Asia-Pacific Edu Res DOI 10.1007/s40299-013-0129-2

REGULAR ARTICLE

Merged or Unmerged School? School Preferences in the Context of School Mapping Restructure in Rural China Dan Zhao • Bruno Parolin

 De La Salle University 2013

Abstract Although the school mapping restructure (SMR) program in rural China mainly aims to improve the efficiency of education, this paper aims to show that the principle of educational equity should be given more attention, as evidence suggests that some children lose the opportunity to learn at a local village and rural families have different preferences regarding merged and unmerged schools. The study on which this paper is based aims to understand the family school choice preferences under SMR, through a combination of questionnaires, interviews, and document analysis. Participating in the study were 10,000 parents whose children were in merged and unmerged primary schools (a total of 986 schools), from 178 towns in 38 counties of six provinces in mid-western China. Data were collected mainly by questionnaires and analyzed by a binary logistic regression model as a tool to estimate school preference probabilities. The study finds that in the context of SMR, several related factors impact the willingness of families to choose certain schools for their children. Although many merged schools have better facilities and more qualified teachers, a number of families still prefer unmerged schools for their children due to shorter commuting distances and smaller school sizes, factors which offer improved school accessibility. This study also reveals that other related factors including parents’ education, economic condition, etc., are strong D. Zhao (&) College of Humanities, Northwest A&F University, Yangling 712100, Shaanxi, China e-mail: [email protected] B. Parolin Faculty of the Built Environment, University of New South Wales, Sydney, NSW 2052, Australia e-mail: [email protected]

influencers of family schooling preferences. Based on the results, the policy implications are that integration of both points of efficiency and equity of education when enacting SMR should be considered. Keywords School preferences  Efficiency and equity of education  School mapping restructure  Merged and unmerged schools  Educational policy  Rural China

Introduction School mapping restructure (SMR) is a term used to describe the combining of 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 consolidation’’: the merging of two or more attendance areas to form a larger school attendance pool (Fitzwater 1953; Sell et al. 1996; Rural School and Community Trust 2000; McHenry-Sorber 2009). Others (e.g., Peshkin 1982) use the term ‘‘school reorganization’’ 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 all refer to the merging together of small schools in relatively poor condition to create larger and better-resourced schools. SMR is targeting improvements in economy of scale, enhancement of educational quality, and equilibrium of educational development. SMR has generated a lot of debate in the literature about the effects of implementation and the relative merits of merged and unmerged schools. In theory, as well as advocated by most of policy makers, larger merged schools are more cost-effective than unmerged smaller schools.

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Thus, SMR is an effective way to achieve ‘‘economy of scale.’’ Additionally, merged larger schools employ a standardized school model which can improve the quality of education and expand accessibility to education (Fan 2006), a similar argument to that in international research (Standard and Poor’s 2007). Some policy makers have advocated the concept of ‘‘bigger is better,’’ believing that educational resources should be centralized in merged larger schools in order to provide better education services, so as to make students enjoy a higher quality of education (Wan and Bai 2010). Mo et al. (2012) found that students who transfer to merged county schools benefit from the transfer during SMR. However, new problems have emerged as by-products of SMR implementation. Negative impacts have been found among poor children in remote villages, including longer travel distances, reduced safety associated with the trip to school, increased financial burdens, and uncertain academic achievement. Fan (2006) conducted a survey to investigate the implementation process of SMR in rural regions: The closure of remote schools may place greater financial pressure on some students and their families because of increased travel or living expenses when attending the merged larger schools, as also argued by international scholars (Laplante 2005; Murry and Groen 2004). Students may also suffer from the longer time required to travel to school (Pang 2006; Wang and Lu¨ 2007), which may limit their after-school free time and present a barrier to participation in extracurricular activities. In addition, the closure of schools may lead to a loss of function and stability of the community, causing the rural culture to face an inheritance dilemma (Feng 2012). Finally, SMR seems to not guarantee an improvement of student performance in step with the expansion of schools (Lu and Du 2010; Dong et al. 2008). Due to the negative impacts of SMR implementation, a number of studies focus on the role of unmerged small rural schools. Zhao and Parolin (2011, 2012) present evidence to show that small rural schools can improve the performance of students, increase accessibility to schools, reduce the cost of school travel and accommodation, satisfy the aspirations of local villagers, etc. These positive benefits can also contribute to the provision of education equity in rural China. They propose that small rural schools have a significant role to play in delivering education services in rural China, as also argued by Fan (2009), Lei and Zhang (2011), and international scholars such as Jimerson (2005) and Blauwkamp et al. (2011). Sun et al. (2008), Tian (2001), and Zhang (2005) argue that the special teaching model, multi-grade teaching, employed in most small rural schools is a very effective way to improve student performance. Additionally, it has also been reported that students and their parents oppose small school

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consolidation because they see the local small school is an important pillar of rural communities both socially and economically (Ilvento 1990). As argued by Howley (1994), the small school can have a strong positive effect on attitudes and satisfaction, participation, attachment to school, low dropout rates, and fairly regular attendance. Based on the debate regarding SMR implementation, it can be concluded that the construction of merged larger schools is the trend, theoretically effective and beneficial to students, while due to the negative impacts of SMR, unmerged small schools are supported by local children. A number of current studies try to discuss the viewpoints of children or parents on SMR. Lei and Zhang (2011) discuss the public participation and satisfaction during SMR and show that the participation rate in SMR is extremely low, and some people are not satisfied with the effects of school closure. The satisfaction rate of low-income residents in remote mountain areas is much lower. Jiang (2010) tries to discuss the viewpoint of children’s parents on SMR, using the theory of identification in social psychology to analyze how children’s parents form an opinion regarding SMR, revealing that only if parents considered SMR as rational and beneficial to their own children would they support it. However, the current studies have not clarified why, in the process of SMR, families of different backgrounds have different school preferences for their children. What is the impact of other factors on rural children and their families’ school preferences? To answer these questions, the focus should be on rural children and their parents themselves, who are the nucleus group directly influenced by SMR implementation. Based on the existing literature and the research gap, this study tries to generate unique benefits to this field, especially for educational management practice and policy. Firstly, the theoretical perspective of this study focuses on educational equity and access, and goes beyond previous studies in these respects. According to the theory, rural schools, whether merged or unmerged, should have balanced quality, giving children equal access to school no matter their socio-economic conditions. The study considers multiple factors including school quality and the socio-economic conditions of children, which act as the backdrop to the debate surrounding school preference, in order to reflect on whether SMR implementation improves educational equity and access. Secondly, the approach here to the impact of SMR on school preference differs from that of previous studies. The authors conducted a largescale survey in mid-western China, and applied a binary logistic regression model as their tool of analysis, based on data provided by those subjects most directly affected by SMR—the students and parents—and found that the information of such direct participants provided much useful information by which to examine SMR

Merged or Unmerged School?

performance. Accordingly, the conditions and impact of SMR implementation are presented in depth. Thirdly, the most significant contribution of this study lies in its implications for educational management. Due to the unbalanced quality of merged and unmerged schools plus decreased access significantly impacting school preference, the authors argue that a better plan focusing on education equity and developed by applying the Geography Information System (GIS), participation of multiple groups, and provision of funds and teachers to remote rural schools, plus sharing educational resources between schools, should be strategically applied to SMR implementation. These suggestions offer useful ways of improving SMR performance and promoting educational equality and access in China. This paper proceeds in eight stages. First, related issues of SMR are reviewed, including its definition, conditions of merged larger schools and unmerged small rural schools, and the viewpoints of participants involved in SMR; further, the focused issue of this study is proposed based on a comprehensive literature review. Second, the context of SMR in rural China is introduced. Third, the theoretical underpinning, educational equity, and access are discussed; it is vital in justifying the main argument of the paper. Fourth, relevant literature review and hypotheses are developed. Fifth, the authors present the methodology used in the large-scale questionnaire-based survey, and the binary logistic model applied to data analysis. The sixth stage presents the research result by applying data derived from the survey. Seventh, discussions based on the research results are presented, and implications to management practice and policy are proposed. Lastly, conclusions are made and several issues associated with SMR which require additional study are raised, regarding the course of further development of rural education.

SMR in Rural China Accompanied by industrialization and urbanization, the geographic areas of China can be classed as rural, county, and urban areas. Rural areas are administrative divisions mainly based on agriculture and consisting of independent villages as the autonomous unit of rural residents. County areas rest between rural and urban areas, and form a center of villages and towns in terms of politics, economics, and culture. Urban areas consist of concentrated non-agricultural populations and well-developed industry and commerce, making them centers of their surrounding regions (Li 2000). Based on this definition of rural, county, and urban areas, it can be concluded that the rural area is the lowest-level administrative division in China, characterized by agricultural production and rural residents. Thus, the

rural school in this study means a school located in a rural area, 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 classroom space and facilities, inhibiting educational quality. These problems have persisted for a long time and have not been successfully resolved. In the implementation of universal nine-year compulsory 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 generated negative impacts on rural school mapping. For example, many villages have their own school despite low birthrates, inadequate physical 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 decline of 5,679,287). The number of rural secondary students had reduced by 44.86 % from 11,445,048 in 1998 to 6,310,685 in 2009 (a decline of 5,134,363; Ministry of Education of the People’s Republic of China 1998, 2009; see Table 1). 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 Ministry of Education of China reveal that over a 12-year period between 1998 and 2009, the number of rural primary schools declined from 493,152 to 234,157 (a reduction of 52.52 %), while the number of small rural schools declined from 178,952 to 70,954 (a reduction of 60.35 %; Ministry of Education of the People’s Republic of China 1998, 2009).

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D. Zhao, B. Parolin Table 1 Number of primary and secondary students in China (1998 and 2009) Total

Rural area

County area

Urban area

2,765,234

Primary school 1998

22,013,814

15,100,116

4,148,464

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

Source Ministry of Education of the People’s Republic of China (1998) and Ministry of Education of the People’s Republic of China (2009)

Theoretical Underpinning: Efficiency and Equity of Education The theoretical underpinning of this study includes two aspects: efficiency and equity of education. Firstly, educational efficiency is theoretically considered to be generated through the SMR program, the argument in favor of SMR being that a wide range of student cohort sizes can be served by roughly the same number of teachers and administrators (Duncombe and Yinger 2010). This point underlines that an effective central administration would only have to increase its staff incrementally as the number of student enrolled increases, which is an important issue for cost control in educational management (Eyre et al. 2002; Reeves and Cynthia 2004). Moreover, SMR could contribute to optimal efficiency, which is the enrollment level at which a school’s cost per pupil is the lowest, and schools could offer a sufficiently diversified curriculum, maximize students’ standardized test scores, minimize dropout rates, or maximize graduation rates (Bard et al. 2006). In other words, with SMR, schools could offer more subjects and the quality of education could generally be improved at lower costs given the larger size of consolidated schools and districts. The other point is educational equity, which means every child has equal access to education, enjoys equal quality of education with balanced resource distribution, and has equable achievements (Shavers 2003; Strange 2003; UNESCO-IIEP 2010; Castellia et al. 2012). In detail, the three interrelated dimensions are as follows: (1) Equitable access to education—all students to have equal access to classes, facilities, and educational programs no matter what their national origin, race, gender, living region, disabilities, first language, or other distinguishing characteristics; (2) Equal treatment—every child should enjoy equal quality of education both in educational resources and interaction. Educational resources including funding, staffing, and other resources (facilities, environmental learning spaces, instructional materials, etc.) are distributed

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in an equitable and fair manner such that the notion that all diverse learners must achieve high academic standards and other school outcomes become possible; and (3) Equal achievement—given the requisite pedagogical, social, emotional, and psychological support, every child has equal opportunities to achieve high standards of excellence and achievement, and performance gaps should be virtually non-existent. In upholding educational equity, educational management is required to provide certain programs for students to insure equal education (Lockheed and Cueto 2006; UNESCO-IIEP 2009, 2010; Zhang and Kong 2009); SMR is not an exception. In sum, both efficiency and equity of education are important drivers of educational policymaking in the SMR context. Undoubtedly, the theory of efficiency is applicable for enacting SMR, and indeed some remarkable results related with educational efficiency have been achieved. However, it is important not to ignore the idea that educational equity is the primary and fundamental principle for education planning including the SMR program (Fan 2009; Zhao and Parolin 2012). Thus, merged and unmerged schools should, regardless of differences in size and location in the context of SMR, give children within a given district equal access to school regardless of aspects of their social-economical situations such as parental educational background, family income and size, and location. In addition, both merged and unmerged schools should be assigned balanced resources including funding, staff, and facilities, so as to insure equitable quality of education. And accordingly, children’s achievements should be virtually equal. Based on this, the study tries to figure out the probabilities of school preferences impacted by factors including parental characteristics, family situation, and school quality. The results of the study are indicative of whether or not educational equity has improved under SMR. It could be explained that, if family school preference differentiated and was significantly impacted by those factors above, this demonstrates that inequity of education is an issue and that SMR performance should be improved, according to the theory of equity of education.

Relevant Literature Review and Hypotheses Development As proposed by Shapiro and Oleko (2001) and Altrichter et al. (2011), the frame of factors impacting family school preference could be defined as parental characteristics, family situation, school accessibility, and quality of education. For each class of factors, there are some relative studies showing the detailed correlations between the factor and school preference, which could be applicable for this study focusing on school preference under the context of SMR.

Merged or Unmerged School?

Correlations Between Parental Characteristics and School Preference Fan (2009) and Zhao and Parolin (2012) propose that, in rural China, the number of children from high education families entering merged larger schools is more than that from lower education families. Zhang (2011, 2013) argues that parental educational backgrounds significantly impact schooling choice willingness; if parents have a higher level of education, they will more likely choose better schools for their children. Altrichter et al. (2011) point out that choice motive is associated with social family characteristic; high education families report more frequent motives to make use of free choice. This is also argued by some other scholars such as Bagley (2006). Thus, based on these studies, years of father’s education and years of mother’s education were thought to affect school preference. Inspired by this, it could be assumed that, under SMR, as the number of years that a parent (father and mother) studied increased, they would more willing to choose schools of better quality for their children. Based on this, the authors used this factor to show whether or not it would influence school preference toward merged or unmerged schools. Correlations Between Family Situation and School Preference Household Income Zeng (2000) and Sun (2004) argue that family economic status is an important element for school preference, especially for rural children. Belfield (2000) states that a family with a higher economical income would more likely choose a better school for their children. Some researchers pointed out that choice motives are associated with social family characteristics. High income and high education families report more frequent motives that help to make use of free choice (Altrichter et al. 2011; Crozier et al. 2008; Bagley 1996). Fan (2006) and Zhao and Parolin (2012) argued that families with better economic conditions prefer merged schools, because they are more able to afford the schooling cost including transportation or accommodation fees required by merged schools, in order to get better human capital and quality education. Therefore, family economic status calculated by per capita income is used to estimate the probability of school preference regarding merged and unmerged school, and the authors hypothesize that the level of household income positively affects the preference for merged school.

obtaining schooling than those in non-mountainous area, and therefore they would not be willing to enter merged school. Thus, the living region of rural children plays an important role in determining which kind of school they prefer. Wu and Shi (2011) and Jia and Zeng (2011) also conclude that the living region of rural children was applied as a determining factor relating to school preference; this is also argued by Harris and Johnston (2008) and Hamnett and Butler (2011). Based on this, mountainous and non-mountainous areas were applied as two categories within the factor of location, in order to estimate the impact of region on school choice preference; the relation assumed is that factor of living region positively affects family preference for merged school. Family Size As the family size increased, willingness to enroll in more costly schools decreased (Zeng 2000; Sun 2004). In rural China, most people with lower incomes have larger family sizes. When an elder child moved on to higher education, the family’s expenditure on education increased; household then preferred to enroll their younger children in schools with fewer associated costs. Thus, the number of siblings also impacted school preference (Fan 2006; Lei 2012; Bai 2012). Based on the literature, the size of the family was applied as a determining factor of school preference and hypothetically has a positive impact on preference for merged school. Correlations Between School Accessibility and School Preference Cost of School Specifically, schooling in merged schools generates both direct costs and opportunity costs (Carnoy 1995); both direct cost and opportunity cost have influence on school preference (Levin 1998; Buddin and Cordes 1998; Brunner and Sonstelie 2003). Under the circumstance of SMR, merged schools are situated further away from children’s homes, forcing families to pay more for transportation, accommodation, etc. This causes a decrease in school preference toward merged schools (Wang and Lu¨ 2007; Jia and Zeng 2011; Lei 2012; Ye 2012). Thus, in this study, the cost of schooling is assumed to negatively affect preferences for merged school. Distance

Location He (2009) and Gao (2011) argue that children living in mountainous areas faced far more difficulties in terms of

Bai (2012) and Jia and Zeng (2011) note that distance indicates the round trip journey from the children’s home to school; the factor of distance is a determinant for school

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preference. In other words, as the schooling distance increases, children will face more difficulties traveling to school and they would less likely to choose the school further away. Rincke (2006) argues that a district’s position relative to its immediate neighbors may be relevant to school preference, due to transportation to more distant schools being either unavailable or prohibitively costly; school districts will be able to attract students only from nearby districts. Some other researchers also point out that travel distance to school and ties with the local community impact school preference significantly (Crozier et al. 2008; Gewirtz et al. 1995; Reay and Lucey 2000). Therefore, in this study, under SMR, schooling distance is predicted to negatively affect preferences for merged schools. Correlations Between Quality of Education and School Preference Among many factors that guide parental attitudes toward school choice, the quality of education including teacher qualification, teacher performance, school facilities, and student achievement is the most important (Carnoy 1995). Normally, during SMR, merged schools were allocated better facilities and qualified teachers, improving the quality of education considerably, which made merged school more attractive (Wu and Shi 2011; Lei and Zhang 2011; Zhao and Parolin 2012; Lu and Du 2010). Rincke (2006), Hoxby (2003), and Black (1999) also point out that districts with better schools with more qualified teachers, better facilities, and better student performance are more likely to be able to attract non-resident students and should therefore be more inclined toward school choice than districts with worse schools. Thus, in this study, the quality of the school is supposed to be a determinate factor for school preference. Additionally, for children who are poor in study achievement, their family would more likely prefer merged schools, in order to improve their grades through higher quality of school services (Fan 2009; Zhao and Parolin, 2012). Therefore, teacher qualification, teacher performance, school environment, and facilities are assumed to positively impact school performance in merged ones, while student achievement negatively impacts school preference for merged schools. Fig. 1 Research design of the study

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Research Method Research Design The basic thought of the research could be explained through Fig. 1: In mid-west China, most rural areas experienced the SMR program, and there emerged mainly two kinds of schools—merged schools and unmerged schools. Under this circumstance, rural families have diverse preferences toward merged school and unmerged schools, due to some factors including parental characteristic, family situation, school accessibility, and quality of education. Thus, this study is focusing on whether and to what extent those factors impact school preference significantly. Based on this, 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. Subjects Survey target groups for the whole project comprised of education administrators, headmasters/teachers, students, and parents. According to relevant studies, particularly households, community, and parents’ characteristics and their perception were important to impede or facilitate school choice (Sun 2004). In fact, due to the age of the students during the basic education period, parents’ preferences were instrumental in determining school choice. For this reason, the questionnaire data derived from parents’ responses were mainly used when examining family school preference. Parents were surveyed by first selecting students using random and clustering sampling methods according to the class lists of students obtained from each school, and then asking the students to deliver the questionnaire to one of their parents. Study Site Multi-stage sampling and stratified sampling were used to select the surveyed areas. Provinces were selected on the basis of various factors including developmental disparity, cultural

Merged or Unmerged School?

diversity, demographics, and geographic factors. The samples were then further broken down to city, county, town, and school levels. Finally, respondents (parents, students, teachers, and school administrators) associated with the schools were selected. In total, six provinces, including Shaanxi, Henan, Hubei, Yunnan, Guangxi, and Inner Mongolia, 38 counties, and 178 towns, were selected for the survey. The schools selected were all situated in central and western China, and their environments may be characterized by comparatively low levels of economic development and strong agricultural or animal-raising contexts. They were 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 nine-year compulsory education schools, and 37 were senior secondary schools. The schools were situated in mountainous, hilly, plain land, pastoral, mining, and lakeland areas. Among the schools surveyed, 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). Data Collection Three instruments were used for collecting data. (a)

SMR planning form This was designed to collect information on enrollment, service population, physical accessibility, number of teachers, and finance of each school surveyed. First, the data of enrollment included the number of students enrolled in the school. Second, the service population not only meant the number of students served by the school but also included all the other people located in the villages where the students came from. Third, physical accessibility was indicated by the distances of children commuting to the school. Fourth, the teachers consisted of teaching staff, administrators, and auxiliary teaching staffs. Fifth, the finance included expenditure on support for students’ academic achievement (instructional materials, teachers’ pay, educational media services, textbooks, etc.); cost of facilities including construction of school buildings (teaching room, library, canteen, gym, etc.), laboratory equipment, food services, etc.; and administrative fees. (b) Interviews and discussions The interviews and discussions proceeded in two ways: group interviews and individual interviews. They consisted of open-ended questions which focused on the viewpoints of children and parents on SMR (involving advantages and disadvantages); preference for schools and reasons for the preference; opinions on the quality of merged schools and unmerged schools, especially small schools; effect of SMR on children’s

(c)

academic achievement, life changes, and difficulties after SMR; and major challenges ahead, opinions, and suggestions for better implementation strategies. Questionnaire surveys For parents, the number of questionnaires sent out was 10000; 7995 were returned, of which 7,421 were valid, making a validity rate of 74.12 %. The investigation included basic respondent information (age, gender, education, etc.); living region; size of family; preference for merged or unmerged schools; children’s distance to school; assessment on quality of schools; children’s academic performance; expense for schooling; and major challenges regarding better implementation of SMR. The questionnaires were tested for content validity and reliability. Validity evidence suggests that, by using a two-rater agreement procedure, the calculation of the Content Validity Index (CVI) generated was 0.79. Reliability evidence was established by applying Kuder– Richardson Formula 20 (KR-20) with the result being 0.82. Both sets of evidence of validity and reliability were thus found to be at acceptable levels.

Data Measures: Definition and Choice of Variables The included variables in this research analysis were as follows: dichotomous dependent variable—type of school during SMR (merged school and unmerged school) where ‘‘willing to choose merged school’’ = 1 and ‘‘willing to choose unmerged school’’ = 0; and the independent variable—11 factors contributing to the school choice preference. These factors can be classed as follows: (1) Parent characteristics including father’s education and mother’s education; (2) Family situation including log of PCI, region, and size of family; (3) School accessibility including cost of schooling and commuting distance; and (4) Educational quality including teacher qualifications, teacher performance, school environment and facilities, and students’ grades (see Table 3). Data Analysis Logistic regression is useful for situations in which one can predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model suited to models where the dependent variable was dichotomous. The logistic regression is applicable to a broader range of research situations than discriminate analysis. In this paper, binary logistic regression model was used to find out the probability relating to willingness toward SMR. A logit model is a univariate binary model. The dependent variable yi takes the value as one or zero, and the variable xi is the independent variable. The model is written as that:

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D. Zhao, B. Parolin Table 2 Number of surveyed schools in six provinces by different categories Schools

Number

predicted logged odds of having the characteristic of interest for a one-unit change in the dependent variables.

Proportion (%)

By province

Results

Shaanxi

221

22.4

Guangxi

369

37.4

97

9.8

Yunnan

141

14.3

Henan

135

13.7

Inner Mongolia Total

23 986

2.4 100

Hubei

By location Mountainous area

657

66.7

Hills

118

12.0

Plains

184

18.7

Pastureland

5

0.5

Mining area

1

0.1

Lakeland area

1

0.1

Not supplied

20

Total

986

1.9

As a result of SMR in rural China, two kinds of schools arose: merged and unmerged schools. Basically, both school types make up primary compulsory education, financed and administrated by the Chinese government. In theory, merged and unmerged schools should get the same level of financing and educational resources. However, in the actual context of SMR, a number of small rural schools in poor condition have been merged into larger schools, which were seen as the key type of school structure, so as to provide better service for an increasing numbers of students. On the other hand, unmerged schools were seen as a type that would eventually be closed down, and in that context they could not get enough finance support, resulting in conditions deteriorating further.

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Parental Responses to Merged and Unmerged Schools Pr ðyi ¼ 1Þ ¼ Fðx0i bÞ where b is a parameter to be estimated and F is the logistic cdf. The logit model is preferable on account of its comparatively lower computation costs. The independent or predictor variables in logistic regression can take any form. The relationship between the predictor and response variable is not a linear function in logistic regression. Instead the logistic regression function is used, which is the logit transformation. pð y i Þ ¼

eðaþbi xi Þ 1 þ eðaþbi xi Þ

where a is the constant of the equation and b is the coefficient of the predictor variables. An alternative form of the logistic regression equation is log it½pðyi Þ ¼ log½

pðyi Þ  ¼ a þ b i xi 1  pðyi Þ

where i = 1……n. In this study, the basic formula for estimating the probability toward SMR was   pðschtyp ¼ 1Þ log it½pðschtyp ¼ 1Þ ¼ log 1  pðschtyp ¼ 1Þ ¼ a þ b1 ðfateduÞ þ b2 ðmomeduÞ þ b3 ðlpciÞ þ b4 ðregionÞ þ b5 ðfmlysizÞ þ b6 ðscstÞ þ b7 ðdiskmÞ þ b8 ðqlfteaÞ þ b9 ðpflteaÞ þ b10 ðschevifacÞ þ b11 ðstugraÞ The logistic regression coefficients show the change (increase when bi [ 0, decrease when bi \ 0) in the

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Regarding parental responses to the two types of schools— merged and unmerged—it depends upon several factors, and to some extent parents in different regions had different opinions. The results of the study show that, for merged schools, quality of education factors including teacher qualifications, teacher performance, and good environment and facilities were the main factors in determining attitudes to school type. Table 4 shows that enrollment in merged schools in mountainous and nonmountainous areas was 59.6 and 53.2 %, respectively, due to teacher qualification. Additionally, it was due to teacher performance in 22.3 and 29.5 % of cases, respectively, and 13.5 and 9.2 % respondents, respectively, felt it was due to good environment and facilities. These are given as reasons pushing parents to prefer merged schools in mountainous and non-mountainous areas, and this is consistent with research by Lei (2012). For unmerged school, cost-effectiveness and school accessibility were the main considerations influencing school preferences. In mountainous areas, positive attitudes toward unmerged school were at 51.8 % due to the low cost of schooling, and 30.3 % due to the family home being near the school. Similarly, non-mountainous areas also presented the same trend regarding positive attitudes to unmerged school choice. 48.5 % of enrollment in unmerged schools was due to ‘‘low cost of schooling’’ and 28.9 % was due to the home being near the school. In addition, teacher performance was also an important factor relating to school choice willingness. This is due to the fact that most teachers in unmerged schools, although having

Merged or Unmerged School? Table 3 Definition of variables

Dependent variable: type of school Independent variable Class 1

1

Father’s education in years

Parental characteristic

2

Mother’s education in years

Class 2

3

Log of PCI

Family situation

4

Living region: 1 = mountainous area; 0 = non-mountainous area

5

Size of family

Class 3

6

Cost of schooling

School accessibility

7

Distance: 1 = 0–2 km; 2 = 2–5 km; 3 = 5–10 km; 4 = 10–20 km; 5 C 20 km

Class 4

8

Teacher qualification: 1 = qualified; 2 = not qualified

Educational quality

9

Teacher performance: 1 = responsible; 0 = irresponsible

10

School environment and facilities: 1 = good; 2 = not good

11

Students’ grades: 1 = low grade; 2 = moderate; 3 = high grade

lower qualifications, had a much higher sense of responsibility for their teaching work and their performance could improve students’ academic scores. Based on this, parents with lower incomes and larger family sizes may prefer to enroll their children in unmerged schools.

statistically significant at 5 % (0.018). One unit increase in log of per capita income will increase the odds ratio toward merged school choice preference by about 1.290 times. (2) The coefficient of region was positive and statistically significant at 5 % (0.029). This showed that the probability of merged school choice preference for children living in non-mountainous areas was higher than that of those living in mountainous areas. The interview results reveal that in the case of pupils (3–6 grades) in one remote village in Nanzheng County, Shaanxi Province, they needed to walk for more than 1 hour on rugged mountain roads for nearly 5 kilometers to go from home to school. Over the course of the day, they needed to spend more than 4 hours traveling between home and school, making their hardship greater since the introduction of SMR. This point was also argued by Yang (2010). (3) Family size was negatively related to the choice preferences toward merged schools. The result showed that the coefficient was negative and statistically significant at 10 % (0.082); the increase of family size will decrease the odds ratio toward merged school choice preference by about 1.019 times.

Note For the factors with two or more categories, the last one was used as the reference category

School Preferences and Relationship to Impact Factors A binary logistic regression model was applied to estimate probability related to attitudes toward school choice. Based on the definition of variables presented above, a binary logistic model was run with them. Regarding dependent variables, the term unmerged was used as a reference category. The results of the statistical model are presented in Table 5. Parental Characteristics Among parental characteristics, the educational level of parents, both father and mother, was positively associated with attitudes toward their children’s choice of school. The results revealed that the coefficient of father’s education was positive and statistically significant at 10 % (0.062). It showed that a one-year increase in the father’s education increases the odds ratio toward merged school preference by 1.265 times. Regarding mother’s education, an additional one year of the mother’s education will increase the odds ratio toward merged school choice preference by about 1.002 times. Family Situation There were three factors included in the family situation group—per capital income, region, and family size. (1) The coefficient on the log of per capita income was positive and

School Accessibility Cost of schooling and commuting distance were included in this class of factor. With regard to cost of schooling, the result showed that it was negatively related to the choice preference on merged school. The coefficient was statistically significant at 5 % (0.020) level of significance. It revealed that an increase in cost of schooling decreased the probability to choice preference on merged schools. Regarding the factor of commuting distance, the coefficient of distance categories (2–5 km), (5–10 km), and ([10 km) for merged school was negative and statistically significant, and the level of significance was 5 % (0.022), 5 % (0.017),

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D. Zhao, B. Parolin Table 4 Frequency distribution of parental responses toward merged/unmerged schools

Factors

Merged school Mountainous areas %

Unmerged school Non-mountainous areas %

Mountainous areas %

Non-mountainous areas %

Teacher qualifications

59.6

53.2

3.5

4.7

Teacher performance

22.3

29.5

10.2

13.8

Good environment and facilities

13.5

9.2

2.9

4.2

Low cost of schooling

1.2

4.6

51.8

48.5

Near to home

2.2

3.1

30.3

28.9

Other factors

1.1

0.4

1.3

0.4

100.0

100.0

100.0

100.0

Total

Table 5 Binary logistic regression model relating school preference to various factors Independent variable Constant Parental characteristics Family situation

School accessibility

Educational quality

Coefficients

Std. error

Sig.

Odds Ratio

-2.532*

0.082

0.000

0.287

1

Father’s education

0.032*

0.083

0.062

1.265

2

Mother’s education

0.208*

0.074

0.081

1.002

3

Log of PCI

1.330**

0.036

0.018

1.290

4 5

Region (non-mountainous areas) Size of family

2.532** -1.008*

0.042 0.024

0.029 0.082

4.387 1.019

6

Cost of schooling

-0.527**

0.005

0.020

0.801

7

Distance (0–2 km)

-0.017

0.039

0.512

0.772

8

Distance (2.1–5 km)

-0.619*

0.056

0.022

0.901

9

Distance (5.1–10 km)

-0.202**

0.051

0.017

0.711

10

Distance (10.1–20 km)

-0.349***

0.061

0.000

0.601

11

Teacher qualifications (qualified)

1.022*

0.027

0.072

6.029

12

Teacher performance (responsible)

3.285***

0.032

0.007

4.871

13

School environment and facilities

2.287**

0.020

0.025

6.012

14

Students’ grades (low grade)

1.281*

0.051

0.066

1.172

15

Students’ grades (moderate)

1.029

0.033

0.219

2.001

Note Dependent variable: School dummy merged school = 1 and unmerged school = 0. Reference category: unmerged school = 0 * Coefficient is significantly different from zero at 0.1 probability level; ** 0.05 probability level; *** 0.01 probability level

and 1 % (0.000), respectively. It was presented that the probability of willingness to choose merged schools was decreased as the distance from home to school increased.

prefer merged schools. Among those four factors of educational quality, teacher performance was the most significant; thus, it could be concluded that parents paid much more attention to teacher performance.

Educational Quality The coefficient of teacher qualification was positive and statistically significant at 10 % (0.072). It was demonstrated that as there were more qualified teachers, parents would be more willing to send their children to merged schools. The coefficient of teacher performance was positive and statistically significant at 1 % (0.007). The coefficient of school environment and facilities was positive and statistically significant at 5 % (0.025). Student grades also significantly impacted school preference; its coefficient was positive and significant at 1 % (0.066) and it meant that children with lower grades would more likely

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Discussion and Policy Implications Parental Characteristics Based on the results of the study, it was demonstrated that the influence of the father’s education on preference for merged schools was greater than that of the mother’s education. This indicates that in rural China, the father, for his higher ability to earn money, has more impact on family issues such as distribution of property, relationships, children’s education, etc. Especially in impoverished areas,

Merged or Unmerged School?

the father can be considered the master of the family, able to make crucial decisions on family issues without asking his wive, and children’s schooling is no exception. To sum up, parental education positively and significantly affected school choice preference in merged schools. Parents act as utility maximizers and are like to send their children to merged schools for better education and to secure their future, a result which is consistent with Sun (2004) and Lu and Du (2010). Some other researchers also point out that parental characteristics impact school choice; it is the social factor which could make sense (Brasington and Hite 2012; Brunner et al. 2010). Therefore, as parental backgrounds significantly impact school preference during the process of SMR implementation, the interests of students and parents should be given more consideration; active participation, such as meeting with respondents involving students and parents, should be practiced to gather the information about educational situations in particular localities. This is also supported by other researchers (e.g., Kennedy and MacDougall 2007; McHenry-Sorber 2009). Family Situation Family Income and Family Size The result revealed that rural families with higher incomes seem to be more willing to send their children to merged schools for the better quality education provided. This is consistent with the argument that households with higher incomes were more likely to contribute more to investment in human capital, from preschool education to compulsory education through to higher education (Carnoy 1995). Another relative factor, family size, also impacted school preference significantly; it was an increase in family size that lead to an increase in household expenditure, which would worsen a family’s economic situation. In general, most households in rural China have large families, with an average size of 3.27, larger than households in urban area (1.97); a majority of them have four to five or more persons in the family (Chen 2008). And, in most remote rural communities, larger-sized households were in bad economic situations or even impoverished, and their children would suffer from this and could not afford the cost of entering merged schools further away, or would even be forced to drop out. Therefore, the poor households were more willing to send their children to unmerged schools due to the low cost (Xiong 2007; Fan 2009). Thus, merged schools are more attractive for wealthier families for the increased fees such as transportation, accommodation, etc.; otherwise, poor students would be more likely to stay in rural unmerged schools in order to save money. But actually, during the process of SMR, there are a large number of poor students who transfer from

unmerged schools to merged schools, and these poor students are being systematically hurt by rural China’s educational system reforms because of the increased economic burden. This generates inequity between poor students and wealthier ones. Thus, our educational policy makers should pay more attention to the poor students from low-income families and more funds should be provided to support needy students, as well as improving the criterion for identifying poor students and increasing the number of students receiving financial support. More assistance to poor students is an important means to promote equity in education (Sandy 1992; Strange 2003; West and Hind 2007; Warner and Lindle 2009). Region The result showed that children living in non-mountainous areas were more likely to prefer unmerged schools. This is likely to be because children living in mountainous areas experience more obstacles in getting to school, including longer and more time-consuming commutes, and higher transportation expenditure. And, for children in nonmountainous area, traveling to school is easier. For this reason, parents in non-mountainous would rather choose merged schools than unmerged schools, while parents in mountainous areas are more likely to choose unmerged schools. Thus, when enacting the SMR program, as the main implementer, the local educational department should pay more attention to the children in mountainous areas, being more cautious when making a decision on closure of small schools. In other words, the positive role of remote small schools should be recognized and more of them should be retained in mountainous areas, so as to provide educational service to remote children who are more willing to enter local schools. This point has also been argued by many researchers both in China and overseas countries (Hargreaves et al. 2009; Hamnett and Butler 2011; Walker and Clark 2010; Zhao and Parolin 2012). School Accessibility: Cost of Schooling and Distance The study pointed out that cost of schooling and traveling distance negatively impacted school preference to merged schools. This is due to the fact that the merged schools are located in central areas of a town or county, further away from the poor households living in mountainous areas. Thus, if children entered merged schools, they would need to travel longer and pay more. Direct costs include accommodation, living expenses, and transportation costs, opportunity costs mean that in order to receive education, students have to give up the opportunity to earn money, such as the value of labor created by children in family production, taking care of younger siblings, or doing other

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D. Zhao, B. Parolin

chores (Carnoy 1995). In rural China, there are still a great number of children attending merged schools who are facing stressful lives with higher transportation fees and living costs, as well as higher opportunity costs due to farm employment opportunities or child labor needs at home (Zeng 2000; Holmes 1999). Our case study also revealed that some children in poor families in Guangxi Province were forced drop out as they had to go to a central school further away from home and could not help herd the sheep for their parents. The research from the World Bank also pointed out that drop-off occurred when children were expected to leave their own community, which would engender physical barriers such as mountains, rivers, forests, or other obstacles that lengthen travel time (Lehman 2003, August). Above all, for the remote children facing obstacles of increased costs of schooling longer and commutes, their parents favor unmerged over merged schools. Based on this, here are mainly two points proposed: (1) policy makers should make more effort to find ways to alleviate the economic burden of poor children who were transferred from unmerged schools to merged schools, by providing not only free education at the compulsory level but also free school bus, living allowances, and subsidized meals for poor students residing on the campus, and these programs should cover all the children in need. (2) For the remote children, especially junior children, the SMR implementer should try best to insure their access to school, deleting obstacles of longer commutes and traveling time. Thus, a scientific plan should be adopted to make decisions on which schools should be closed or retained, especially reserving small schools for remote children (Brunsdon 2001; Gordon and Knight 2009). And, in order to improve the scientificalness of SMR, the key strategy is to apply geographic information systems (GIS) approaches, as popularized in many countries, which is also advocated by the International Institute for Educational Planning (Attfield et al. 2002; Hite and Hite 2004). Through GIS, the data including all the related elements such as terrains, elevations, roads, locations of schools, villages, and any other institutions can be imported into GIS, generating the visualized maps. Moreover, GIS could do the analysis explaining how to make micro-plans for mapping new schools, consolidating schools, solving real difficulties faced by students during SMR, etc. Educational Quality The result showed that the four factors—teacher qualification, performance, school facilities, and student grades— strongly determined school choice preference. This clearly indicated that parents would like to send their children to

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merged schools for better education. (1) Teacher qualification and school environment and facilities. During SMR, more qualified teachers and facilities were assigned into merged schools, aiming to concentrate educational resources. However, under the circumstance of shortage of educational resources in rural areas, unmerged schools did not have enough qualified teachers and facilities, engendering negative influences on educational quality. This makes parents hesitate to choose an unmerged school, although it is located nearer their residence. (2) Teacher performance. The size of the merged school became larger; this also caused the size of class to become larger. For this reason, the teacher had to spend more time and energy on each student, especially on some children with lower learning abilities or some who were disturbing the class. Thus, to some extent, merged schools required more from teachers, not only by way of qualification but also their responsibility and performance. As a result, teacher performance was much more crucial for school choice willingness (Zhao and Parolin 2011; Fan 2009). (3) Student grades. Families whose children had lower grades were more willing to choose merged school because merged schools have more qualified teachers and other educational resources, which could help improve quality. Thus, the situation of unbalanced distribution of educational resources between merged and unmerged schools has made parents very ambivalent about choosing merged schools over unmerged schools. Here, the most fundamental solution is to improve the equilibrium of educational resources between schools. This first consists of increasing resources for school operations. The detailed information with respect to a shortage of funds and facilities in each school (both merged and unmerged) should be investigated and identified by educational administrators, and the requested funds should be allocated to the schools (Lei 2012). To guarantee this, it is also suggested that the current financial management system should be reformed by way of making the governments at the central and provincial level take key responsibility for financing rural education, rather than county governments which are facing serious financial constrains especially in mid-western China. Second, more efforts should be made to increase the supply of qualified teachers to rural schools, especially for unmerged smaller schools (Ehrenberg and Brewer 1994; Hopkins and Stern 1996; Yang et al. 2013). Several strategies associated with remuneration policies should be applied. First, living allowances for teachers in small rural schools should be increased, by establishing job-specific subsidies and improving housing conditions. Second, teachers of special subjects such as fine arts, English, and information technology should be allowed to move between neighboring rural schools, reducing teacher

Merged or Unmerged School?

shortages in these subjects. Third, a rotation plan for rural teachers should be implemented. For some remote, small rural schools, the problem of teacher shortages is much more serious; 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. Third, on account of the international trend for balanced development of rural schools, a school cluster among merged and unmerged schools could be organized and their shared services increased, so as to improve educational quality (Spradlin et al. 2010; Plucker et al. 2007; Rincke 2006). The strategies of sharing resources should include sharing certified specialists, adopting the same calendar and schedule for sharing curriculum, transportation between the schools for students, implementing a half-day model where students would have the option of spending half of the school day taking courses at the other school, utilizing distance learning opportunities, establishing a committee to assess joint textbook adoption, upgrading to a joint student data system, etc. Through school cluster and educational resource sharing, students in unmerged small schools could enjoy the same quality of education as the students in merged schools, which could improve the balanced development of rural schools, and the equity and access to education as well. SMR is substantially a program which is in accordance with the objective changes of the population. In rural areas of China, as populations grow thinner, further SMR appears inevitable. Under this condition, merged and unmerged schools are the different school styles in terms of size of school and location, due to the diversity of their service areas. We could not define which is better or more efficient in the long run as both merged and unmerged schools are important for universal education; the truth is that these two kinds of schools should be equal in quality through allocation of the same level of educational resources, so as to provide as high a level of education as they possibly can. And, for the students with different levels of grades, from merged or unmerged schools, policy makers should level off the base or bridge the achieving gap for low achievers with a view toward inclusive education (Hung et al. 2013). Summary and Theoretical Implications In general, the identified factors classified by parental characteristics, family conditions, school accessibility, and quality have significant effects on the preferences of parents regarding the schools their child attends. Specifically, parents with higher levels of education, smaller-sized families with better economic conditions, and those living in non-mountainous areas have a positive preference for

merged schools. The cost of schooling and commuting distance involved play negative roles on preferences toward merged schools. Teacher qualifications and performance, school environment and facilities are factors that positively impact school preference toward merged schools, while students’ grades have negative effects. However, for a long period during SMR, family opinions about these factors were not paid sufficient attention, displaced by the idea of educational efficiency (Fan 2009; Jia and Zeng 2011; Ye 2012). Although efficiency is rational and applicable for SMR, dynamically, in the long run, the social-economical factors identified in this study should gain more notice from policy makers, so as to avoid negative results and inequity issues raised by SMR implementation. That is also to say, fundamental targets that improve educational equity should take precedence during SMR (Fan 2009; Bard et al. 2006). Further, theoretically, this study proposes the implications and contributes to the rationale for SMR. It argues that the theoretical underpinnings of SMR in educational efficiency and equity are not contradictory to each other, but should be integrated in SMR planning. On this basis, this paper recommends that, during the SMR process, the theory of educational equity should be given priority to insure equal educational opportunities for each student. On the other hand, the perspective of economies of scale could be applied to implement SMR, due to the condition that educational resources are not unlimited. However, there is a significant point that ‘‘diseconomies of scale’’ should be paid close attention. The reason is that, during SMR, merged schools could educate multiple children to promote efficiency, but could run into diseconomies of scale if too many children are taken in, at which point educational equity would be damaged. Thus, another point is made— optimally, the point at which we stop enjoying economies of scale and hit diseconomies of scale should be taken into account. And, only under this condition could it take a positive role to improve educational equity (Elchanan and Terry 1990). Overall, the theoretical implications and contributions consist of priority of equity of education and rational economies of scale that do not exceed the point of diseconomies of scale, and these points go beyond the traditional view that educational efficiency dominates the process of SMR. In addition, the SMR program is not an issue only in China; it is a kind of worldwide program both in developed and developing countries. The policy suggestions proposed in this study are also suitable for other counties, because there are similarities in conditions of SMR implementation: proponents tout the benefits of fiscal efficiency, a broadened curriculum, and an increase in student achievement, while critics argue that the community and children suffer when schools close. The public officials who set school

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funding formulas favor SMR as a cost-saving measure, and rural citizens typically do not have sufficient representation to counter those policies. And, too often, the judiciary has refused to protect the interests and needs of the rural minority, leaving the matters of school funding and educational quality to be determined by the legislature, based on majoritarian preferences (Nitta et al. 2010; Cooley and Floyd 2013). Under this condition, the inequitable educational opportunities for rural students resulted from the SMR process, and merged schools were better in quality, while unmerged schools were lower. Thus, policy practices including better participation, micro-planning, more equal quality of merged and unmerged schools, and collaborative efforts of sharing services should be adopted during SMR, so as to improve educational equity (UNESCO 2005).

Conclusions This study discussed some important factors which influenced the parents’ school preferences, in the context of SMR in rural China, and the policy suggestions for improving equity and access of education were also proposed. Based on the result of the study, in general, parents’ willingness to send their children to school mainly depends upon the four classes of factors including parental characteristics, family conditions, school accessibility, and quality. Among those impacting factors are the following: (1) parents with higher-level education had stronger motivation to enroll children in merged school; (2) families with better economic conditions and smaller size, as well as living in non-mountainous areas, were more willing to choose merged schools; (3) the cost of schooling and commuting distance play a negative role on school preference toward merged schools; and (4) quality of education is also an important factor for child school choice willingness. Teacher qualifications and performance, and school environment and facilities positively impact school preference toward merged schools; students’ grades have negative effects on preference for merged ones. Overall, the present model is good in explaining social-economical and educational factors affecting school preference. Based on the research results, this study also proposed relative policy implications for educational management. Conclusively, during SMR, the essential reason for different school choice willingness was the big gap between merged schools and unmerged small schools. And, this gap was mainly caused by the over-chasing for efficiency during SMR. Especially for the financial constraints of a county government, they tended to merge more small schools into larger schools, and set priority funding for merged larger schools in order to improve their conditions,

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while the unmerged schools could not receive adequate funds. In addition, for rural children and families, they are themselves unable to take the initiative for school choices; family preferences as shown in this study are covered by SMR plans controlled by county educational departments, and were often ignored by implementers. For these reasons, the authors have tried to provide some policy implications for SMR implementation: (a) participation of multiple groups; (b) scientific plan using GIS for micro-planning; and (c) provision of more funds and qualified teachers to rural schools, and promotion of sharing of educational services between schools. Through these strategies, the SMR program will contribute more to educational equity and access, and not to economical effectiveness alone; as a result, its performance could be improved much and rural children could benefit from this. As previous studies have found, there was big gap between merged and unmerged schools, in terms of educational quality including funds, facilities, teacher qualification, student grades, etc. (Howley 2006; Mo et al. 2012; Zhao and Parolin 2012; Cooley and Floyd 2013). This study recommends that more educational resources should be allocated to unmerged schools, plus collaboration efforts made as well. Accordingly, future studies can focus on what detailed strategies could be adopted and how to implement those strategies for strengthening unmerged schools. In addition, the data collected from children’s parents could represent the school preference of the family, and the children’s own attitude could also make sense for this issue. Thus, further study could also focus on children’s preference toward different kinds of schools, which is helpful to predict children’s need for education. Moreover, school preference during SMR is also related to the issue of the participant’s attitude toward the SMR program; thus, the model in this study could also be applied for further study on people’s attitude or satisfaction of SMR implementation and reflect the performance of the SMR. Acknowledgments The Project [The Progress of the Project on Rational School Mapping Structure of the Rural Primary and Secondary Schools in central and western China (RSMS)] was funded by a Sino-UK bilateral grant-in-aid ‘‘Basic Education in Western Areas Project,’’ the DFID, the World Bank, and the Finance Bureau of MOE of China. We are very grateful to the 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 ‘‘Construction of sharing model of educational resource through GIS econometrics analysis_Background of balanced development of regional compulsory education in Northwest China,’’ funded by the National Natural Science Foundation of China (Project Number: 71203180) and Project ‘‘SMR in western rural China: Issues of small rural schools_Empirical study by GIS application,’’ funded by the Chinese Ministry of Education (Project Number: 12YJC880157).

Merged or Unmerged School?

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