gurukul–madrasa–gumba
Other
Total students

64.97
34.01
–
0.51
0.51

49.52
49.40
0.60
–
0.48

38.17
60.92
0.39
0.26
0.26

67.78
31.91
–
–
0.61

54.07
45.22
0.43
–
0.29

394

836

765

329

701

46.33
53.15
0.13
0.39

–
762

Expenditure (NRs)

Community or government
Institutional or private
Technical or vocational
Gurukul–madrasa–gumba
Other
Total expenditure

1,668.60
7,737.93
–
3,627.5
130
3,734.92

2632.90
12,321.05
14,471
–
3,077.5
7,064.96

5,297.97
21,375.64
31,400
2020
12,735
15,204.92

1,768.25
7,907.66
–
–
195
3718.07

2,220.13
12,951.31
1,635
–
65
7,064.23

4,588.66
22,741.32
23,200

1,983.3
–
14,250.89

remained fairly stable in rural areas, there has been a marginal rise in expenditure on
boys in urban centers (with the share of girls’ fees to boys’ fees dropping from 0.99
A 0.93). This trend is noticeable in rising gaps across the years in expenditure levels
in both private and public schools in addition to a faster rate of growth in private
school participation for boys (from 34% A 61%) compared with girls (from 32% A
51%). In rural areas, rising gaps in expenditure in public schools were observed over
time, although surprisingly the average expenditure gap in private schools became
negative. Tuttavia, this negative expenditure gap is offset by a disparity in private
school participation growth rates with the enrollment of boys in private schools
increasing from 4% A 18% compared with the rate of girls increasing from 2% A
11%.11

The ﬁrst set of regressions were simple OLS models with gender as a
dependent variable (Tavolo 5). The coefﬁcient of the major variable of interest
(female) was signiﬁcant with the semi-elasticity of fees at between –0.098 and
–0.202, indicating lower levels of education expenditure for girls. Other control
variables showed the expected outcomes. The semi-elasticity of total family income
was positive and signiﬁcant, but the level of inﬂuence on total education expenditure

11Inequality in private school enrollment extends far beyond gender. Spatially, private schools constitute
only 1% E 20% of all secondary schools in mountainous areas of the far-western and mid-western regions in
Nepal, rispettivamente. Allo stesso modo, enrollment of other marginalized groups such as Dalits, ethnic minorities, and the
disabled—is also found to be disproportionately low in private schools (Department of Education 2015). Differences
in rural–urban private school enrollment rates can be observed in Tables 2 E 3.

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166 Asian Development Review

Variable

Description

Tavolo 4. Descriptions of Control Variables

Exp
Female
Income
Poor
Birthorder

HHsize
Fatheredu
Motheredu
Ethni

Total expenditure on education
Dummy variable where 1 is girl and 0 is boy
Total income of the households in thousands of Nepalese rupees
Dummy variable where 1 implies a household is poor and 0 implies it is nota
Ordinal variable where 1 represents a ﬁrstborn child, 2 represents a second child, E

so on

Size of the household
Education qualiﬁcation of father with 10 representing 10th grade
Education qualiﬁcation of mother with 10 representing 10th grade
Dummy variable where 1 represents member of the upper caste and 0 represents

other ethnicities

Currentclass
Distschool

Current grade of the student
Distance from home to school (in kilometers in 2010–2011 and hours in 1995–1996

and 2003–2004)

Schooltype

Dummy variable where 1 E 0 mean enrollment in private and public schools,

rispettivamente

Rural

Dummy variable where 1 represents rural and 0 represents urban

aThe poverty line has been drawn based on nutritional requirements included in the NLSS.
Fonte: Author’s compilation.

Tavolo 5. Ordinary Least Squares Regression with
Gender as a Dependent Variable

Log(esp)

Female
Income
Poor
Birthorder
HHsize
Fatheredu
Motheredu
Ethni
Currentclass
Schooltype
Distschool
Rural

1995–1996
−0.105***
0.011
−0.609***
0.024
−0.027***
0.037*
0.121
0.096**
0.221***
0.502***
0.006
−1.153***

2003–2004
−0.098***
0.021***
−0.708***
−0.017
−0.004

0.026***
0.048***
0.123***
0.203***
1.067***
0.073***
−0.698***

2010–2011
−0.202***
0.003***
−0.601***
−0.054***
−0.021***
0.029***
0.034***
0.054*
0.158***
0.882***
−0.0005***
−0.483***

Note: ***, **, E * denote signiﬁcance at the 1%, 5%, E 10%
level, rispettivamente. See Table 4 for a description of the variables.
Fonte: Author’s calculations based on NLSS Surveys.

was very low. This perhaps is indicative of the poor quality of income data collected
in the survey since data on income are notoriously unreliable (Vedere, Per esempio,
Deaton 1997, 29–31). As expected, poverty has a strong negative inﬂuence on total
education expenditure, with poor families expected to spend up to 50% less on
education expenditure than nonpoor families. Expenditure fell as household size
increased and rose with the educational attainment of parents. Allo stesso modo, the grade
of students and type of school had the expected strong and positive impact on

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Gender Discrimination in Education Expenditure in Nepal 167

Tavolo 6. Ordinary Least Square Regressions with Separate Results for the Population
Cohorts

Boys

1995–1996

2003–2004

2010–2011

Income
Poor
Birthorder
HHsize
Fatheredu
Motheredu
Ethni
Currentclass
Schooltype
Distschool
Rural

0.247***
−0.589***
0.022
−0.028***
0.040
0.139
0.056
0.213***
0.430***

−0.007
−0.979***

0.0153***
−0.719***
−0.004
0.002
0.025***
0.036
0.103**
0.198***
1.071***
0.071**
−0.710***

0.005**
−0.596***
−0.080***
−0.016*

0.029***
0.026***
0.034
0.147***
0.944***
−0.0005***
−0.430***

1995–1996
−0.001
−0.611***
0.028
−0.037***
0.025
0.057
0.173***
0.223***
0.574***
0.037
−1.213***

Girls

2003–2004

2010–2011

0.092***
−0.682***
−0.026
−0.014**
0.023***
0.062**
0.138***
0.209***
1.022***
0.078**
−0.640***

0.002
−0.587***
−0.033
−0.025**
0.028***
0.041***
0.073*
0.168***
0.805***
−0.015***
−0.494***

Note: ***, **, E * denote signiﬁcance at the 1%, 5%, E 10% level, rispettivamente. See Table 4 for a description of
the variables.
Fonte: Author’s calculation based on NLSS Surveys.

education expenditure. È interessante notare, regressions also showed that members of the
upper caste were more likely to spend more on education than people from other
ethnicities.12

types,

Gender-wise classiﬁcation of the OLS regression also provided interesting
insights (Tavolo 6). For variables like poverty, grade, and school
IL
coefﬁcients were comparable for boys and girls, while other variables impacted
the two cohorts unequally. The impact of the size of the household was found
to be relatively insigniﬁcant for boys but was highly signiﬁcant and negative for
girls, suggesting that a reduction in education expenditure per child due to an
increase in household size primarily impacts girls. Therefore, a focus on family
planning measures would lead to increased education opportunities for girls.13 The
importance of the mother’s education was also reﬂected unequally. A woman’s
level of education is likely to play a more important role in a daughter’s education
compared with a son’s; questo è, the semi-elasticity of a mother’s education on
education expenditure is higher for girls than boys.14 Distance from school had

12This discrepancy is explained both by differences in school preferences and expenditure categories. Not
only were upper caste households more likely to send their children to private schools (22% private school enrollment
for households from other ethnicities compared to 28% for members of the upper caste), but they also were more
likely to spend on other educational expenditure and tuition fees. The cultural reasons behind these differences are
beyond the purview of this study. Tuttavia, basic analysis reveals that parents from the upper caste earn more than
everyone else and are more likely to be educated than counterparts from other ethnicities.

13The average household size in the sample was 5.94 persons, which provides sufﬁcient space for family

planning interventions.

14The reasons behind this phenomenon are not clear but evidence suggests that mothers prefer allocating
educational resources to daughters and fathers to sons (Glick and Sahn 2000). Education empowers women and
increases their bargaining power in the family, thus allowing them to spend more resources on girls. This ﬁnding
is supported by additional evidence from Africa and Asia (King and Lillard 1987, Lillard and Willis 1992, Tansel
1997).

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Tavolo 7. Results from Blinder–Oaxaca Decomposition

Log(esp)

Difference
Explained
Unexplained

1995–1996
−0.045
0.054
−0.099**

2003–2004
−0.098**
−0.008
−0.089***

2010–2011
−0.264***
−0.020
−0.243***

Note: ***, **, E * denote signiﬁcance at the 1%, 5%, E 10% level,
rispettivamente.
Fonte: Author’s calculation based on NLSS Surveys.

Tavolo 8. Results from Ñopo Decomposition

Log(esp)

Difference
Dx
Dm
Df
Fare

1995–1996
−0.007
0
0.014
−0.013
−0.008

2003–2004
−0.014
0
−0.025
0.032
−0.021

2010–2011
−0.030
0.0003
−0.179
−0.190
−0.018

Fonte: Author’s calculation based on NLSS Surveys.

a larger negative impact on girls than boys, suggesting proximity to school is an
important factor contributing to a better education for children.15

To differentiate the roles of endowments and discrimination in explaining
the differences in education expenditure between boys and girls, I conducted a
Blinder–Oaxaca decomposition analysis on the same observations (Tavolo 7). Results
from NLSS II show that in log terms, expenditure on boys was 0.098 higher than
on girls, of which only about 9% could be explained by differences in the control
variables and about 90% could be attributed to discrimination. Allo stesso modo, risultati
from NLSS III show that expenditure on girls is lower than expenditure on boys by
around NRs0.264 per child in log terms. Only about 8% of this gap can be explained
via differences in household characteristics and the remaining 92% can be attributed
to discrimination.

Results from the Ñopo decomposition also display an incidence of
discrimination, although the extent of discrimination appears to be much smaller
(Tavolo 8). This technique shows that in 2010–2011, almost 60% of the expenditure
gap was due to unexplained factors (discrimination). The results were more
dramatic in 1995–1996 and 2003–2004, when in both cases the endowment effects
of men and women constituted more than 100% of the expenditure gap. Therefore,
if boys and girls were to have the same distribution across the controlled variables,
the expenditure gap would be even higher, suggesting that, given prevailing
conditions, socioeconomic status and other factors are more favorable in households
incurring girls’ expenditure compared to boys’. The Blinder–Oaxaca and Ñopo

15The distance needed to travel to attend school is an important impediment to educating girls. In developing
societies, girls’ safety is a crucial consideration. The United Nations Girls’ Education Initiative (2014) has made
reducing the distance to the nearest school an important component of its activities.

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Gender Discrimination in Education Expenditure in Nepal 169

Tavolo 9. Decomposition Results Based on Quantile
Regressions

Log(esp)

1995–1996

2003–2004

2010–2011

Quantile 0.2

Raw difference
Endowment
Coefﬁcients

−0.100
0.027
−0.127**

−0.084**
−0.015
−0.069*

−0.306***
−0.070**
−0.235***

Quantile 0.4

Raw difference
Endowment
Coefﬁcients

−0.045
0.029
−0.075*

−0.031
−0.017
−0.014*

−0.228***
−0.088***
−0.140***

Quantile 0.6

Raw difference
Endowment
Coefﬁcients

0.024
0.048
−0.024

−0.024
−0.019
−0.004

−0.211***
−0.113***
−0.098***

Quantile 0.8

Raw difference
Endowment
Coefﬁcients

0.063
0.072
−0.009

−0.102**
−0.062
−0.040

−0.325***
−0.186***
−0.138***

Note: ***, **, E * denote signiﬁcance at the 1%, 5%, E 10% level,
rispettivamente.
Fonte: Author’s calculation based on NLSS Surveys.

methodologies both demonstrate the existence of widespread gender discrimination
in household education expenditure, albeit to different degrees.

The results of the quantile decomposition reinforce the ﬁndings of the
Blinder–Oaxaca decomposition method by using four quantiles (20th, 40th, 60th,
and 80th percentiles) of education expenditure (Vedi la tabella 9). While in NLSS I and
II, there are signiﬁcant differences in expenditure, large differences are observed
in NLSS III. Among all four quantiles, education expenditure on girls was lower
and signiﬁcant in comparison with boys. Differences in expenditure were found
to be the largest among the highest and the lowest spenders, and smallest among
the 60th percentile. The ratio of unexplained to total differences fell among the
higher quintiles, with the largest share of unexplained differences found in the
poorest population segments.16 The regressions suggest that, despite controlling
for factors such as school enrollment (which already displays a signiﬁcant source
of discrimination in favor of boys), parents still choose to spend more on boys’
education than on girls’ education, which is clearly indicative of the differential

16In the lower quintiles, the participation of students in private schools is almost negligible, with only
Di 4% of boys and 3% of girls enrolled in private schools at these income levels. In the upper two quintiles,
the participation ratio of boys in private schools is about 62% compared with 56% for girls. Therefore, while the
unexplained differences are larger in poorer segments of the population, discrimination is also prevalent at higher
income levels, primarily through the school selection process.

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170 Asian Development Review

treatment of boys and girls in Nepalese households. Worryingly, this phenomenon
is new and coincides with rising average costs of education in Nepal.

V. Conclusions

Discrimination in school participation has been widely reported in the
literature as a major source of gender inequality in Nepal. Even with improving
participation rates for girls at all grade levels, the inequality persists. This study
has explored discrimination among school-going boys and girls by analyzing the
expenditure behavior of their parents and found that boys are better represented in
private schools and girls are better represented in public schools, which stands as
the most important form of discrimination. This phenomenon is more pronounced
in rural Nepal, although a noticeable difference in participation is observed in urban
areas as well.

Through simple OLS regressions, the effects of various control variables
on total education expenditure across two genders were investigated. The data
substantiate the ﬁndings of existing literature, including Vogel and Korinek (2012),
that parental expenditure patterns in education are discriminatory. My analysis
ﬁnds that even after controlling for school type, parents spend as much as 20%
less on girls compared with boys. The data show that differences in expenditure
comprise unequal spending on private tuition, textbooks and supplies, and other
education-related expenditure. The paper also found that while the mother’s
education is an important equalizer, household size and distance to the school
disproportionately affect household expenditure on a girl’s education.

The Blinder–Oaxaca decomposition method,

the Ñopo decomposition
method, and a decomposition based on quantile regressions were used to further
investigate the level of gender discrimination in education expenditure. All three
of these methods revealed a high level of discrimination in education expenditure
in favor of boys among households in Nepal. At times, more than 60% del
difference in education expenditure between genders could be explained by such
bias. Findings from the quantile decomposition show that discrimination has risen
over time and that households in the lowest and highest quintiles of income were
the ones most likely to discriminate between boys and girls. The latter result is
counterintuitive and therefore should be a matter of further research. Another
area for further research could be the impact of such differential treatment on the
performance of children at schools.

The study ﬁnds sufﬁcient evidence to conclude that discrimination in
education expenditure is prevalent among Nepalese households. It also suggests
that such discrimination might be on the rise. Therefore, it is imperative for the
government to improve the quality of education at public schools to not only
provide better quality education for girls, but also to encourage parents to review

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Gender Discrimination in Education Expenditure in Nepal 171

the decision-making processes in which they are more likely to send boys than
girls to private schools. I also ﬁnd that educating parents (especially mothers) E
improving access to schools can potentially reduce unequal expenditure, albeit to
a small extent. To the extent that unexplained differences (discrimination) still
account for the largest share of differences in education expenditure, I conclude
that parental choices are still largely governed by a patriarchal mindset within
Nepalese society, even among families at the highest income levels. Therefore,
the medium-term approach should be accompanied by a longer-term strategy of
changing the perception of women’s roles in Nepalese society so that household
investment decisions are not biased against girls.

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