Caste, Gender and School Enrollment: Evidence from the Nepalese LivingStandard Survey

Margarita Pivovarova∗

This draft: June, 2011

Abstract

This paper explores the role of community effects - ethnic composition of the community andaggregate level of village development - on the probability of school enrollment among the low castegirls in rural Nepal. Using data from the Nepalese Living Standards Surveys 1996 and 2004 andemploying difference-in-difference framework, I find that girls from the underprivileged groups (lowcastes) who live in the villages with the high proportion of upper castes households are more likely tobe enrolled in school, thus implying the presence of positive externalities. This finding is at odds withthe almost uniform agreement about the negative impact ethnic heterogeneity has on the provisionof public goods and performance of the members of underprivileged groups within heterogeneouscommunities. A number of potential channels through which this positive effect might operate areanalyzed: improvements in the school and village infrastructure, increased labour market opportuni-ties through expansion of tourism industry, and peers effects. The empirical findings suggest that theprevalence of the high caste households in the community, along with the overall village developmentand infrastructure, plays an important role in improving school participation and progression amongthe most vulnerable group of children - girls from the low caste households.

JEL CLASSIFICATION: J15; J16; O15KEYWORDS: caste, gender, education, development, heterogeneous community, social norms,

Nepal.

∗PhD Candidate, Department of Economics, University of Toronto. e-mail: rita.pivovarova@utoronto.ca. I am indebted tomy supervisor, Dwayne Benjamin, for his patience and support, and for enlightening ideas he generously shared with me. Ialso would like to thank Gustavo Bobonis for his helpful suggestions and discussion. Sincere thanks go to seminar participantsat the University of Toronto Department of Economics. I thank the Central Bureau of Statistics Nepal for providing me withthe data.

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1 Introduction

Nepal, a small landlocked country in South Asia, for the last two decades has experienced shift to a new

social, political and economic society. Previous research has found that transition may affect different

population groups disproportionately. For instance, low caste girls in India were able to exploit new

labor market opportunities better than the boys (Munshi and Rosenzweig 2006), while female wages

in transition in Russia have fallen relative to male wages tremendously (Brainerd 2000). The focus of

this paper is on the dynamic changes in educational outcomes for the most underprivileged group of

Nepalese population - girls from the low castes households, and on the determinants of these outcomes

at the community level1.

Although Nepalese educational statistics has improved a lot for the past three decades, the participation

of the low caste girls remains on extremely low levels compared to the girls from other castes and boys

from the low castes: 53 percent versus 74 percent for other girls and 71 percent for the low caste boys

2. However, in communities where proportion of advantaged castes is above the average for Nepal,

disadvantaged girls are more likely to attend school. This finding is surprising and contradictory for at

least two reasons. First, socio-economic literature has established a broad link between discrimination,

social identity and behavior. Discrimination affects structure of opportunities open to different social

groups. Experimental findings show that when caste identity is publicly revealed in a mixed caste group,

a significant caste gap emerges (Hoff and Pandey 2005a, 2005b, 2006, Kochar 2004) and members of

the disadvantaged castes tend to underperform even in simple tasks. Second, there is almost uniform

agreement among scholars about the direct effect of ethnic heterogeneity on public goods provision both

at macro (Easterly and Levine 1997, Alesina, Baqir and Easterly 1999) and micro level. Ethnic diversity

is negatively correlated with levels of publicly provided public goods (Alesina and La Ferrara 2005).

Higher levels of local ethnic heterogeneity are associated with lower primary school funding and worse

school facilities in Kenya (Miguel and Gugerty 2005), lower access to public goods in India (Banerjee,

Iyer, Somanathan 2004), social heterogeneity negatively affects the maintenance of community projects

1The effect of the caste status on educational outcomes has been extensively explored in studies based on Indian data. See,for instance, Deshpande (2000), Dreze and Kingdon (2001), Dostie and Jayaraman (2006) among others.

2Nepalese society is divided into groups called castes which are linked to one or more traditional occupations. For moredetailed information on castes in Nepal and their classification, see Appendices C1 and C2.

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(Khwaja 2006). At the same time, heterogeneity has been found to be associated with greater willingness

to contribute privately to a public good (M.Schundeln 2007). The results of the present research are

also in line with Banerjee and Somanathan (2007) who find that in rural India, the population share of

Brahmans (highest caste in Indian hierarchy) in a constituency is positively correlated with access to

primary, middle and secondary schools, to post offices and to piped water.

This paper seeks to explore the relationship between heterogeneity of the community and individual

outcomes from a different angle: instead of looking at the provision of public goods at the community

level in the presence of caste diversity, I will analyze the demand for one of such publicly provided goods

– education – in highly fragmented along caste lines Nepalese society3.

I will rely on two rounds of nationally representative household survey to analyze the determinants of

school enrollment among Nepalese children. More specifically, I will provide evidence that contrary to

the findings in the literature on heterogeneity, social identities and economic outcomes, low caste girls’

participation in school and their school progression are positively associated with the proportions of upper

caste households in the community.

To preview the results of the empirical analysis and provide intuition for the quantitative exercise in this

paper, figure 1 in Appendix B depicts the relationship between the enrollment rates of the low caste girls

and caste compositions of the community where they live. I disaggregate the data by the proportion

of advantaged castes below the median (low intensity) and above the median (high intensity). In 2004,

attendance rates of the low caste girls in the communities with larger proportion of the upper castes

households were consistently higher than those in communities where proportion of high castes was

below the median. That pattern does not hold for the sample of high caste girls in 2004, low caste boys

in 2004, and for the low caste girls in 1996. The raw data suggest that there are changes happening

across time and across communities shifting the attitude towards education among low caste households.

I propose several explanations for the observed trends in school enrollment among the low caste girls

and try to discriminate between those explanations. Among the possible channels are the better school

quality and overall village infrastructure in the communities where the majority belongs to the high

3The effect of the community caste structure in the Indian context has been explored by Dostie and Jayamaran (2006) whofind that caste composition matters but only if one does not control for unobserved heterogeneity within communities

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castes; response of the households to either decrease in the child wages, or increase in the return to

education; pro-educational and anti-discriminating policies of the Maoist insurgents. I understand that

one or more of the proposed channels are complementary to each other and thus cannot be distinguished

in the empirical analysis. In such cases, I use auxiliary data to provide additional evidence in favour or

against the competing hypothesis. The possible explanations are: (1) response of the household to the

increased returns to education (Munshi and Rozenzweig); (2) response to decrease in current wages for

child labour which lower opportunity cost of going to school; (3) village development, such as better

infrastructure, school availability; (4) peer pressure; (5) pro-educational and non-discriminating policy

of the Maoist insurgents; (6) government policy targeting underprivileged members of Nepalese society,

development programs.

The findings of the the present research indicate that not only an individual’s identity matters – belong-

ing to the disadvantaged caste reduces the probability of school enrollment – but also community caste

composition might significantly affect chances of school participation. The probability of being enrolled,

attend school, or complete primary school conditional on enrollment for low caste girls is higher if they

live in a community where the proportion of upper-caste households is higher. Part of the positive effect

is indeed channeled through better village infrastructure. However, when unobserved heterogeneity be-

tween communities is taken into account, the positive effect vanishes and thus can be attributed to the

social norms prevalent in some of the communities. For instance, perception of education as a high-

valued good might increase the willingness of all households in the community to send their children to

school. [Add]

This paper essentially consists of two parts: the next section describes the data used in the present re-

search, and section 3 presents largely descriptive analysis of the recent developments in the Nepalese

education based on the survey data, as well as detailed statistics for the sample of rural Nepalese children

whose participation in schooling is the subject of the present research. Section 4 outlines empirical strat-

egy based on a series of the OLS regressions. Results of the empirical analysis are presented in section

5. Section 6 concludes with the final remarks.

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2 Data

The empirical analysis in this paper is based on the data from two rounds of the Nepal Living Stan-

dard Survey (NLSS). The NLSS is a nationally representative survey of households and communities

conducted between June 1995 and June 1996 (NLSS I) and April 2003 and April 2004 (NLSS II) by

the Nepal Central Bureau of Statistics with the assistance of the World Bank. Both rounds use similar

modules to collect data on the demographic composition of the interviewed households, the labour status

of the household members, their health and educational achievements, and various sources of household

income. The NLSS I sample includes information on 3,373 households in 274 primary sampling units

(PSUs), while the NLSS II sample is based on 326 PSUs enumerating 3,912 households.

The sample for the empirical analysis is limited to the rural areas of Nepal for two reasons: 86% of the

Nepalese population live in rural areas, while the remaining 14% live mostly in Kathmandu (capital of

Nepal) and other emerging urban areas. Second, school characteristics and indicators of village devel-

opment have been recorded in a community questionnaire only for rural areas. I further limit the sample

to children 6-15 years - which corresponds to primary, lower secondary and secondary school as only a

small fraction of children continue to higher education (grades 11 and 12). I have to drop observations

for which one or more variables used in the empirical analysis are missing. My final sample consists of

3117 rural children in 1996 NLSS, of whom 1591 are boys and 1526 are girls, and 3776 rural children in

2004 NLSS of whom 1944 are boys and 1832 are girls.

3 Descriptive analysis

For the recent decades, the government of Nepal has implemented various policy measures targeted at im-

provements in educational sector: free primary education for all, government-provided school facilities,

teachers salaries and educational materials, scholarships for poor and female students. The literacy rates

in Nepal increased significantly from 24% in 1981 to 33% in 1990 and to 51% in 20044. However, Nepal

still lags behind its South Asian neighbours in literacy and enrollment rates in post-primary education

levels (secondary, high secondary and tertiary). Table 2 in Appendix A provides descriptive analysis of

4Data from the Central Bureau of Statistics, Nepal. Literacy rate is defined as proportion of population 6 years age andolder who can read and write.

5

the trends in primary (Grades 1 to 5), lower secondary (Grades 6 to 8) and secondary (Grades 9 and 10)

education in Nepal along several dimensions — gender, social status, ethnicity, income, and also captures

regional and geographical differences in those trends. The admission to school begins when child is 6

years old, but some of the children enter pre-school at 5 years. The education system consist of 10 years

of primary and secondary school with an option to continue to high secondary after passing the nation-

wide examination and obtaining the School Leaving Certificate. Overall, net enrollment rates5increased

by 17 percent for primary school, 13 percent for lower secondary, and 9 percent for secondary educa-

tion. Still, about quarter of all primary school aged children were out of school and only 17 percent of

14-15 years old were enrolled in secondary school. Similar to other developing countries, children in

Nepalese urban areas were more likely to attend school, both at the primary and secondary levels, and

this difference was more pronounced at higher levels of education. While the gender gap in enrollment

rates into primary schools has been reduced significantly – from 21 percent in 1994 to 9 percent in 2004

for primary school children, the majority of out-of-school children are girls.

As evident from the table, the differences in the enrollment rates are strongly related to the consump-

tion level for both primary and secondary school. The difference is significant - children from wealthy

households are by 35 percent more likely to attend primary school, and almost eighteen times more likely

then poor children to attend secondary school. Similarly, there are disparities between ethnic and caste

groups6. Highest rates of enrollment at all levels of education are among Hindu groups with lowest rates

for Muslims minorities. More pronounced are differences in the enrollment rates among castes: dominant

groups such as Brahman and Newar have their children enrolled in primary school almost uniformly. The

lowest rates are among Dalits - historically peasants and laborers, also called ”untouchables” low castes.

Table does not include enrollment rates for the rest of the 9 castes group - Hills Dalits, Muslims, Janajatis

from Hills and Tarai and other non-caste groups. Enrollment rates for those groups are roughly 70% for

primary, 20% for lower secondary and 11% for secondary levels, thus being comparable to average rates

in Nepal. Gross enrollment rates also increased among boys and girls and for both low caste children

5Net enrollment rate is the fraction of children in the age group who attend school, gross enrollment rate is the fraction ofchildren out of all children at any level of education.

6Nepal is the only Hindu state in the world with 84 percent of the population identifying themselves as Hindu, 10 percentof Janajatis or Indigenous population (communities that have their own ethnic language other than Nepali and observe theirown distinct customs and tradition than the Hindu), and 4 percent of Muslims.

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and children from the middle and high castes. While both boys and girls from the low castes were more

likely to attend school in 2004 than in 1996, the proportional increase in the enrollment rates among the

girls was much higher.

Table 3 summarizes individual, household and village level variables describing the sample for present

research, as well as indicators of school quality. The main dependent variable is an indicator of school

enrollment at the time of the survey as reported by an individual or his or her parent. In 1996, 73 percent

of the rural boys in my sample attended school and only 50 percent of the rural girls reported to be

enrolled in school. In 2004, 83 percent of boys and 69 percent of girls attended school. The enrollment

rates among girls in my sample experienced larger proportional increase relative to boys. Among the

individual controls are indicators of caste and gender, and age of the child. The mean age of children

in my sample was 10.2 years, proportion of low caste children was 15 percent in 1996 and 19 percent

in 2004, children from the high caste households was about 43 percent in 1996 and roughly 40 percent

in 2004 7. The set of the household controls includes completed level of education for both parents - to

some extent, this measure capture preference of parents for education. In 1996 sample, the mean years of

completed education for mother and fathers of the sampled children were 0.42 and 2.5 respectively, thus

reflecting the striking gender inequality in access to education among the adult Nepalese population. The

situation was only slightly better in 2004: mothers had on average 0.8 years of education, while fathers

had 3.4 years. Expenditure per capita and land ownership measure child’s family welfare. The average

level of annual expenditure for rural households in 1996 was not different from the average for Nepal:

the mean expenditure per capita in Nepal was 14, 809 rupees, while for the rural household with at at

least one child of 6-15 years in my sample this figure was 16, 557 rupees a year. In 2004, the mean per

capita expenditure deflated by the 1996 prices was 15, 977 rupees, while the corresponding number for

Nepal was 22, 555 rupees. The divergence of rural incomes from the Nepalese average in 2004 reflects

growth of manufacturing and service sectors in the urban areas and higher return to labor in those sectors

in which the majority of the urban population have been employed in 2004. For households living in the

rural areas not only the availability of school matters, but also proximity of the school. The distance to

7The coding of the caste and ethnicity differs among two waves of the survey in that there was less number of ethnic/castegroups identified in 1996, and for some of the households in 1996 sample ethnicity is missing.

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the nearest primary school captures the direct costs of attending school. In 1996, an average household

in my sample had a school within 23 minutes of walking time, in 2004, that number was 18.5 minutes.

Village level variables in Table [3] capture variation in infrastructure and caste composition across com-

munities. I use village development indicators - availability of electricity, public standpipe, and all

weather road - as a measure of the demand for skilled labor in a given community, and fraction of landless

households and proportion of households below the national poverty line as a measure of overall level of

wealth in a village. Descriptive statistics reveal that compared to 1996, overall infrastructure in Nepalese

villages improved: the proportion of villages with accessible all-weather roads increased from 22% to

69%, fraction of households connected to electricity source increased from 22% to 53%. Public stand-

pipes became available in 58% of the communities compared to 50% in 1996. The fraction of households

who live below the national poverty line decreased by 9 percent and was 30 percent in 2004. This reduc-

tion in poverty rate is consistent with the national trend in general8. For rural areas, landholding is an

important indicator of wealth. Between two waves of the survey, the proportion of landless households

did not change among the sampled villages: on average, 16 percent of households did not own any land

in 1996 and 2004 surveys. The members of such households are usually employed by land owners in the

same or neighbor village, or are engaged in traditional crafts, or other non-agricultural activities.

The main focus of the analysis in this paper is on two variables which describe the ethnic composition of

the community: the caste fractionalization index (CFI) and the proportion of advantaged castes (PHC)9.

Next set of the village level variables measures school quality and quantity. Those measures include

indicators of whether school is a permanent structure, availability of the school supplies (desks and

chairs), number of days school has been closed over the past year and presence of a female teacher in

school. Quantity indicators include the number of primary, secondary or incomplete schools normalized

by the number of households in a village. I also use district level variables – the number of schools,

8NLSS I has found Nepal poverty rate to be 42% , while NLSS II recorded reduction by 11% to 31%. In rural areas,poverty declined from 43% to 35%

9Caste fractionalization index is computed asCFI = 1−∑I

i=1

(ni

N

)2, where I is a number of different ethnic group/castes

in a village, ni is a number of household that belongs to ethnic group/caste i, and N is a total number of households in avillage. The proportion of high caste households is the ratio of the number of households from the advantaged castes to thetotal number of households in the community/village. Both indicators take values between 0 and 1. The closer CFI to 1, themore heterogeneous the community is.

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pupils and teachers in a district – to construct pupil-teacher ratio at an aggregate level 10.

4 Theoretical Considerations and Empirical Specification

4.1 Theoretical Consideration

To be completed

4.2 Empirical Specification

This section presents a series of ordinary least squares regressions aimed to analyze the relationship

between individual and community characteristics and schooling decision in households where at least

one child currently attends school or has attended school in the past, allowing for this relationship to

differ between boys and girls. The dependent variable is either a school attendance indicator that equals

one if the child attends school during the survey year, or a continuous measure of relative education

level represented by the grade-for-age z-score which is a normalized measure of school progression

for each age-gender group. From the theoretical point of view, schooling choice is determined by the

intersection of the demand and supply curves. In the following specification, demand side variables

are represented by the individual and household characteristics: age of the child, gender, distance to

school and per capita expenditure, mother and father completed years of education; variables representing

village infrastructure. From supply side, I use variables describing school quality: number of primary

and secondary school per household, pupil-teacher ratio, suitability and quality of the school building,

availability of desks and chairs in school. The estimation considers the relationship between community

level indicators and children’s schooling status as follows:

Yihc = α+ βXi + γXh + δXc + εihc (1)

where Yihc is an outcome of interest for child i in household h from community c, Xi is a set of individual-

level controls, Xh is a set of household level characteristics, and Xc is a set of community characteristics.

The estimation allows for low caste × gender effects by the interaction of low caste dummy and the

female indicator.

Yihc = α+ βXi + β1Female + β2Low caste + β3Female × Low caste + γXh + δXc + εihc (2)

10The administrative unit in Nepal is district. There are 75 districts in Nepal.

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The inclusion of the caste × gender interaction causes the coefficient of the low caste dummy to be inter-

preted as an average change in the outcome associated with the caste status of a child. The interpretation

of the coefficient is as follows: β2 is an estimate of the effect associated with the low caste status for

boys, β3 is the extra increment in the outcome associated with being female in addition to the effect for

boys.The corresponding effect of low caste status for girls is given by the sum of coefficients β2 + β3,

after controlling for age and gender differences. If I believe that the children from disadvantaged caste

are less likely to attend school at any given age and have less years of completed education on average

compared to the children from upper and middle caste households, then β2 < 0 and β2 + β3 < 0 . This

approach also allows me to test whether the effect of the low caste status is the same for boys and girls,

or whether β3 = 0.

The relationship between propensity of the low caste girls to attend school and composition of the com-

munity where they live can be depicted in a standard difference-in-difference table.

School Attendance

PHC<median PHC>median Difference11

LC girls 2004 0.44 0.70 -0.26***

[0.03] [0.04] [0.05]

LC girls 1996 0.37 0.45 -0.08

[0.05] [0.03] [0.06]

Difference 0.07 0.25 -0.18

Z-score

PHC<median PHC>median Difference

LC girls 2004 -0.43 0.05 -0.48***

[0.05] [0.07] [0.09]

LC girls 1996 -0.67 -0.49 -0.19*

[0.05] [0.08] [0.10]

Difference 0.24 0.54 -0.30

This table demonstrates that in 1996, the propensity to attend school for the low caste girl was the same

whether she lived in the village where the majority of the households was from the high castes or not. In

2004, the girls from the disadvantaged castes were more likely to attend school if they lived in a village

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where the fraction of high castes was above average. Not only the propensity to attend school was higher,

but these girls progressed better as measured by their z-score.

This observation suggest the use of the difference-in-difference strategy where I will exploit potentially

exogenous measure of caste composition.

To analyze the association between own caste, gender and caste composition of the village, the last

specification allows for caste composition×caste×gender effects by the interaction of female and low

caste dummies with continuous variable PHC which measures the proportion of high castes in a village12:

Yihc = α+ βXi + β1Female + β2Low caste + β3Female × Low caste

+β4PHC + β5Female× PHC + β6PHC × Low caste

+β7Female × Low caste × PHC + γXh + δXc + εihc

(3)

As before, the interpretation of the coefficients after controlling for community and gender×low caste

effects is as follows: β6 is and estimate of the effect associated with changes in proportion of high

caste households in a village for low caste boys on schooling outcomes, β7 is an extra increment in the

outcome associated with being female for that of boys, and the compound effect of increasing proportion

of advantaged castes in community for low caste girls is given by β6 + β7. If I believe that the low caste

girls are more likely to attend school and get more years of education when they live in the communities

where proportion of privileged castes is higher, then I expect the sum of coefficients β6 and β7 to be

positive. This approach will also allow me to test whether the girls from the low castes are better off

living in those communities than boys (β7 = 0). Another specification uses village fixed effects, thus

controlling for unobservables at the community level:

Yihc = α+ βXi + γXh + δXc + FEc + εihc (4)

where FEc is a community c fixed effect and all the interactions terms are as in (3). The inclusion of

fixed effects allows me to account for the unobserved village heterogeneity.

The last step is to analyze the relationship between schooling indicators and individual, household and

community characteristics separately for boys and for girls to see whether the aggregation hinders gender

12This specification also includes interaction of CFI - caste fractionalization index with female and low caste dummies.

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specific effects.

4.3 Identifying assumption

In this section I will discuss why the variation in caste composition of a village maybe considered ex-

ogenous. Appendix [] provide very brief discussion of caste system in Nepal and classification of castes

into three main groups. The caste barriers in Nepal are very rigid and members of low caste by no means

may assume middle or high status. The intercaste marriages in Hindu system was not allowed in order to

keep the characteristics of a caste pure. The change in a caste status of individual might happen through

marriage: if a member of high caste marries low caste, then he or she accepts the lower status. Another

avenue through which the caste composition of the community might change is migration. Since the

movement of individuals from one caste into another is impossible, the caste composition of a village

may change because of migration. To prove my first point, I use data from the survey to analyze the

total number of married individuals and their ethnicity/caste. In 2004, there were 8811 couples for whom

I have data on caste/ethnicity. Out of those 8811 there are only 26 intercaste marriages, or merely 0.3

percent. There was only one case when husband and wife were from the low and upper castes, the rest

are unions between middle and high castes. In 1996, ethnicity/caste was recorded only for the head of

the household, thus explicitly assuming that all the members of the household belong to the same caste.

Also, in the community questionnaire in both survey years, ethnicity of the village residents is reported

at the level of household, again assuming that only members of one caste/ethnicity may cohabitate13.

The second argument requires more detailed analysis on the patterns of migration within Nepal across

districts and villages.

I am using data on migration on both aggregate level - district migration, and household level - probability

of individuals to migrate depending on their social status and controlling for observables. The statistics

at the district level is: number of individuals who migrated during 1996-2006 on average 1.2 individuals

per thousand of population in a district. Three districts have no statistics for migration, but there is

no reason to suspect that these three district have had abnormal migration during the reported period.

13I plan on using Demographic and Health Survey to provide more detailed evidence on the inter caste marriages. Thissurvey is available for 1987, 1996, 2001 and 2006 years.

12

Another way to look at the problem is to show that intercaste marriages are rare. After the marriage,

woman moves to the village/household of her husband and given that husband and wife are from the

same caste, new marriages do not affect caste composition of a given village. Table 1 and Table 2 in

Appendix provide regression results for migration. Numbers for the intercaste marriage in NLSS I and II

are respectively:Another way to show identification assumption holds is to use marriage statistics from

both waves of the survey [can reference here UBC paper - for instance, such and such provide indirect

evidence that there is no caste based migration and almost 99 percent of all marriages are within castes]

Figure 2 in Appendix plots the distribution of ever migrated individuals by gender and caste. Overall, 37

percent in 2004 sample reported ever migrated, and 70 percent of those who ever migrated are women.

This is not surprising given that after the marriage women move to the residence of their husband. This

assumption is confirmed in the data: out of all female who ever migrated, only 7 percent have never

been married and majority of those female are either children or young women below 32 years old (95th

percentile). Those statistics together with the absence of inter caste marriages imply that migration is

unlikely to affect the caste composition of the village or community. One potential threat is a non-

random displacement during the war. However, 72 percent of those who migrated stated family reason

as the primary reason for their decision to migrate, and not disasters or extreme circumstances. For

comparison, similar findings from 1996 confirms migration and marriage patterns. Interestingly, that in

1996, caste was recorded only for the head of the household, which assumes that all members of the

household then belong to the same caste. Also, question about migration is only asked from the head of

the household. Out of 3,372 households in the survey, about 12 percent of the heads of the households

reported that they have ever migrated. Only 15 percent of them are women. The distribution across caste

looks similar: among high, middle and low caste from 11 (middle) to 14 (high) percent of the heads ever

migrated14.

14Population monograph on Nepal based on the 2001 Census provides the following statistics: the volume of within districtmigration in 2001 was 13.2 percent of total population. However, the data collected in 1996 at a smaller smaller spatial level– Village Development Committees – indicated that 32.7 of the native-born population has ever migrated from their place ofbirth. The main reason for migration reported by 27 percent of the migrants was marriage.Source: Bal Kumar KC, PopulationMonograph of Nepal, Central Bureau of Statistics Nepal, 2002

13

5 Discussion of Results

The idea of the empirical estimation is to look at how the magnitude and significance of the interaction

coefficient which measures the marginal change in schooling outcomes for the low caste girls changes

if they live in communities with higher proportion of advantaged castes. By introducing different sets

of controls into regression equation, I am able to track the potential channels for the “spillover” effect

described in the introduction and plotted in Figure [1]. First two columns of Table [7] report the results

for the baseline regression (Eq. 1) where I only include individual and household controls. Since Nepal

is an extremely poor country, it is not surprising that the coefficient on the expenditure variable is not

only statistically significant, but also large in magnitude, implying an increase in the probability of en-

rollment by 14 percent with every income increase by 10 percent. Father education is also an important

determinant of both boys and girls enrollment, which confirms finding from the previous studies. The

low level of mother’s education is reflected in the insignificant impact of that variable on schooling de-

cision. Distance to school, though has been found to be an important determinant of school attendance

in a number of studies, is economically and statistically insignificant in the present analysis, most likely

due to the satisfactory supply of schools and given that all children in my sample live sufficiently close

to the primary school. Coefficients of interest - female and low caste dummies - have expected negative

sign and are large in magnitude conforming patterns from the data: low caste girls are 20% less likely

to attend school comparing to the boys of the same age and caste. However, the impact of belonging

to the low caste on the grade-for-age z-score does not differ by gender among children from the low

castes: on average, grade-for-age z-score is by 0.16 standard deviation lower for both and girls (the mean

grade-for-age z-score for low caste boys and girls are -0.30 and -0.34 respectively).

Next, I include proportion of high castes (PHC) and caste fractionalization index (CFI) as additional in-

dependent variables to test whether children in villages with higher share of advantaged castes perform

better in terms of school participation and grade-for-age z-score (Columns 3 and 4 of Table [7]). The

coefficient of PHC is positive and large in magnitude and statistically significant. However, level of

heterogeneity in the community has even larger positive effect on the probability of enrollment for all

rural children independent of gender. Since the supply of schools and school quality seem to be indepen-

14

dent of the level of caste diversity in the data, positive effect of caste fractionalization might be channeled

through greater demand for schooling in heterogeneous communities. The main purpose of this empirical

exercise is, however, to see how participation of the low caste girls depends on the ethnic composition of

the community. This is done by estimating Eq. 4 with interaction terms. Results are reported in Columns

(5) and (6) in Table [7]. Presence of upper castes in the community has strong positive impact on low

caste girls’ enrollment, while low caste boys are not subject to this influence: one standard deviation

increase in proportion of high castes is associated with 5.3% increase in probability of enrollment for low

caste girls relative to boys from the low castes (as measured by the magnitude and significance of the

coefficient for the interaction of female and PHC variables).

The two last columns of Table [7] present estimation results for the full sample of children with additional

inclusion of the village development indicators. If participation in school is associated with the better

developed village infrastructure, then inclusion of the community-level controls should have reduced the

magnitude and significance of the caste composition effect. However, there is only slight decrease in the

magnitude of the coefficient which measures the marginal change in low caste girls school enrollment:

the implied effect is equal to 7% increase in enrollment rates for one standard deviation increase in

proportion of the high caste households.

Table [1] reports summary statistics on village development indicators to rule out the most obvious reason

why the propensity to attend school for low caste girls is higher in the communities with high fraction

of advantaged caste - households from high caste might care about the overall quality of life and quality

of schooling. Thus, the development and infrastructure of the common property might be better in those

villages. As can be seen in the table, almost all indicators are numerically comparable across two types

of the communities15. The data do not seem to indicate differential trends in the village development -

that, for instance, villages with the high fractions of Brahmins and Chetry households have experienced

faster development in the years between two survey waves.

15For the purpose of the descriptive analysis I define communities where proportion of high caste households is below themedian as low intensity, and where fraction of high caste households is above the median as high intensity. The mean andmedian for the proportion of high caste in sampled Nepalese villages are 0.32 and 0.23 respectively. The number of villageswhere proportion of high caste is above the mean is 98 out of 229 (or 43%), the number of villages where more than half ofthe households belong to the high caste is 65 out of 229 (28%). The village development indicators numerically are equivalentwhether I compare villages above and below the mean or median.

15

Table 1: Village Development Indicators, by intensity of PHCNLSSI NLSSII

PHC<median PHC>median PHC<median PHC>medianNumber of primary schools 0.025 0.032 0.025 0.031(normalized by the number of hhs)Number of secondary schools 0.011 0.018 0.010 0.013(normalized by the number of hhs)Accessible road 0.27 0.17 0.71 0.64HHs are connected to electricity 0.25 0.22 0.53 0.54Fraction of landless HHs 0.20 0.11 0.19 0.12Public standpipe 0.33 0.66 0.44 0.73Number of observations 103 100 114 115

Village development indicators have significant impact on the school participation of rural children and

are interesting to look at on their own16 (this is also true for Indian villages, see Dostie and Jayaraman

(2006) who find positive effect of village road on boys participation). The availability of all weather

road and public standpipe are associated with increase in probability of enrollment for both boys and

girls17. This is an anticipated result since children are responsible for collecting wood and fetching water

in Nepal. Access to clean water saves time and all-weather roads reduce time walking to the forest and

collecting woods, thus reducing opportunity cost of schooling. Presence of the electricity source in a

village also has a positive impact on both girls and boys’ enrollment. This might reflect the fact that

when the village is electrified, children spend less time in the kitchen where they usually maintain labor

intensive and inefficient stoves (those stoves use wood and are very hard to maintain; moreover, they

are very harmful for the health of those present at home throughout the day). Village electrification also

makes the use of the efficient technologies at home more feasible. As expected, higher proportion of

landless households in a village has a negative impact on schooling outcomes. This continuous measure

serves both a proxy for aggregate village welfare and income inequality. In rural Nepal, land is the main

household asset providing living, and 80% of the population depend on agriculture for their income and

employment. The landownership serves as a measure of wealth. Thus, on the aggregate level, villages

where only a few household do not own land, are on average wealthier, with better living conditions and

16Results for the village level indicators estimation are not reported here, but are available upon request.17This is also true in the context of Indian villages, see Dostie and Jayaraman (2006) who find positive effect of village road

on boys participation

16

better school facilities.

To further investigate the differential impact of community caste composition on low caste girls en-

rollment and grade progression, I estimate Equations (2)-(4) separately for boys and girls. Coefficient

estimates for the low caste girls 2004 sample are presented in Table [8]. One notable feature of the

regression analysis in Table [8] is that despite the number of included left-hand side variables, the pos-

itive effect on low caste girls’ enrollment from living in a community with the prevalence of high caste

households remains statistically significant and economically important implying roughly 11 percent bet-

ter chances of attending school and 13.5 points increase in the grade-for-age z-score for every standard

deviation increase in proportion of high caste in a village. This finding suggest that better infrastructure

is not the only channel through which participation in school and grade progression are related to the

community caste composition. The second likely channel as hypothesized in the introduction maybe

peer pressure and social attitudes prevalent in the diverse communities. To test this hypothesis, I need

more detailed data on school attendance across villages with different caste composition. For instance,

I need to know whether children from different castes attend same school, or there are several school

available in the same village and those school ate attended by children from different social groups. The

alternative channel mentioned in the introduction is the labour market externalities. The mechanism sug-

gested and empirically tested by Munshi and Rosenzweig (2006) in Bombay, India, works as follows: in

the presence of the new labour market opportunities, boys from the low caste continued to be channeled

into their traditional occupations, while girls from the same caste were not binded by the caste occupa-

tional constraints. Girls from the low caste were found to be more responsive to the new labour market

opportunities opened by the modernization of Indian society.

One of the new opportunities opened in Nepal for the last two decades was the expansion of the tourism

industry. However, for the period between two waves of the survey from 1996 to 2004 the flow of tourists

did not increase, but rather slightly decreased (385,297 in 2004 and 393,613 in 1996) due to the ongo-

ing civil conflict and associated security concerns. The main purposes of visit reported by those who

traveled were trekking (26%), holiday/pleasure (8%), and business (4%). Although the absolute earnings

from foreign tourism in Nepal have almost doubled from 1995 to 2004, the share of tourism industry in

Nepalese GDP remained the same - 3.8%. Number of travel and trekking agencies increased roughly by

17

50%. However, female population is remains underrepresented in these sectors: only 3% of those em-

ployed are females. Similar pattern is observed in the service sector (hotels and lodges which traditionally

employ more female labour): only 13% percent of the employees are women18. These evidences suggest

that the increased school enrollment among low caste girls can not be solely explained by the expansion

and attractiveness of the tourism industry in Nepal. Female representation in the industry remains at low

levels and the gender composition of the labour force in the tourism industry has been stable for the past

14 years. Data from the NLSS I and II imply that in 1996 only in 3% of the surveyed villages households

owned a lodge (in 7 villages out of 205 rural villages in the sample). In 2004, this number doubled to 8%,

representing 19 villages out of 226. Only in 1 village out of 7 in 1996 the lodges were primarily used

by foreigners rather than locals (14%). In 2004, households reported that 74% of the lodges were used

solely by locals, 10% both by locals and foreigners, and 14% mostly by foreigners. Thus, since 1996

the number of households owning lodges doubled. This finding is in accordance with the aggregate data

for Nepal on the number of hotels and lodges. I have also checked whether the presence of lodges in a

village servicing foreigners is related to the caste composition of the community. It turns out that there is

no direct relationship between the two: in 2004, half of the villages where households reported owning

a lodge belonged to the communities with high intensity of PHC (above the median), and half - to the

low intensity PHC villages. Back in 1996, only quarter of low intensity PHC villages reported to have

lodges. The growth in the number of lodges servicing foreign tourists was mostly through the increase

within the communities with lower proportions of high castes. These evidences from the data need to be

treated with caution. Even though the surveys were designed to be representative for Nepal, the sampling

procedure might not fully reflect the aggregate picture of lodges’ construction and maintenance. One

implication of the survey data to the current analysis is that there is no direct evidence of the impact that

expansion of the tourism industry in Nepal might have had on the low caste girls’ enrollment.

There is a valid concern that the communities where proportion of privileged households is higher are

less likely to be affected by the 1996- 2006 People’s war in Nepal. People’s war has affected Nepal

unevenly: not all of the 75 administrative districts experienced violence and damage to the infrastructure.

During the armed conflict, children often do not go to school for security reasons (long and unsafe

18Source: Nepal Tourism Statistics 2009 (Annual Statistical Report).

18

walk to school, danger of attack), or schools may be closed for the period of the conflict, or the school

building might be destroyed. If districts that were subject to the conflict at a lesser degree, also have

higher fractions of advantaged caste households, then the effect that I find for the low caste girls who

live in those communities can not be attributed to the caste composition of the village. To address this

concern, I provide correlations between the proportion of high caste households in a community and

(1) the number of causalities per 1000 population from 1996 to 2004, (2) number of deaths caused by

maoists, (3) number of deaths caused by the state. The corresponding correlations are: 0.077, 0.077 and

0.067 19. These correlations are positive and small, thus implying that the association between low caste

girls attendance and proportion of high caste households in a community cannot be explained only by the

absence of the armed conflict20.

To test the impact of the supply side variables on the demand for schooling, I use available indicators of

school quality and school supply21. Only one indicator has persistent positive impact on girls’ enrollment

- presence of at least one female teacher in school. Potential positive influence of female teachers on girls

enrollment has been recognized by the Nepalese government in 1997 in the Education for All program

which requires at least one female teacher in multi-teachers schools. Although the number of female

teaches has been steadily increasing until 2003 and it has been reported that on average there was one

female teacher in each primary school, in reality, there were more than 10,000 schools that did not have

a single female teacher22.

The results of the empirical analysis are robust to changes in the specification of regression equation to

probit or logit models, as well as the villages fixed effects model. In all the specifications the marginal

effect of community caste composition (PHC) on low caste girls attendance is positive and significant.

Another potential reason for the higher propensity of school enrollment in the high caste villages is the

less need in child labour. Child labour and schooling have been found to be substitutes in the household

production function. Clearly, if a child is working, he or she has less time left to study. Going to school

involves not only time spent in school, but also time at home doing homework and other extra curriculum

19The data on causalities across Nepalese districts were kindly provided by L. Iyer, Harvard Business School.20Pivovarova and Swee (2011) find no effect of conflict on schooling using the same data, Valente (2011) finds small

positive effect of war on girls’ education independent of caste.21Results are available upon request22Source: Ministry of Education and Sports (2004) “School Level Educational Statistics of Nepal“.

19

activities. To check the patterns of school enrollment and other activities of children, I can simply contrast

proportions of children who go to school and who is engaged in child labour across different communities.

There is a well-known challenge with estimating impact of child labour on schooling and in the absence

of the right instrument this problem cannot be [ ] in the empirical analysis. The best I can do here is to

compare the hours of work for children in high caste communities and the rest. I find that the difference in

hours worked is statistically significant with almost one hour magnitude difference. On average, children

in the high caste communities work 2.2 hours while in the rest of the communities average hours are 3.2.

Table [9] reports results for two other samples: 6-15 years old girls in 1996 and low caste boys in

2004. The same pattern as in Figure [1] emerges as a result of empirical analysis: there is no significant

association between caste composition of the community and school attendance and grade-for-age z-

score for the low caste boys in 2004 and low caste girls in 1996. It is interesting to note, that in 1996 the

gender gap in enrollment rates was much more pronounced than the caste gap: probability of enrollment

for girls was on average 30% lower that for the boys controlling for individual, family and community

characteristics. Low caste status did not have any impact on girls’ or boys enrollment as indicated by the

insignificance of the coefficient for the low caste dummy and low caste*female interaction.

Finally, I run falsification tests using high caste girl dummy in a place of low caste girl dummy. Results

do not hold, moreover, interaction PHC*high caste*female is always negative and never significant23. In

other words, girls from the high caste households on average have the same probability to be enrolled in

school independent of the caste composition of the community compared to other girls and boys from

the high castes.

6 Conclusion

In this paper, I attempted to discriminate among several competing hypotheses in order to explain a con-

tradicting observation from the recent educational data in Nepal: significant increase in the enrollment

rates among young girls from the low castes if they live in communities where majority of the households

belongs to the privileged castes. I analyzed the determinants of school participation in rural Nepal for all

children controlling for family background characteristics and village development indicators with the

23Results are not reported here and available upon request

20

emphasis on the girls from the disadvantaged households. While I find that improved village infrastruc-

ture has positive effect on the propensity to attend school for both boys and girls in rural Nepal, these

findings cannot explain why in communities with larger proportion of high caste households low caste

girls are more likely to attend school and have relatively higher grade-for-age score compared to the boys

from the low castes and girls from other caste groups. My findings suggest that the positive effect of

the community caste composition operates not only through the better developed village infrastructure

(regular supply of electricity and piped water, presence of all-weather road and quality school facilities

in the community). I also analyzed the trends in the tourism industry in Nepal and compared them to

the village-level developments to assess the impact the expansion in the tourism sector might have had

on the employment opportunities of the low caste girls. Both the survey and aggregate Nepal data point

out that even though the tourist industry has experienced growth for the recent two decades, the relative

proportion of females employed in the hotels and services for the tourists did not increase. The third

channel – peer effects and pressure – can not be analyzed with the available data.

The results of the empirical analysis show that the households’ decision to send children to school is

determined both by the adequate supply and quality of the schools, overall level of village development,

and ethnic and caste composition of the community.

My findings suggest that the schooling decisions among rural Nepalese households are subject to ag-

gregate level influences. Thus, in the villages with developed infrastructure, children are more likely to

attend school, and this result is stronger for girls. Older girls whose participation is especially low in

rural Nepal, are responsive to such improvements on the village level as regular supply of piped water

and electricity. Apart from the economic development indicators, important role belongs to the social

attitudes in the community as measured by the village ethnic and caste composition.

In shaping development policy targeted at attracting and keeping girls at school in a country where most

of the population lives in rural communities, it is important to account for social attitudes and tradi-

tions. Another implication is that providing one girl with the scholarship will not change much while

the overall level of development is low. Girls still be forced out of school to fill the needs of families

and communities. In order to promote education in Nepalese rural communities, it is necessary to act at

both individual and village level, closely work with village development committees, and target the most

21

vulnerable groups - poor and low caste children.

References

[1] Alesina A., A. Devleeschauwer, W. Easterly, S. Kurlat, and R. Wacziarg (2003): Fractionalization,

Journal of Economic Growth Vol. 8 (2), 155-94.

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Journal of Economics, Vol.114(4), 1243-1284.

[3] Alesina, A. and E. La Ferrara (2005): “Ethnic Diversity and Economic Performance,”Journal of

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[4] Banerjee, A. and R. Somanathan (2001): “Caste, Community and Collective Action: The Political

Economy of Public Good Provision in India,” Manuscript, MIT.

[5] Banerjee, A. and R. Somanathan (2007) “The political economy of public goods: Some evidence

from India,” Journal of Development Economics, Vol. 82(2), 287-314.

[6] Becker, Gary S., (1962): Investment in Human Capital: A Theoretical Analysis, Journal of Political

Economy, Vol. 70, 9-49.

[7] Bista, D.B. (1972): People of Nepal , Ratna Pustak Bhander.

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Books, London.

[9] Dostie, B. and R. Jayaraman (2006): ”Determinants of School Attendance in Indian Villages,”

Economic Development and Cultural Change, Vol.54(2), 405-421.

[10] Dreze, J. and Geeta G. Kingdon (2001): ”School Participation in Rural India,” Review of Develop-

ment Economics, Vol.5(1), 1-24.

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[11] Easterly, W. and R. Levine (1997): Africas Growth Tragedy: Policies and Ethnic Divisions, Quar-

terly Journal of Economics, Vol.112(4), 1203-1250.

[12] Hoff, K. and P. Pandey (2006): “Discrimination, Social Identity, and Durable Inequalities,” Ameri-

can Economic Review Vol. 96 (2), 206-211.

[13] King E. M. and M. A. Hill, eds, Women’s education in developing countries: barriers, benefits, and

policies, Johns Hopkins University Press, 1993. Published for the World Bank.

[14] Khwaja, A. (2006): “Can Good Projects Succeed in Bad Communities?”, Manuscript, Harvard

University.

[15] Lewis, M. A. and M. E. Lockheed (2006): Inexcusable Absence: Why 60 Million Girls Still Arent

in School and What to Do About It. Washington, DC: Center for Global Development.

[16] Lewis, M. A. and M. E. Lockheed (2008): ”Social Exclusion and the Gender Gap in Education,”

The World Bank Policy Research Working Paper WPS 4562.

[17] Miguel, E. and M. K. Gugerty (2005): “Ethnic Diversity, Social Sanctions, and Public Goods in

Kenya,” Journal of Public Economics, 89 (11-12), 2325-2368.

[18] Moffitt, (2001): ”Policy Interventions, Low Level Equilibria, and Social Interactions” in

S.N.Durlauf and H.P. Young, eds. Social Dynamics, Cambridge, Massachusetts: The MIT Press.

[19] Munshi, K. and M. Rosenzweig (2006):”Traditional Institutions meet the Modern World: Caste,

Gender and Schooling Choice in a Globalizing Economy.” American Economic Review Vol 96, n.4

[20] Orazem, P. and E.King (2008): Schooling in Developing Countries: The Roles of Supply, Demand

and Government Policy, Chapter 55 in T. P. Schultz and John Strauss, eds. Handbook of Develop-

ment Economics, Volume 4. Amsterdam: North Holland.

[21] Pivovarova, M. and E.L.Swee (2011):“Quantifying the Microeconomic Effects of War: How Much

Can Panel Data Help?” University of Toronto mimeo.

23

[22] Schultz, T. P. (1993b): Returns to Womens Education, In Womens Education in Developing Coun-

tries: Barriers, Benefits, and Policies, eds. Elizabeth M. King and M. Anne Hill. Baltimore, MD:

Johns Hopkins University Press.

[23] Schundeln, M.(2007): “Ethnic Heterogeneity and the Private Provision of Public Goods,” Harvard

University mimeo.

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Exposed to Civil Conflict in Nepal,” The University of Sheffield, Department of Economics Working

Paper 2011009.

24

A Statistical Tables

Table 2: Net Enrollment Rates for Primary and Lower Secondary Schools, 2004(1996), %Indicator Primary Lower Secondary SecondaryAll Nepal 74.27 (57) 31.67 (19) 17.27 (9)Urban 83.14 (71) 47.89 (37) 33.48 (23)Rural 71.79 (56) 26.20 (18) 12.09 (8)GenderBoys 78.95 (67) 33.71 (23) 19.05 (13)Girls 69.50 (46) 29.34 (14) 15.48 (6)Development RegionEast 75.95 (59) 31.13 (26) 18.92 (17)Central 66.02 (51) 35.27 (18) 16.76 (10)West 84.72 (70) 34.11 (19) 17.22 (8)Mid West 80.23 (52) 21.69 (18) 14.62 (2)Far West 74.07 (47) 26.32 (9) 19.28 (0)Ecological ZoneMountains 76.95 (47) 29.77 (17) 10.34 (2)Hills 80.45 (65) 35.39 (21) 16.56 (11)Tarai 68.10 (51) 28.05 (18) 19.52 (9)Quintile1 (poorest) 52.42 (37) 7.86 (6) 2.07 (2)2 71.83 (49) 17.36(9) 4.85 (4)3 82.14 (59) 27.83(18) 13.99(6)4 82.85 (74) 39.94(30) 17.37(10)5 (most well-off) 87.85 (78) 58.97(41) 37.14(29)Ethnic groupHindu 76.76 (60) 35.73 (24) 21.22 (14)Janajatis 75.07 (55) 29.29 (21) 14.04 (12)Muslims 51.01 (47) 5.33 (4) 1.00 (4)CasteBrahman 85.04 (70) 45.39 (29) 27.96 (16)Newar 89.80 (76) 57.14 (37) 24.39 (36)Tarai Middle Castes 62.28 (53) 24.43 (18) 14.86 (6)Dalits (Tarai) 36.00 (42) 7.69 (7) 4.76 (0)Conflict IntensityHigh Intensity 75.63 (56) 27.26 (19) 12.10 (6.2)Low Intensity 72.75 (61) 36.76 (27) 22.97 (21)

Source: Author’s calculation based on NLSS I and NLSS II (2004)

25

Table 3: Descriptive Statistics, Children 6-15 years, Rural NepalNLSSI NLSSII

Boys Girls Boys GirlsPanel A: Individual and Household ControlsSchool Attendance 0.73 (0.44) 0.50 (0.49) 0.83 (0.38) 0.69 (0.45)Years of Schooling 2.64 (2.54) 1.74 (2.27) 3.28 (2.55) 2.69 (2.52)Grade-for-age Z-score -0.16 (1.01) -0.23 (0.96) 0.16 (0.96) 0.22 (0.98)Age 10.2 (2.84) 10.1 (2.83) 10.3 (2.9) 10.23 (2.9)Mother’s years in school 0.41 (1.6) 0.43 (1.6) 0.83 (2.2) 0.82 (2.1)Father’s years in school 2.56 (3.6) 2.49 (3.5) 3.37 (3.9) 3.5 (3.9)Per capita expenditure 17.0 (10.4) 16.0 (9.2) 16.0 (8.9) 15.9 (9.0)Below poverty line 0.41 (0.49) 0.43 (0.49) 0.31 (0.46) 0.34 (0.47)Land owner 0.88 (0.32) 0.90 (0.30) 0.87 (0.34) 0.87 (0.34)Distance to primary school 22.9 (25.0) 24.2 (26.0) 18.4 (17.7) 18.9(17.3)Low Caste 0.15 (0.34) 0.15 (0.34) 0.19 (0.39) 0.19 (0.39)High Caste 0.43 (0.49) 0.43 (0.49) 0.39 (0.48) 0.41 (0.49)Number of observations 1591 1526 1944 1832

Panel B: Village Level ControlsAccessible road 0.22 (0.41) 0.69 (0.47)HHs are connected to electricity 0.22 (0.42) 0.53 (0.5)Fraction of landless HHs 0.16 (0.26) 0.16 (0.26)Public standpipe 0.5 (0.5) 0.58 (0.23)Fraction of HHs below poverty line 0.39 (.25) 0.30 (0.23)

Panel C: School Level ControlsNumber of primary schools 2.75 4.0Number of secondary schools 1.4 1.9Number of incomplete schools 1.8 2.0

One randomly selected primary schoolNumber of days school closed 7.77 17.4School is a permanent structure 0.24 0.47School has electricity 0.11 0.11Desks provided for all students 0.28 0.37Chairs provided for all students 0.19 0.42Female teacher in school 0.35 0.60Number of years school has been in operation 21 27.52Number of observations 203 229

Administrative data, by districtPupil-teacher ratio, primary school, by district 37.25 35.50Number of teachers per primary school, by district 3.8 3.8Number of pupils per primary school, by district 141.1 131.4Number of observations 72 72

26

27

Tabl

e4:

Eff

ecto

fCom

mun

ityC

aste

Com

posi

tion

onSc

hool

Enr

ollm

enta

ndG

rade

-for

-Age

Z-s

core

,All

Chi

ldre

n20

04D

epen

dent

Var

iabl

e(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)at

tend

ance

zsco

reat

tend

ance

zsco

reat

tend

ance

zsco

reat

tend

ance

zsco

re

Low

cast

e-0

.04

-0.1

6**

-0.0

5*-0

.19*

**-0

.06

-0.2

4*-0

.07

-0.2

8**

(0.0

3)(0

.07)

(0.0

3)(0

.06)

(0.0

7)(0

.13)

(0.0

7)(0

.13)

Hig

hca

ste

0.08

***

0.23

***

(0.0

2)(0

.05)

Gen

der

-0.1

1***

0.06

*-0

.11*

**0.

06*

-0.1

5***

0.06

-0.1

5***

0.06

(0.0

2)(0

.03)

(0.0

2)(0

.03)

(0.0

6)(0

.10)

(0.0

6)(0

.10)

Fath

ered

ucat

ion

0.01

***

0.04

***

(0.0

0)(0

.01)

Mot

here

duca

tion

-0.0

00.

01(0

.00)

(0.0

1)L

and

owne

rshi

p0.

15**

*0.

29**

*(0

.03)

(0.0

7)D

ista

nce

tosc

hool

-0.0

0***

-0.0

1***

(0.0

0)(0

.00)

Exp

endi

ture

0.14

***

0.38

***

(0.0

2)(0

.04)

Fem

ale*

Low

Cas

te-0

.09*

*-0

.09

-0.0

8**

-0.0

8-0

.04

-0.0

2-0

.04

-0.0

1(0

.04)

(0.0

7)(0

.04)

(0.0

7)(0

.10)

(0.1

6)(0

.10)

(0.1

6)PH

C0.

08**

0.26

***

-0.0

10.

15-0

.02

0.13

(0.0

4)(0

.08)

(0.0

4)(0

.09)

(0.0

4)(0

.09)

CFI

0.12

**0.

34**

*0.

13**

0.38

***

0.10

0.28

**(0

.05)

(0.1

0)(0

.06)

(0.1

2)(0

.06)

(0.1

3)L

owca

ste*

PHC

0.12

0.30

0.15

0.37

*(0

.10)

(0.2

2)(0

.10)

(0.2

1)L

owca

ste*

Fem

ale*

PHC

0.21

*0.

110.

20*

0.08

(0.1

2)(0

.24)

(0.1

2)(0

.24)

Fem

ale*

PHC

0.10

**0.

090.

11**

0.09

(0.0

5)(0

.10)

(0.0

5)(0

.10)

Obs

erva

tions

3776

3776

3776

3776

3776

3776

3760

3760

R2

0.22

20.

238

0.22

90.

250

0.23

60.

252

0.24

30.

263

Rob

usts

tand

ard

erro

rsar

ein

pare

nthe

ses,

and

are

clus

tere

dby

the

LSM

Ssa

mpl

ing

unit:

***s

igni

fican

tat1

%,*

*sig

nific

anta

t5%

,*si

gnifi

cant

at10

%.

Sam

ple

cons

ists

ofal

lrur

alch

ildre

n6-

15ye

ars

old

for

who

min

form

atio

non

left

-han

dsi

deva

riab

les

isno

tmis

sing

.D

epen

dent

vari

able

sar

esc

hool

atte

ndan

ce(o

dd-n

umbe

red

colu

mns

)an

dag

e-fo

r-gr

ade

z-sc

ore

(eve

n-nu

mbe

red

colu

mns

).A

llre

gres

sion

sin

clud

efu

llse

tof

indi

vidu

alan

dfa

mily

cont

rols

,an

dag

edu

mm

ies.

Col

umns

(1)

and

(2)

repo

rtes

timat

esof

equa

tion

[2]

with

incl

usio

nof

indi

vidu

alan

dfa

mily

leve

lco

ntro

lson

ly.

Col

umns

(3)

and

(4)

pres

ent

regr

essi

onco

effic

ient

whe

nPH

Can

dC

FIar

ein

clud

ed,c

olum

ns(5

)and

(6)-

estim

ates

ofeq

uatio

n[4

]with

inte

ract

ion

term

s,an

dco

lum

ns(7

)and

(8)-

with

villa

ge-l

evel

cont

rols

.

28

Tabl

e5:

Eff

ecto

fCom

mun

ityC

aste

Com

posi

tion

onSc

hool

Enr

ollm

enta

ndG

rade

-for

-Age

Z-s

core

,Rur

alG

irls

2004

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

atte

ndan

cezs

core

atte

ndan

cezs

core

atte

ndan

cezs

core

atte

ndan

cezs

core

Low

cast

e-0

.10*

*-0

.22*

**-0

.11*

**-0

.24*

**-0

.09

-0.2

5*-0

.10

-0.2

9*(0

.04)

(0.0

7)(0

.04)

(0.0

7)(0

.08)

(0.1

5)(0

.09)

(0.1

6)Fa

ther

educ

atio

n0.

02**

*0.

05**

*(0

.00)

(0.0

1)M

othe

redu

catio

n0.

000.

03**

(0.0

0)(0

.01)

Lan

dow

ners

hip

0.17

***

0.30

***

(0.0

4)(0

.08)

Dis

tanc

eto

scho

ol-0

.00*

*-0

.01*

**(0

.00)

(0.0

0)E

xpen

ditu

re0.

18**

*0.

42**

*(0

.03)

(0.0

5)PH

C0.

13**

0.30

***

0.07

0.23

*0.

060.

23**

(0.0

5)(0

.11)

(0.0

6)(0

.12)

(0.0

5)(0

.11)

CFI

0.11

*0.

27**

0.13

*0.

28*

0.10

0.19

(0.0

7)(0

.13)

(0.0

8)(0

.16)

(0.0

8)(0

.16)

Low

cast

e*PH

C0.

34**

*0.

39*

0.36

***

0.43

**(0

.11)

(0.2

1)(0

.11)

(0.2

1)

Obs

erva

tions

1832

1832

1832

1832

1832

1832

1825

1825

R2

0.23

50.

282

0.24

40.

293

0.25

10.

295

0.25

90.

307

Rob

usts

tand

ard

erro

rsar

ein

pare

nthe

ses,

and

are

clus

tere

dby

the

LSM

Ssa

mpl

ing

unit:

***s

igni

fican

tat1

%,*

*sig

nific

anta

t5%

,*si

gnifi

cant

at10

%.S

ampl

eco

nsis

tsof

allr

ural

girl

s6-1

5ye

arso

ldfo

rwho

min

form

atio

non

left

-han

dsi

deva

riab

lesi

snot

mis

sing

.Dep

ende

ntva

riab

lesa

resc

hool

atte

ndan

ce(o

dd-n

umbe

red

colu

mns

)and

age-

for-

grad

ez-

scor

e(e

ven-

num

bere

dco

lum

ns).

All

regr

essi

ons

incl

ude

full

seto

find

ivid

ual

and

fam

ilyco

ntro

lsan

dag

edu

mm

ies.

Col

umns

(1)

and

(2)

repo

rtes

timat

esof

equa

tion

[2]

with

incl

usio

nof

indi

vidu

alan

dfa

mily

leve

lco

ntro

lson

ly.C

olum

ns(3

)and

(4)p

rese

ntre

gres

sion

coef

ficie

ntw

hen

PHC

and

CFI

are

incl

uded

,col

umns

(5)a

nd(6

)-es

timat

esof

equa

tion

[4]w

ithin

tera

ctio

nte

rms,

and

colu

mns

(7)a

nd(8

)-w

ithvi

llage

-lev

elco

ntro

ls.

29

Tabl

e6:

Eff

ecto

fCom

mun

ityC

aste

Com

posi

tion

onSc

hool

Enr

ollm

enta

ndG

rade

-for

-Age

Z-s

core

,Rur

alG

irls

1996

and

Rur

alB

oys

2004

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

atte

ndan

cezs

core

atte

ndan

cezs

core

atte

ndan

cezs

core

atte

ndan

cezs

core

atte

ndan

cezs

core

atte

nd3

zsco

re

Low

cast

e-0

.01

0.07

-0.0

40.

14-0

.02

-0.2

1-0

.04

-0.0

7-0

.07

-0.2

4*-0

.07

-0.2

9**

(0.0

7)(0

.21)

(0.0

8)(0

.17)

(0.1

2)(0

.19)

(0.1

2)(0

.19)

(0.0

8)(0

.13)

(0.0

8)(0

.13)

Gen

der(

Fem

ale=

1)-0

.32*

**-0

.28*

**-0

.32*

**-0

.30*

**(0

.04)

(0.0

9)(0

.04)

(0.0

9)L

owca

ste*

Fem

ale

0.00

-0.1

00.

04-0

.02

(0.0

7)(0

.10)

(0.0

8)(0

.13)

PHC

AST

E0.

10**

*0.

150.

08**

0.11

0.13

**0.

22**

0.09

0.16

*0.

010.

150.

000.

14(0

.03)

(0.0

9)(0

.04)

(0.1

0)(0

.06)

(0.1

0)(0

.05)

(0.1

0)(0

.04)

(0.1

0)(0

.04)

(0.1

0)C

FI0.

12**

0.14

0.11

**0.

100.

24**

*0.

41**

*0.

22**

*0.

37**

*0.

14**

0.41

***

0.12

*0.

30**

(0.0

5)(0

.13)

(0.0

5)(0

.13)

(0.0

7)(0

.13)

(0.0

7)(0

.13)

(0.0

6)(0

.12)

(0.0

6)(0

.14)

Low

Cas

te*P

HC

0.08

-0.1

40.

07-0

.25

0.03

0.15

-0.0

9-0

.12

0.12

0.31

0.14

0.38

*(0

.14)

(0.2

3)(0

.15)

(0.2

4)(0

.17)

(0.2

6)(0

.20)

(0.3

0)(0

.10)

(0.2

2)(0

.10)

(0.2

2)L

owca

ste*

Fem

ale*

PHC

-0.0

60.

33-0

.12

0.22

(0.1

8)(0

.29)

(0.2

0)(0

.32)

Fem

ale*

PHC

0.03

0.06

0.02

0.05

(0.0

5)(0

.10)

(0.0

5)(0

.10)

Obs

erva

tions

3113

3113

2805

2805

1522

1522

1363

1363

1944

1944

1935

1935

R2

0.26

40.

232

0.27

20.

231

0.26

60.

276

0.28

50.

280

0.18

60.

215

0.19

20.

227

Rob

usts

tand

ard

erro

rsar

ein

pare

nthe

ses,

and

are

clus

tere

dby

the

LSM

Ssa

mpl

ing

unit:

***s

igni

fican

tat1

%,*

*sig

nific

anta

t5%

,*si

gnifi

cant

at10

%.

Sam

ple

cons

ists

ofal

lrur

algi

rls

6-15

year

sol

dfo

rwho

min

form

atio

non

left

-han

dsi

deva

riab

les

isno

tmis

sing

.D

epen

dent

vari

able

sar

esc

hool

atte

ndan

ce(o

dd-n

umbe

red

colu

mns

)an

dag

e-fo

r-gr

ade

z-sc

ore

(eve

n-nu

mbe

red

colu

mns

).A

llre

gres

sion

sin

clud

efu

llse

tof

indi

vidu

alan

dfa

mily

cont

rols

and

age

dum

mie

s.C

olum

ns(1

)an

d(2

)re

port

estim

ates

ofeq

uatio

n[2

]w

ithin

clus

ion

ofin

divi

dual

and

fam

ilyle

vel

cont

rols

only

.C

olum

ns(3

)an

d(4

)pr

esen

tre

gres

sion

coef

ficie

ntw

hen

PHC

and

CFI

are

incl

uded

,col

umns

(5)a

nd(6

)-es

timat

esof

equa

tion

[4]w

ithin

tera

ctio

nte

rms,

and

colu

mns

(7)a

nd(8

)-w

ithvi

llage

-lev

elco

ntro

ls.

30

B Figures

Figure 1: Enrollment rates among Nepalese girls 6-15 years

31

Figure 2: Migration patterns in 2004

1162

773

1911

1388

3381

2447265

806

8481699

916

2139

0

1000

2000

3000

4000

5000

LC male LC female HC male HC female MC male MC female

Distribution of ever-migrated individuals by caste and gender

Migrated

Never migrated

32

C Additional Information

C.1 Social Exclusion in Nepal

A small landlocked country in South Asia, bordered with China and India, Nepal is one of the 49 least

developed countries in the world, ranked as low as 145 out of 179 countries by Human Development

Index and 99 out of 135 developing countries by Human Poverty Index 24.For the last decade, GDP

growth barely topped the rate of population growth - growth rate of real GDP for 1999-2008 was on

average 3.9% accompanied by 2.2% population growth. Nepal’s pace of growth and poverty reduction

has lagged behind that of other South Asian countries although share of population living below poverty

line decreased from 42% in 1995/1996 to 31% in 2003/2004 25. Nepal has a very diverse society in

several dimensions. Although the majority of the population belongs to the Hindu religion, there are

deep caste divisions in the Nepalese society, and discrimination and human right abuses against the lower

castes are not uncommon. Traditionally, political and economic power was consolidated by interlinking

it with the Hindu caste system.26 The priestly Brahmans were at the top of the ritual order, with the

Kshatriya (kings and warriors) just beneath them and in command of the political order; next came

the Vaishya (merchants) and the Sudra (peasants and labourers). Beneath everyone were occupational

groups, considered ”impure”, and ”untouchable” or acchut. Officially abolished in 1963, caste-based

discrimination, while diluted, remains even today. About twenty Dalit caste groups exist in Nepal at

present. As per Nepalese Census 2001, low occupational castes (Dalits) represents about 14% of the

total Nepal population. Dalits are discriminated on the basis of caste and ”untouchability”. Dalit women

suffer both from discrimination from high caste and within the same caste group.

Dalits have been relegated to do caste-based work as black/goldsmith, tailors, shoemakers and street

cleaners. All these occupations are considered to be of low social status. Persistent poverty and lack of

other means (most of the Dalits do not own any land) force Dalits to continue their traditional occupations.

Dalit women and children often work in the households of their landlords of help in the traditional jobs

of Dalits. Those Dalits who work in bonded labor or forced labor are not even earning from their work,

24United Nations, Human Development Indicator 2008.25Nepal Living Standard Survey I (1996) and II (2004).26This section draws heavily from the World Bank report on Caste, Gender and Ethnic Exclusion in Nepal, 2006.

33

but getting in-kind payment (usually it is food grains). Even when Dalits work for wages they face

differential treatment compared to other caste and ethnic group, with Dalit women earning less than Dalit

men. Therefore, when Dalits change their traditional occupation to wage labor, it does not necessarily

mean that they improve their economic status. The lack of financial resources and modern technology

skills prevents Dalits from participating in slowly modernized Nepalese economy. There exists wide-

spread practice of social and cultural discrimination against Dalits. They are prohibited from entry to

the houses, temples and other public spaces. Untouchability is practiced in schools. There are numerous

evidence from the field surveys and case studies that Dalit children refuse to enrol in school, particularly

if the school lies outside their immediate neighborhood. It should be noted that in case of polarized

communities not only physical distance but also social distance matters for the cost of schooling. In

the school context, discrimination can stem from both peers and teachers: Dalit children are offered

segregated seating in class and are subject to physical and mental bulling.27 Financial support in the form

of Dalit scholarship seems not to bring desired results: Dalits children prefer not to apply for the stipend

which demonstrates their low status in a society.

Studies also show that incidence of caste-base discrimination is prevalent in western region of the country,

implying positive correlation with the degree of development in the region.

Nepals new Constitution (1990) established a more inclusive state. It describes Nepal as ”multi-ethnic,

multi-lingual and democratic” and declares that all citizens are ”equal irrespective of religion, race, gen-

der, caste, tribe or ideology”. However, it also retained some ambiguities by declaring Nepal a Hindu

Kingdom and Nepali as the only official language, denying women the right to pass their citizenship to

their children and explicitly protecting traditional practices, which has been used to bar Dalits (low caste,

occupational caste and ”untouchables” now call themselves Dalits) from temples and to permit continued

caste discrimination.

The archaic caste system has left its mark on education, and continues to influence it today. Traditionally

confined to the elite, education has only recently been recognized as a fundamental right, and in many

areas this attitude has yet to become the norm when considering girl children. The state assumed re-

sponsibility for the education system in the 1970s; previously locally run schools were turned over to a

27UNESCO report ”Winning People’s Will for Girl Child Education” 2005.

34

centralized educational administration. Public education expanded rapidly thereafter. To help poor and

socially excluded children access the kind of education that will open opportunities for them, Nepalese

government committed to provide equal access to educational resources for all excluded groups girls,

linguistic minorities, Dalits and Janajatis. In an effort to reform the system, in 2001 the parliament passed

the Seventh Amendment of the Education Act, allowing management of local public schools to be handed

over to School Management Committees (SMCs). The rules require at least one woman member but do

not mandate Dalit or Janajati representation. Participation of both Dalits and women in the SMCs re-

mains low. Schools with female teachers tend to attract more female students. For that reason the policy

of having at least one female teacher per school in multi-teacher schools was established over a decade

ago, and the Nepal Education for All programme requires at least two female teachers in such schools.

However, neither policy has yet been fully implemented. Not surprisingly that the excluded groups are

also under represented in higher education with Dalits being less than one percent of those with Bache-

lor degree and above and this is largely due to exclusion at the lower levels. Although the government

of Nepal has taken steps to remedy these inequities by subsidizing education and reserving positions in

institutions on tertiary level and in the public sector for the low castes, a large caste-gap in education and

income continues to persist in both rural and urban Nepal today.

C.2 Caste Groups in Nepal

Table below presents caste division in Nepal. NLSS II provides a list of 103 ethnic group living in Nepal.

I use classification of caste in Hindu and Newar communities described in Bista (1972) to construct

measures of caste fractionalization.

35

High CastesChhetri Brahman NewarThakuri Sanyasi KayashtaBaniya Rajput MarwadiJaine Nurang Bengali

Middle CastesKalwar Teli YadavSudi Sonar LoharKoiri Kurmi KanuHaluwai Hajam/Thakur BadheRajbhar Kewat Mallah NumharKahar Lodha Bing/BandaBhediyar Mali Kamar DhuniaMagar Tamang RaiGurung Limbu SherpaBhote Walung BuansiHyolmo Gharti/Bhujel KumalSunuwar Baramu PahariAdivasi Janajati YakkhaShantal Jirel DaraiDura Majhi DunuwarThami Lepcha ChepangBote Raji HayuRaute Kasunda TharuDhanuk Rajbans TajpiriyaGangai Dhimal MecheKisan Munda Santhal/Satar/DhangadKoche Pallarkatta/Kusbadiya

Low castesKami Damai SarkiGaine Badi ChamarMusahar Tatme BantarDhusadadh/Paswan Khatway DomChidimar Dhobi HalkhorMuslim Churoute Bhujel/GhartiNuniya Baantar

C.3 Education system in Nepal

The formal education system in Nepal was established in 1971 and is divided into five levels: 1) pre-

primary or early childhood education for 3 to 5 years of age; 2) primary education (Grade 1-5) for

children from 6 to 10 years; 3) lower secondary education (Grade 6-8) for eleven to thirteen year-old

36

children; 4) secondary education (Grade 9-10) for fourteen and fifteen year-old; 5) higher secondary

education (Grade 11-12) for sixteen and seventeen year-old. Secondary education generally refers to

Grade 6-12. Before the Interim Constitution of 2007, only primary education was provided free charge

for all children enrolled in community schools and textbooks are also provided at no cost, and secondary

education was free only for female students and children from low caste 28, poor29, and ethnic minorities.

The main budget source for financing education in Nepal is the government. The government provides

teachers salaries, management costs, and program costs. At the decentralized level, district, municipal-

ity and village development committees provides support in terms of physical infrastructure and also

teachers’ salaries. In addition, schools collect fees from students. In the case of community lower

secondary/secondary schools, students pay NPR 500 30 (equivalent of US$ 7.06) or more per year in

community schools. Fees for private schools vary. In addition, each school collects various kinds of fees

for activities such as sports, special training, as well as for repair and maintenance of facilities such as

libraries and laboratories. There are mainly two types of institutions providing primary and secondary

education in Nepal: community schools and institutional, or private, schools. Community schools are

run by the government and receive regular grants from the government. Institutional schools are pri-

vately managed and do not receive regular government funding. In Nepal, 17 percent of the students are

enrolled in private schools at all levels of education. In Nepal, transition from primary to lower secondary

education depends on the results of the final examinations that is conducted at the end of Grade 5. In

order to be admitted to Grade 6, students are required to obtain at least 32% in the examination. As for

the higher secondary education (Grade 11-12), admission is decided based on the results of the admis-

sion test at the end of Grade 10, or School Leaving Certificate (SLC). SLC test is considered nation-wide

examination.28Nepali society is based on the Hindu caste system. Dalits are considered one of the lowest and underprivileged groups

called ”untouchables” and have been deprived of basic rights,including the right for education.29poor are defined as those whose family income falls below the poverty line.30Mean nominal expenditure per capita in NLSS II was 15848 rupees and GDP per capita in current US$ in 2004 was 240

(Penn World Tables 6.1).

37

C.4 Survey design

The first round of Nepalese Living Standards Survey (NLSS I) was conducted by the Central Bureau

of Statistics (CBS) in 1995/96. The goal of the survey was to collect information on the extent, nature

and determinants of poverty covering different aspects of household welfare, including consumption,

income, housing, access to facilities, education, health, employment, access to credit and remittances.

The second round of the survey (NLSS II) was originally planned for 2002/03, but was implemented one

year later in 2003/04 with the intention to track changes in the living standards and access the impact

of different government policies and programs targeted on poverty and other indicators of economic and

social development in a country. NLSS II has two parts - cross-section to estimate trends and levels of

the socio-economic indicators, and a smaller panel survey in order to track changes for the last eight

years. Both NLSS I and II followed methodology of the World Bank Living Standards Survey applied

in more than 50 developing countries. The general characteristics of the World Bank suveys comparing

to the specific purpose survey are smaller sample size, intergrated household questionnaire, inclusion of

panel survey part and community questionnaire. The basis for the NLSS II sample frame was provided

by the 2001 Population Census of Nepal. The design of the NLSS I and II was based on the two-stage

stratification method. First, the sample of 334 primary sampling units (PSU, or villages, or communi-

ties) was selected from six strata using probability proportional to size (PPS) sampling method with the

number of households as a measure of size. On a second stage, 12 households from each of 334 PSU

units were selected by systematic sampling from all households listed with the total sample size of 4,008

households. The final size of the survey consists of 3, 912 households in 326 PSUs for cross-sectional

sample, and 1165 households in 95 PSUs as some of the wards could not be reached due to the military

conflict in the area (in total, 12 PSUs could not be reached even after repeated attempts, 8 of them in

cross-section and 4 in panel sample).

38