#  Fewer, better pathways for all?
Intersectional impacts of rural school consolidation in China’s minority
regions††thanks:  This paper is published as: Hannum, Emily, and Fan Wang.
“Fewer, Better Pathways for All? Intersectional Impacts of Rural School
Consolidation in China’s Minority Regions.” World Development 151 (March 1,
2022): 105734. https://doi.org/10.1016/j.worlddev.2021.105734.

Emily Hannum and Fan Wang Emily Hannum: Department of Sociology and Population
Studies Center, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA
19104 (email:hannumem@sas.upenn.edu); Fan Wang: Department of Economics,
University of Houston, 3623 Cullen Boulevard, Houston, TX 77204 (email:
fwang26@uh.edu). We gratefully acknowledge support from Penn’s University
Research Foundation and School of Arts and Sciences Research Opportunity Grant
Programs and from Grand Challenges Canada (PI: Jere Behrman). We also
acknowledge support from Scholar Grant GS040-A-18 from the Chiang Ching-Kuo
Foundation for coverage of Wang’s time. Thanks to Anita Lai for providing
editorial suggestions. This paper has benefited from helpful comments provided
by two anonymous reviewers and by seminar participants at the Department of
Sociology at the Chinese University of Hong Kong, the Center for Social
Research at Peking University, the Population Research Institute at Duke
University, the Population Research Institute at Penn State University, and
the Education and Inequality Workshop at the University of Pennsylvania. We
also thank Jere Behrman, Aimee Chin, Elaine Liu, and Xiuqi Yang for
suggestions and comments.

(September 8, 2021; March 1, 2022)

###### Abstract

Primary school consolidation—the closure of small community schools or their
mergers into larger, better-resourced schools—is emerging as a significant
policy response to changing demographics in middle income countries with large
rural populations. In China, large-scale consolidation took place in the early
21st century. Because officially-recognized minority populations
disproportionately reside in rural and remote areas, minority students were
among those at elevated risk of experiencing school consolidation. We analyze
heterogeneous effects of consolidation on educational attainment and reported
national language ability in China by exploiting variations in closure timing
across villages and cohorts captured in a 2011 survey of provinces and
autonomous regions with substantial minority populations. We consider
heterogeneous treatment effects across groups defined at the intersections of
minority status, gender, and community ethnic composition and socioeconomic
status.

Compared to villages with schools, villages whose schools had closed reported
that the schools students now attended were better resourced, less likely to
offer minority language of instruction, more likely to have Han teachers,
farther away, and more likely to require boarding. Much more than Han youth,
ethnic minority youth were negatively affected by closure, in terms of its
impact on both educational attainment and written Mandarin facility. However,
for both outcomes, significant penalties accruing to minority youth occurred
only in the poorest villages. Penalties were generally heavier for girls, but
in the most ethnically segregated minority villages, boys from minority
families were highly vulnerable to closure effects on educational attainment
and written Mandarin facility. Results show that intersections of minority
status, gender, and community characteristics can delineate significant
heterogeneities in policy impacts.
JEL: J15, I24, O15

## Keywords

Keywords: school closure and consolidation, rural education, minoritized
populations, China

## Highlights

  1. 1.

Primary school consolidation is a policy response to changing demographics in
middle income countries with large rural populations.

  2. 2.

We analyze the effects of consolidation on educational attainment and language
ability among rural minority populations in China.

  3. 3.

After closure, better resourced and farther away schools offer less minority
language instructions and have more Han teachers.

  4. 4.

Minority youth were negatively affected by closure, in terms of both
educational attainment and written Mandarin facility.

  5. 5.

Penalties were heavier for girls from poorer villages and for boys from more
ethnically segregated minority villages.

##  1 Introduction

School consolidation—the closure and merger of small schools to form larger
schools—is a common strategy to economize and create a better-resourced
environment for staff and students in the context of demographic changes [18,
56]. In OECD countries, for example, a recent report observes that “shrinking
student numbers, limited access to qualified and experienced teachers and a
relatively high proportion of disadvantaged students make the efficient
provision of high-quality education in rural areas a difficult undertaking”
and that “consolidation may be unavoidable to guarantee adequate learning
environments for all students” [58, 14, 129]. Consolidation is also emerging
in some middle income countries with large rural populations,111For example,
see press reports about consolidation such as malik_apprehensions_2013,
chowdhury_cramped_2017, saengpassa_ministrys_2017, harun_schools_2017,
tawie_sarawak_2017. as fertility trends, population aging, and rural
outmigration set the stage for demographic decline.222The top 10 countries in
the world in terms of projected rural population decline between 2018 and
2050, in descending order, are China, India, Indonesia, Bangladesh, United
States of America, Russian Federation, Thailand, Viet Nam, Brazil, and Iran
[68, 4, 46]. In these contexts, consolidation is often linked to concerns
about performance, capacity to provide quality schooling at reasonable cost,
and “demographic scarcity” in the school-aged population.333See [72, 923-925]
for similar rationales for consolidation in urban and rural areas in the
United States.

Yet, a recent OECD report on learning in rural schools by [18, 56] also
observes that there are “serious risks and costs associated with school
consolidation, including “increased transportation costs for students’
families and relocated staff, reduced parental involvement, deteriorated local
services, reduced school choice and other social costs.” The review also
raises the concern that socio-economically disadvantaged students are more
negatively affected following school closure [18, 56]. Whether consolidation
enhances or detracts from educational opportunity for students from
economically and socially marginalized populations is particularly important
to address.444See [72] for a discussion of evidence regarding the presence or
absence of racial unevenness in the experience of school consolidation in
United States.

Extremely rapid fertility decline and urbanization have accelerated pressures
for system consolidation in China, and the policy response has been striking
in pace and scale. In 2001, the State Council initiated a massive national
push to consolidate educational infrastructure. The school consolidation
initiative, implemented by county level officials, sought to address sparse
demand, inefficiencies in provision, and perceived quality problems in rural
education [54]. [54, 141-143] cite official statistics showing that between
1998 and 2010, the number of primary schools dropped by 58 percent.

For students in affected communities, consolidation often means traveling
longer distances but attending better-equipped schools, though larger schools
can also mean larger class sizes. Distance can sometimes bring greater
expense, particularly if children need to board. Studies published on the
phenomenon as recently as 2014 and 2015 commented on the surprisingly paucity
of empirical research on the consequences of this important policy [7, 54].
While a handful of empirical studies in English have begun to address
implications of system consolidation for access, cost, and performance (for
example, see [6, 45, 15, 7, 48, 4, 32, 36]), the implications of system
consolidation for patterns of stratification have not been fully explored.

In particular, how the policy has affected China’s minority youth is a
significant question. As of the 2015 intercensal survey, more than one in ten
youth ages 10 to 19 in China were members of officially-recognized minority
groups [75, 3]. Despite absolute gains in access over time, minority youth in
China, as a group, remain educationally vulnerable. Focusing on compulsory
education as a minimum standard for access, in 2015, the percent of
adolescents aged 10 to 19 not on track to receive the nine-year cycle of
compulsory education was fairly negligible among majority Han adolescents, at
1.7 percent, but was 7.2 percent among minority youth [75, 7].555Not on track
includes youth who had never been to school, had graduated from primary school
only, or had dropped out of primary school or junior secondary school.

As a group, minority youth are more likely than others to attend poorly
resourced schools [28, 519]. The access vulnerability of minority youth is
closely tied to geographic marginalization [10, 321-322].666[59] suggest that
an additional factor in differential educational outcomes is a legacy of the
more stringent fertility controls imposed on Han families, which translated to
smaller sibships, on average, for Han students. Children who are members of
minority populations, as a group, are more likely than members of the Han
majority to reside in remote, poor, and rural areas. In the 2010 census, 71.4
percent of ethnic minority children ages 0 to 17 lived in rural areas,
compared with only 53.4 percent of Han children [73, 2]. A more recent
estimate based on the 2015 intercensal survey indicated that nearly half of
all ethnic minority adolescents aged 10 to 19—45.4 percent—lived in poor rural
areas, counties officially-recognized at that time as poverty-stricken,
compared to 24 percent of the total adolescent population [75, 4].777A
substantial body of literature has emerged that investigates links among
economic development, geography, and ethnic disparities in education and other
outcomes (for reviews of literature on ethnicity and education in China, see
[10] and [12]. For near-national evidence on educational testing differences
by minority status, albeit evidence that does not include the experiences of
youth in Xinjiang, Tibet, Qinghai, Inner Mongolia, Ningxia, or Hainan, see
[78]. Speaking to the importance of an intersectional perspective, the
minority-majority performance gap found by [78] is reversed in the urban and
rural samples, though findings for the urban sample are not statistically
significant. Thus, minority youth disproportionately reside in the kinds of
communities that are appealing targets for school consolidation.

Beyond the general risks and benefits associated with consolidation, for
minoritized communities, there may be additional considerations. For example,
consolidated schools may offer better national language instruction, but could
be less equipped to offer mother-tongue instruction, if children from multiple
language groups are coming together in consolidated schools.888[65, 493]
points out that while there are 56 recognized ethnic groups in China, an
important, regularly updated reference work on global linguistic diversity
registers many more languages—306 in the 2021 edition [17]. As minority group
boundaries were defined and languages recognized, linguistic diversity within
groups was downplayed as standards were chosen, which created challenges to
full preservation of non-standard languages spoken by some included within
defined ethnic categories harrell_linguistics_1993,dwyer_texture_1998,
roche_articulating_2019. For example, [65, 494] observes that Mongolians speak
at least six distinct languages. Chakhar Mongolian is taught as standard in
the Inner Mongolia Autonomous Region and is replacing not only several
languages spoken by Mongolians but also those of other ethnic groups, such as
the Evenki, even as a language shift from Mongolian to Mandarin is also
underway. Roche describes similar phenomena of standard versions of Yi and
Tibetan languages edging out languages spoken by some members of these groups.
Similarly, some Chinese topolects—not recognized as separate languages—are
mutually unintelligible with Mandarin and are as distant from each other as
Romance languages [81]dwyer_texture_1998. This linguistic complexity
highlights that whether or not children are receiving mother tongue education
is hard to fully parse with existing data. Facility with the national language
might be viewed as essential for economic mobility by some rural minority
parents, and if this were the case, the greater Mandarin focus in consolidated
schools might appeal. At the same time, a more Mandarin-focused teaching
environment might create barriers to engagement and learning for some minority
youth. A related possibility is that minority parents’ decisions about sending
children to school are more sensitive to increased distance (signifying cost
or safety risk) or less sensitive to the kinds of school resources on offer in
consolidated schools. Families may be less inclined to allow their children to
board at school if there are linguistic mismatches or contexts of ethnic
tension. Linguistic minority parents may hesitate to send young children far
away to board at a school if they cannot communicate with teachers. Linguistic
barriers in centralized schools might thus counteract, to some degree,
benefits of better facilities at these larger schools for minority youth.

Drawing on a 2011 rural household and community survey of provinces and
autonomous regions with substantial minority populations, we analyze the
impact of school consolidation on minority and Han youth in provinces and
autonomous regions with substantial minority populations. The dataset contains
both villages that experienced closure between 2000 and 2011 as well as those
that did not. In closure villages, older individuals who were beyond primary
school age at the village-specific year of closure were not directly impacted,
but younger individuals had to transition from village schools to consolidated
schools. Using a difference-in-differences strategy, we identify causal
effects of school closure by exploiting the variations in closure and closure
timing across villages, as well as variations in age at closure within closure
villages.

In our empirical analysis, we first compare school features in consolidation
villages and those that have maintained village schools. Next, we test whether
ethnic minority youth were disproportionately affected when they experienced
school closure. We estimate impacts on educational attainment and reported
facility in Mandarin, because consolidated schools are likely to promote the
use of the national language from an earlier age. Importantly, given
literature suggesting that gender, impoverishment, and the isolation of
minoritized communities might condition impacts, we allow for heterogeneous
treatment effects across groups defined at the intersections of minority
status, gender, and community ethnic composition and socioeconomic status.

We show that average negative effects of closure on educational attainment and
reported written Mandarin ability for minority youth mask considerable
heterogeneities across groups defined by intersections with gender, community
economic status, and community ethnic composition. Penalties for minority
youth were pronounced in poorer villages, while there was no evidence of a
significant minority penalty in wealthier villages. Penalties were generally
heavier for minority girls. However, in the most ethnically segregated
minority villages, boys from minority families were highly vulnerable to
closure effects on educational attainment and written Mandarin facility.

The remainder of this paper is organized in the following sections. Section 1
provides background on school consolidation and stratification, in comparative
perspective and in China. In Section 2, we describe the data and estimation
strategy. Section 3 presents and discusses estimation results. Section 4
concludes.

##  2 Motivation and research questions

###  2.1 Consolidation in global perspective

####  2.1.1 Global trends

In recent years, school consolidation has been advanced as a strategy to
reduce costs and improve educational quality in the context of new demographic
realities in many countries, with the idea being that consolidated schools
have the potential to provide better facilities and more academic and extra-
curricular programs and activities in an economical fashion [18, 56]. For
example, in a majority of OECD countries, only 20 percent or less of children
up to the age of 14 live in rural areas–a share which is even lower in the
15-29 age group–and this situation is creating what a recent report
characterized as “unsustainable” excess capacities in rural areas [58, 125].
The same report observes that consolidation has often been proposed to
“increase the size, improve the resources and broaden the educational
[offerings] of the remaining rural schools” [58, 128].

The strategy is emerging as a response to changing demographics in some middle
income countries with large rural populations. For example, in the case of
Brazil, official education statistics show that the number of rural primary
schools dropped 31 percent between 2007 and 2017, from 88,386 rural primary
schools to 60,694 [3]. Thailand’s Ministry of Education recently announced a
plan to merge thousands of small schools with fewer than 120 students each
with other schools within a six-kilometer radius [66]. In Punjab, Pakistan,
new reports indicate that of 51,602 primary and middle public schools in the
province, about 5,500 primary schools have been merged [53]; in Rajasthan,
India, in 2014, the government merged 17,000 of the over 80,000 government
schools in the state with other schools, with more mergers planned [13].
Further, India’s National Education Policy 2020 recommends consolidation of
schools (section 7.4) and the creation of ‘school complexes/clusters’ (section
7.6) to address problems of small rural schools
[3]ramanand_national_2020india_ministry_of_human_resource_development_national_2021.

####  2.1.2 Consolidation and educational stratification

The implications of rural school consolidation for educational attainment and
educational stratification in low- and middle-income countries are not yet
well theorized or researched.999See [72] and [18], respectively, for reviews
of research in the US and OECD countries. When considering the impact of
consolidations on inequality, one important question is, which communities
experience closures? In principle, there are three common, related rationales
for school closure decisions: demographic, economic, and performance-based.
Demographic scarcity–-too few students for a school—is a common factor cited
in closure decisions. Economic efficiency is also commonly cited–-there may be
insufficient resources to provide curricular offerings in small, sparsely-
populated schools. Finally, schools whose students perform poorly are also
commonly targeted, with the hope that the better resources available in a
consolidated school will address performance problems. These conditions are
often associated with socioeconomically marginalized communities, whether
poorer urban communities or remote rural ones. As [5] has observed in a very
different setting—a case study of school closures in the city of
Philadelphia—the metrics involved in selecting schools for closure tend to
predispose poor and minority communities to institutional loss.101010For a
similar argument made more broadly about the United States, see [72].

Separate from the question of whether there are disparate exposures to school
consolidation, and more directly tied to the purpose of this paper, is whether
school consolidation may have heterogeneous impacts on different groups of
children. There is not yet a great deal of evidence on this point. In one of
the first evaluations of the policy in India, [2] investigated the impact of
closures in Rajasthan. Basic infrastructure was better in consolidated
schools, though the number of students per classroom did increase post
consolidation [2, 22]. However, [2, 23] report a greater decline in enrollment
in consolidated schools compared to all government schools across Rajasthan,
and that impacts were not equally borne. The authors conclude, “worryingly,
the decline in [enrollment] seems to be the highest for students with
disability, followed by that of [Scheduled Caste] and [Scheduled Tribe]
students” [2, 23].111111[62, 5] cites a Save the Children report [63] about
school mergers in Telangana, Odisha, and Rajasthan, which found that
enrollments were negatively affected by school mergers, and that “harm was
more serious for students belonging to marginalized communities like Dalits
and Tribes.” The study reported no evidence of school mergers improving the
quality of learning; instead, the authors reported inadequate numbers of
teachers in cramped classrooms.

One common effect of consolidation is to increase distance to schools, and the
effects of distance might be heterogeneous. A cross-national analysis of
Demographic and Health Surveys carried out in the 1990s considered the
relationship between the school enrollment of 6 to 14 year-olds and the
distance to primary and secondary schools in 21 rural areas of low-income
countries [21]. Findings suggested that distance and enrollment are
statistically significantly related, but magnitudes of the associations are
small: simulating large reductions in distance yielded only small increases in
average school participation [21]. A multi-level analysis of survey data from
220,000 children in 340 districts of 30 developing countries estimated that
parental decisions regarding children’s enrollment were associated with
distance from school, after adjusting for other school, family and community
characteristics [38]. One recent review suggested that greater distance to
school or the absence of a nearby school carries stronger negative
implications for girls’ than boys’ enrollments in many settings [42, 25].
Similarly, [26, 1632] reports that many studies have suggested that girls’
schooling responds more strongly than boys’ to changes in school distance or
availability, while the opposite is very rarely found. This pattern could
emerge if families are more worried about girls’ safety than boys’, if parents
depend more on girls’ labor at home than boys’, if parents are less willing to
pay added expenses for girls than boys, or if parents respond differently to
the improved resources in consolidated schools for girls than for
boys.121212See [41, 77-78] for a brief discussion of distance and other
concerns about girls’ enrollment). However, [26] also acknowledges that
greater vulnerability to distance for girls is not a pattern that is
consistent in cross-national studies. [21]’s cross-national analysis of
Demographic and Health Surveys found little impact of increasing the
availability of primary schools on gender inequality. [21, 923] plotted
national male-female ratios of enrollment from a simulation of the impact of
reducing the distance to all primary schools to zero against the actual ratio
of male to female enrollment and found that almost all points fell on the 45
degree line. Along the same lines, [38] found similar magnitudes of effect of
distance on enrollment for girls and boys at ages 8 to 11.

####  2.1.3 Consolidation and linguistic and cultural minority populations

In societies with significant linguistic and cultural minority populations, it
is possible that consolidation could carry different implications for
minoritized children than for other children. For example, the educational
benefits of improved resources might be tempered if accompanied by reduced
access to mother-tongue education. To the extent that consolidation implies
switching from a small village school offering mother-tongue language of
instruction to a larger, possibly multi-ethnic consolidated school offering
national language of instruction, the shift could present significant barriers
to learning for children. A number of studies have sought to identify the
impact of mother-tongue education on educational and economic outcomes. In
India, [40, 473] compared districts in which the official language matched the
district’s language and some where it did not. Linguistically mismatched
districts had 18.8 percent lower literacy rates and 27.6 percent lower college
graduation rates, which the authors attribute to difficulty in acquiring
education due to a different medium of instruction in schools. Further, [40]
found that educational achievement recovered in mismatched districts after a
1956 reorganization of Indian states on linguistic lines.

In South Africa, [19] examined the impact of the 1955 Bantu Education Act,
which mandated eight years of mother-tongue education for Black students. [19,
311-313] drew on the 1980 South African census and estimated a difference-in-
differences model to reveal the effect of increasing potential exposure to
mother-tongue instruction for Black students from four to six years. Results
show positive effects on wages. Evidence also suggested that English speaking
ability increased as a result of the policy, but only in predominantly English
parts of the country. Finally, the paper found larger effects on wages and
educational attainment for women than men, consistent with the idea that
mother-tongue education increased accessibility more for girls than boys.

However, a review of these and other pieces by [25] highlights the mixed and
context-specific evidence about implications of mother-tongue instruction,
versus national, colonial, or international language instruction, for
schooling and returns to schooling. A significant counterexample to the
importance of mother-tongue education is provided in [1]. The authors argue,
with evidence from Puerto Rico during a period of change from English to
Spanish language of instruction, that changes in language of instruction were
not significantly associated with changes in reported English proficiency.

Apart from considerations of learning, it is conceivable that the prospects
presented by consolidated schools might be weighed differently by families
from linguistic and cultural minorities. Differences might emerge if minority
parents, more than others, had concerns about risk in a larger school, about
children’s ability to communicate with teachers, or about cultural
preservation. Differences could also emerge if minority parents perceived
fewer prospects for their children realizing economic returns from better-
resourced, consolidated schools.

####  2.1.4 Intersectional perspectives

Theoretically, the impact of consolidation in the case of minoritized youth
might also be tied to other domains of social status. An intersectional
perspective suggests that “individuals are simultaneously situated in multiple
social structures, which interact in complex ways to influence their
experiences, social relations, and well being” [70, 540]. With regard to
education, evidence from some countries suggests that significant cumulative
disadvantages in education can occur for children at the nexus of female
status and minority or indigenous status [70], perhaps especially under
conditions of material deprivation. However, we have not found studies of
consolidation that adopt an intersectional perspective.

In short, consolidation might bring particular educational benefits to
children from minority communities, if they have full access to better-
resourced schools. However, benefits could be tempered by the potential
educational costs of linguistic mismatch or if the risk associated with
children leaving home communities, to larger schools, is perceived to be
greater by parents in minority communities. The contrast between consolidated
and village schools–both in terms of resources and in terms of language and
cultural difference–might differ for boys and girls, and might be most
pronounced in economically marginalized communities or more isolated minority
communities.

###  2.2 Consolidation and stratification in China

####  2.2.1 Consolidation policies

National school consolidation policies in China commenced in 2001, though [14]
report that consolidation experiments were piloted in some provinces in 1993.
The State Council’s “Decision on Basic Education Reform and Development” [22]
required local governments to make reasonable adjustments to schools’
geographic distribution to improve efficiency. As elsewhere, concerns with
quality and efficiency loomed large [47, 79, see, for example,]. Resource-
constrained counties may have faced particular pressures toward consolidation
[20]. By 2012, the Ministry of Education and then the General Office of the
State Council issued documents calling for an end to consolidation [23].

However, persistent population decline in rural China continues to exert
demographic pressures toward consolidation. From a long-term perspective,
China ranks at the top of all countries in terms of projected rural population
loss in the coming years: the rural population of China is projected to be
around 305 million less in 2050 than it was in 2018 [68, 4,46]. More
immediately, [36, 93] report that ongoing fiscal pressures create incentives
for local governments to encourage the practice. Policy debates continue,
given concerns that have arisen around consolidated schools. A recent
communique from the State Council committed to support for the establishment
of small-scale rural schools (primary schools in villages and teaching points
of less than 100 students) and improvement of township boarding schools [24];
see discussion of this communique in [74, 108].

####  2.2.2 Impact of school consolidation

Several studies have investigated the impact of consolidation on student
achievement [45, 55, 7]. [45] find that the timing of consolidation in
students’ lives mattered: higher-grade students’ grades rose after merging,
while grades of younger students fell. [55] and [7] find that elementary
school students’ academic performance improved when they transferred from less
centralized schools to more-centralized schools. Boarding at primary school
can become necessary with longer distances that come with consolidation.
However, as discussed in [7], the need to board at school at early ages may
jeopardize the benefits of centralized schools. Parents may not wish to avail
themselves of centralized schools if these schools are too far for daily
commuting.

Are certain groups more vulnerable than others to school closure? Some studies
have suggested that economically disadvantaged families may be most vulnerable
due to costs imposed by consolidation. [4] found that the compulsory school
consolidation program increased educational expenditures, including
expenditures on transportation and boarding due to greater distance to school.
[83] found that children from poorer families had difficulties paying for a
bus or boarding at school and are more likely to endure longer commutes. Other
studies suggest that some poor rural families might be more likely to shoulder
costs for boys than girls: research in China suggests that girls’ educational
attainment has been more susceptible than boys’ to poverty [11, 49]. Earlier
research has also shown that rural girls are particularly affected by school
consolidation, possibly because enrollment decisions for boys are more
sensitive to quality, while those for girls are more sensitive to distance
[32].

Another study has directly investigated socioeconomic and gender differences
in the implications of consolidation. [36] analyze survey data from 137
township schools with boarding facilities to study “premature” early grade
boarding that resulted from consolidation, and its impact on human capital
accumulation. They show that children from more adverse family circumstances
are at greater risk than others to be early-grade boarders. Families were more
likely to send boys than girls to be early-grade boarders. Further analyses
showed that early-grade boarding was associated with lower test scores, more
depression, and bullying victimization. Subsample analyses showed that girls’
outcomes were particularly affected by early boarding.

####  2.2.3 Consolidation and ethnic disparities in education

As a group, youth who are members of officially recognized minority ethnic
groups are vulnerable to consolidation due to disproportionate residence in
poorer rural communities. Poverty in China is mainly concentrated in remote
mountainous areas, border areas, and minority areas of central and western
China [50, 66]. [36, 93] note that villages in central and western regions are
often scattered in mountainous areas with poor access to transportation, which
necessitates boarding at school131313Separate from boarding associated with
the left-behind children phenomenon (留守儿童) and school consolidation, there
have been “dislocated” boarding schools (內地班) for minority students from
Xinjiang and Tibet in inland China for many years [84, 60, 8, 27, 9, 44, 61].
These schools are sometimes referred to as “inland” or “hinterland” schools or
classes, or Xinjiang or Tibet classes. when there are closures of village
primary schools or teaching points. A recent policy review indicated that by
2007, about 18 percent of primary students boarded, and the proportion was
higher in western regions [69, 76]. The review also reported that boarders in
compulsory education are mainly located in large ethnic minority provinces and
autonomous regions [69, 76].141414In 2017, 10.66 million primary school
students were boarders. The vast majority–9.3 million–were in rural areas, and
most of these in central and western China [74, 121].

Sude and his colleagues also suggest that consolidation policies have paid
insufficient attention to the particular educational circumstances and needs
in minority communities [69]. For example, consolidation may imply a shift in
language of instruction for some minority youth. [64] conducted fieldwork on
bilingual education models in use in the Liangshan Yi Autonomous Prefecture in
Sichuan Province in 2016, including “first model” schools using minority
language of instruction and “second model” schools offering minority language
classes. With official statistics, the authors trace a rapid decline in “first
model” schools, evident in the late 1990s and accelerated in the 2000s, which
the authors speculate may be linked to consolidations already underway in this
area in the 1990s.151515[64] also observed that schools in which “first model”
instruction was abolished were then counted as ‘second model’ schools, with
the result that the superficial numbers of ‘second model’ schools did not
change much since the 1990s. This trend is also consistent with a refocusing
of language policy to stress learning the national language in the twenty-
first century, relative to the 1990s [82, 16-17].

To the extent that consolidation implied a shift away from minority language
use in schools,161616Linguistic mismatch at school can occur not only because
children who speak minority languages at home come into contact with Mandarin
at school. Children from smaller minority groups may come together in schools
where the dominant language is that of larger minority group. In addition,
because of the ways that ethnic boundaries were drawn in China, subgroups of
some minorities may speak a language other than the standard language
recognized for their group, and may come into contact with a dominant group
language at larger schools. Finally, Han children who speak topolects mutually
unintelligible with standard Mandarin are, in effect, not receiving mother
tongue education in schools. For a discussion of linguistic diversity among
Mongolian, Yi and Tibetan populations, see [65]. See [34] and [16] for a
discussion of linguistic diversity in the Han population. language
difficulties might negatively affect the foundations of learning for children
from minority backgrounds (for example, see [77] on the experiences of
Dongxiang children starting school). Language difficulties may greatly reduce
the benefits of schooling [52, 238]. In a study in northwest China, [80] found
a minority disadvantage in school performance that was much more pronounced
among language minority students than among minority students from groups that
did not have a separate mother tongue. Further, the performance of linguistic
minority children benefited less from the same school inputs in mixed schools,
which lacked minority language classes, compared to their Han counterparts.
[64, 10] reported that about 39 percent of the teachers they surveyed in 2016
agreed or strongly agreed that inadequate knowledge of Mandarin posed an
obstacle to learning for some students. Language difficulties are likely to be
particularly pronounced for students from isolated minority villages, who
would have had fewer opportunities to have been exposed to the national
language before starting school.171717A Mandarin learning environment could be
perceived in a variety of ways in minority communities. For example, [33]’s
fieldwork in the mid-1990s among Tai (Dai), Naxi, Akha (Hani) and Jinuo
communities in Yunnan Province found, among other factors, that historical
relations with the Han majority and the dominant minority group in the region,
and group experiences of educational mobility and the lack thereof, shaped the
degree to which minority youth and families found Mandarin-language education
to be instrumental, beneficial, or subtractive [33]. Separate from issues
related to learning foundations, parents might be wary of sending young
children to board at a school in which communication is a
challenge.181818Apart from effects on educational outcomes, there may also be
cultural diffusion effects of school consolidation. [46] found that gender
gaps in risk-taking become more similar for Mosuo children, who come from a
matriarchal culture, and Han children, for whom patriarchal families are the
norm, when they are placed in school together or assigned to board together in
middle school.

From a different perspective, it is conceivable that better-resourced schools
teaching in the national language associated with consolidation might lead to
stronger Mandarin skills and, ultimately, greater educational attainment. [81]
investigated impact on education and employment outcomes of a nationwide
language education reform—-the Chinese Pinyin Act of 1958-1960—-using a
difference-in-differences approach by interacting a birth cohort exposure
dummy with the linguistic distances between local languages and standard
Mandarin. The paper suggests that the policy shift led to modest short-term
negative effects, but long-term positive effects, on educational attainment,
and that it increased rural households’ non-agricultural employment and
improved workers’ capacity to migrate across provinces and language regions.
Earlier work using the Chinese Household Ethnic Survey (CHES) data employed
here shows that Mandarin ability and minority language ability are associated
with quite distinct employment opportunities in young adulthood, with Mandarin
ability more closely linked to economic opportunity [31].

Finally, the benefits of access to consolidated schools might also be
unequally distributed, as suggested by a case study of a 2000 elementary
school system consolidation in a county in a poor, mountainous minority area
in southwestern Sichuan [6]. Through review of aggregate data and comparisons
of the experiences of five elementary schools, the authors report that while
consolidation increased the average quality of basic education, schools became
less accessible to students living in remote areas—-consolidation exacerbated
polarization between remote, likely poorer, villages, compared to villages in
the county periphery and developing areas in the county core.

###  2.3 Research questions

In short, an emerging cross-national literature suggests that closures tend to
produce larger, better-resourced schools that are farther away. In the context
of China, consolidation implies a higher likelihood of requiring boarding.
Consolidation may also imply a stronger orientation to the national language
and less access to mother-tongue education, for some minority youth. Some
studies in China and elsewhere suggest that girls’ enrollment might be
particularly vulnerable to greater distances to school.

For minority youth, the potential benefits of better resourced schools might
be tempered if consolidation implies a shift in language of instruction and
language of communication, because educational engagement might be reduced, or
because parents may perceive higher risk associated with children living at
school when communication is a barrier. Some international studies suggest
that early grade mother-tongue education has positive impacts on educational,
language, and economic outcomes, but evidence is mixed. Minority children from
more demographically isolated villages might experience the greatest
challenges from a more Mandarin-oriented educational environment, while one
case study suggests that children from more economically marginalized villages
might have lower access than others in the same county to consolidated schools
when village schools close.

With this context in mind, we address three research questions: 1) How are
schools serving closure villages different from schools in villages with no
reported closures? 2) Compared to non-minority counterparts, are youth from
officially-recognized minority groups differently affected by the experience
of school closure, in terms of policy impact on both educational attainment
and facility in the national language? And, 3) Are there important treatment
effect heterogeneities across groups defined at the intersections of minority
status, gender, and community ethnic composition and socioeconomic status?

##  3 Data and methods

###  3.1 Study site and sample

This paper utilizes data from the rural household and village questionnaires
from the China Household Ethnic Survey (CHES 2011), which covers households
and villages from 728 villages in 81 counties of 7 provinces with substantial
minority populations in China.191919Please see [30] for an authoritative
description of the survey and discussion of some of the key variables
collected. [51] provides detailed source data in Chinese. [37] and [32]
provide detailed discussions on survey design and the distribution of survey
villages across provinces. In order to investigate the economic and social
conditions of people in minority areas, CHES 2011 utilized subsamples of the
National Bureau of Statistics’ Rural Household Survey (RHS) in seven Chinese
provinces and autonomous regions with substantial minority populations.
Household information at the end of 2011 was collected through diaries and
single-round visits in early 2012. Village closure information, along with
information on the closest primary school to the village, is taken from a
village head survey, which was collected in conjunction with household
surveys.

For our analysis, to evaluate educational attainment and capture variations in
closure effects, we focus on a sub-sample of the CHES survey with individuals
between 10 to 34 years of age in 2011.202020We do not consider individuals
below 10 years of age in 2011 who have just started their educational
trajectories. We also do not consider individuals beyond 34 years of age in
2011, who were born before market reforms. Additionally, for individuals from
villages that experienced school closure, we consider only those who were
older than 5 years of age at the time of closure.212121Given closure dates and
the survey time-frame in 2011, 82 percent of individuals who were below 6
years of age at the year of closure are below 10 years of age in 2011. Given
that we focus on individuals above 10 years of age in 2011, we do not include
individuals who were below 6 years of age at the year of closure.  Given our
study selection criteria, we consider 638 villages from 80 counties in the 7
provinces.222222In each of these 638 villages, there were at least two
surveyed individuals that were between 10 to 34 years of age in 2011.

###  3.2 Measurement

Table 1: Means of individual-level variables by cohort (age in 2011) and
ethnic category

Age in 2011 10-14 15-19 20-24 25-29 30-34 Panel A: All individuals Male
fraction 0.51 0.53 0.52 0.55 0.54 Currently enrolled fraction 0.93 0.56 0.15
0.02 0.00 Years of education 5.52 9.37 9.91 8.60 7.59 Strong spoken Mandarin
fraction 0.20 0.34 0.36 0.27 0.22 Strong written Mandarin fraction 0.16 0.34
0.30 0.20 0.16 Observations 1879 2392 3153 2425 1788 Panel B: Minority
individuals Male fraction 0.53 0.52 0.52 0.55 0.55 Currently enrolled fraction
0.93 0.53 0.12 0.01 0.00 Years of education 5.41 9.06 9.46 8.34 7.34 Strong
spoken Mandarin fraction 0.17 0.28 0.29 0.22 0.17 Strong written Mandarin
fraction 0.14 0.28 0.23 0.16 0.13 Observations 1308 1594 2087 1675 1209 Panel
C: Han individuals Male fraction 0.49 0.54 0.51 0.54 0.52 Currently enrolled
fraction 0.93 0.64 0.21 0.02 0.01 Years of education 5.76 9.98 10.78 9.18 8.13
Strong spoken Mandarin fraction 0.27 0.47 0.49 0.37 0.34 Strong written
Mandarin fraction 0.21 0.45 0.44 0.29 0.22 Observations 571 798 1066 750 579
Individual-level information is based on responses from the household
respondent. See Section 3.1 for details on the study site and sample. See
Section 3.2 for discussions on the educational outcomes and demographic
variables shown in Table 1.

Table 2: Means (s.d.) of village and closest primary school variables (2011)

Han fraction inclusion threshold All villages Han $<90$% Han $<70$% Han $<50$%
Panel A: Closure, school distance and quality Village reports school closure
0.27 0.25 0.24 0.24 (0.45) (0.44) (0.43) (0.43) Distance measure from village
head survey 2.71 2.54 2.49 2.54 (5.18) (5.28) (5.24) (5.49) Primary school
facilities index 5.37 5.22 5.19 5.21 (1.99) (1.93) (1.93) (1.90) Has no unsafe
buildings 0.82 0.81 0.82 0.83 (0.39) (0.39) (0.39) (0.38) Panel B: School
language offerings and facilities Has teachers from Han ethnic group 0.81 0.75
0.72 0.68 (0.40) (0.44) (0.45) (0.47) Has teachers from minority ethnic groups
0.82 0.93 0.96 0.97 (0.39) (0.26) (0.20) (0.18) Has minority language classes
0.22 0.25 0.27 0.29 (0.41) (0.44) (0.44) (0.45) Has minority language of
instruction 0.27 0.32 0.35 0.39 (0.44) (0.47) (0.48) (0.49) Panel C: School
boarding facilities Requires living away from home 0.31 0.32 0.32 0.32 (0.46)
(0.47) (0.47) (0.47) Has dormitory 0.39 0.40 0.40 0.40 (0.49) (0.49) (0.49)
(0.49) Panel D: Other village attributes Number of households 446.0 420.0
417.7 421.7 (305.9) (287.0) (294.7) (302.7) Fraction Han in village 0.40 0.20
0.13 0.076 (0.42) (0.29) (0.21) (0.14) Fraction female in village 0.48 0.48
0.48 0.48 (0.054) (0.053) (0.053) (0.053) Observations 638 477 426 382 Village
level information is based on responses from the village head. Primary school
information is based on the closest full primary school to the village in
2011.

In this section, we first discuss individual-level educational attainment,
language facility and demographic variables. We then discuss variables
reported by the village head on village ethnic composition, school closure,
and language offerings and facilities of the closest primary school to the
village in 2011.

####  3.2.1 Outcome variables

##### Educational attainment

We measure educational attainment based on years of schooling completed for
each individual at the time of the survey in 2011. Table 1 shows that, in our
sample, minority and Han individuals who are between 20 to 24 years of age in
2011 have on average 9.5 and 10.8 years of schooling, respectively.
Individuals who are 20 to 24 years of age in 2011 have higher educational
attainment compared to younger individuals who are still proceeding through
school as well as to individuals from older cohorts who were born in the late
1970s and early 1980s (between 25 to 34 years of age in 2011).

A significant fraction of the younger sample is currently enrolled. The
percentages enrolled are around 93, 56 and 15 percent for individuals at 10 to
14, 15 to 19 and 20 to 24 years of age in 2011, respectively. Minority
enrollment is generally lower than enrollment for Han individuals. For
example, in 2011, for individuals between 20 to 24 years of age, 12 percent of
minority individuals and 21 percent of Han individuals were enrolled in
school.

##### Reported Mandarin language facility

Mother-tongue languages in China include numerous Chinese topolects (方言), some
of which are not mutually intelligible, as well as numerous minority languages
that span a broad range of language families. Topolects share a largely common
written form with the national language, while the diverse range of minority
languages in China do not.

To index facility with the national language, for each individual in the
household, spoken Mandarin ability and written Mandarin ability are reported.
These variables were originally coded into four categories: strong, good,
simple ability, or none.232323The household survey respondent rated the spoken
Mandarin communication ability and written Mandarin reading and writing
ability of each household member. The questions had four categorical
responses: have strong ability, have good ability, have simple ability, and no
ability.  This approach is similar to that used in the US Census, in which a
report (or proxy report) is given about speaking ability ([76] explains the
approach; [43] describes past validations of the approach).242424[43] reported
that verification exercises using the 1980 census and an English Language
Proficiency Study showed that people who were reported to speak English ”very
well”, but not others, had passing levels similar to the control group of
English speakers. Census alternatives to ”very well” are ”well”, ”not well”
and ”not at all”. This report also suggested analyses of a 1986 test census
showed that language speaking capacity measures showed a general consistency
between the items used in the census and a series of other language related
items, including background, current use contexts, and other operational
measures presumed to be less subjective than the ”how well” questions. For
discussion, see [1]. Other work with the CHES rural data, using a combined
measure of reported writing and speaking capacity, has shown that better
reported Mandarin facility is associated with greater Internet use and better
economic outcomes for rural minority young adults [31]. We generated a
contrast of strong reported language facility, coded as one, with all other
capabilities coded as zero.252525For our main analysis on Mandarin ability, we
exclude non-responses which account for approximately 10 percent of the
sample. In Appendix Section LABEL:sec:app:esti:mandwithna, we re-estimate
Mandarin ability regressions by including non-respondents jointly with
individuals reported to have good, simple, or no Mandarin ability. In Appendix
Table A.3, we show that, conditional on age in 2011, non-respondents of the
Mandarin ability question have lower enrollment and lower years of grades
completed than strong Mandarin ability respondents.

Table 1 shows that Han individuals in our sample report higher fractions with
strong Mandarin language skills than minority individuals. For reading and
writing ability, the fraction of Han individuals reporting strong ability is
between 50 to 100 percent higher than minority individuals. In particular,
individuals between 20 to 24 years of age in 2011 have the largest gap, with
23 and 44 percent of minority and Han individuals reporting having strong
written Mandarin ability. Across cohorts, the strong Mandarin ability fraction
doubles between individuals who were in early to later teen years in 2011, and
then halves for older cohorts between 30 to 34 years of age in 2011.

####  3.2.2 Child demographics and socioeconomic status

##### Minority status

Minority status is coded 1 if a household member is a member of an officially-
designated minority ethnic group, or 0 if the individual is reported as a
member of the Han majority. In our analytic sample of individuals from these
predominantly minority area villages, 7873 individuals belong to minority
ethnic groups and 3764 individuals are members of the Han majority.262626In
Appendix Section A.3, we provide a province-by-province breakdown of the main
ethnic groups in the sample. Our sample includes eight of the ten largest
minority groups in China. Manchus and Yi are the 3rd and 6th largest minority
groups in China, but reside in areas outside of our sampled regions.,272727For
our main analysis, we include all minority individuals in the regression. In
Appendix Section B.1, we test the robustness of our results to the exclusion
of minority individuals reported not to have their own language. Those
excluded individuals predominantly come from the Hui minority group, who
mostly reside in the Ningxia Hui Autonomous Region.

##### Gender

The survey collected binary responses regarding sex of individuals in the
household. A household member is coded 1 if reported as male, otherwise 0. Our
analytic sample includes 6161 male individuals and 5476 female individuals.

##### Age cohorts

Age at the end of 2011 is reported and is used to define single-year-of-age
cohorts. Models incorporate province-specific cohort fixed effects, as part of
the identification strategy defined below. We focus on outcomes for
individuals who are between 10 and 34 years of age in 2011. Table 1 and
Appendix Tables A.1, A.2, and A.3 present age structure, educational
attainment and enrollment, and spoken and written Mandarin ability statistics
for cohort groups in 2011.

####  3.2.3 Closure variables

##### School closure

School closure information is reported in the village questionnaire. Table 2
includes the closure variable in Panel A. Village heads were asked if the
village had a primary school, and asked about the year of school closure if
the village school had been closed. Among the 638 villages in the analytic
sample, there are 175 closure villages that did not have village schools in
2011 and experienced school closure between 1999 and 2010. The analytic sample
includes 2698 individuals from these villages with school closure.

##### Age at closure

There is heterogeneity in the timing of school closure. Out of the 175 closure
villages, 20 experienced school closure between 1999 and 2002, 49 between 2003
and 2006, and 106 between 2007 and 2010.282828Among the 638 villages in our
analytic sample, school closure took place in all seven provinces and in all
the year ranges listed. The intensity of school closure varied across
provinces. Between 1999 and 2010, 46 and 53 percent of the surveyed villages
from Hunan Province in south-central China and the Inner Mongolia Autonomous
Region in north China reported village school closures, respectively. 24, 21,
and 20 percent of surveyed villages from Ningxia Hui Autonomous Region in
northwestern China, the Xinjiang Uygur Autonomous Region in northwestern
China, and Guizhou Province in southwestern China experienced closures,
respectively. 17 and 19 percent of surveyed villages from the Guangxi Zhuang
Autonomous Region in south-central China and Qinghai Province in northwest
China had school closure, respectively. Given variations in the village-
specific year of closure, there are two different dimensions of time: the year
when a child was born and the year of school closure in that child’s village.
We use the difference between these two variables to calculate age at closure.
We consider individuals who were 6 to 9, 10 to 13, 14 to 21 and 22 to 29 years
of age in the year of school closure. Appendix Table A.1 summarizes
educational attainment and Mandarin ability by cohorts (age in 2011) and ages
at years of closure. For example, among individuals from closure villages who
were 10 to 13 years of age at the village-specific years of closure, 129
individuals were between ages 10 to 14 in 2011, 222 individuals were between
ages 15 to 19 in 2011, and 115 individuals were over 20 years of age in 2011.

####  3.2.4 Village and province context

##### Village ethnic composition

The village questionnaire provides information on the proportion of Han
individuals in the village population. Column 1 of Table 2 presents village
and closest primary school statistics for all villages. Columns 2, 3, and 4
focus on villages with less than 90, 70, and 50 percent Han population,
respectively. There are 382 villages with less than 50 percent Han
individuals, 95 villages with between 50 and 90 percent Han individuals, and
161 villages with greater than 90 percent Han individuals.

##### Village school characteristics

The village questionnaire contains information on distance to school as well
as nine yes-no questions about basic facilities and infrastructure for the
closest primary school to the village that is attended by village children.
Facility questions include whether the primary school has no unsafe buildings,
has heating, tap water, kitchen, shower, sufficient desks, a library, personal
computers, and internet access. We generate a facilities index based on
summing these binary variables. Panel A of Table 2 shows that distance and the
facilities index are similar across Han fraction inclusion thresholds.
Distance to the closest primary school is on average 2.71 km and 2.54 km for
all villages and predominantly minority villages, respectively. Across the
columns, around 82 percent of primary schools report having no unsafe
buildings and the average school facilities index varies between 5.21 to 5.37
(out of 9 points maximum).

In Panel B and C of Table 2, we include measures of teacher demographics (has
any Han teachers, has any minority teachers) and boarding status (requires
living away from home, school has dormitories). Additionally, we include
measures of minority language of instruction and minority language classes as
indicators of a minority-centered curriculum. The share of closest primary
schools reported to have a Han teacher drops from 81 percent in all villages
to 68 percent in predominantly minority villages. The fraction of closest
primary schools having a teacher of minority ethnic background increases from
82 percent in all villages to 97 percent in predominantly minority villages.
For all villages, the fraction with minority language classes and the fraction
that employ a minority language as the language of instruction are 22 and 27
percent, respectively. These fractions rise to 29 and 39 percent for
predominantly minority villages. Across Han fraction inclusion thresholds, 40
percent of schools have dormitories and 32 percent of schools require living
away from home.

###  3.3 Methods

We undertake a parallel set of analyses for two education-related outcomes:
educational attainment and facility with the national language. For
individuals of the same province and age cohorts (age in 2011), we first
compare the difference in educational attainment (number of grades completed
by 2011) or language facility between those from closure villages who were
exposed to closure to individuals from non-closure villages. This first
difference is attributable to the consolidation policy as well as existing
differences in educational attainment across villages that would have occurred
in the absence of the policy. We make a second comparison of the difference in
educational attainment between closure village individuals not impacted by the
policy and individuals of the same age cohorts from non-closure villages. This
second difference should only reflect existing differences in educational
attainment across villages. The impact of the policy is, in effect, the
difference in the first and the second differences.

In closure villages, we classify individuals into groups who are exposed to
the policy upon entry into primary school (6 to 9 years old at the time of
primary school closure) or who experienced closure in the second-half of
primary school (10 to 13 years old at the time of primary school closure).
Exposed individuals are distinguished from older individuals of the same
village who were above elementary school age (age 13) at the time of closure.
Individuals from villages without closure do not have age at closure.

The primary underlying assumption of our strategy is that, in the absence of
the closure policy, rural educational attainment or language facility follows
a common province-specific age cohort trend. Cohort-invariant differences
across villages, which might be correlated with school closure, are captured
by village-specific fixed effects.292929Controlling for provincial fixed
effects, [32] find no statistically significant differences between closure
and non-closure villages in terms of village per-capita income, local wage,
fraction of workers in the labor force, the fraction of migrant workers,
village per-capita spending overall and on education, and the implementation
rate of health and insurance policies between closure and non-closure
villages. The key statistically significant difference is that closure
villages have on average 11 percent less households than non-closure villages.
To test for pre-existing cohort trends, we separate older individuals from
closure villages into those who were 14 to 21 and those who were 22 to 29 at
the year of closure. In the absence of cohort trends, the difference in
attainment or language facility between each of these age-at-closure groups
and individuals of the same age cohorts (age in 2011) from non-closure
villages should be similar.303030In Appendix Section A.5, we compare
educational attainment and strong Mandarin ability between individuals from
non-closure villages and individuals from closure villages who were not
exposed to closure. We find that differences in raw averages disappear once we
control for province fixed effects, age fixed effects, and gender. Additional
concerns might arise if individuals or families move as a result of the
closure policy. In the rural China context, however, mandatory household
registration limited mobility of entire households. Additionally, the CHES
survey, conducted around the New Year holidays, captures the educational
status of all household members including migrant workers.

Equation (3.3) presents the estimation equation. Our regression approach
assumes that in the scenario without policy intervention, educational outcome,
$E$—which might be number of grades completed or reported language facility—of
a child $i$ from village $v$ in province $p$ and whose cohort (age in 2011) is
$a$ could be decomposed into five parts: a village fixed effect $\beta_{v}$, a
province-specific cohort fixed effect $\gamma_{pa}$, an ethnic-minority-
specific cohort fixed effect $\delta_{a}$, and idiosyncratic terms including
one part that can be explained by observed characteristic $X_{i}$ and another
unobserved error term $\epsilon_{i}$. $M_{i}$ indicates whether an individual
is Han ($M_{i}=0$) or minority ($M_{i}=1$). Under school closure, the policy’s
effect is assumed to be additive and captured by $\theta^{H}_{z}$ for Han
individuals and $\theta^{M}_{z}$ for minorities, both of which are specific to
group $z$ defined by age-at-closure $t_{i}$:

$\displaystyle E_{pvia}$ $\displaystyle=$
$\displaystyle\alpha+\beta_{v}+\gamma_{pa}+\delta_{a}\cdot M_{i}$
$\displaystyle+\sum_{z=1}^{Z}\left(\theta^{H}_{z}\cdot\left(1-M_{i}\right)+\theta^{M}_{z}\cdot
M_{i}\right)\cdot\mathbf{1}\left\\{l_{z}\leq t_{i}\leq u_{z}\right\\}\cdot
C_{v}$ $\displaystyle+X_{i}\cdot\gamma+\epsilon_{i}$

In Equation (3.3), $\alpha$ is a constant, and $C_{v}$ is a binary variable
indicating if individual $i$ is from a village $v$ with school consolidation
(i.e. treatment village). We group children in villages with school closure
into $Z$ groups based on their age at closure $t_{i}$, with lower and upper
bounds for each group $l_{z}$ and $u_{z}$. Therefore $\theta^{H}_{z}$ and
$\theta^{M}_{z}$ capture the average effects across cohorts for age-at-closure
group $z$ at the time of survey measurement. For individuals from villages
without closure, $C_{v}=0$. Including individuals from villages without
closure isolates the policy effects from the province-specific cohort patterns
captured by $\rho_{pa}$.313131The specification assumes that $\theta_{z}$ is
not specific to 2011 age $a$ and $\theta_{z}$ is not specific to the calendar
year of closure. Additionally, we use common age-at-closure cut-offs, $l_{z}$
and $u_{z}$ for all demographic groups.

Our second research question addressed whether there was a differential impact
of closure by ethnic minority status. We address this question by comparing
the estimated policy effects $\theta^{H}_{z}$ for Han individuals and
$\theta^{M}_{z}$ for minorities. Educational outcome cohort trajectories might
differ for Han and minority youth for reasons that are unrelated to the
consolidation policy, which the inclusion of $\delta_{a}$ helps to account
for.

Our third research question asked whether there are important treatment effect
heterogeneities across groups defined at the intersections of minority status,
gender, and community ethnic composition and economic status. To allow for the
possibility that more ethnically segregated minority areas were differently
affected by closures, we estimate policy effects separately for predominantly
Han villages and predominantly minority villages. To estimate ethnic gaps in
closure impact conditioned on gender and community economic status, we
estimate Equation (3.3) separately for sex groups and obtain group-specific
coefficients–$\theta^{H,g}_{z}$ and $\theta^{M,g}_{z}$–for age-at-closure and
sex groups, with $g\in\left\\{\text{male},\text{female}\right\\}$. The village
fixed effects and provincial-cohort trends are allowed to be gender-specific.
We show estimates for all villages as well as more minority-centric villages.
Additionally, we also split the sample into villages with below and above
median average per-capita income levels, and estimate sex group-specific
regressions for relatively poorer and richer villages separately. This
approach, in effect, constitutes a treatment effect heterogeneity analysis in
which the intersections of minority status, gender, and village ethnic
composition and economic status might delineate substantially distinct
vulnerabilities to policy effects.

##  4 Results

###  4.1 Schools serving closure- and non-closure villages

Table 3: Means of closest primary school variables (2011), closure and non-
closure villages

Group averages P-values Non-closure Closure Closure vs non-closure† Year of
closure trend‡ Panel A: All villages Distance (km) to primary school 1.80 5.41
0.00 0.15 Primary school facilities index (0 to 9) 5.07 6.28 0.00 0.65 Has no
unsafe buildings 0.79 0.90 0.00 0.40 Has teachers from Han ethnic group 0.75
0.96 0.00 0.02 Has teachers from minority ethnic groups 0.83 0.79 0.71 0.91
Has minority language classes 0.23 0.16 0.56 0.07 Has minority language of
instruction 0.31 0.15 0.00 0.67 Requires living away from home 0.25 0.50 0.00
0.22 Has dormitory 0.31 0.62 0.00 0.38 Panel B: Villages where Han fraction is
less than 50 percent Distance (km) to primary school 1.73 5.24 0.00 0.15
Primary school facilities index (0 to 9) 4.91 6.20 0.00 0.65 Has no unsafe
buildings 0.80 0.91 0.00 0.40 Has teachers from Han ethnic group 0.61 0.93
0.00 0.02 Has teachers from minority ethnic groups 0.97 0.94 0.71 0.91 Has
minority language classes 0.31 0.22 0.56 0.07 Has minority language of
instruction 0.43 0.22 0.00 0.67 Requires living away from home 0.26 0.49 0.00
0.22 Has dormitory 0.33 0.63 0.00 0.38 Village level information is based on
responses from the village head. Primary school information is based on the
closest full primary school to the village in 2011. † Column 3 tests whether
the mean differences between non-closure and closure villages are
statistically significant after controlling for provincial fixed effects. ‡
Column 4 tests whether the means within closure villages are statistically
different across years of closure, also controlling for provincial fixed
effects.

Are the pathways “better” in consolidated schools? We can not directly
estimate the causal effects of closure on facilities and curricular offering
because we do not observe how facilities and curricular offerings changed with
closure. We can observe differences in school characteristics in villages that
have and have not experienced closure in 2011. Table 3 shows descriptive mean
comparisons of key closure and language related variables reported by the
village head for the closest primary school to the village between villages
that experienced closure and villages that did not experience closure. We
present means in columns 1 and 2. In column 3, we test whether the mean
differences are statistically significant after controlling for provincial
fixed effects. In column 4, we test whether the means within closure villages
are statistically different across years of closure, also controlling for
provincial fixed effects. In separate panels, we present statistics for all
villages and villages where the Han fraction is less than 50 percent.

Table 3 suggests, as expected, that villages reporting closures also report
schools that are more distant, but have better resources than non-closure
villages. In Panel A, across all villages types, the average distance to
school reported for closure villages was 5.78 kilometers, while the average
distance to school reported for non-closure villages was 1.81 kilometers. This
difference is highly statistically significant. Closure villages in the sample
report better facilities. Out of a maximum index value of 9, closure villages
have an average aggregate school facilities index value of 6.07 compared to
5.09 for non-closure villages. One of the component measures of the index is
whether school has no unsafe buildings. For this measure, we find that 19
percent of non-closure villages report that the closest primary schools have
unsafe buildings, compared to 9 percent for closure villages.323232The means
for seven of the nine individual facility measures that generate the
facilities index are statistically different between closure and non-closure
villages. The exceptions are having sufficient desks and having access to tap
water. For all nine measures, the share of closure villages with the facility
is larger than the share of non-closure villages with it. We do not find
significant time-trends among closure villages for distance and facility
measures. Results in Panel B for villages where the Han fraction is less than
50 percent are similar to statistics for all villages.

Table 3 also presents means for teacher demographics, linguistic and cultural
focus, and boarding facilities. In Panels A and B, there is a pattern of
significantly greater likelihood of schools having a Han teacher,
significantly less likelihood of schools having minority language of
instruction, and significantly greater likelihood that schools requiring
boarding for schools serving closure villages. Closure village schools are
also somewhat less likely to have minority language classes, but this
difference, after controlling for provincial fixed effects, is not
significant. Additionally, in the sample, closure villages and non-closure
villages report schools with similarly high levels of having teachers from
minority ethnic groups. For all variables, the gaps between closure and non-
closure villages are similar across village level of segregation, though more
ethnically isolated minority villages in the sample have lower rates of Han
teachers and higher rates of minority teachers and language offerings.

Given these descriptive findings, consolidated schools tend to be better than
other village schools in terms of resources, but due to teacher demographics,
curricular offerings, and greater distance and the need to board, consolidated
schools may have been viewed by some minority parents as both less oriented
toward cultures and languages of minority students and more risky for young
children without fluency in the national language. However, it is also
possible that the prospect of stronger national language acquisition might be
particularly valued by language minority parents, due to its instrumentality
for economic mobility.

###  4.2 Heterogeneity of closure impact by minority status and village ethnic
composition

Table 4: Regressions of educational attainment with school closure, Han-
minority interaction

Outcome: grades completed by year 2011 Village Han fraction inclusion
threshold All villages Han $<90$% Han $<70$% Han $<50$% Han $<10$% Han $\geq
90$% Baseline group: Child was 14-21 years old at village primary school
closure year Child is minority interaction: Closure $\times$ age at closure
6-9 -0.50∗∗ -0.54∗∗ -0.45∗ -0.44∗ -0.46 (0.23) (0.24) (0.25) (0.26) (0.31)
Closure $\times$ age at closure 10-13 -0.60∗∗∗ -0.66∗∗∗ -0.56∗∗∗ -0.58∗∗∗
-0.56∗∗ (0.19) (0.19) (0.19) (0.20) (0.24) Closure $\times$ age at closure
22-29 0.076 0.025 0.12 0.10 0.012 (0.19) (0.19) (0.20) (0.21) (0.28) Child is
Han interaction: Closure $\times$ age at closure 6-9 0.0058 -0.45 -0.065 -0.60
0.40 (0.26) (0.39) (0.41) (0.55) (0.32) Closure $\times$ age at closure 10-13
-0.28 -0.25 0.010 -0.52 -0.20 (0.20) (0.32) (0.41) (0.38) (0.26) Closure
$\times$ age at closure 22-29 -0.19 -0.087 0.00036 -0.077 -0.15 (0.25) (0.31)
(0.38) (0.40) (0.39) Observations 11637 9197 8371 7653 5686 2451 Statistical
significance:* 0.10 ** 0.05 *** 0.01. Robust standard error clustered at
village level. Each column represents a separate regression. All regressions
include village fixed effects, province-specific age FEs, Han-specific age
FEs, and control for household size. Columns 2 to 5 sequentially drop villages
with more than 90, 70, 50, and 10 percent Han population. Samples for columns
5 and 6 include only minority and Han individuals, respectively.

Table 5: Regressions of Mandarin ability with school closure, Han-minority
status interaction

Outcome: strong Mandarin ability = 1, no/simple/good = 0 Village Han fraction
inclusion threshold All villages Han $<90$% Han $<70$% Han $<50$% Han $<10$%
Han $\geq 90$% Panel A: Written Mandarin ability Baseline group: Child was
14-21 years old at village primary school closure year Child is minority
interaction: Closure $\times$ age at closure 6-9 -0.14∗∗∗ -0.15∗∗∗ -0.16∗∗∗
-0.18∗∗∗ -0.23∗∗∗ (0.044) (0.045) (0.047) (0.048) (0.057) Closure $\times$ age
at closure 10-13 -0.054∗ -0.067∗∗ -0.067∗∗ -0.073∗∗ -0.073∗ (0.031) (0.031)
(0.031) (0.033) (0.040) Closure $\times$ age at closure 22-29 -0.016 -0.020
-0.013 -0.018 -0.061 (0.032) (0.032) (0.034) (0.036) (0.047) Child is Han
interaction: Closure $\times$ age at closure 6-9 0.076 -0.041 -0.035 -0.018
0.18∗∗ (0.055) (0.074) (0.098) (0.14) (0.075) Closure $\times$ age at closure
10-13 -0.034 -0.026 -0.027 -0.059 -0.043 (0.043) (0.061) (0.093) (0.12)
(0.058) Closure $\times$ age at closure 22-29 -0.022 0.020 -0.052 -0.075
-0.034 (0.038) (0.045) (0.048) (0.063) (0.056) Observations 10495 8126 7328
6656 4745 2383 Panel B: Spoken Mandarin ability Baseline group: Child was
14-21 years old at village primary school closure year Child is minority
interaction: Closure $\times$ age at closure 6-9 -0.035 -0.033 -0.030 -0.043
-0.078 (0.049) (0.051) (0.053) (0.055) (0.076) Closure $\times$ age at closure
10-13 -0.014 -0.024 -0.020 -0.023 -0.027 (0.033) (0.034) (0.036) (0.037)
(0.052) Closure $\times$ age at closure 22-29 -0.015 -0.011 -0.0072 -0.010
-0.061 (0.029) (0.030) (0.031) (0.032) (0.042) Child is Han interaction:
Closure $\times$ age at closure 6-9 0.053 -0.042 0.028 -0.050 0.11 (0.052)
(0.075) (0.072) (0.098) (0.070) Closure $\times$ age at closure 10-13 -0.0028
-0.040 0.052 -0.038 0.0096 (0.035) (0.057) (0.069) (0.10) (0.048) Closure
$\times$ age at closure 22-29 -0.0021 0.040 0.0023 0.0100 -0.013 (0.040)
(0.056) (0.068) (0.089) (0.053) Observations 10488 8126 7332 6662 4745 2373
Statistical significance:* 0.10 ** 0.05 *** 0.01. Robust standard error
clustered at village level. Each column in each Panel represents a separate
linear probability regression. All regressions include village fixed effects,
province-specific age FEs, Han-specific age FEs, and control for household
size. Columns 2 to 5 sequentially drop villages with more than 90, 70, 50, and
10 percent Han population. Samples for columns 5 and 6 include only minority
and Han individuals, respectively. For these regressions, we consider
individuals who responded to the written or spoken Mandarin ability questions.

In Tables 4 and 5, we present the impacts of closure on educational attainment
and Mandarin language ability for children who were 6 to 9 and 10 to 12 years
of age at the time of closure, along with a “placebo test” of a closure effect
for individuals who were 22 to 29 at the time of closure. In both tables, we
interact age at the time of closure with whether a child is Han or minority.
Moving left to right from columns one to four in both tables, we present
estimates that focus on successively more minority-centric villages. For
example, column 1 contains all villages, and column 4 does not include
majority-Han villages. In column 5, we include only minority children from
nearly all minority villages by considering villages where the fraction of Han
individuals is less than 10 percent. In column 6, we include only Han children
from nearly all Han villages by considering villages where the fraction of Han
individuals is greater than 90 percent.

Because models include ethnicity- and province-specific single-year-of-age
cohort controls as well as village fixed effects, the age-at-closure reference
category, 14 to 21 year olds, accommodates any base differences in closure
villages compared to non-closure villages, but for unaffected cohorts–those
for whom primary school closure should not have had any impact. The age-at-
closure coefficients for ages 6 to 9 and 10 to 13, in turn, offer the
decrement in years of schooling in affected cohorts in affected villages,
compared to the years of schooling predicted by village and cohort fixed-
effects. It is this quantity, estimated for different groups, that we will
refer to in the discussions below as “the decrement” and treat as the policy
effect.333333As shown in Appendix Table A.1, in 2011, due to differential
school closure dates, individuals who were 6 to 9 at year of closure
predominantly are between 10 to 19 years of age in 2011. Individuals who were
10 to 13 at year of closure predominantly are between 10 to 24 years of age in
2011. The policy effect is the average effects on years of schooling from both
individuals who have possibly completed schooling as well as individuals who
are still progressing through school. Finally, a term for age at closure of 22
to 29 is also estimated and presented.

Table 4 analyzes closure effects on educational attainment, with cohort-
specific closure effects allowed to vary with minority status of the child.
Results show that, by 2011, minority youth ages 10 to 13 at time of closure in
a closure village experienced around a 0.6-year decrement in years of
schooling. For those 6 to 9 years old at closure, the decrement is around a
half-year. Results are similar in more and less minority-centric villages,
with a weakening of statistical significance for estimates from columns based
on smaller numbers of villages. In contrast to the situation of minority
youth, the same set of estimates for Han youth shows no statistically
significant decrements in attainment associated with experiencing school
closure. For both minority and Han individuals, we do not find a closure
effect for the placebo group of individuals that were 22 to 29 at the year of
closure.

We ran a parallel set of linear probability regressions for closure effects on
reported ability in written and spoken Mandarin in Table 5.343434Results in
Table 5 exclude non-respondents. We find very similar results in Appendix
Table LABEL:tab:interhan:mandwritetalkm2c:nsfto:withna where we include non-
respondents in the same group as individuals who answered no/simple/good to
the Mandarin ability question. Results for written Mandarin ability in the top
panel show decrements in the probability of reporting strong written ability
for those affected by closures at the beginning of their primary school
careers. The estimated decrement for minority individuals is larger in the
more isolated samples: 14 percentage points in the most integrated villages
compared to 23 percentage points in the most isolated minority villages. There
is a smaller decrement in reported written ability for those exposed to
closure at ages 10 to 13, which suggests about a 5 to 7 percentage points
reduction in the likelihood of reporting strong Mandarin written ability.
Significant decrements are not observed for Han children across village
groups. On the contrary, in the most Han-centric villages in column 6, a 18
percentage points increase in the probability of reporting strong written
Mandarin ability is reported for Han children who started in consolidated
schools. For reported spoken Mandarin communication ability, shown in the
bottom panel of Table 5, we find no significant effects. For this reason,
subsequent analyses focus exclusively on attainment and written Mandarin.

###  4.3 Intersectional perspectives: Adding gender

Table 6: Regressions of educational attainment with school closure, Han-
minority status interaction by gender

Outcome: grades completed by year 2011 Female Male All villages Han $<90$%
villages Han $<50$% villages All villages Han $<90$% villages Han $<50$%
villages Baseline group: Child was 14-21 years old at village primary school
closure year Child is minority interaction: Closure $\times$ age at closure
6-9 -0.58 -0.55 -0.26 -0.19 -0.31 -0.25 (0.36) (0.35) (0.39) (0.26) (0.26)
(0.30) Closure $\times$ age at closure 10-13 -0.77∗∗ -0.75∗∗ -0.63 -0.49∗∗
-0.62∗∗∗ -0.72∗∗∗ (0.32) (0.33) (0.38) (0.24) (0.24) (0.26) Closure $\times$
age at closure 22-29 0.23 0.23 0.13 0.083 0.031 0.18 (0.30) (0.31) (0.33)
(0.28) (0.29) (0.30) Child is Han interaction: Closure $\times$ age at closure
6-9 -0.35 -0.46 0.36 -0.55 (0.34) (0.51) (0.41) (0.53) Closure $\times$ age at
closure 10-13 -0.45 -0.36 0.018 -0.11 (0.35) (0.55) (0.27) (0.34) Closure
$\times$ age at closure 22-29 -0.23 0.019 0.013 -0.32 (0.33) (0.42) (0.37)
(0.46) Observations 5476 4328 3328 6161 4869 3830 Statistical significance:*
0.10 ** 0.05 *** 0.01. Robust standard error clustered at village level. Each
column represents a separate regression. All regressions include village fixed
effects, province-specific age FEs, Han-specific age FEs, and control for
household size. Columns 1, 2, and 3 regressions include only females. Columns
4, 5, and 6 regressions include only males. Columns 2 to 3 (and 5 to 6)
sequentially drop villages with more than 90 and 50 percent Han population.
Samples for columns 3 and 6 regressions include only minority individuals.

Table 7: Regressions of written Mandarin ability with school closure, Han-
minority status interaction by gender

Outcome: strong written Mandarin ability = 1, no/simple/good = 0 Female Male
All villages Han $<90$% villages Han $<50$% villages All villages Han $<90$%
villages Han $<50$% villages Baseline group: Child was 14-21 years old at
village primary school closure year Child is minority interaction: Closure
$\times$ age at closure 6-9 -0.13∗∗ -0.12∗∗ -0.14∗∗ -0.12∗∗ -0.14∗∗ -0.19∗∗∗
(0.056) (0.057) (0.061) (0.053) (0.055) (0.056) Closure $\times$ age at
closure 10-13 -0.11∗∗ -0.13∗∗∗ -0.12∗∗ -0.013 -0.028 -0.046 (0.047) (0.047)
(0.050) (0.042) (0.044) (0.048) Closure $\times$ age at closure 22-29 -0.0050
-0.0032 -0.033 -0.016 -0.026 -0.022 (0.038) (0.036) (0.040) (0.047) (0.049)
(0.051) Child is Han interaction: Closure $\times$ age at closure 6-9 0.028
-0.049 0.12 -0.019 (0.071) (0.11) (0.079) (0.094) Closure $\times$ age at
closure 10-13 -0.074 -0.087 0.0020 0.020 (0.059) (0.082) (0.056) (0.073)
Closure $\times$ age at closure 22-29 0.00091 -0.0054 -0.044 0.010 (0.050)
(0.067) (0.056) (0.066) Observations 4917 3800 2865 5578 4326 3333 Statistical
significance:* 0.10 ** 0.05 *** 0.01. Robust standard error clustered at
village level. Each column in each Panel represents a separate linear
probability regression. All regressions include village fixed effects,
province-specific age FEs, Han-specific age FEs, and control for household
size. Columns 1, 2, and 3 regressions include only females. Columns 4, 5, and
6 regressions include only males. Columns 2 to 3 (and 5 to 6) sequentially
drop villages with more than 90 and 50 percent Han population. Samples for
columns 3 and 6 regressions include only minority individuals.

Building on the results from the last section, we next consider whether the
significant negative closure impacts on educational attainment and writing
facility observed among minority youth are borne equally by boys and girls. In
Tables 6 and 7, we present the impacts of closure on educational attainment
and mandarin language writing abilities for females and males, Han and ethnic
minority children.353535In both tables, to account for differences in gender
that are unrelated to closure, we estimate the model for females and males
separately, which allows for different village fixed effects for females and
males as well as different ethnicity and province-specific age fixed effects.
Conditional on gender, following Tables 4 and 5, we interact age at the time
of closure with whether a child is Han or minority. We present results for
females in the first three columns and results for males in the latter three
columns. Our sample includes all villages in columns 1 and 4, villages with
less than 90 percent Han individuals in columns 2 and 5, and less than 50
percent Han individuals in columns 3 and 6. Given the small number of female
and male Han children that remain for the restricted sample of villages with
less than 50 percent Han children, we include only minority children in the
estimation samples for columns 3 and 6.

By 2011, Table 6 shows that closure decrements for minority girls who were
ages 10 to 13 at time of closure are about three quarters of a year, except
for a non-significant decrement among the most isolated villages at about 0.63
years. For boys who were ages 10 to 13 at time of closure, the pattern shows
about a half-year decrement among youth in all villages; this number becomes
0.72 in the most ethnically segregated minority villages. For girls and boys
who were 6 to 9 at time of closure, similar to the results in Table 4, closure
effects are negative as well, but small in magnitude compared to individuals
who were older at time of closure. No significant findings are present among
Han youth, although the direction of effects tends to be relatively more
negative for Han girls. Thus, in the sample of all villages, minority girls
are most vulnerable, but in the most ethnically segregated villages, minority
boys become more vulnerable. For minority youth across genders, we find weakly
positive but not significant closure effects for the placebo group of
individuals.

Considering the analysis of reported writing facility in Table 7, from linear
probability regressions, we observe a consistent decrement for minority girls
in the 6 to 9 and 10 to 13 age groups of 11 to 13 percentage points, with no
pattern across villages.363636Table 7 results exclude non-respondents.
Appendix Table LABEL:tab:interhan:bygender:mandwritem2c:nsfto:withna shows
similar estimates when non-respondents are included in the same group as
individuals who answered no/simple/good to the Mandarin ability question.
However, for boys, the pattern is different. Only the youngest boys at closure
are affected, and the decrement is much larger in the most ethnically isolated
villages (about 19 percentage points, compared to 12 percentage points for all
villages). We do not find significant results for Han youth, although the
magnitude and direction of the effects for Han girls are more negative than
Han boys.

###  4.4 Intersectional perspectives: Adding village income

Table 8: Regressions of educational attainment and written Mandarin ability
with school closure, Han-minority status interaction by gender and village-
income

Outcomes in 2011 Grades completed Strong written Mandarin Female Male Female
Male Panel A: Villages with below 4000 Yuan per-capita income in 2011 Baseline
group: Child was 14-21 years old at village primary school closure year Child
is minority interaction: Closure $\times$ age at closure 6-9 -1.08∗∗ -0.25
-0.12∗ -0.18∗∗∗ (0.45) (0.34) (0.070) (0.066) Closure $\times$ age at closure
10-13 -1.11∗∗ -0.69∗∗ -0.12 -0.0045 (0.51) (0.32) (0.074) (0.057) Closure
$\times$ age at closure 22-29 0.060 0.020 -0.038 -0.045 (0.41) (0.26) (0.047)
(0.056) Child is Han interaction: Closure $\times$ age at closure 6-9 -0.31
0.35 0.064 0.17 (0.65) (0.71) (0.097) (0.12) Closure $\times$ age at closure
10-13 -0.23 0.075 0.11 -0.079 (0.49) (0.35) (0.089) (0.075) Closure $\times$
age at closure 22-29 0.014 -0.61 -0.022 -0.087 (0.44) (0.41) (0.079) (0.088)
Observations 2867 3253 2593 2980 Panel B: Villages with above 4000 Yuan per-
capita income in 2011 Baseline group: Child was 14-21 years old at village
primary school closure year Child is minority interaction: Closure $\times$
age at closure 6-9 0.011 0.018 -0.14 -0.032 (0.59) (0.41) (0.086) (0.090)
Closure $\times$ age at closure 10-13 -0.33 -0.46 -0.075 -0.040 (0.43) (0.39)
(0.052) (0.070) Closure $\times$ age at closure 22-29 0.49 0.061 0.056 0.035
(0.42) (0.64) (0.066) (0.080) Child is Han interaction: Closure $\times$ age
at closure 6-9 -0.44 0.44 -0.022 0.16 (0.44) (0.57) (0.099) (0.10) Closure
$\times$ age at closure 10-13 -0.56 -0.069 -0.17∗∗ 0.064 (0.48) (0.37) (0.076)
(0.075) Closure $\times$ age at closure 22-29 -0.14 0.64 0.040 0.00075 (0.49)
(0.62) (0.066) (0.068) Observations 2609 2908 2324 2598 Statistical
significance:* 0.10 ** 0.05 *** 0.01. Robust standard error clustered at
village level. Each column in each Panel represents a separate linear
regression. All regressions include village fixed effects, province-specific
age FEs, Han-specific age FEs, and control for household size. Columns 1 and 3
include only females. Columns 2 and 4 include only males. Columns 1 to 2 have
educational attainment (grades completed by year 2011) as the outcome
variable. Columns 3 and 4 have strong written Mandarin ability (strong = 1,
no/simple/good = 0) in 2011 as the outcome variable.

In this section, we study heterogeneous effects of closure for richer and
poorer villages. Building on our prior results, within village groups, we
continue to allow for heterogeneous effects for minority and Han children and
by gender. In Table 8, in the top panel, we present results for villages with
lower than 4000 Yuan per-capita income in 2011. In the bottom panel, results
from separate regressions are shown for villages with higher than 4000 Yuan
per-capita income in 2011.373737Running separate regressions for relatively
richer and poorer villages allow for different ethnicity- and provincial-
specific age fixed effects for relatively richer and poorer villages. Among
the villages we study, 4000 Yuan was approximately the median village per-
capita income level in 2011. We present estimates for educational attainment
in columns 1 and 2 and estimates for written Mandarin ability in columns 3 and
4. To ensure sufficient sample sizes, we limit the analysis here to all
villages.

In columns 1 and 2 of Table 8, it is very clear that decrements to educational
attainment with school closure emerge most strongly among minority youth in
the relatively poorer villages. Negative attainment effects are more
consistent for minority girls, shown in the top panel of column 1. Minority
girls who experienced closure at ages 6 to 9 as well as ages 10 to 13
experienced around a year decrement. Among minority youth, boys who started
school with closure experienced no decrement, but those who were likely in
school when it closed (ages 10 to 13) experienced a 0.69 year decrement. For
individuals in relatively richer villages and for Han youth, we do not observe
significant educational attainment decrements due to closure. We do find that
the direction of closure effects are generally relatively more negative for
girls, both from relatively richer and poorer villages.

In columns 3 and 4 of Table 8, we find that closure has the strongest negative
effects on Mandarin writing for relatively poorer minority girls as well as
boys who were 6 to 9 at year of closure. The effects are also negative for
minority girls who were 10 to 13 at year of closure. The magnitudes or the
effects are similar to the results from Table 7 in the combined sample. In the
relatively poorer villages, we do not find significant effects of closure on
Han individuals. In relatively richer villages, the effects of closure on
Mandarin writing for girls are negative, but generally insignificant. The
exception is the finding of significant negative effects for Han girls who
were 10 to 13 years of age at the time of closure.

In this section, given the limited sample and the larger number of
heterogeneous effects parameters, estimates are less precise and the chance of
obtaining spuriously significant estimates is larger. Overall, we continue to
find stronger negative effects of closure for minority youth compared to Han
youth, and the negative effects are more concentrated in relatively poorer
villages.

##  5 Conclusion

With a focus on regions of China with substantial minority populations, this
paper has investigated whether there are different impacts of China’s rural
school consolidation initiative on educational attainment and written Mandarin
capability for minority students, compared to Han students. Descriptive
results showed, first, that consolidated schools were safer and had generally
better equipment than village schools, but were farther away and more likely
to require boarding, which might increase perceived risk and cost.
Consolidated schools were also larger, though we are not able to observe
whether classes were larger. These pull- and push- factors in consolidated
schools are likely to affect minority and Han youth in a similar though not
necessarily identical fashion. However, consolidated schools were reportedly
less likely than remaining village schools to offer a minority language of
instruction, and the differences were stark. Across all sample villages, 31
percent of villages with schools and 15 percent of villages with closure
reported a minority language of instruction. In predominantly minority
villages, 43 percent of villages with schools, but only 22 percent of closure
villages, reported that the primary school offered minority language of
instruction. The difference in language of instruction associated with
consolidation would likely be relevant to minority but not Han youth–perhaps
hindering early engagement and learning in ways that might translate to poorer
educational foundations, and perhaps heightening the perceived risks
associated with distance and boarding.

Beyond language mismatch, children from culturally distinct groups might also
find themselves in classrooms in which they need to learn different behavioral
norms. For example, [46] reported that when children from the matriarchal
Mosuo ethnic group and Han students came together at school, ethnic
differences in gendered patterns of risk-taking behavior observable at early
ages diminished as Mosuo children adopted the risk preferences of the
majority. We are unable to observe subtle differences in school, classroom, or
peer culture in the current study, but these topics might be very relevant for
understanding minoritized students’ experiences of consolidation.

Our main analyses show clear evidence of an average negative effect of closure
on two of the three outcomes studied, educational attainment and reported
written Mandarin ability, for minority youth, compared to Han youth.
Importantly, the average decrements experienced by minority youth masked
significant heterogeneities. This paper is novel in attending to the
possibility that minoritized status, together with gender and community
economic status and ethnic composition, might intersect to generate distinct
vulnerabilities to policy impact. Our analysis shows that the intersectional
perspective does yield useful insights about heterogeneous policy impacts. For
example, for both educational attainment and written language facility,
penalties accruing to minority youth occurred only in poorer villages and not
to minority youth in wealthier villages. In addition, penalties were generally
heavier for minority girls. However, in the most ethnically segregated
minority villages, boys from minority families were highly vulnerable to
closure effects on educational attainment and written Mandarin facility. This
latter pattern could emerge if boys were particularly affected by the
linguistic mismatch potentially associated with consolidation in predominantly
minority villages, but we do not have a means to test this speculation.

In an age of declining fertility and urbanization, sparse school-aged
populations in rural communities are becoming more common. Press reports
suggest that school consolidation initiatives are emerging in a variety of
settings as a policy response [13, 53, 35, 66, 67, 71, for example, see].
China has been at the leading edge of this policy trend, with large-scale
rural primary school consolidations occurring over the course of the early
2000s. While the likely impacts of school consolidations are very context-
specific, the rationales across the world are strikingly similar, as are the
concerns about potential implications for children in socially and
economically marginalized communities. Our findings speak to the importance of
considering intersecting dimensions of stratification together, when assessing
the policy’s impacts.

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ONLINE APPENDIX
Fewer, better pathways for all?
Intersectional impacts of rural school consolidation in China’s minority
regions Emily Hannum and Fan Wang

##  Appendix A Additional data details

###  A.1 Location of closures

This paper utilizes data from the rural sample of the China Household Ethnic
Survey (CHES 2011), which covers households and villages from 728
villages.A.1A.1A.1There are 751 unique village IDs in the survey, but 17
villages do not have school closure information, and 6 villages report closure
without a closure year. Following discussions in Section 3.1, our analytic
sample includes 638 villages across 7 provinces: Qinghai Province (107
villages); Ningxia Hui Autonomous Region (88 villages); Xinjiang Uygur
Autonomous Region (80 villages); Inner Mongolia Autonomous Region (74
villages); Qiandongnan Miao and Dong Autonomous Prefecture in Guizhou Province
(110 villages); Hunan Province (87 villages); and Guangxi Zhuang Autonomous
Region (92 villages).

###  A.2 Sampling procedure

The CHES survey is the largest-scale cross-province survey ever gathered to
study the socio-economic conditions of minorities in ethnically diverse
regions of China. It was designed by China’s Academy of Social Sciences and
the Central Nationalities University, and it was administrated by local
offices of the National Bureau of Statistics (NBS). The survey villages were
selected based on a subset of the NBS’s Rural Household Survey (RHS) [57].
Selected villages are not representative of their respective provinces and
autonomous regions overall, but are from prefectures with substantial minority
populations in order for the survey to capture socio-economic conditions of
minorities and Han individuals in ethnically diverse areas. Households in
villages were selected by systematic sampling based on their agricultural
census address codes. The survey was implemented in early 2012 and asked
households to report information from the end of 2011. Household surveys are
complemented by surveys of villages heads from the sampled villages. [29]
provide more information on the sampling procedure and other information
related to the survey.

###  A.3 Ethnic composition of the sample

The sample we analyze comes from seven provinces of China with large
concentrations of minority populations. China is home to 56 minority
nationalities, including the Han majority and 55 minority nationalities. In
our analytic sample, the predominant minority group in each of the seven
provinces differs. We report here minority groups that account for more than 3
percent of the overall analytic sample, including Han individuals, for each
province: Tibetan, Hui, Salar, and Tu account for 31, 19, 9, and 3 percent of
the analytical sample in Qinghai province; Hui account for 46 percent of the
analytical sample in the Ningxia Hui Autonomous Region; Uygur, Kazaks, and Hui
account for 58, 9, and 3 percent of the analytic sample in the Xinjiang Uygur
Autonomous Region; Mongolians account for 23 percent of the analytic sample in
the Inner Mongolia Autonomous Region; Miao and Dong account for 49 and 27
percent of the analytic sample from the Qiandongnan Miao and Dong Autonomous
Prefecture in Guizhou Province; Miao, Tujia, Dong, and Yao account 34, 17, 16,
and 6 percent of the analytical sample in Hunan province; and Zhuang, Yao,
Miao, Dong, and Mulao account for 37, 9, 8, 5, and 4 percent of the analytical
in the Guangxi Zhuang Autonomous Region.

There are 28 minority groups included in the analytical sample. The ten
largest minority groups in our analytic sample are Miao, Hui, Uygur, Dong,
Zhuang, Tibetan, Tujia, Mongolian, Yao, and Salar; they account for,
collectively, 94 percent of the overall minority population within the
analytic sample. These ten minority groups accounted for 66 percent of the
overall minority population in China in 2010, and eight of these ten minority
groups are among the largest ten minority groups in China overall [56].
Manchus and Yi are the 3rd and 6th largest minority groups in China, and they
jointly account for 17.1 percent of the minority population in China in 2010.
The analytical sample has less than 0.1 percent Manchus and no Yi
individuals.A.2A.2A.2Manchus largely reside in Northern and Northeastern
China, generally in provinces with predominantly Han populations. The Yi
people largely reside in Yunnan province, which is not a part of the survey
sample we use in this study.

In Section B.1, we consider the robustness of our overall estimation results
to the inclusion of minorities without their own language.

###  A.4 Age of enrollment, age at closure, and age in 2011

In this study, we define age cutoffs for the educational attainment based on
primary school enrollment age patterns in our data from 2011. Overall
enrollment rates into primary school jump from 11 percent at age 5 to 76
percent between ages 6 to 8. Enrollment rates decrease from 50 percent at age
13 to 15 percent at age 14. A lower proportion of 5 year old ethnic minorities
are enrolled in primary schools (compared to Han children), and a lower
proportion of 12 to 15 year old ethnic minorities are enrolled between ages 12
and 15 in middle school and beyond. Overall, children are predominantly
enrolled in primary schools between 6 and 13 years of age in 2011. Hence, for
villages with closure, we consider children who were between 6 and 13 years of
age at the year of closure as most likely to have been enrolled in primary
schools.

In Table A.1, we present statistics on educational attainment and language
facilities for individuals who experienced closure at different ages
conditional on their cohorts (age in 2011). The columns are grouped by age in
2011, using five-year intervals between 10 to 34 years of age. Row Groups A,
B, C and D show statistics for individuals experiencing closure when they were
6 to 9, 10 to 13, 14 to 21, and 22 to 29 years of age. Row Group E shows
statistics for individuals from villages without closure.

Table A.1: Educational attainment and Mandarin ability by cohort (2011 age)
and age at closure

Age in 2011 10-14 15-19 20-24 25-29 30-34 Group A: Age 6 to 9 at year of
closure Age in 2011 (mean) 11.85 16.84 20.20 Years of education (mean) 5.46
9.76 10.93 Strong spoken Mandarin fraction 0.23 0.62 0.53 Strong written
Mandarin fraction 0.21 0.41 0.53 Observations 169 58 15 Group B: Age 10 to 13
at year of closure Age in 2011 (mean) 12.59 16.77 21.53 25.00 Years of
education (mean) 5.91 9.38 10.38 8.50 Strong spoken Mandarin fraction 0.23
0.44 0.55 1.00 Strong written Mandarin fraction 0.17 0.38 0.45 1.00
Observations 129 222 113 2 Group C: Age 14 to 21 at year of closure Age in
2011 (mean) 14.00 17.76 21.91 26.26 30.74 Years of education (mean) 7.00 9.72
10.23 8.96 8.41 Strong spoken Mandarin fraction 0.38 0.42 0.42 0.42 0.41
Strong written Mandarin fraction 0.19 0.40 0.37 0.34 0.30 Observations 16 275
581 224 27 Group D: Age 22 to 29 at year of closure Age in 2011 (mean) 23.26
27.18 31.72 Years of education (mean) 9.98 8.75 8.09 Strong spoken Mandarin
fraction 0.31 0.28 0.33 Strong written Mandarin fraction 0.21 0.23 0.24
Observations 101 452 314 Group E: Non-closure villages individuals Age in 2011
(mean) 12.03 17.06 22.00 26.89 31.82 Years of education (mean) 5.48 9.30 9.79
8.51 7.47 Strong spoken Mandarin fraction 0.19 0.31 0.33 0.25 0.20 Strong
written Mandarin fraction 0.15 0.32 0.28 0.18 0.14 Observations 1565 1837 2343
1747 1447 Group A individuals were 6 to 9 at the year of closure and were
exposed to consolidated primary school for up to half of their primary school
years. Group B individuals were 10 to 13 at the year of closure and
transitioned from village schools to consolidated primary schools during the
final years of primary school. Group C and D individuals are from villages
that experienced closure but were beyond primary school age at the year of
closure. Group E individuals are from villages without school closure.

###  A.5 Years of education and Mandarin ability by ethnic category and
closure

In Table A.2, we present individual-level summary statistics by minority and
closure status. Across 5-year interval cohort groups, Table A.2 shows that Han
and minority individuals in closure villages have higher years of education
completed and larger fractions reporting strong Mandarin abilities.

The raw differences in years of education and strong Mandarin ability between
closure and non-closure villages is largely explained by province fixed
effects, age fixed effects, and gender. Specifically, we consider a sample of
individuals who were over 20 years of age in 2011 and include only individuals
who were older than 13 at the year of closure—this group includes individuals
in columns 3, 4 and 5 and Groups C, D and E from Table A.1. Using this sample,
we regress years of education on village closure status, controlling for
province fixed effects, cohort (age in 2011) fixed effects and gender. The
coefficient for closure on years of education is 0.09 (s.e. 0.10) and
insignificant. We interpret this as a measure of the existing difference in
educational attainment between closure and non-closure villages in the absence
of school closure. Interacting closure with Han status, results remain
insignificant: we find that among Han individuals, the coefficient for closure
is -0.20 (s.e. 0.19), and among minority individuals, the coefficient for
closure is 0.17 (s.e. 0.12).

With the same sample and controls, we also regress strong written Mandarin
ability on closure. The coefficient from linear probability regression for
closure on reporting strong Mandarin ability is 0.025 (s.e. 0.013).
Interacting closure with Han status, we find that among Han individuals, the
coefficient for closure is -0.015 (s.e. 0.025), and among minority
individuals, the coefficient for closure is 0.030 (s.e. 0.016).

Table A.2: Means of individual-level variables by cohort (2011 age), ethnic
category and closure

Age in 2011 10-14 15-19 20-24 25-29 30-34 Panel A: Minority in closure
villages Male fraction 0.51 0.58 0.51 0.55 0.60 Age 6-9 at year of closure