1 23

Asia Pacific Education Review ISSN 1598-1037Volume 13Number 4 Asia Pacific Educ. Rev. (2012) 13:713-726DOI 10.1007/s12564-012-9231-z

School mapping restructure in rural China:achievements, problems and implications

Dan Zhao & Bruno Parolin

1 23

Your article is protected by copyright and

all rights are held exclusively by Education

Research Institute, Seoul National University,

Seoul, Korea. This e-offprint is for personal

use only and shall not be self-archived in

electronic repositories. If you wish to self-

archive your work, please use the accepted

author’s version for posting to your own

website or your institution’s repository. You

may further deposit the accepted author’s

version on a funder’s repository at a funder’s

request, provided it is not made publicly

available until 12 months after publication.

School mapping restructure in rural China: achievements,problems and implications

Dan Zhao • Bruno Parolin

Received: 13 February 2012 / Revised: 5 August 2012 / Accepted: 4 September 2012 / Published online: 18 September 2012

� Education Research Institute, Seoul National University, Seoul, Korea 2012

Abstract This study considers the experience of school

mapping restructure (SMR) in areas of rural China. It aims

to understand what happened after SMR implementation.

Through a combination of instruments such as question-

naires, interviews and document analysis, the study finds

that SMR has impacted positively on the development of

education in terms of generating scaling benefits, improved

school conditions and educational quality, and more equi-

table distribution of educational resources. However, there

remain several associated problems including longer

schooling commute, household financial restrictions and

student pressures, and increased teacher work-loads. The

study recommends that more structured plans should be

developed. Increased stakeholder participation should be

enabled, and by providing increased teaching resources to

rural schools, implementation should be improved.

Keywords School mapping restructure � Educational

policy � Rural China � Achievements � Problems and

implications

Introduction

School mapping restructure (SMR) is a term used to

describe the process of combining schools or districts in an

effort to generate administrative efficiencies and improved

academic and social experiences for students in sparsely

populated areas. It is also used to describe ‘‘school con-

solidation’’: the merging of two or more attendance areas to

form a larger school attendance pool (Fitzwater 1953;

Rural School and Community Trust 2000; McHenry-Sor-

ber 2009; Cooley 2012). This term is applied to the

investigations of efficient and equitable distribution of

resources within and between school systems when large-

scale reform or significant expansion of an educational

system takes place (Caillods 1983). Others use the term

‘‘school reorganization’’ (e.g. Peshkin 1982; Howard et al.

2010) or ‘‘school merger’’ (Mo et al. 2012) to describe the

combination of two or more previously independent school

districts in one new and larger school system. What these

terms have in common is that they refer to the merging

together of small schools in relatively poor condition to

create larger and better resourced schools.

A number of issues are taken into account when policy-

makers consider the implementation of SMR. First, larger

schools are seen as more cost-effective than smaller

schools. Thus, SMR is considered an effective way to

achieve ‘‘economy of scale’’ (Fanning 1995; Laplante2005;

Standard and Poor’s 2007; De Haan et al. 2011). Second,

rural schools are losing students due to a combination of

urbanization and lower birth rates (Shavers 2003; Shakrani

2010). Third, larger schools employ a standardized school

model which can improve the quality of education and

expand accessibility to education (Kay et al. 1982; De-

Young and Howley 1990; Alvarez et al. 2010). Some

policy-makers have advocated the concept of ‘‘bigger is

better’’ (Theobald and Nachtigal 1995; Berry 2004). They

believe that resources should be centralized in larger

schools in order to provide better education services.

School mapping restructure implementation may, as a

by-product, lead to the emergence of new problems. For

D. Zhao (&)

College of Humanities, Northwest A&F University, Yangling,

Shaanxi 712100, China

e-mail: zhaodan821225@gmail.com

B. Parolin

Faculty of the Built Environment, University of New South

Wales, Sydney, NSW 2052, Australia

123

Asia Pacific Educ. Rev. (2012) 13:713–726

DOI 10.1007/s12564-012-9231-z

Author's personal copy

example, the closure of remote schools may place greater

financial pressure on some students and their families

because of the increased travel or living expenses needed to

attend the new schools (Rural School and Community

Trust 2002; Murry and Groen 2004; Laplante 2005; Nitta

et al. 2010). Longer commutes to school may be to the

detriment of students, limiting their after-school free time

and presenting a barrier to participation in extracurricular

activities. In addition, the closure of schools may lead

to the loss of function and stability of the community

(Voth and Danforth 1981; Fitchen 1991; Johnson 2004;

Purcell and Shackelford 2005; Blauwkamp et al. 2011).

Finally, SMR seems not to guarantee the improvement of

student performance in step with the expansion of schools

(Walberg and Fowler 1987; University of Arkansas, Office

for Education Policy 2005; Lu and Du 2010; Cato 2011).

Solutions for improving SMR performance have been

discussed in several papers. Some argue that factors such as

student enrollment, financial impact, transportation (dis-

tance, terrain, time and cost) and impact on community

should be synthetically considered in order to minimize

negative impact (Kennedy and MacDougall 2007; Spradlin

et al. 2010). McHenry-Sorber (2009) stresses that SMR

should provide local community with the opportunity to

express their concerns regarding the local context (p. 14).

Some researchers suggest that special aid should cover all

small poor schools which, they believe, should be sup-

ported in the long run (Hargreaves et al. 2001; Jimerson

2007; Blauwkamp et al. 2011). McHenry-Sorber (2009)

encourages cost- and service-sharing programs between

neighboring schools or districts; this is also argued by

Plucker et al. (2007).

Very few studies have looked specifically at the

implementation of SMR in China. Fan (2006), Pang

(2006) and Xiong (2007) argue that SMR has a number of

negative impacts on poor children in remote villages, such

as longer travel distances, safety associated with the trip

to school and increased financial burdens. Fan (2009)

conducted a survey to investigate the implementation

process of SMR in rural regions. Chan and Harrell (2009)

took Yanyuan County in Sichuan Province as a case study

to examine some of the difficulties entailed in SMR. Yang

(2010) produced a review of SMR issues including poli-

cies and problems, suggesting the retention or reopening

of small rural schools. Mo et al. (2012) examine the

impact of the SMR on the academic performance of

students in a poor county of Shaanxi Province and find

that students who transfer to the county school benefit

from the transfer. A similar study is conducted by Liu

et al. (2010). Zhao and Parolin (2011, 2012) applies GIS

to assess the effect of SMR, showing that children in

sparsely populated areas suffer longer commutes follow-

ing the sharp decrease in the number of small rural

schools, and proposes that SMR decision making should

employ scientific tools.

To some extent, international studies, based on the

experiences of other countries, consider issues such as

background, reasons, difficulties, impacts and policy sug-

gestions, when discussing the issue of SMR. These con-

cerns are also applicable to this study. Regarding Chinese

research, however, despite the broad implementation of

SMR in rural China, relatively little has been learned about

how SMR has affected rural schools, and the attitudes of

the groups involved, such as administrators, educators,

students and parents. The current studies typically focus on

township and county level, thus neglecting to come to any

overall national level assessment of SMR performance.

Additionally, there has been no attempt to propose policy

directions based on overseas experience, which would put

SMR into the broader international context.

To help fill this gap, this study aims to provide an

overall assessment of experiences of SMR in rural China

and to recommend strategies from a broader international

perspective. The approach here to the achievements,

problems and strategies of SMR differs from that of pre-

vious studies. The real conditions and processes of SMR

implementation are presented in depth, based on evidence

provided by a large-scale survey conducted in the central

and western provinces of China in 2008. The authors

limited this study to those subjects most directly affected

by SMR: school administrators, teachers, students and

parents, and focused on the following concerns: what are

the effects of SMR implementation in rural China? Are

there any achievements or problems generated by SMR?

What are the policy implications for SMR improvement?

In detail, the evidence shows that SMR has achieved

scale improvements, enhancement of school conditions and

educational quality and equilibrium of educational devel-

opment, among other benefits. The problems faced by SMR

involve longer school commutes, household financial bur-

dens, study pressure and increased teacher workloads. It is

argued here that a better plan, via the application of GIS

(Geography Information System), participation of multiple

groups, and increased provision of teachers to rural

schools, should be developed. Such strategies may improve

SMR performance and promote the future development of

Chinese rural education.

This paper proceeds in six stages. First, related issues of

SMR are reviewed from an overseas perspective, including

definitions, reasons, problems and solutions. Studies con-

ducted by Chinese researchers are also introduced. Second,

the context of SMR in rural China is introduced. Third, the

authors present the methodology used in the large-scale

questionnaire-based survey. Fourth, achievements and

problems of SMR are presented by applying data derived

from the survey. The fifth stage makes conclusions and

714 D. Zhao, B. Parolin

123

Author's personal copy

recommends implications for policy-makers. Lastly, sev-

eral issues associated with SMR which require additional

study are raised, regarding the course of further develop-

ment of rural education.

Context of SMR in rural China

Accompanied by industrialization and urbanization, China

can be geographically classed according to three catego-

ries, namely rural, county and urban areas. Rural areas are

administrative divisions mainly based on agriculture, and

consisting of independent villages as autonomous units of

rural residency. County areas rest between rural and urban

areas and form a network of villages and towns in terms of

politics, economics and culture. Urban areas consist of

concentrated non-agricultural populations and well-devel-

oped industry and commerce, making them the centers of

their surrounding regions (Li 2000). Based on this defini-

tion of rural, county and urban areas, it can be concluded

that rural areas are the lowest level administrative divisions

in China, characterized by agricultural production and a

rural population. Thus, rural schools in this study are taken

to refer to schools located in rural areas, as classified by the

Education Statistical Yearbook in China.

For a long time, Chinese primary and middle schools

have faced problems such as irrational distribution of

schools; a large number of small rural schools are located

in neighboring villages despite class sizes of around ten

pupils each, while some larger schools are short of class-

room space and facilities, inhibiting educational quality.

These persistent problems have yet to be successfully

resolved. In the implementation of universal 9-year com-

pulsory education starting in 1986, local governments have

conducted measures to achieve the goal of ‘‘every village

establishing a primary school and a middle school’’ (Fan

2006), which was part of a larger effort to expand access to

education. However, this policy has had a negative impact

on rural school mapping. For example, many villages have

their own school despite low birthrates, inadequate physi-

cal facilities, and insufficiently trained or unmotivated

teachers. On this basis, local governments commenced a

program of SMR in the early 2000s, as part of the National

Basic Education Plan (State Council of the People’s

Republic of China 2001). Since 2001, the SMR policy was

applied across China, especially in rural areas. This was

driven by existing school mapping and the rapid decline of

school-age populations in rural China. In 2009, compared

to the number of enrolled students in rural primary schools

in 1998, there had been a 37.61 % decline from 15,100,116

students to 9,420,829 (a reduction of 5,679,287). The

number of rural secondary students had declined by

44.86 %, from 11,445,048 in 1998 to 6,310,685 in 2009 (a

reduction of 5,134,363; Ministry of Education of the Peo-

ple’s Republic of China 1998, 2009; See Table 1). Factors

that have influenced SMR include rural tax reform,

urbanization and the changing geography of the adminis-

trative structure of China (Fan 2006).

The main objectives of SMR in China include equitable

distribution of educational resources, greater economy of

scale, urban–rural balanced development of education,

improved management capacity and enhanced education

quality, especially in poorer rural areas (State Council of

the People’s Republic of China 2001). The objectives are

also the standards which can be used to assess the results of

SMR implementation. One of the key methods of SMR has

been the closure of schools in remote isolated areas and the

transfer of students, teachers and other resources to central

schools located further away. The scale of school closures

in China has been dramatic, as evidenced by primary

schools and small rural schools. Statistics from the Min-

istry of Education of China reveal that over a 12-year

period between 1998 and 2009, the number of rural pri-

mary schools has declined from 493,152 to 234,157 (a

reduction of 52.52 %), while the number of small rural

schools has declined from 178,952 to 70,954 (a reduction

of 60.35 %; Ministry of Education of the People’s

Republic of China 1998, 2009).

Methodology

A large-scale survey of six provinces was conducted in

2008 with funding from the World Bank and the Chinese

Ministry of Education. In total, 52 researchers were

involved. They were divided into six groups, and each was

made responsible for one provincial investigation.

Survey area and target group

Multistage sampling and stratified sampling were used to

select the surveyed areas. Provinces were selected on the

basis of various factors including developmental disparity,

cultural diversity, demographics and geographical factors.

Table 1 Number of primary and secondary schools in China (1998

and 2009)

Total Rural area County area Urban area

Primary school

1998 22,013,814 15,100,116 4,148,464 2,765,234

2009 16,377,978 9,420,829 4,127,154 2,829,995

Secondary school

1998 19,613,640 11,445,048 5,087,299 3,081,293

2009 17,863,912 6,310,685 8,075,391 3,477,836

School mapping restructure in rural China 715

123

Author's personal copy

The samples were then further broken down to city and

county, town and school levels. Finally, respondents (par-

ents, students, teachers, school administrators) associated

with the schools were selected. In total, six provinces,

namely Shaanxi, Henan, Hubei, Yunnan, Guangxi and

Inner Mongolia, including 38 counties and 178 towns, were

selected for the survey. All situated in central and western

China, these schools may be characterized by compara-

tively low levels of economic development and strong

agricultural or animal raising contexts. They are located on

varying terrain, with some in highly mountainous areas.

In total, 986 rural primary and secondary schools were

investigated, of which 764 were primary schools (including

small rural schools), 140 were junior secondary schools, 45

were 9-year compulsory education schools and 37 were

senior secondary schools. The schools were situated in

mountainous, hilly, plain land, pastoral, mining and lake-

side areas. The percentage of schools in mountainous areas

was 66.7 %, those in hilly areas accounted for 12.0 %, and

those on the plains accounted for 18.7 % (see Table 2).

The sample schools include boarding schools, day schools

and mixed boarding and day schools.

Survey target groups comprised of education adminis-

trators, headmasters/teachers, students and parents/custo-

dians. Within these target groups, teachers and students

were selected using random sampling and cluster sampling

methods, respectively. As for the teachers, with an average

number of teachers in each school of between 30 and 40, a

sampling probability of 0.5 was used, according to the list

of teachers in each school, so as to improve the represen-

tativeness of the teacher’s sample. For students, due to the

fact that the number of students was large, and the students

in each type of school were homogeneous; cluster sampling

(a class as one unit) was applied in most of consolidated

schools, according to the list of class in schools. Different

sampling method made the teacher sample a little bit

higher than the student sample. Additionally, given that

schools were not evenly distributed between the six prov-

inces, respondents in different provinces were accorded

different weights, so that the results have been adjusted and

weights calculated. Moreover, it proved to be more difficult

to select parent groups because so many had migrated to

the cities seeking jobs, leaving their children in the village

with custodians. For this reason, the parent group com-

prises of both parents and custodians.

Data collection

Four instruments were used for collecting data: (a) SMR

planning form to collect information on enrollment, service

population, physical accessibility, number of teachers and

finance; (b) Interviews with groups and individuals, con-

sisted of open–ended questions; (c) Questionnaire surveys

for administrators, teachers, students and parents (see

Table 3).

Based on the objective of this study, the investigation

included: basic respondent information; district back-

ground variables; objective and motivations of SMR;

viewpoints of different groups (education administrative

official, head teacher, school administrative staff, teacher

and teaching auxiliary) toward SMR; successes and con-

straints of SMR; issues (e.g. distance, expense, life diffi-

culties) of concern to students and parents; major

challenges and recourses of different groups regarding

better implementation of SMR.

The SMR questionnaires used in this study were tested

for content validity and reliability. Validity evidence

Table 2 Number of surveyed

schools in six provinces by

different categories

By province By location

Schools Number Proportion (%) Schools Number Proportion (%)

Shaanxi 221 22.4 Mountainous area 657 66.7

Guangxi 369 37.4 Hills 118 12.0

Hubei 97 9.8 Plains 184 18.7

Yunnan 141 14.3 Pastureland 5 0.5

Henan 135 13.7 Mining area 1 0.1

Inner Mongolia 23 2.4 Lakeland area 1 0.1

Not supplied 20 1.9

Total 986 100 Total 986 100

Table 3 Questionnaire statistics

Questionnaire

type

Sent

out

Returned Return

rate (%)

Valid

surveys

Validity

rate (%)

Officials/

administrators

210 194 92.3 181 86.2

Headmasters/

teachers

15,000 12,490 83.3 11,463 76.4

Parents 12,000 7,995 66.6 7,421 62.0

Students 12,000 11,997 99.9 11,990 99.9

Total 39,210 32,476 83.0 31,055 79.2

716 D. Zhao, B. Parolin

123

Author's personal copy

suggests that, by using a two-rater agreement procedure,

the calculation of the Content Validity Index (CVI) gen-

erated 0.84, 0.76, 0.79 and 0.81, consistent with the ques-

tionnaire for administrators, teachers, parents and students.

Reliability evidence was established by applying Kuder-

Richardson Formula 20 (KR-20), the result for each cate-

gory of questionnaire—in the same order as above—being

0.78, 0.80, 0.82 and 0.77. Both sets of evidence of validity

and reliability were thus found to be at acceptable levels.

Research findings

The following sections present evidence from the ques-

tionnaires and interviews regarding the experience of SMR

implementation, showing both achievements and problems.

Effects of SMR

Economy of scale improvements

From the survey, the school size of all the three types of

schools—primary school, junior secondary school and

senior secondary school—increased once SMR had been

undertaken. In detail, the average primary school size, in

the six provinces investigated, rose from 228 to 295 stu-

dents (a 29.5 % increase), the average junior secondary

school size rose from 874 to 1,020 students, and the

average senior secondary school rose from 773 to 2,025

students. Along with an increase in school size, the average

cost per student at all three types of school decreased. As

this survey showed, after SMR, a student’s average cost at

primary school, junior secondary school and senior sec-

ondary school was, respectively, 2,998 RMB (a decrease of

15.8 %), 4,561 RMB (a decrease of 4.6 %) and 4,012 RMB

(a decrease of 20.05 %; see Table 4). Nationally, the

average scale of rural primary schools increased from 191

to 242 students, while the average cost per student

decreased from 4,087 to 3,620 RMB (Ministry of Educa-

tion of the People’s Republic of China 1998, 2009). These

changes demonstrate that SMR has increased economies of

scale in schools.

Additionally, from the perspective of the stakeholder

groups interviewed, 71.2 % of education administrative

officials, 59.7 % of head teachers, 58.1 % of school

administrative staff and 52.6 % of teachers felt that SMR

had improved economies of scale in rural schools (see

Table 5).

Overall enhancement of school conditions and educational

quality

Three of the indicators, namely the percentage of qualified

teachers, assets value of teaching instrument and equip-

ment, and books in school library, were applied to examine

the change in school conditions following SMR imple-

mentation. As this study shows, the percentage of qualified

teachers per school in our investigated areas increased from

92.73 to 97.21 %, the average assets value increased from

42,600 to 62,800 RMB and the average number of library

books increased from 4,110 to 6,014. Therefore, numerical

increases in terms of those three indicators can demonstrate

that educational operating conditions after SMR had been

improved (see Table 6). This could also help to show that,

from the perspective of ‘‘school conditions,’’ the educa-

tional quality increased during SMR implementation.

The statistical data also shows that 74.6 % of education

officials, 65.5 % of head teachers, 55.3 % of school

administrators and 45.8 % of teachers agree that educa-

tional quality improved with SMR. In addition, 73.2 % of

parents consider that teachers are more responsible than

before SMR, and 65.1 % of students agree that teachers

spend more time instructing them than before SMR. A total

of 52.3 % of parents think that the grades of their children

improved, and 50.5 % of students think that their grades

improved (see Table 7). In summary, as aspects including

‘‘responsibility of teachers,’’ ‘‘grades,’’ ‘‘period of

instruction by teachers,’’ ‘‘drop-out rate’’ and ‘‘attendance

rate’’ are all related to educational quality, these evalua-

tions could demonstrate that, overall, SMR has somewhat

generated a positive impact on educational quality.

More equitable distribution of educational resources

As an indicator for assessing the degree of dispersion of data,

coefficient of variation (CV) was applied in order to measure

the degree of equal distribution of educational resources

among schools during the process of SMR. Coefficient of

variation was calculated by standard deviation (and can also

Table 4 Average cost of studying in rural primary and secondary

schools before and after SMR (in the six provinces investigated)

Types Pre-

SMR

Post-

SMR

Changed

proportion (%)

PS 228 295 29.5

Student’s average cost

(RMB)

3,561 2,998 15.8

JSS 874 1,020 16.6

Student’s average cost

(RMB)

4,780 4,561 4.6

SSS 773 2,025 162.0

Student’s average cost

(RMB)

4,991 4,012 20.0

PS primary schools, JSS junior secondary schools, SSS senior sec-

ondary schools

School mapping restructure in rural China 717

123

Author's personal copy

therefore be called the coefficient of standard deviation). Its

formula is expressed as follows:

CV ¼ S�X

� �� 100 % thereinto; S

¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXn

jðXj � XÞ=ðN � 1Þ

r;

Xj is observation, X is the mean value of sampling obser-

vations, S is the standard deviation of sampling observations.

According to the principle of CV, the higher the CV

value, the higher the degree of dispersion. In this study,

higher CV value would also indicate a higher degree of

equal distribution of educational resources. From the sur-

vey statistics, the three indicators in Table 5 were chosen

to represent educational resources. The value of CV within

each county of the six provinces was calculated, showing

the degree of equal distribution of educational resources

between schools within county. As presented in Table 6,

for all three indicators, the value of CV of each county

decreased after SMR was undertaken. These changes

affected both primary and secondary schools after SMR.

For example, for primary schools in county 1 (C1) in

Shaanxi Province, the value of CV for indicator 1, Per-

centage of Qualified Teachers, decreased from 0.77 pre-

SMR to 0.69 post-SMR; thus, the allocation of resource of

qualified teachers turned to be more equal after SMR. The

other counties in the other provinces the same effect is

recorded (see Table 8). Hence, SMR has been shown to

have played a positive role in eliminating the educational

resource gap between different categories of schools and

between different areas.

Conceptually, changes in educational resource distribu-

tion can be explained by way of the logical model in Fig. 1.

Before SMR, schools were dispersed in the villages (see

the left frame), had low enrollment rates, and lacked basic

facilities. After SMR, however, a central school (A) has

Table 5 Achievements of SMR from the perspective of each group

Groups Valid

samples

Improvement of

school scale

economies (%)

Improvement of

education quality

(%)

Equilibrium of

educational

development (%)

More reasonable

distribution of

educational

resources (%)

Others (%)

Administrative officials 178 71.2 74.6 72.2 93.8 2.2

Head teachers 893 59.7 65.5 57.3 78.2 2.9

School administrators 736 58.1 55.3 52.9 75.8 3.2

Teachers 8,884 52.6 45.8 54.2 70.2 3.7

Table 6 Changes in school

conditions in terms of three

indicators (average per school in

the six provinces surveyed)

Pre-SMR Post-SMR

Percentage of qualified teachers (%) 92.73 97.21

Assets value of teaching equipment (RMB) 42,600 62,800

Books in the school library 4,110 6,014

Table 7 Changes in educational quality since SMR

Groups Valid samples Contents Increase (%) Not clear/About

the same (%)

Decrease (%)

Parents 7,306 Teachers’ responsibility 73.2 20.1 6.7

7,235 Grades 52.3 16.5 31.2

Students 11,226 Period of instruction by teachers 65.1 20.7 14.2

11,610 Number of class drop-outs 13.1 9.9 77.0

11,737 Grades 50.5 27.1 22.4

Teachers 10,549 School attendance rate 36.1 46.7 17.2

9,977 Drop-out rate 21.0 37.8 41.2

Administrative officials 176 School attendance rate 26.1 67.6 6.3

159 Drop-out rate 12.6 61.6 25.8

718 D. Zhao, B. Parolin

123

Author's personal copy

been constructed by merging small schools. Meanwhile,

other comprehensive schools (from B to F) have been

centralized and distributed in sub-central areas. Some small

rural schools that were considered irreplaceable have been

retained in remote areas. Thus, as SMR proceeds, the

number of schools is reduced, allowing educational

resources to be channeled to a smaller number of them. In

other words, as schools are centralized, educational

resources are centralized, consequently being more ratio-

nally distributed than pre-SMR, in which the limited edu-

cational resources were distributed to a larger number of

schools with different levels of enrollment, facilities and

staff, which can also be figured out in Table 4. This is also

pointed out by Fan (2006).

Problems

Complicated effect on access to quality education

and students’ academic achievement

In the provinces investigated, the longest distances traveled

by students in Inner Mongolia, Yunnan, Shaanxi, Guangxi,

Hubei, Henan were 250, 200, 100, 100, 100 and 50 km,

respectively. A total of 32.4 % of students travel more than

5 km to school, with some commuting over 20 km; the

longest traveling time taken by the students in these six

provinces was 12, 7, 20, 14, 8 and 4 h, respectively. In

detail, of the students traveling more than 20 km, the junior

secondary school students, boarding students and the stu-

dents experiencing SMR accounted for higher proportions

(See Table 9). For example, as to the students traveling

more than 100 km, the students from junior secondary

school and boarding school accounted for 63.6 and 94.7 %,

respectively, and 66.9 % of students who experienced

SMR traveled more than 100 km, further than the students

not experiencing SMR. Furthermore, the value of v2 (Chi-

square) for schooling distance and type of school, boarding

status and SMR experience are strongly correlated.

Therefore, during SMR, students from secondary schools

and boarding schools experienced longer commute to

school. It should be noticed that, despite boarding at

school, many students return home once a week, such long

commute indeed caused their difficulties attending con-

solidated schools, which was revealed by the remote stu-

dents and their parents interviewed. Additionally, the

primary students who traveled long distances such as more

than 5 km or more also account for more than 20 % among

three types of school (see Table 9); this may cause hard-

ship of younger students for access to consolidated schools.

The micro statistics has been done, in order to assess the

students’ academic achievement related with SMR. ‘‘Grade

of students’ academic score’’ was chosen as the indicator.

Table 8 Degree of equal distribution of educational resources pre- and post-SMR (indicator of coefficient of variation)

Province Type of school Primary school Secondary school

Indicator 1 2 3 1 2 3

County Pre Post Pre Post Pre Post Pre Post Pre Post- Pre Post

Shaanxi C1 0.77 0.69 0.82 0.75 1.26 1.16 0.34 0.31 0.82 0.76 0.66 0.58

C2 0.83 0.72 0.87 0.73 1.11 1.00 0.51 0.49 0.79 0.67 0.49 0.35

C3 0.62 0.54 0.73 0.61 0.54 0.43 0.89 0.72 0.78 0.65 0.56 0.39

Guangxi C1 0.86 0.76 1.09 0.98 1.26 1.12 0.99 0.87 0.80 0.77 0.87 0.78

C2 0.97 0.78 1.12 0.99 0.91 0.87 1.01 0.90 0.91 0.80 0.76 0.67

C3 0.93 0.79 0.87 0.77 0.98 0.80 1.12 1.03 0.83 0.79 0.87 0.79

Yunnan C1 0.99 0.81 0.90 0.78 1.10 1.02 1.00 0.91 0.76 0.68 0.82 0.73

C2 0.88 0.80 0.78 0.63 1.01 0.88 0.90 0.83 0.81 0.78 0.85 0.72

C3 0.81 0.62 0.98 0.79 0.89 0.80 1.09 0.99 0.74 0.67 0.79 0.70

Hubei C1 0.42 0.33 0.55 0.43 0.47 0.43 0.56 0.43 0.50 0.43 0.37 0.25

C2 0.67 0.61 0.52 0.34 0.71 0.66 0.26 0.21 0.58 0.34 0.55 0.40

C3 0.26 0.22 0.31 0.22 0.52 0.43 0.09 0.06 0.25 0.21 0.45 0.38

Henan C1 0.48 0.40 0.88 0.76 0.71 0.68 0.33 0.28 0.54 0.38 0.30 0.21

C2 0.70 0.61 0.67 0.56 0.34 0.31 0.56 0.33 0.39 0.23 0.43 0.36

C3 0.67 0.60 0.55 0.41 0.58 0.44 0.24 0.19 0.40 0.38 0.32 0.29

Inner Mongolia C1 1.03 0.99 1.39 1.12 1.16 1.03 1.28 1.10 1.11 0.98 0.66 0.59

C2 1.11 1.05 1.22 1.10 0.98 0.90 1.13 1.04 0.87 0.76 0.63 0.54

C3 0.93 0.87 1.02 0.92 1.02 0.91 0.98 0.88 1.03 0.99 0.82 0.77

Indicator 1 = Percentage of qualified teachers; Indicator 2 = Assets value of teaching equipment; Indicator 3 = Books in school library

School mapping restructure in rural China 719

123

Author's personal copy

The Ordinal Logit model was applied to analyze the aca-

demic performance influenced by SMR implementation. As

shown in Table 10, there were several factors including

‘‘Occupation of parents, boarding in school, schooling dis-

tance,’’ negatively impacting on students’ academic perfor-

mance. In detail, first, as to the occupation of parents, if

parents are both working in agriculture, the economic con-

dition in this kind of family would at a low level influenced

by the overall situation in rural China, causing children to

face hardship at school, such as not being able to buy extra

learning materials, transportation tools or afford boarding

costs, etc. If the mother is a migrant worker or both parents

are migrant workers, children would miss out on family

education and parental care and supervision, and this works

against students’ learning effectiveness. Second, ‘‘boarding

in school’’ impacted negatively on academic performance,

this may due to the low quality of dormitories in some

schools after SMR: poor conditions of dorms with nearly 20

students living in a single dorm and two or three children

sleeping in one bed; students boarding in school normally

watch TV once a week and go to sleep as early as 7 pm, etc.,

investigated by this survey. Third, long commutes make

students spend much more time on journey, and mean they

do not have time for extracurricular activities and revision

work, and this is likely to impact negatively on these

students’ performance at school.

Fig. 1 Conceptual model of

educational resource

distribution Pre-/post-SMR

(circles are in proportion to

school size)

Table 9 Schooling distance of students before and after SMR

Distance (km) Type of school Boarding status Experience SMR or not

Primary Junior secondary Senior secondary Yes No Yes No

0–5 4,545 1,571 67 1,467 4,522 2,399 3,420

% 73.5 25.4 1.1 24.5 75.5 41.2 58.8

5.1–10 1,012 792 30 1,272 475 684 1,045

% 55.2 43.2 1.6 72.8 27.2 39.6 60.4

10.1–20 671 815 36 1,327 118 925 489

% 44.1 53.5 2.4 91.8 8.2 65.4 34.6

20.1–30 204 299 17 457 31 342 134

% 39.2 57.5 3.3 93.6 6.4 71.8 28.2

30.1–50 139 239 50 379 26 286 102

% 32.5 55.8 11.7 93.6 6.4 73.7 26.3

50.1–100 55 178 28 218 18 180 56

% 21.1 68.2 10.7 92.4 7.6 76.3 23.7

[100 35 89 16 125 7 85 42

% 25.0 63.6 11.4 94.7 5.3 66.9 33.1

Total 6,661 3,983 244 5,245 5,197 6,983 3,906

v2 (Chi-square test) Likelihood ratio = 1220.277*** Likelihood ratio = 4339.398*** Likelihood ratio = 101.271***

720 D. Zhao, B. Parolin

123

Author's personal copy

On the other hand, factors such as ‘‘father is migrant

worker’’ and ‘‘number of qualified teachers’’ engendered

significant positive impact on students’ academic perfor-

mance. Specifically, if the father works in the city as a

migrant worker, he can earn more money than by working

in agriculture, making improvements in the family’s eco-

nomic condition and thus supplying more resources for the

children’s schooling, so as to promote their learning

effectiveness. In addition, if there are more qualified

teachers in school, they can deliver high quality education

to students and, as a result, improve students’ academic

performance.

Additionally, from the result of the survey, the attitudes

of different surveyed groups also revealed that ‘‘longer

commute to school’’ was definitely a pressing problem

during SMR implementation. As many as 76.1 % of edu-

cation officials, 78.9 % of principals, 72.3 % of school

administrators and 71.3 % of teachers felt that ‘‘longer

commute to school’’ is the issue that concerns them most.

As for students and parents, the issue of ‘‘longer commute

to school’’ constituted 26.4 and 31.6 % of their most

important issues, respectively, second out of all the options

(see Table 11).

Economic constraints and hardship for poor students

Specifically, schooling in consolidated schools generates

both direct costs and opportunity costs (Carnoy 1995).

First, direct costs include accommodation, living expenses

and transportation costs. Our case study of Pingpeng cen-

tral primary school in Pingmeng Village, Shiquan County,

Shaanxi Province, demonstrated that the extra expenditure

per semester for students transferred from small rural

schools to the consolidated central primary school was 80

RMB for accommodation, 600 RMB for food and 240

RMB for transportation, a considerably elevated expendi-

ture per semester (see Table 12). As a fact, families in

underdeveloped rural areas, whose major income is from

agricultural production, spend a huge amount on children’s

schooling, which consequently puts a huge pressure on

their economic condition.

Second, opportunity costs refer to the fact that in order

to receive school education, students must give up the

opportunity to earn money. This may be the value of labor

created by children in family production, taking care of

younger siblings or doing other chores (Carnoy 1995). For

low-income families, every laborer plays an important role

in the proper functioning of the household economy. For

example, in a sheep raising household, if there are no

young people to graze the sheep, parents have to put their

other work aside for this task. Consequently, for students in

remote rural areas, if they are transferred to consolidated

school far from their own village, this will inhibit them

from participating in farm or house work, thereby

increasing opportunity costs. This is an indicator that SMR

has caused increased opportunity costs to students in

remote poor areas.

With regard to respondents’ attitudes, 71.6 % of parents

and 57.0 % of students felt they had economic pressure

resulting from both direct and opportunity costs, which had

increased post-SMR (see Table 13). These economic dif-

ficulties have been observed before (California County

Superintendents Educational Services Association 2002).

Table 10 Ordinal logit results analyzing factors impacting students’

academic performance

Dependent variable: grade of academic score (1 = high level;

2 = middle level; 3 = low level)

Male = 1, female = 0 -.031

Age, year 0.249

Experience SMR (1 = yes) -.025

Parents are both working in agriculture (1 = yes) -.010*

Father is migrant worker (1 = yes) .125*

Mother is migrant workers (1 = yes) -.189*

Parents are both migrant workers (1 = yes) -.537**

Boarding in school (1 = yes) -.482***

Schooling distance (km) -.428**

Number of qualified teachers .868***

Size of class -.002

Constant 1 -.497*

Constant 2 .501*

*Significant at 10 %; ** Significant at 5 %; *** Significant at 10 %.

Pearson v2 = 11362.756, sig = 0.752; Deviance test = 0.807

Table 11 Issues of most

concern according to students

and parents since SMR

The issue which the students most worry

about

Students Parents

Number Percentage (%) Number Percentage (%)

Increased the economic burden on parents 6,107 53.5 3,213 45.2

Longer commute to school 2,968 26.4 2,229 31.6

Others 1,070 9.6 1,477 17.8

Bullied by the students from other villages 891 7.3 234 3.9

Cannot get used to the school environment 544 3.2 89 1.5

School mapping restructure in rural China 721

123

Author's personal copy

In addition, SMR has aggravated pressures on student

life. Based on our survey, this point can be explained by

two tracks. First, for students walking between home and

school, either accompanied by parents or walking inde-

pendently, the potentially dangerous terrain is a big chal-

lenge, especially in bad weather, provoking additional

parental worry for their charges’ safety. Second, for stu-

dents boarding at school from a young age, they face daily

difficulties, such as an inability to handle daily tasks such

as laundry and washing. Some rural schools have no toilets

inside dormitory buildings, which results in an increased

incidence of bed-wetting among young children. In sum-

mary, in remote rural areas, neither students who walk to

school nor those who board can conveniently enjoy

schooling following SMR. Students must suffer hardships

in order to gain an education, as has been argued by some

researchers (Fan 2006; Pang 2006; Xiong 2007).

Increased teacher workload

The authorized size of classes in primary schools is

between 40 and 45 students, and between 45 and 50

students in county and rural secondary schools (Ministry of

Education of the People’s Republic of China 1982).

However, many class sizes have exceeded this limit,

especially since SMR. It is fairly common for there to be

50–60 students per class in most rural primary schools, and

60–70 students per class in rural middle schools. Addi-

tionally, the number of teachers did not increase enough to

follow the increase in class size. For example, from our

survey, the average ratio of teacher to student of primary

school in the six provinces decreased from 1/20.7 to 1/25.2,

and the average teacher to student ratio in secondary school

decreased from 1/19.3 to 1/19.9 (see Table 14). As such,

teachers have a huge organizational task to perform to

teach such large classes. Their time is almost fully occu-

pied in teaching and lesson preparation, giving them little

personal time. In addition, managing boarding school

arrangements is also tasked to teachers despite their already

large teaching workloads. In rural areas, there is a lack of

teachers, especially at auxiliary level, due to an unwill-

ingness to work in rural areas with poor conditions and low

salaries. Therefore, the ‘‘irrational expansion of class size’’

during SMR has resulted in an increased teacher workload.

Conclusions and implications

Based on the empirical study of SMR undertaken in the six

surveyed provinces, evidence suggests that SMR may have

resulted in some positive effects regarding greater econo-

mies of scale, improved school conditions and educational

quality, and more equitable distribution of educational

resources.

First, post-SMR, the overall number of schools in the six

surveyed provinces decreased while the scale of schools

increased. With the increase in school scale, gains were

made in terms of educational resources allocated to each

school, resulting in improvements in economies of scale.

Therefore, evidence regarding the scale of investigated

schools and rural primary schools nationally, combined

with the viewpoint of survey respondents, supports the

view that SMR has increased economies of scale in

schools. This was also presented in the theory of educa-

tional economy of scale (Carnoy 1995), in which the

economic efficiencies brought on by an expansion in

Table 12 Extra expenditure for students in consolidated primary

schools: the case of Pingmeng central primary school in Pingmeng

village, Shiquan County, Shaanxi province

Items of expenditure Expenditure per semester

Small rural schools Consolidated

schools

Accommodation (RMB) 0 80

Food (RMB) At home, \200 600

Transportation (RMB) 0 240

Table 13 Family difficulties providing school expenditures

Respondents Yes No Not clear

answer

Total

Parents Quantity 1,918 759 – 2,677

Percentage (%) 71.6 28.4 – 100

Students Number 3,133 1,515 852 5,500

Percentage (%) 57.0 27.5 15.5 100

Table 14 Ratios of teachers to students in rural primary and secondary schools before and after SMR in six provinces

Average Shaanxi Guangxi Hubei Yunnan Henan Inner Mongolia

Primary School Pre-SMR 1:20.7 1:21.1 1:24.7 1:23.6 1:20.0 1:22.1 1:12.6

Post-SMR 1:25.2 1:28.6 1:29.5 1:26.5 1:24.1 1:27.2 1:15.2

Secondary School Pre-SMR 1:19.3 1:18.3 1:22.5 1:17.9 1:17.2 1:20.5 1:15.9

Post-SMR 1:19.9 1:20.1 1:21.4 1:20.7 1:19.2 1:21.5 1:16.6

722 D. Zhao, B. Parolin

123

Author's personal copy

school scale were discussed. This paper suggests that these

theoretical considerations were borne out by SMR.

Second, SMR has improved educational quality by

providing better educational resources, including better

teachers and better facilities. After SMR, a large number of

rural school-age children were transferred from small rural

schools to larger central schools and thus could enjoy

higher quality education; the outcome of which was felt by

students and parents alike. Illustrated by evaluations made

by each group, it may be concluded that SMR has gener-

ated a positive impact on educational quality. This also

chimes with the findings of Zhuo (2006), suggesting that

SMR enhances equity by providing poorer children with

access to schools that are supported with better educational

resources.

Third, SMR has promoted a reasonable and balanced

distribution of educational resources, evidenced by the CV

value of educational resource distribution in each sampling

county. This was also consistent with the orientation of the

Chinese government. As issued by the national government

in 2005 and 2010 (Ministry of Education of the People’s

Republic of China 2005, 2010), ‘‘equilibrium of educa-

tional development’’ has been an important policy in recent

years, aiming at equalizing educational resources across

schools. In other words, educational resources should be

distributed evenly among schools and areas. In this context,

SMR was considered a powerful instrument for achieving

this goal, and as intended, SMR has indeed promoted the

balanced development of education.

Despite significant progress associated with SMR in

Chinese rural schools in recent years, the emergence of

new problems has posed a challenge to SMR. These

problems have been compounded by the disparity of eco-

nomical and geographical conditions of the rural regions

concerned. Based on this investigation, the main problems

associated with SMR can be summarized into four aspects:

time taken to commute to school, hardships for access to

quality education faced by poor students, family financial

constrains and increased teacher workload.

First, SMR has engendered longer commutes for some

children, especially those living in remote poor areas.

Despite increasing numbers of rapidly growing urban areas

in China today, some people continue to live in small,

isolated villages or dispersed settlements. Thus, in isolated

mountainous areas with high peaks and low-quality road

infrastructure, students from poor families have no viable

transport options except to walk, making it hard to attend

school. From an international perspective, the longest

traveling distances and times are significantly beyond the

normal catchment radius of 1 km and 0.5 h recommended

by the IIEP (Lehman 2003). Learnt from this study, com-

paring to the international standard, there are still a number

of hard-to-reach children in China who carry considerable

travel burdens in order to attend school, a situation which is

at odds with international standards.

Second, such long commutes are likely to impact neg-

atively on these students’ performance at school. Although

a number of people in each group saying that SMR posi-

tively impacting educational quality in consolidated

schools, the micro statistics of this survey revealed that,

when facts including parents’ occupation, schooling dis-

tance, etc., taken together, the impact of SMR on students’

academic performances became much more complicated.

In other words, SMR did not have significant positive

impacts on students’ academic performance, while the

other factors such as parents’ working in agriculture,

boarding at school, longer commute to school, etc., sig-

nificantly and negatively impacted on students’ academic

performance. Thus, it can be concluded that, in the process

of SMR implementation, there are many hidden issues that

need additional focus, so as to improve SMR performance.

Third, the survey results also suggest that students in

consolidated schools had elevated costs, which caused

economic constraints to most families in remote areas.

Furthermore, some parents expressed concern that students

from low-income families might even drop out due to the

high costs of attending consolidated schools. Increased

expenditure caused by SMR therefore has indeed con-

strained family economies, negatively impacting on chil-

dren’s likelihood of completing compulsory education.

Research on educational economics also shows that an

increase or decrease in the private cost of education can

significantly influence the rate of universalization of edu-

cation (Sheehan 1973).

Fourth, increased numbers of students in classes made it

harder for teachers to handle class organization and man-

agement. Our survey has showed that, during SMR, along

with the number of small rural schools centralized into

larger consolidated schools, the size of schools and some-

times the size of classes increased. However, the number of

teachers appeared not to match this development. There-

fore, limited numbers of teachers have to teach ever more

students and be responsible for their discipline manage-

ment, as well as other issues resulting from the larger class

sizes. This increases the work burden of the teachers.

Due to problems involving longer commutes and eco-

nomic constraints for the students and their families, plus

the work burden on teachers, it is quite clear that there is

little scientific plan and verification for SMR implemen-

tation in rural China. In fact, as the key actors for SMR

implementation, governments at county level generally

rely on their direct experience to make decisions as to

which school should be closed or retained, rather than

adopt scientific approaches. Largely, as a consequence of

this, negative impacts on the students and their families

have emerged following SMR.

School mapping restructure in rural China 723

123

Author's personal copy

Thus, the key strategy should be to apply a scientific

approach to SMR implementation. From the international

perspective, as popularized in many countries, GIS should

be employed when designing scientific plans for SMR

implementation in rural China, as advocated by IIEP (e.g.

Caillods 1983; Attfield et al. 2002; Hite and Hite 2004).

Through GIS, all related data such as terrains, elevations,

roads, locations of schools, villages, any other institutions,

etc., can be gathered so as to generate visualized maps.

More importantly, with all the data imported, GIS can do

the analysis explaining how to make micro-plans for

mapping new schools, consolidating schools, solving real

difficulties faced by students during SMR, etc. In summary,

there is no doubting the role of GIS in the monitoring,

evaluation and impact assessment of SMR in China or its

role in the whole educational planning and management

environment.

On the other hand, multiple groups of participants

should be involved in the SMR process. As an educational

policy which focuses on rationalizing physical locations of

schools based on the demands of populations within special

catchment areas, SMR mainly involves the interests of four

groups: educational administrators, teachers, students and

parents. Thus, participation by the respondents is very

significant. This suggests that active participation, such as

meeting with respondents involving teachers, students and

parents, should be practiced to gather the information of

educational situations in different localities. The process,

as presented above, also indicates that multiple groups

should work together to form partnerships, examine all

possible variables, and make well-informed decisions when

implementing SMR.

The other major recommendation would be for efforts

to be made to increase the supply of qualified teachers to

rural schools. Several strategies associated with the

remuneration policy should be applied. First, more qual-

ified teachers should be increased in the consolidated

schools, according to the national standard of the ‘‘Ratio

of teacher to student.’’ Second, living allowances for

teachers in small rural schools should be increased, by

establishing job-specific subsidies and improving housing

conditions. Third, teachers of special subjects such as fine

arts, English and information technology should be

allowed to move between neighboring rural schools,

reducing teacher shortages in these subjects. Fourth, a

rotation plan for rural teachers should be implemented.

For some remote small rural schools, the problem of

teacher shortages is much more serious than at other

schools. Thus, a certain number of qualified teachers

could be sent to small rural schools to work for a period

of three to 5 years, with a new group of teachers sent to

replace them on their return.

Limitations and further research

This research has found that SMR has contributed to a

greater economy of scale in education, enhanced educa-

tional conditions and more balanced development of edu-

cation. However, it is also found that SMR has created

problems, such as longer traveling distance, economic

constraints on families and increased teacher workloads. It

appears that there is still a fundamental lack of structured

plan and groups’ participation. To deal with these issues,

the authors have proposed that policy-makers should make

scientific plans using GIS technology as is done in the other

countries, let multiple groups participate in the SMR pro-

cess, and enable governments at national and provincial

level to take responsibility of funding rural schools. In

summary, the implementation of SMR must be a two-step

process. If adjustments can be made to SMR implemen-

tation in the future, better performance is a likely outcome.

This study has suffered from a number of limitations,

namely a lack of information to justify conclusive state-

ments on indicators for economy of scale and equilibrium

in educational development. Some explanations rely on the

views of respondents involved in SMR, theoretical

approaches and data derived from the Ministry of Educa-

tion. To gain better results, a more detailed set of data must

be acquired. Improving data collection and administrator

training are necessary.

In future studies, room for research exists regarding

issues of transportation of remote rural students. How to

design school bus routes based on physical distance and

topographical conditions while taking economic efficien-

cies into consideration is a topic likely to attract attention

from researchers and policy-makers.

• The construction of boarding schools is another related

question of merit. Based on difficulties faced by

boarding students after SMR, a thorough investigation

of boarding schools in terms of financing the construc-

tion, assessing academic performance and psycholog-

ical changes of boarding students is worthy of further

study.

• As found in this survey, the closure of large number of

small rural schools in remote areas has inevitably

resulted in longer commute for students attending

school, as well as other negatives for their families.

Thus, issues associated with small rural schools such as

educational quality should be of major concern to

researchers.

• The relationship between school size, class size and

academic performance is another key question to

consider. Alongside SMR, school size and class scale

have increased significantly. One of the goals of SMR

724 D. Zhao, B. Parolin

123

Author's personal copy

was to improve educational quality as it has close

relationship with academic performance. A fundamen-

tal questions remain therefore: Have SMR-related

increases in school size and class scale had an impact

on student achievement? Is there a perfect school and

class size which will maximize student academic

performance?

Acknowledgments The project (The Progress of the Project on

Rational School Mapping Structure of the Rural Primary and Sec-

ondary Schools in central and western China (RSMS)) was funded by

a Sino-UK bilateral grant-in-aid ‘‘Basic Education in Western Areas

Project,’’ DFID, World Bank and Finance Bureau of MOE of China.

We are very grateful to DFID and the World Bank for their long-term

support of and contribution to rural basic education, especially in

central and western China. This paper is also one of the research

results of the Project ‘‘SMR in western rural China: Issues of small

rural schools—Empirical study by GIS application,’’ funded by

Chinese Ministry of Education (Project Number: 12YJC880157).

References

Alvarez, J. A., Loucagos, D., & Rashid, S. (2010). A cost-benefitanalysis of school regionalization in Massachusetts. Paperprepared for interactive qualifying project. Worcester: Worces-

ter Polytechnic Institute.

Attfield, I., Tamiru, M., Parolin, B., & DeGrauwe, A. (2002).

Improving micro-planning in education through a geographicalinformation system: Studies on Ethiopia and Palestine. Paris:

IIEP/UNESCO.

Berry, C. (2004). School inflation: Did the twentieth-century growth

in school size improve education? Education Next, 4(4), 56–62.

Blauwkamp, J., Longo, P., & Anderson, J. (2011). School consoli-

dation in Nebraska: Economic efficiency versus rural community

life. Online Journal of Rural Research and Policy, 6(1), 1–20.

Caillods, F. (1983). Module I: School mapping and micro-planning

concepts and processes. In F. Caillods, J. Casselli, T. N. Chau, &

G. Porte (Eds.), Training materials in educational planning,administration and facilities: School mapping and micro-plan-ning in education. Paris: IIEP/UNESCO.

California County Superintendents Educational Services Association

(2002). Declining enrollment in California. Retrieved from July

14, 2011. http://wwwstatic.kern.org/gems/ccsesaAtWork/Declining

Enrollmentpaper3.pdf.

Carnoy, M. (1995). International encyclopedia of economics ofeducation (2nd ed.). Oxford: Elsevier Science Press.

Cato, H. B. (2011) The effects of school enrollment size on student

achievement. A Thesis submitted to the faculty of the graduate

school of arts and sciences of Georgetown University. Wash-

ington, DC. April 15.

Chan, C. Y., & Harrell, S. (2009). School consolidation in rural

Sichuan: Quality versus equality. In M. L. Zhou & A. M. Hill

(Eds.), Affirmative action in China and the US: A dialogue oninequality and minority education (pp. 143–166). New York:

Palgrave Macmillan.

Cooley, D. A. (2012). Small school district consolidation in Texas: Ananalysis of its impact on costs and student achievement(Unpublished doctoral dissertation). Stephenville, TX: Tarleton

State University.

De Haan, M., Leuven, E., & Oosterbeek, H. (2011). Scale economies

can offset the benefits of competition: Evidence from a school

consolidation reform in a universal voucher system. (IZA DP

No. 5528).

DeYoung, A. J., & Howley, C. (1990). The political economy of

school consolidation. Peabody Journal of Education, 4(67),

63–89.

Fan, X. Z. (2006). Studies on the reason, motivation and pattern

selection of adjusting primary and secondary school distribution

in rural areas. Education and Economy, 82(1), 26–29. (in

Chinese).

Fan, X. Z. (2009). Rational structure of primary and secondaryschools in rural mid-western China. Chinese social science

press. (in Chinese).

Fanning, J. (1995). Rural school consolidation and student learning.

Retrieved from ERIC database. (ED384484).

Fitchen, J. M. (1991). Endangered spaces, enduring places, change,identity and survival in rural America. Boulder: Westview Press.

Fitzwater, C. O. (1953). Educational change in reorganized schooldistricts. Washington: US Government Printing Office.

Hargreaves, E., Montero, C., Chau, N., Sibli, M., & Thanh, T. (2001).

Multigrade teaching in Peru, Sri Lanka and Vietnam: An

overview. International Journal of Educational Development,21, 499–520.

Hite, S. J., & Hite, J. M. (2004). Geographical information systems ineducation planning and management: A training module createdfor the IIEP/UNESCO. Paris: IIEP/UNESCO.

Howard, J. Y., Wrobel, S. L., & Nitta, K. A. (2010). Implementing

change in an urban school district: A case study of the

reorganization of the little rock school district. Public Admin-istration Review, 70(6), 934–941. doi:10.1111/j.1540-6210.

2010.02229.x.

Jimerson, L. (2007). Slow motion: Traveling by bus in consolidated

districts in West Virginia. Rural school and community trust.(ERIC Document Reproduction Service No. ED499440).

Johnson, J. (2004). Small works in Nebraska: How poverty and thesize of school systems affect school performance in Nebraska.

Arlington, VA: Rural School and Community Trust. Retrieved

from August 10, 2009. www.files.ruraledu.org/docs/nebraska/

Small_Works_In_Nebraska_FINAL_01-04.pdf.

Kay, S., Hargood, N., & Russell, R. K. (1982). The effect ofconsolidation on fidelity to traditional value systems. Frankfort:

Kentucky State University Community Research Service.

Kennedy, N., & MacDougall, M. (2007). School closure processreview: Review committee report and recommendations.

Retrieved from September 30, 2011. www.pomquet.ednet.ns.ca/

School_Closure_Review_Final_Report.pdf.

LaPlante, R. J. (2005). School consolidation: An ineffective way of

improving education. Flint Hills Center for Public Policy, 2(9),

1–2.

Lehman, D. (2003). Bringing the school to the children: Shorteningthe path to EFA. Retrieved from September 30, 2011. www.

siteresources.worldbank.org/EDUCATION/Resources/Education-

Notes/EdNotesRuralAccessInitiative.pdf.

Li, S. Y. (2000). Theory of rural education. Nanjing, China: Jiangsu

Education Publishing House. (in Chinese).

Liu, C. F., Zhang, L. X., Luo, R. F., Rozelle, S., & Loyalka, P. (2010).

The effect of primary school mergers on academic performance

of students in rural China. International Journal of EducationalDevelopment, 30, 570–585.

Lu, K., & Du, Y. H. (2010). Effects of school consolidation on

students’ academic performance. Tsinghua Journal of Educa-tion, 31(6), 64–73.

McHenry-Sorber, E. (2009). School consolidation in Pennsylvania:

An analysis of Governor Rendell’s policy proposal. The Beacon:A Publication of the Pennsylvania School Study Council, 5(4),

1–17.

School mapping restructure in rural China 725

123

Author's personal copy

Ministry of Education of the People’s Republic of China (1982).

Planning for elementary and primary school buildings’ area quotas

(on trail). Retrieved from September 30, 2011. www.

people.com.cn/item/flfgk/gwyfg/1982/206013198201.html.

Ministry of Education of the People’s Republic of China. (1998).

Education statistical year book. Beijing: People’s Education

Press.

Ministry of Education of the People’s Republic of China. (2005).

Notice of advancing balanced development of compulsoryeducation. Retrieved from May 04, 2010. http://www.moe.gov.

cn/publicfiles/business/htmlfiles/moe/s3321/201001/xxgk_81809.

html.

Ministry of Education of the People’s Republic of China. (2009).

Education statistical year book. Beijing: People’s Education

Press.

Ministry of Education of the People’s Republic of China. (2010).

National program for medium- and long-term educational reform

and development. http://www.moe.gov.cn/publicfiles/business/

htmlfiles/moe/moe_177/201008/93785.html.

Mo, D., Yi, H. M., Zhang, L. X., Shi, Y. J., Rozelle, S., & Medina, A.

(2012). Transfer paths and academic performance: The primary

school merger program in China. International Journal ofEducational Development, 32(3), 423–431.

Murry, V., & Groen, R. (2004). Competition or consolidation? Theschool district consolidation debate revisited (Report No. 189).

Phoenix, AZ: Goldwater Institute. Retrieved from January 12,

2011. www.goldwaterinstitute.org/article/1223.

Nitta, K., Holley, M., & Wrobel, S. (2010). A phenomenological

study of rural school consolidation. Journal of Research in RuralEducation, 25(2), 1–19.

Pang, L. J. (2006). School mapping restructure in rural China:

Problems, reasons and solutions. Exploring Educational Devel-opment, 4, 1–6. (in Chinese).

Peshkin, A. (1982). The imperfect union: School consolidation andcommunity conflict. Chicago: University of Chicago Press.

Plucker, A. J., Spradlin, E. T., Magaro, M. M., Chien, W. R., Zapf, S.

J. (2007). Assessing the policy environment for school corpora-tion collaboration, cooperation, and consolidation in Indiana(Policy Brief No. 5). Retrieved from September 30, 2011.

http://ceep.indiana.edu/projects/PDF/PB_V5N5.pdf.

Purcell, D., & Shackelford, R. (2005). An evaluation of the impact of

rural school consolidation: What challenges may a new round of

rural school consolidations have on the ‘‘safety,’’ ‘‘educational

performance’’ and ‘‘social environment’’ of rural communities?

Paper presented at National Rural Education Association, Fort

Collins, CO.

Rural School and Community Trust. (2000). School consolidation andtransportation policy: An empirical and institutional analysis.

Arlington, VA: K. Killeen & J. Sipple.

Rural School and Community Trust. (2002). Closing costs: A

summary of an award winning look at school consolidation in

West Virginia, a state where it has been tried aggressively.

Retrieved from September 30, 2011 Hhttp://www.ruraledu.

org/articles.php?id=2043H.

Shakrani, S. M. (2010). School district consolidation study in 10Michigan counties: Is district consolidation cost effective? Whatis the alternative to consolidation? (Working Paper No. 15). East

Lansing, MI: Education Policy Center at Michigan State

University.

Shavers, A. W. (2003). Rethinking the equity versus adequacy debate:

Implications for rural school finance reform litigation. Universityof Nebraska Law Review, 82, 133–189.

Sheehan, J. (1973). The economics of education. London: George

Allen and Unwin Press.

Spradlin, T. E., Carson, F. R., Hess, S. E., & Plucker, J. E. (2010).

Revisiting school district consolidation issues. Education PolicyBrief, 8(3), 1–20.

Standard & Poor’s. (2007). Study of the cost-effectiveness ofconsolidating Pennsylvania districts: Profiles of paired districts.

New York: Standard & Poor’s School Evaluation Services.

State Council of the People’s Republic of China. (2001). The decision

to reform and develop basic education. Retrieved from Septem-

ber 30, 2011. www.edu.cn/20010907/3000665.shtml.

Theobald, P., & Nachtigal, P. (1995). Culture, community and the

promise of rural education. Phi Delta Kappan, 2(77), 132–135.

University of Arkansas, Office for Education Policy (2005). A firstlook at the impacts of district and school consolidation (PolicyBrief No. 20). Fayetteville, AR: Author.

Voth, D. E., & Danforth, D. M. (1981). Effect of schools upon small

community growth and decline. The Rural Sociologist, 1(6),

364–369.

Walberg, H., & Fowler, W. (1987). Expenditure and size efficiencies

of public school districts. Educational Researcher, 16(7), 5–13.

Xiong, X. M. (2007). Reflection on the present adjustment of primary

and secondary school distribution in rural areas. Education andEconomy, 84(2), 50–53. (in Chinese).

Yang, L. (2010). Reviews and reflections upon the policy of school

mapping restructure. Blue book of education: Annual report onChina’s education 2010 (pp. 73–85). Beijing, China: Social

Sciences Academic Press. (in Chinese).

Zhao, D., & Parolin, B. (2011). School mapping restructure in China:

What role for the small rural school? Frontiers of Education inChina, 6(2), 248–278.

Zhao, D., & Parolin, B. (2012). GIS analysis of school consolidation

in rural China: Process, problems and conclusion. Education andEconomy, 1, 13–18.

Zhuo, M. (2006). Primary school consolidation in rural China:

advantages, disadvantages and solutions. Education Forum, The

Education Department of Fujian Province. Retrieved February 9,

2011. from http:/218.85.72.34/newsInfo.aspx?pkld=10103. (in

Chinese).

726 D. Zhao, B. Parolin

123

Author's personal copy