View in EDS HTML Full Text PDF Full Text

Does School Consolidation in Rural Areas Affect Students' Education? Empirical Evidence from China

Saved in:
Bibliographic Details
Title: Does School Consolidation in Rural Areas Affect Students' Education? Empirical Evidence from China
Language: English
Authors: Yu Li (ORCID 0000-0003-1848-8975), Bo Gao
Source: European Journal of Education. 2024 59(4).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 13
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Foreign Countries, Rural Areas, Consolidated Schools, Educational Change, Educational History, Urban Schools, School Closing, Educational Attainment, Achievement Gap, Rural Youth, High School Students, Data Analysis, Enrollment Influences, Government Role, Achievement Gains
Geographic Terms: China
DOI: 10.1111/ejed.12790
ISSN: 0141-8211
1465-3435
Abstract: In the late 1990s, an extensive consolidation of schools in rural China led to the amalgamation of numerous primary and secondary schools into urban schools or their discontinuation. The study aimed to investigate whether China's school consolidation movement has influenced the educational attainment of rural students. The data are sourced from the China Household Income Project (CHIP) and regional statistical yearbooks, and the cross-section difference-in-differences (DID) method is used to analyse the differences in high school enrolment of rural students between regions with different degrees of school consolidation. Empirical analysis results indicate that school consolidation substantially enhances the probability of rural students receiving high school education, as well as having a positive impact on rural students taking the college entrance examination and receiving a longer formal education. Moreover, the findings of the empirical analysis remain significant even after controlling for variations in government educational expenditures, substituting the explained variables, as well as eliminating the influence of school-entering age. The adjustment of rural schools, as an inevitable historical trend, is beneficial to improving rural educational attainment.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1450294
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwFnZSZ5dVLwTD8mcvmiswmsAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDA6eTJbOhxCULhILigIBEICBm2yrR3uoOEf1OZrbexP7a9QXrkG7h4fUbJ_bGcmmKdUH0OTprpulwW_6n82HV4hU7znxNdF15BhFZk0kgOSV7zwNJdsISLszHHwsYALwWbdkTFPID997934rQ8cLmrV5y4TX9v6e2h5GtOXjymDHQDMFogLMOPhMgxpQ8MmZX6HPchR_f5fbYIQ1Ft3_ed6VyC3j97pKP00riqsI
Text:
  Availability: 1
  Value: <anid>AN0181057390;eje01dec.24;2024Nov26.04:09;v2.2.500</anid> <title id="AN0181057390-1">Does School Consolidation in Rural Areas Affect Students' Education? Empirical Evidence From China </title> <p>In the late 1990s, an extensive consolidation of schools in rural China led to the amalgamation of numerous primary and secondary schools into urban schools or their discontinuation. The study aimed to investigate whether China's school consolidation movement has influenced the educational attainment of rural students. The data are sourced from the China Household Income Project (CHIP) and regional statistical yearbooks, and the cross‐section difference‐in‐differences (DID) method is used to analyse the differences in high school enrolment of rural students between regions with different degrees of school consolidation. Empirical analysis results indicate that school consolidation substantially enhances the probability of rural students receiving high school education, as well as having a positive impact on rural students taking the college entrance examination and receiving a longer formal education. Moreover, the findings of the empirical analysis remain significant even after controlling for variations in government educational expenditures, substituting the explained variables, as well as eliminating the influence of school‐entering age. The adjustment of rural schools, as an inevitable historical trend, is beneficial to improving rural educational attainment.</p> <p>Keywords: education level; education policy; high school enrolment opportunities; school consolidation</p> <hd id="AN0181057390-2">Introduction</hd> <p>It is both a fundamental human right and an effective means of eradicating inequality and poverty to have access to high‐quality education and opportunities for lifelong learning. Nonetheless, not all school‐age children, particularly those in rural and impoverished areas, receive an equivalent standard of education. Furthermore, inequalities based on regional economy and population mobility persist. Globally, 244 million children and adolescents remain behind academically because of cultural, economic and social factors, and merely 70% of countries legally guarantee compulsory education for at least 9 years (UNESCO [<reflink idref="bib31" id="ref1">31</reflink>]). Meanwhile, a large number of schools in less populated and disadvantaged places could enhance students' educational attainment and income by shortening commuting distances (Duflo [<reflink idref="bib8" id="ref2">8</reflink>]). Consequently, ensuring equity in education, notably equity in access to school, is essential for developing countries and poor areas.</p> <p>Providing educational resources to all children is a challenging task with each government. As the 21st century commenced, China completed the popularisation of 9‐year compulsory education. Subsequently, in 2020, the net enrolment ratio of school‐age children in elementary schools was 99.96%, and the gross enrolment ratio in junior secondary schools reached 102.5%.[<reflink idref="bib1" id="ref3">1</reflink>] Undoubtedly, China's achievements in the realm of compulsory education have substantially advanced international endeavours to promote educational equality. Nevertheless, the increasingly widening educational gap between urban and rural areas has hindered the overall development of education in China. The number of schools and students is increasing, but rural schools are vanishing.</p> <p>The historical trend of urbanisation is one of the reasons for the disappearance of rural schools. School distribution was typically closely correlated with population (Hershkovitz [<reflink idref="bib12" id="ref4">12</reflink>]; Zhang et al. [<reflink idref="bib33" id="ref5">33</reflink>]). The spatial distribution of urban and rural populations changed consequently of urbanisation, the rural permanent population and school‐age children declined, and school adjustments and mergers were inevitable (Sageman [<reflink idref="bib28" id="ref6">28</reflink>]; Hannum and Wang [<reflink idref="bib11" id="ref7">11</reflink>]). Moreover, high‐quality teaching facilities and high enrolment rates in cities and towns had also accumulated a surge of resources, including rural teachers, and accordingly, rural schools could no longer operate (Sunday and Olatunde [<reflink idref="bib30" id="ref8">30</reflink>]; Ling et al. [<reflink idref="bib19" id="ref9">19</reflink>]; Hannum and Wang [<reflink idref="bib11" id="ref10">11</reflink>]; Ju, Wang, and Lin [<reflink idref="bib14" id="ref11">14</reflink>]).</p> <p>Another essential reason for the disappearance of rural schools is the government's active promotion. In determining school layout, the government is confronted with the challenge of reconciling efficiency and equity. Large‐scale schools ensured efficiency but increased commute times, whereas small‐scale schools considered equity yet had lower enrolment rates (Bhatnagar and Bolia [<reflink idref="bib4" id="ref12">4</reflink>]). Moreover, local governments incur substantial costs in maintaining numerous rural schools and building more urban schools. Education fiscal policy that consolidates schools to reduce financial strain through economies of scale is more economical (Karakaplan and Kutlu [<reflink idref="bib15" id="ref13">15</reflink>]; Nguyen‐Hoang [<reflink idref="bib27" id="ref14">27</reflink>]; Zhang and Rozelle [<reflink idref="bib32" id="ref15">32</reflink>]).</p> <p>Rural school closures are also influenced by China's unique educational fiscal decentralisation system and tax reform. Local governments are primarily responsible for education expenditures under China's education fiscal decentralisation system. Nonetheless, as a result of the 1994 Tax Sharing System reform, local governments experienced a decline in fiscal revenue and lost interest in carrying out public service duties. In the late 1990s, some local governments began to adjust schools because of financial difficulties.</p> <p>The Chinese government implemented an education reform in 2001 that restructured rural primary and secondary schools. The goal of this reform was to abandon the approach of 'village‐run primary schools', maximise the utilisation of educational resources in rural areas, improve education quality and investment returns, and promote the long‐term development of rural basic education. Rural elementary schools declined by 85.3% between 1995 and 2021, junior high schools declined by 70.4%, and high schools declined by 74.2%.[<reflink idref="bib2" id="ref16">2</reflink>] Consequently, what would be the impact of school consolidation on students?</p> <p>Advantageously, the long‐term improvement in school quality would counterbalance the decline in academic achievement among rural students compelled to relocate to municipalities endowed with more advanced educational facilities (Engberg et al. [<reflink idref="bib10" id="ref17">10</reflink>]; Beuchert et al. [<reflink idref="bib3" id="ref18">3</reflink>]; De Haan, Leuven, and Oosterbeek [<reflink idref="bib7" id="ref19">7</reflink>]). Additionally, there would be more opportunities for high school education (Liang and Wang [<reflink idref="bib18" id="ref20">18</reflink>]). Meanwhile, concentrating limited funds on schools with economies of scale could also improve students' academic achievement (Card and Payne [<reflink idref="bib6" id="ref21">6</reflink>]; De Haan, Leuven, and Oosterbeek [<reflink idref="bib7" id="ref22">7</reflink>]). A suitable school adjustment could resolve the trade‐off between large schools with excellent facilities and tiny schools with inadequate infrastructure, as well as the issue posed by an excess of schools and inadequate enrolment (Bhatnagar and Bolia [<reflink idref="bib4" id="ref23">4</reflink>]).</p> <p>Nonetheless, with increasing distances to schools, there would be a corresponding decline in average years of education and a rise in dropout rates (Liang [<reflink idref="bib17" id="ref24">17</reflink>]; Mandic et al. [<reflink idref="bib25" id="ref25">25</reflink>]; Al‐Sabbagh [<reflink idref="bib1" id="ref26">1</reflink>]; Larsen [<reflink idref="bib16" id="ref27">16</reflink>]). Boarding students were those who attended school due to their residence being at a considerable distance from their homes or enrolled in urban junior high schools had poorer nutritional status and shorter heights than non‐boarding students (Luo et al. [<reflink idref="bib23" id="ref28">23</reflink>]). For the privilege of accompanying their children to school, parents who rented a house near the institution were required to pay a premium (Long et al. [<reflink idref="bib20" id="ref29">20</reflink>]). School consolidation impacted children in the merged areas and negatively impacted students in the receiving schools, including making their grades worse (Angrist and Lang [<reflink idref="bib2" id="ref30">2</reflink>]; Brummet [<reflink idref="bib5" id="ref31">5</reflink>]; Steinberg and Macdonald [<reflink idref="bib29" id="ref32">29</reflink>]). Additionally, the closure of rural schools has led to the degradation of rural culture and civilisation. Following the disappearance of schools as an essential cultural communication platform and the vitality provided by education (Mei et al. [<reflink idref="bib26" id="ref33">26</reflink>]; Eacott and Freeborn [<reflink idref="bib9" id="ref34">9</reflink>]), it turned out that school closures contributed to the decline in population (Sageman [<reflink idref="bib28" id="ref35">28</reflink>]; Lykke Sørensen et al. [<reflink idref="bib24" id="ref36">24</reflink>]).</p> <p>Concentrating schools in cities and counties may benefit rural students in obtaining an improved education. Nonetheless, blindly cancelling schools and teaching sites transferred the government's economic expenditures to rural families as well as increased students' time costs and safety risks. Besides, the Ministry of Education proposed in 2012 to 'prudently advance the adjustment of compulsory education schools, and continue in running necessary village primary schools and teaching locations', putting the brakes on the preceding 10 years' overheated school consolidation. The majority of rural students' educational trajectories have been influenced to some extent by this activity over the past two decades after rural schools were withdrawn and merged. To analyse the shortcomings and deficiencies of school consolidation and to more effectively allocate educational resources between urban and rural areas, it is imperative to conduct an inquiry into the consequences for rural students.</p> <p>This study explores the influence of rural school adjustment on students' educational attainment by utilising cross‐sectional difference‐in‐differences (DID). Rural students may drop out of school, or their education may improve. This research aimed to determine whether school consolidation boosts high school enrolment or enhances educational outcomes for rural students. The 2018 China Household Income Project (CHIP2018) and county statistics are primarily employed in this paper. The rest of this paper is structured as follows. Section 2 provides an overview of the policy context, whereas Section 3 proceeds with an empirical analysis. The concluding segment is written.</p> <hd id="AN0181057390-3">Policy Background</hd> <p></p> <hd id="AN0181057390-4">The Role of the Government in School Consolidation</hd> <p>The development of combined instruction,[<reflink idref="bib3" id="ref37">3</reflink>] as well as national education and literacy initiatives in China during the 1950s, contributed substantially to the massive expansion of elementary and secondary schools. Ordinary primary schools in China numbered 1.09 million during the 1970s, whereas the number of middle schools surpassed 200,000. As a result of the massive migration of rural surplus labour to urban areas in the early 1980s, insufficient enrolment compelled the closure of several small rural schools and teaching sites. The concentration of secondary education in urban areas contributed to an increase in the enrolment of junior high school students in cities and towns. Nonetheless, because urbanisation remained in its early stages, the number of ordinary elementary schools in rural areas accounted for approximately 90% of the total in the 1990s.</p> <p>Financial stress relief is the primary objective of the Chinese government's advocacy for the closure and consolidation of rural schools. The promulgation of the school consolidation strategy coincided with the State Council assuming the initiative to implement the pilot program for rural tax and fee reform in specific provinces during the year 2000. Twenty provinces (municipalities and autonomous regions) were included in the pilot program for rural tax and fee reform 2 years later. Furthermore, the reform primarily involved the abolition of administrative fees and government funds particularly imposed on farmers and the alteration of agricultural tax policies and agricultural specialised tax collection systems.</p> <p>Several fundamental authorities were delegated to lower levels during the 1994 overhaul of the Tax Sharing System.[<reflink idref="bib4" id="ref38">4</reflink>] The county government was entrusted with certain duties on food supervision, mandatory education, infrastructure development, social security and environmental preservation. Besides, numerous of these public services and goods are not within the jurisdiction of local governments, resulting in a lack of motivation and a lack of municipal governments to fulfil these responsibilities. Meanwhile, on the condition that the Tax Sharing System reform was first designed, it employed a strategy that benefitted the central government. Even though there were numerous sorts of municipal taxes, the revenue fell well short of satisfying the needs of local fiscal expenditures. Moreover, the reorganisation of rural taxes and fees has exacerbated the already formidable financial situation at the local level, and education funding has been squeezed. Prior to the formal document on the adjustment of rural schools issued by the central government, various regions had initiated small‐scale school consolidation and closure.</p> <p>Prior to the reform of rural taxes and fees, the majority of investments in rural primary and secondary education were comprised of township education surcharges, education fundraising and financial appropriations. In 2001, China made substantial changes to the rural basic education system and began to adopt an education management system and financial system in which 'local governments are liable to oversee hierarchical management under the direction of the State Council'. Additionally, it was the responsibility of every tier of local government to guarantee the remuneration of educators. The reduction in county and township fiscal revenue has rendered it incredibly difficult to ensure the provision of fundamental school operating conditions. Considering the impracticability of implementing institutional reforms to augment funding shortly, local governments were compelled to explore alternative methods of spending reduction, and the policy of consolidating schools was widely advocated.</p> <p>The State Council released a document in 2001 demanding alterations to the structure of rural compulsory education schools on the basis of local conditions to enhance the general level of basic education. An officially initiated nationwide educational reform has been designed to restructure primary and secondary schools located in rural areas. Following the policy's implementation, a large number of small‐scale primary and secondary schools in rural areas were closed, and the personnel and students were transferred to urban schools, boarding schools or primary schools for completion.</p> <hd id="AN0181057390-5">The Process of School Consolidation</hd> <p>Figure 1 indicates that the total number of primary and secondary schools nationwide as a decline commenced in the late 1980s. Despite the most abrupt decrease, rural primary schools remained to account for a sizable proportion of total primary schools. Even though the number of rural school‐age children was dropping consequently of population control policies, the decline in the number of schools exceeded the decrease in the enrolment of students. Between 1986 and 2020, the number of students attending rural primary schools and middle schools decreased by 77.29% and 77.55%. However, the number of schools decreased by 89.04% and 87.19%, respectively.[<reflink idref="bib5" id="ref39">5</reflink>]</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12790-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12790-fig-0001.jpg" title="1 Primary schools, teaching points and middle schools in urban and rural areas of China from 1986 to 2020. Middle schools include junior middle schools, senior middle schools and complete middle schools." /> </p> <p></p> <p>Figure 2 shows the changes in the number of primary and secondary schools in counties from 2000 to 2020. The provinces with the highest prevalence of primary school closures were predominantly situated along the eastern coast. Additionally, there has been a reduction of over 90% in the number of schools in certain provinces characterised by substantial populations and swift urbanisation within the central and western regions. As a result of greater demand for primary education, primary schools can be found in nearly every village. Consequently, secondary schools experience less change than primary schools when adjustments are made. Nonetheless, Chongqing and Guizhou in the west have been most resistant to the closure of middle schools. Potentially attributable to the provinces' greater proportion of mountainous regions, allocating educational resources proved to be more difficult for the governments. Despite the absence of county‐specific middle school counts in the figure, our data analysis revealed that the number of middle schools in some counties has decreased to one or two.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12790-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12790-fig-0002.jpg" title="2 Number of primary schools (a, b) and middle schools (c, d) in China from 2000 to 2020. Middle schools include junior middle schools, senior middle schools and complete middle schools. The map comes from the 2022 China Administrative Division Map of the Ministry of Natural Resources of China." /> </p> <p></p> <hd id="AN0181057390-8">Data and Model</hd> <p></p> <hd id="AN0181057390-9">Data</hd> <p></p> <hd id="AN0181057390-10">Sample Data From Micro‐Surveys</hd> <p>The China Institute for Income Distribution provided rural resident data from the CHIP2018. The CHIP is a thorough, representative and large‐scale socioeconomic survey conducted in China, which employs systematic sampling methods to acquire rural and urban samples from 15 provinces, covering basic personal information of residents, as well as basic family information and income and expenditure data. Consequently, it is convenient to assess the impact of school changes on rural students.</p> <p>The China Institution for Income Distribution has currently completed six surveys, once every 5 years. CHIP2018 is the latest survey data released by the agency. It is also the only sizable social survey for which county data are made accessible to the general public. Nevertheless, CHIP is cross‐sectional data rather than a tracking survey, which is a limitation of the data itself. Panel data that have not been tracked continuously cannot be analysed using the traditional DID method. Referring to Duflo's ([<reflink idref="bib8" id="ref40">8</reflink>]) design for investigating the Indonesian INPRES project, the identification strategy we used is to generate a cross‐sectional DID using differences in policy implementation efforts in different regions and population birth cohort data.</p> <hd id="AN0181057390-11">School and Student Data</hd> <p>The consolidation of small schools and teaching sites into large‐scale complete primary schools occurred, whereas junior and senior high schools in towns replaced schools managed by villages. When considering administrative divisions, the consolidated and closed schools were primarily located in rural areas. Regarding numbers, village primary schools made up a sizable proportion of closed schools. Furthermore, the statistical calibre of data varied by location and year, and junior and senior high school numbers were occasionally combined. Consequently, complete data on the number of junior high schools could not be obtained. For the time being, the empirical analyses that follow are limited to fluctuations in the quantity of primary schools.</p> <p>We can only utilise the total number of schools because county statistics do not distinguish between urban and rural schools. Furthermore, a limitation of this study is the inability to differentiate between the specific impacts of voluntary school closures and mandated school mergers. The school and regional data are sourced from the <emph>China Education Expenditure Statistical Yearbook</emph> and <emph>China Education Statistical Yearbook</emph>, as well as provincial and municipal statistical yearbooks. Meanwhile, we obtained data on the number of students in these yearbooks. In subsequent sections, data on the number of schools and students will be used to calculate the degree of school consolidation.</p> <p>CHIP2018 discloses the administrative region codes[<reflink idref="bib6" id="ref41">6</reflink>] of the samples. By adopting these codes, the samples can be localised to the county in which they live, which allows us to have both the student's personal information and data concerning the county in which the student resides. After measuring the county's school consolidation intensity indicator, the impact on student education can be analysed through model regression.</p> <hd id="AN0181057390-12">Model Design</hd> <p>This section begins by explaining how to construct the two important variables required for the model, followed by designing the empirical model. To assess the educational attainment of students residing in rural areas, we employ the cross‐section DID approach. Comparable to how the DID approach generates policy dummy variables, the cross‐sectional DID method does the same. The methodology takes into account the birth cohort that is not a beneficiary of the policy intervention as an alternative reality resulting from the birth cohort that is provided with the policy intervention, and the age of the sample determines whether or not the sample is impacted by the policy.</p> <p>Typically, the DID method entails the construction of two dummy variables: time and policy intervention. We substitute these two variables with variations in the two dimensions of birth cohort and regional school consolidation intensity, which are discussed separately below.</p> <hd id="AN0181057390-13">Birth Cohort</hd> <p>As mandated by the Compulsory Education Law of China, all children must attend school and complete compulsory education once they reach the age of six. Within the research, all children are assumed to begin school at the age of 6. Children receive primary and junior high school education from the ages of 6–15 and high school education from the ages of 15–18. Because the key explained variable in this research is whether the sample has attended high school, the birth years of the selected individuals range from 1978 to 2000. In 2018, the age group under consideration for the survey should have attained a high school diploma.</p> <p>A policy implementation dummy variable, denoted as young, is generated in a manner that is precise to that of the DID method. Using 2001 as the baseline year, samples are divided into two groups: those aged 13 and above are assigned to the control group, whereas those aged 12 and below are assigned to the experimental group, and the screened samples are spread over 77 counties in 13 provinces. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0001" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Young</mtext><mo linebreak="goodbreak">=</mo><mfenced close="" open="{">0,if13≤Age_2001i≤231,ifAge_2001i≤12</mfenced></mrow></semantics></math> </ephtml></p> <hd id="AN0181057390-14">Intensity of School Consolidation</hd> <p>The intensity of school consolidation <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0002" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>policy</mtext><mo>_</mo><msub><mtext>city</mtext><mi>j</mi></msub></mrow></semantics></math> </ephtml> , is measured by the change in the number of schools per unit of students from 2000 to 2006, which can eliminate the natural fluctuations in the number of schools induced by changes in student enrolment. In the interim, this facilitates a more gradual adaptation of the number of educational institutions relative to scholastic enrolment, thereby enabling a more empirically grounded deduction (Liang and Wang [<reflink idref="bib18" id="ref42">18</reflink>]). Due to policy revisions and the Ministry of Education's initiation of corrective measures regarding the process of local school consolidation, 2006 was designated as the node year. Moreover, the central government intended to establish a rural education funding guarantee mechanism in which the central and local governments jointly proportionally contribute to projects, and progressively included compulsory education in rural areas within the public financial guarantees. By limiting the time to the active period of school consolidation activity, it is possible that the situation could be represented more precisely. Furthermore, establishing an earlier start date allows for a policy reaction period for following a discussion of the influence of students' education levels. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0003" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Policy</mtext><mo>%5f</mo><msub><mtext>city</mtext><mi>j</mi></msub><mo linebreak="goodbreak">=</mo><mfrac><mrow><mtext>Number of primary schools in</mtext><mspace width="0.25em" /><mn>2000</mn></mrow><mrow><mtext>Number of primary school students in</mtext><mspace width="0.25em" /><mn>2000</mn></mrow></mfrac><mo linebreak="goodbreak">−</mo><mfrac><mrow><mtext>Number of primary schools in</mtext><mspace width="0.25em" /><mn>2006</mn></mrow><mrow><mtext>Number of primary school students in</mtext><mspace width="0.25em" /><mn>2006</mn></mrow></mfrac></mrow></semantics></math> </ephtml></p> <p>Then construct the cross‐sectional DID model as follows:1 <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0004" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>Y</mi><mrow><mi>i</mi><mspace width="0.25em" /><mi>j</mi><mspace width="0.25em" /><mi>t</mi></mrow></msub><mo linebreak="goodbreak">=</mo><msub><mi>α</mi><mi>j</mi></msub><mo linebreak="goodbreak">+</mo><msub><mi>α</mi><mn>1</mn></msub><msub><mtext>young</mtext><mi>i</mi></msub><mo linebreak="goodbreak">×</mo><msub><mtext>policy</mtext><mi>j</mi></msub><mo linebreak="goodbreak">+</mo><msub><mi>α</mi><mn>2</mn></msub><msub><mi>age</mi><mi>t</mi></msub><mo linebreak="goodbreak">+</mo><msub><mi>α</mi><mn>3</mn></msub><msub><mtext>region</mtext><mi>j</mi></msub><mo linebreak="goodbreak">+</mo><mi>δ</mi><msub><mi>X</mi><mrow><mi>i</mi><mspace width="0.25em" /><mi>j</mi></mrow></msub><mo linebreak="goodbreak">+</mo><mi>γ</mi><msub><mtext>County</mtext><mrow><mi>j</mi><mspace width="0.25em" /><mi>t</mi></mrow></msub><mo linebreak="goodbreak">+</mo><msub><mi>ε</mi><mrow><mi>i</mi><mspace width="0.25em" /><mi>j</mi><mspace width="0.25em" /><mi>t</mi></mrow></msub></mrow></semantics></math> </ephtml></p> <p>In Equation (<reflink idref="bib1" id="ref43">1</reflink>), <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0005" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mtext>young</mtext><mi>i</mi></msub></mrow></semantics></math> </ephtml> represents a dummy variable of the age group. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0006" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mtext>policy</mtext><mi>j</mi></msub></mrow></semantics></math> </ephtml> indicates the degree of consolidation of schools. The explained variable, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0007" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>Y</mi><mrow><mi>i</mi><mspace width="0.25em" /><mi>j</mi><mspace width="0.25em" /><mi>t</mi></mrow></msub></mrow></semantics></math> </ephtml> , demonstrates whether the sample has received a high school education. Besides, age and region‐fixed effects are represented by <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0008" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>age</mi><mi>i</mi></msub></mrow></semantics></math> </ephtml> and <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0009" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mtext>region</mtext><mi>j</mi></msub></mrow></semantics></math> </ephtml> , respectively. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0010" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>X</mi><mrow><mi>i</mi><mspace width="0.25em" /><mi>j</mi></mrow></msub></mrow></semantics></math> </ephtml> symbolises the individual characteristic variable, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0011" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mtext>County</mtext><mrow><mi>j</mi><mspace width="0.25em" /><mi>t</mi></mrow></msub></mrow></semantics></math> </ephtml> embodies the regional characteristic variable, and <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0012" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>ε</mi><mrow><mi>i</mi><mspace width="0.25em" /><mi>j</mi><mspace width="0.25em" /><mi>t</mi></mrow></msub></mrow></semantics></math> </ephtml> is the disturbance item. Moreover, <emph>i</emph>, <emph>j</emph> and <emph>t</emph> indicate individual, region and age, respectively.</p> <p>The classification of individuals into the 'treatment group' and 'control group' in this article is predicated on the degree of intensity exhibited by school consolidation, which is comparable to the DID study by Duflo ([<reflink idref="bib8" id="ref44">8</reflink>]). Treatment groups are defined as regions with significant policy implementation; otherwise, they are classified as control groups. The DID coefficient captures the difference in high school enrolment rates among affected and unaffected students across regions with varying policy strengths. The key parameter we are interested in is the cross‐term coefficient <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12790:ejed12790-math-0013" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>1</mn></msub></mrow></semantics></math> </ephtml> . After controlling for age and region‐fixed effects, it reflects the impact of one fewer school per 1000 students on the probability of rural children receiving high school education. In addition, standard errors in all regressions are clustered by region. Now that the model's dummy variables have been identified, the explanatory variables and control variables will be introduced.</p> <hd id="AN0181057390-15">Variable Selection and Measurement</hd> <p>In 1992, the gross enrolment rate for high school[<reflink idref="bib7" id="ref45">7</reflink>] students aged 15–17 was barely 26%; by 2021, it had risen to 91.4%.[<reflink idref="bib8" id="ref46">8</reflink>] Nevertheless, this growth in rural and urban areas is not uniform. As a result of better instructional facilities and teacher resources, enrolment rates are typically greater in cities and towns. A significant proportion of students whose education is interrupted or diverted to secondary vocational schools as a result of the high school entrance examination diversion policy are from rural families. Even after enrolling in high school, rural high school students are more likely than urban students to drop out (Luo and Meng [<reflink idref="bib22" id="ref47">22</reflink>]). Rural students did not experience an equivalent degree of advantage from the sustained surge in higher education enrolment resulting from the expansion of colleges. Consequently, the disparities in high school enrolment between the two regions serve as a significant indicator of the educational inequality that exists between rural and urban areas.</p> <p>The significance of rural basic education cannot be overstated for students who are unable to alter their origins and family backgrounds. Students' access to opportunities for higher education is substantially influenced by the three educational streaming that occur after primary school, junior high school and high school. The transition from junior high to high school exerts the most significant influence on prospects for pursuing higher education. Consequently, the primary explained variable utilised in this study is whether the students have received high school education.[<reflink idref="bib9" id="ref48">9</reflink>] For rural students, high school attendance has a substantial impact on their future educational trajectory and financial standing.</p> <p>Control variables cover three distinct dimensions: household, individual and region. Besides, individual characteristic variables include age, gender, hukou, number of siblings and ranking among siblings. The majority of households have two or more children as population control regulations in rural areas are not as stringent. As a family's financial capacity diminished and the number of children in the household increased, there was a corresponding rise in the probability that each child would discontinue their education (Huang [<reflink idref="bib13" id="ref49">13</reflink>]; Lugauer, Ni, and Yin [<reflink idref="bib21" id="ref50">21</reflink>]). Children who are the eldest in the family may forego educational opportunities to support their siblings. Variables representing household and regional characteristics encompass financial assets, education expenditures, parental education level, per capita GDP, population density and urbanisation rate. Detailed information, calculation methodologies and descriptive statistics for the primary variables are presented in Tables 1 and 2.</p> <p>1 TABLE Variable assignment and calculation methods.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center">Assignment and calculation method</th></tr></thead><tbody valign="top"><tr><td align="left">Education</td><td align="center">Dummy variable, 1 for high school education and 0 otherwise</td></tr><tr><td align="left">Age</td><td align="center">2018—year of birth</td></tr><tr><td align="left">Gender</td><td align="center">Dummy variable, 1 for men and 0 for women</td></tr><tr><td align="left">Hukou</td><td align="center">Dummy variable, 1 for rural household registration and 0 for other cases</td></tr><tr><td align="left">Brother</td><td align="center">Number of children in the family</td></tr><tr><td align="left">Rank</td><td align="center">Rank among all children in the family</td></tr><tr><td align="left">Parent</td><td align="center">Stages of education completed by parents</td></tr><tr><td align="left">HDI</td><td align="center">Household disposable income in 2018</td></tr><tr><td align="left">Expenditure</td><td align="center">Household education expenditure in 2018</td></tr><tr><td align="left">GDP</td><td align="center">County GDP per capita</td></tr><tr><td align="left">Urbanization</td><td align="center">Urban population/total population</td></tr><tr><td align="left">Density</td><td align="center">Total population/administrative area</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> The educational level of parents is a hierarchical system, ranging from 1 to 9: no schooling (including non‐formal education such as literacy classes), primary school, junior high school, high school, vocational high school/technical school, technical secondary school, junior college, undergraduate and postgraduate. Household disposable income, education expenditure and GDP per capita are logarithmised in the regression. The regional characteristic variables are all from the counties where the samples are located.</p> <p>2 TABLE Descriptive statistics.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center">Observations</th><th align="center">Mean</th><th align="center">Median</th><th align="center">Standard deviation</th></tr></thead><tbody valign="top"><tr><td align="left">Education</td><td align="center">3983</td><td align="center">0.4577</td><td align="center">0</td><td align="center">0.4983</td></tr><tr><td align="left">Age</td><td align="center">3983</td><td align="center">28.9576</td><td align="center">29</td><td align="center">6.2536</td></tr><tr><td align="left">Gender</td><td align="center">3983</td><td align="center">0.5308</td><td align="center">1</td><td align="center">0.4991</td></tr><tr><td align="left">Hukou</td><td align="center">3978</td><td align="center">0.8542</td><td align="center">1</td><td align="center">0.3530</td></tr><tr><td align="left">Brother</td><td align="center">3975</td><td align="center">2.4133</td><td align="center">2</td><td align="center">1.1099</td></tr><tr><td align="left">Rank</td><td align="center">3974</td><td align="center">1.7325</td><td align="center">1</td><td align="center">1.0075</td></tr><tr><td align="left">Parent</td><td align="center">1835</td><td align="center">2.0463</td><td align="center">2</td><td align="center">0.9436</td></tr><tr><td align="left">HDI</td><td align="center">3956</td><td align="center">58,154.73</td><td align="center">45,513.55</td><td align="center">50,327.07</td></tr><tr><td align="left">Expenditure</td><td align="center">3956</td><td align="center">6407.55</td><td align="center">3021.5</td><td align="center">10,510.81</td></tr><tr><td align="left">GDP</td><td align="center">3236</td><td align="center">46,224.47</td><td align="center">33,378</td><td align="center">36,722.33</td></tr><tr><td align="left">Urbanization</td><td align="center">3634</td><td align="center">0.5077</td><td align="center">0.4691</td><td align="center">0.1366</td></tr><tr><td align="left">Density</td><td align="center">3982</td><td align="center">374.77</td><td align="center">333.5459</td><td align="center">367.6697</td></tr><tr><td align="left">Policy</td><td align="center">3983</td><td align="center">1.6123</td><td align="center">1.0137</td><td align="center">2.0721</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181057390-16">Empirical Results</hd> <p>We created birth cohort and school merger intensity indicators to replace the two dummy variables of policy and time, which are slightly different from the classic DID model. In the regression, the cross‐term coefficient captures the differences in high school enrolment rates between affected and unaffected students across regions with varying policy intensities. The effect of having one fewer school per 1000 students on the probability of children receiving high school education can be analysed. If the coefficient is positive, it implies that school consolidation has a beneficial effect on the high school education of local students. Additionally, the regression also controls for age and region‐fixed effects. Methods including replacing the explained variables, eliminating the influence of school‐entering age and considering changes in government educational expenses were employed in the robustness checks.</p> <hd id="AN0181057390-17">Cross‐Sectional DID Regression Results</hd> <p>In the first place, we conducted a DID regression analysis on Equation (<reflink idref="bib1" id="ref51">1</reflink>). The results of school consolidation and withdrawal on the enrolment of rural students in high school are illustrated in Table 3. At the 5% level of significance, the cross coefficient indicates that there is a significant positive correlation between the degree of school consolidation and the probability that rural students will complete high school.</p> <p>3 TABLE Impact of school consolidation on high school education for rural students.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center">Explained variable: whether to receive high school education</th></tr><tr><th align="center">(1)</th><th align="center">(2)</th><th align="center">(3)</th></tr></thead><tbody valign="top"><tr><td align="left">policy × young</td><td align="center">0.0152<xref ref-type="fn" rid="tfn3" /> (0.0087)</td><td align="center">0.0212<xref ref-type="fn" rid="tfn4" /> (0.0098)</td><td align="center">0.0297<xref ref-type="fn" rid="tfn4" /> (0.0116)</td></tr><tr><td align="left">Gender</td><td align="center">0.0011 (0.0157)</td><td align="center">0.0116 (0.0171)</td><td align="center">0.0086 (0.0174)</td></tr><tr><td align="left">Brother</td><td align="center">0.0585<xref ref-type="fn" rid="tfn5" /> (0.0130)</td><td align="center">0.0462<xref ref-type="fn" rid="tfn5" /> (0.0146)</td><td align="center">0.0421<xref ref-type="fn" rid="tfn5" /> (0.0146)</td></tr><tr><td align="left">Rank</td><td align="center">0.0027 (0.0092)</td><td align="center">0.0040 (0.0120)</td><td align="center">0.0034 (0.0122)</td></tr><tr><td align="left">Hukou</td><td align="center">−0.1182<xref ref-type="fn" rid="tfn3" /> (0.0626)</td><td align="center">−0.0866<xref ref-type="fn" rid="tfn3" /> (0.0514)</td><td align="center">−0.0658 (0.0503)</td></tr><tr><td align="left">Parents</td><td align="center" /><td align="center">0.0296<xref ref-type="fn" rid="tfn3" /> (0.0156)</td><td align="center">0.0244<xref ref-type="fn" rid="tfn3" /> (0.0160)</td></tr><tr><td align="left">Expenditure</td><td align="center" /><td align="center">0.0376<xref ref-type="fn" rid="tfn5" /> (0.0081)</td><td align="center">0.0352<xref ref-type="fn" rid="tfn5" /> (0.0085)</td></tr><tr><td align="left">HDI</td><td align="center" /><td align="center">0.0672<xref ref-type="fn" rid="tfn5" /> (0.0164)</td><td align="center">0.0562<xref ref-type="fn" rid="tfn5" /> (0.0155)</td></tr><tr><td align="left">GDP</td><td align="center" /><td align="center" /><td align="center">0.0151 (0.0246)</td></tr><tr><td align="left">Urbanization</td><td align="center" /><td align="center" /><td align="center">0.0388 (0.0852)</td></tr><tr><td align="left">Density</td><td align="center" /><td align="center" /><td align="center">0.0251<xref ref-type="fn" rid="tfn3" /> (0.01560)</td></tr><tr><td align="left">Age fixed effect</td><td align="center">Control</td><td align="center">Control</td><td align="center">Control</td></tr><tr><td align="left">Regional fixed effects</td><td align="center">Control</td><td align="center">Control</td><td align="center">Control</td></tr><tr><td align="left">Observations</td><td align="center">3971</td><td align="center">1738</td><td align="center">1723</td></tr><tr><td align="left">R<sup>2</sup></td><td align="center">0.18</td><td align="center">0.30</td><td align="center">0.31</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 <emph>Note:</emph> GDP per capita, urbanisation rate and population density are controlled at the regional level, and GDP per capita is logarithmic.</item> <item>3 * <emph>p</emph> < 0.10.</item> <item>4 ** <emph>p</emph> < 0.05.</item> <item>5 *** <emph>p</emph> < 0.01.</item> </ulist> <p>By utilising hukou, gender, number of siblings and ranking of siblings with control variables being the only in column (<reflink idref="bib1" id="ref52">1</reflink>), a one‐school reduction per thousand primary school students results in a 1.52% augmentation of the probability that students residing in rural areas could excel to the tenth grade. Following controlling for family and region factors, the likelihood of students receiving high school increases by 2.12% and 2.97%, respectively, and is remarkable at the 5% statistical level. A statistically significant inverse correlation has been observed between the number of siblings and hukou and the completion of high school education by students, concerning control variables. Conversely, family education expenditure and financial assets demonstrate a statistically significant positive influence on student education. Even though the closure of rural schools has resulted in some students experiencing some inconvenience in attending classes, the educational standing of students residing in rural areas has significantly advanced.</p> <hd id="AN0181057390-18">Parallel Trend Test</hd> <p>Satisfying the parallel trend assumption is essential for the DID setting. If there is no significant trend difference between the treatment and control groups before the policy is implemented, the regression result will reflect the policy intervention's causal effect. An event study method is utilised to test parallel trends and investigate the dynamic effects of school consolidation. The 13‐year‐old population at the time of the policy was used as a benchmark to assess the impact on rural adolescents aged 7–20. The absence of an impact on children who completed primary education in 2001 suggests the existence of a parallel trend prior to the implementation of the policy.</p> <p>As illustrated in Figure 3, the impact of school consolidation on rural adolescents aged 15 and older in 2001 was not statistically significant. A parallel trend was evident prior to the implementation of the policy. From the perspective of dynamic effects, the likelihood of rural children under the age of 14 attending high school has increased dramatically. As some children in rural areas did not enter school at the legal age and may not have completed primary education at the age of 13 or 14, we will discuss the issue of age interference later.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12790-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12790-fig-0003.jpg" title="3 Parallel trend test." /> </p> <p></p> <hd id="AN0181057390-20">Robustness Checks</hd> <p>The school consolidation indicator, which was created in the preceding empirical section, assessed the change in rural schools from 2000 to 2006. Prior to the central government's official documentation being released, a number of local governments had undertaken autonomous initiatives to consolidate rural schools. We intend to prolong the duration covered by the school consolidation indicator to 1995–2006 to capture a wider range of school changes. The findings of the analysis are presented in column (<reflink idref="bib1" id="ref53">1</reflink>) of Table 4. Upon modifying the indicator period, it is noted that a decrease in one school per thousand primary school students is associated with a 2.28% rise in the probability of rural students accessing high school education, a trend that aligns closely with the outcomes of the earlier regression analysis.</p> <p>4 TABLE Impact on rural students after changing variables.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center">Explained variable</th></tr><tr><th align="center">(1)</th><th align="center">(2)</th><th align="center">(3)</th></tr></thead><tbody valign="top"><tr><td align="left">policy × young</td><td align="center">0.0228<xref ref-type="fn" rid="tfn7" /> (0.0094)</td><td align="center">0.0238<xref ref-type="fn" rid="tfn7" /> (0.0103)</td><td align="center">0.1891<xref ref-type="fn" rid="tfn8" /> (0.0561)</td></tr><tr><td align="left">Control variables</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td></tr><tr><td align="left">Observations</td><td align="center">618</td><td align="center">1509</td><td align="center">1698</td></tr><tr><td align="left">R<sup>2</sup></td><td align="center">0.38</td><td align="center">0.25</td><td align="center">0.34</td></tr></tbody></table> </ephtml> </p> <ulist> <item>6 <emph>Note:</emph> The control variables are consistent with column (<reflink idref="bib3" id="ref54">3</reflink>) of Table 3.</item> <item>7 ** <emph>p</emph> < 0.05.</item> <item>8 *** <emph>p</emph> < 0.01.</item> </ulist> <p>The national unified university entrance examination is administered to students who have completed high school to determine their eligibility for further academic pursuits. Hence, to assess the evolution of students' educational attainment, we consider replacing the explicated variable with the length of formal education and the status of college admission examination participation. Columns (<reflink idref="bib1" id="ref55">1</reflink>) and (<reflink idref="bib2" id="ref56">2</reflink>) of Table 4 display the results of the regression analysis conducted subsequent to the substitution of the variables that were explained. Upon adjusting for pertinent variables, it becomes immediately apparent that a reduction of one primary school per thousand students results in a 2.51% increase in the likelihood of rural students undertaking college entrance examinations and a 0.22‐year increase in the duration of formal education. The possibility that some members of the survey sample have pursued additional education introduces the possibility that the impact of years of formal education may be undervalued.</p> <p>It is prevalent for school‐age children in developing countries, notably in impoverished regions and among girls, to commence their education beyond the nationally required age. In accordance with China's compulsory education policies, children are required to commence their academic endeavours at the age of 6. Nonetheless, this criterion is frequently disregarded in rural regions. During the survey year, students who commenced their academic journey at the age of seven or eight had not yet attained the certification of high school graduation. Furthermore, particular regions' continued implementation of the 5‐year primary school system may result in the graduation of some children prior to the age of 12. Due to the possibility of measurement error introduced by the entry age specification utilised in prior analyses, the effect of school consolidation potentially is undervalued. Given the possibility that children commenced formal education over the age of six or attended 5‐year primary school, we omit from the samples two cohorts whose birth years are (1987,1990) and (1998, 2000).</p> <p>The regression results are demonstrated in Table 5. After controlling for entry age and educational system, it becomes apparent that the likelihood of rural students obtaining a high school education increased by 3.88% when the number of primary schools per thousand students was reduced. Additionally, the likelihood of taking the college entrance examination rose by 2.46%, whereas the duration of formal education increased by 0.27 years. As opposed to the prior regression, the current results appear to be more favourable, indicating that the influence of school consolidation may have been underestimated.</p> <p>5 TABLE Impact on rural students after excluding the interference of entry age.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center">Explained variable</th></tr><tr><th align="center">(1)</th><th align="center">(2)</th><th align="center">(3)</th></tr></thead><tbody valign="top"><tr><td align="left">policy × young</td><td align="center">0.0388<xref ref-type="fn" rid="tfn10" /> (0.0134)</td><td align="center">0.0246<xref ref-type="fn" rid="tfn9" /> (0.0145)</td><td align="center">0.2722<xref ref-type="fn" rid="tfn10" /> (0.0856)</td></tr><tr><td align="left">Control variables</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td></tr><tr><td align="left">Observations</td><td align="center">1220</td><td align="center">1065</td><td align="center">1199</td></tr><tr><td align="left">R<sup>2</sup></td><td align="center">0.30</td><td align="center">0.28</td><td align="center">0.37</td></tr></tbody></table> </ephtml> </p> <ulist> <item>9 ** <emph>p</emph> < 0.05.</item> <item>10 *** <emph>p</emph> < 0.01.</item> </ulist> <p>Local governments are substantially motivated to merge rural schools by the need to alleviate the pressure on fiscal expenses. As fewer schools are necessary to be maintained, local education costs change. Subsequently, we analyse changes in local education expenditures and further control the impact of education expenditures on school consolidation. As total primary education expenditure and per‐student expenditure data for the county are not available, we utilise total education expenditure as a proportion of fiscal revenue as an indicator. A regression analysis is performed using panel data spanning the Years 1995 to 2006, encompassing the 77 counties that comprise the sample.</p> <p>Column (<reflink idref="bib1" id="ref57">1</reflink>) of Table 6 illustrates the results. A reduction of one primary school per thousand students results in a 0.12 percentage point decrease in the fiscal proportion of education expenditures. As demonstrated by this, school consolidation has mitigated the fiscal strain on local governments to a certain extent. Column (<reflink idref="bib2" id="ref58">2</reflink>) indicates that following controlling for local education expenditure, school consolidation continues to sustain a substantial positive impact on the access of rural students to high school education, with the probability of receiving high school education increasing by 1.85%.</p> <p>6 TABLE Changes in education expenditures during rural school consolidation.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center">Explained variable</th></tr><tr><th align="center">(1)</th><th align="center">(2)</th></tr></thead><tbody valign="top"><tr><td align="left">policy</td><td align="center">−0.1227<xref ref-type="fn" rid="tfn12" /> (0.0071)</td><td align="center" /></tr><tr><td align="left">policy × young</td><td align="center" /><td align="center">0.0185<xref ref-type="fn" rid="tfn12" /> (0.0092)</td></tr><tr><td align="left">Control variables</td><td align="center">Yes</td><td align="center">Yes</td></tr><tr><td align="left">Observations</td><td align="center">407</td><td align="center">1621</td></tr><tr><td align="left">R<sup>2</sup></td><td align="center">0.81</td><td align="center">0.31</td></tr></tbody></table> </ephtml> </p> <ulist> <item>11 <emph>Note:</emph> The control variables used in column (<reflink idref="bib1" id="ref59">1</reflink>) include urbanisation rate and logarithmic GDP per capita, fiscal revenue, total population and rural population size. The control variables in column (<reflink idref="bib2" id="ref60">2</reflink>) are the same as those in column (<reflink idref="bib3" id="ref61">3</reflink>) of Table 3.</item> <item>12 ** <emph>p</emph> < 0.05.</item> </ulist> <hd id="AN0181057390-21">Discussion</hd> <p>Although the disappearance of rural schools may diminish the educational enthusiasm of rural families and students, it may also increase opportunities for students to access high‐quality educational resources. To the extent that school clustering is also beneficial to students' educational outcomes. We utilised the cross‐sectional DID method to investigate whether rural students have a higher likelihood of enrolling in high school after school consolidation. It is not mandatory for students to attend high school in China, and they have the right to decide whether or not to pursue further education.</p> <p>Our empirical analysis of micro‐data revealed that school consolidation increased the likelihood of rural students receiving high school education and had a positive effect on the completion of the college entrance examination and the duration of their formal education. The findings match those observed in earlier studies (Engberg et al. [<reflink idref="bib10" id="ref62">10</reflink>]; Beuchert et al. [<reflink idref="bib3" id="ref63">3</reflink>]; De Haan, Leuven, and Oosterbeek [<reflink idref="bib7" id="ref64">7</reflink>]; Liang and Wang [<reflink idref="bib18" id="ref65">18</reflink>]).</p> <p>In contrast, certain studies argued that school consolidation increased dropout rates (Liang [<reflink idref="bib17" id="ref66">17</reflink>]; Mandic et al. [<reflink idref="bib25" id="ref67">25</reflink>]; Al‐Sabbagh [<reflink idref="bib1" id="ref68">1</reflink>]; Larsen [<reflink idref="bib16" id="ref69">16</reflink>]). We contend that distance may be a significant factor contributing to discrepancies in the analysis results. The influence of distance may be more noticeable for younger students, as they may struggle more with adapting to longer commutes and boarding. Besides, the impact of the increased distance to school varies among students from varying economic and geographical contexts. The adaptation to the increase in commuting distance is more challenging for students in mountainous areas than those in plain areas. Furthermore, students' enthusiasm for education is likewise influenced by the coverage area of merged schools. On the condition that the radiation range of the school is excessively broad, some students are required to spend extra time commuting.</p> <p>The empirical analysis is conducted using large‐scale micro‐survey data and county statistics matching, which is a somewhat innovative approach adopted in this study. National surveys can collect samples from a broader geographic area than the surveys that focus on specific counties or schools. Biases that are a result of a region's history, topography or economics may be mitigated by this. The data acquired through the application can also reduce the cost of research. Furthermore, the analysis of school change processes is specific to the county‐level administrative regions, which is more realistic than other surveys that focus solely on provinces and prefecture‐level cities. The use of maps also more intuitively illustrates the fluctuations in the number of primary and secondary schools.</p> <p>Nevertheless, the large‐scale micro‐survey data available upon application are insufficient to satisfy the requirements of all researchers. For instance, the survey utilised in this study does not include inquiries regarding the sample's distance from school and commuting time. Consequently, it remains impossible to conduct a detailed examination of the moderating impact of distance. Some of the sample may not have completed their education by the time of the survey. The results of the regression on the duration of education may be understated, and the long‐term impact on variables such as individual income cannot be analysed. Except for that, schools that were forcibly consolidated and those that disappeared naturally cannot be distinguished by the yearbook data that are currently available. The negative effects of mandatory school consolidation may be offset. These issues could potentially be addressed by either updating the questionnaire and regional data or improving the empirical method.</p> <p>The education of rural students is the sole subject of the previous discourse. As a result of the school consolidation, rural families, urban students and local governments have all been affected to varying extents. Furthermore, consideration of these subjects contributes to the enhancement of our research. We have made an effort to account for the impact of education expenditures by local governments in the robustness section. This represents a promising start for our work. Subsequent research directions may also include the educational burden of rural families and the impact on urban students.</p> <hd id="AN0181057390-22">Conclusions</hd> <p>The progress of rural primary and secondary education in China experienced an initial surge, followed by a subsequent decrease. Typically, rural children were educated at schools built under the layout of the village. On the contrary, as a result of policy encouragement and urbanisation expansion, the number of rural schools began to decrease substantially in the late 1990s. Between 2000 and 2020, there was a substantial decrease in the number of primary schools. Middle schools in certain counties have been reduced to one or two.</p> <p>This research investigates whether the school consolidation movement that occurred in China during the late 1990s affected on the academic performance of rural students. Empirical evidence from China's micro‐surveys and county data indicate that school consolidation increased the likelihood of rural students receiving high school education and positively influenced their chances of taking the college entrance examination and obtaining a longer duration of formal education. After adjusting the period of the indicator, incorporating the impact of education expenditure and controlling for school entry age, the aforementioned analysis results continue to be statistically significant.</p> <p>Several essential small‐scale schools and teaching sites have been cancelled because of the absence of preliminary studies on school consolidation in some places. Despite the fact that the consolidation of schools could lead to reduced spending on education, the preservation of small‐scale schools is crucial for developing countries that have significant rural populations.</p> <p>Where the economy is behind, students should utilise education as a tool to alter their fate. Consequently, local governments should contemplate how to evaluate the efficiency and fairness of educational resource allocation following actual conditions. We believe that differentiated measures should be applied to schools at different educational stages.</p> <p>Students without schools do exist in addition to schools devoid of students. Small rural primary schools ought to be maintained as well, considering that rural areas will continue to exist over time. It is essential to ensure that primary school students' commuting time is within the half‐hour stipulated by the Chinese government. If maintaining small‐scale schools or teaching points is not feasible, it is essential to ensure the effective use and supervision of school buses. If students expend a significant amount of time and money on commuting, parents are faced with the choice of either increasing the cost of sending their children to school or having their children drop out.</p> <p>We tend to concentrate junior and high school educational resources in towns and cities. Secondary education requires better infrastructure and teachers. Equipping rural schools with laboratories or computer classrooms is an expensive task. Students in this age group are more likely to quickly adapt to boarding school and demonstrate better self‐care abilities. Additionally, this situation may motivate students to study hard and strive to enter high schools with higher enrolment rates and improved teaching conditions. We believe that transferring students to urban schools at the junior high school stage or earlier will assist them in gaining admission to high school and extending their formal education.</p> <hd id="AN0181057390-23">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <hd id="AN0181057390-24">Data Availability Statement</hd> <p>The survey data are available upon application to China Institution for Income Distribution of Beijing Normal University at <ulink href="http://www.ciidbnu.org/chip/about.asp?lang=CN">http://www.ciidbnu.org/chip/about.asp?lang=CN</ulink>. The institution requires that data obtained from CHIPS will not be passed on either wholly or partially with or without profit to any other data user or disseminator of data with or without commercial purpose.</p> <ref id="AN0181057390-25"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref3" type="bt">1</bibl> <bibtext> The gross enrolment rate refers to the percentage of students in a certain grade to the population of that grade specified by the state. The gross enrolment ratio may exceed 100% because of the inclusion of non‐formal age group (underage or overage) students.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref16" type="bt">2</bibl> <bibtext> In addition to 3‐year schools, junior high schools also include 9‐year consistent schools and high schools include complete middle schools and 12‐year consistent schools.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref18" type="bt">3</bibl> <bibtext> Combined instruction is typically used in villages with shortages of teachers, inadequate enrolment or poor economic circumstances. One teacher employs distinct educational materials to teach students of varying grade levels inside a single classroom that is comprised of two or more students in the lower grade level. Students must move to larger complete elementary schools, middle schools or high schools in order to continue their education once they have reached the upper years of primary school.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref12" type="bt">4</bibl> <bibtext> In January 1994, the Tax Sharing System was instituted in China. Principal contents include: In light of the separation of powers between the central and local governments, rationally ascertain fiscal expenditures across all tiers. Taxes are uniformly classified as central taxes, local taxes and shared taxes, and two sets of central and local tax agencies are established to collect and manage them; Determine the amount of local revenue and spending scientifically, and progressively establish a uniform tax return and transfer payment system.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref31" type="bt">5</bibl> <bibtext> The data come from the <emph>China Rural Statistical Yearbook</emph> and <emph>China Demographic Yearbook</emph>.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref21" type="bt">6</bibl> <bibtext> The administrative division code is an alternative code compiled by the Chinese government. It consists of six Arabic numerals, with every two numerals representing the administrative divisions of provinces, prefecture‐level cities and counties.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref19" type="bt">7</bibl> <bibtext> The high school stage includes general high schools, secondary vocational schools and adult high schools.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref2" type="bt">8</bibl> <bibtext> The data come from the <emph>China Education Statistics Yearbook</emph>.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref34" type="bt">9</bibl> <bibtext> The high school education discussed in this paper solely pertains to whether students attend regular high schools and does not consider if they attend vocational schools, drop out or get a diploma but do not take the college entrance examination.</bibtext> </blist> <blist> <bibtext> Funding: The authors received no specific funding for this work.</bibtext> </blist> </ref> <ref id="AN0181057390-26"> <title> References </title> <blist> <bibtext> Al‐Sabbagh, T. A. 2022. " GIS Location‐Allocation Models in Improving Accessibility to Primary Schools in Mansura City‐Egypt." Geo Journal 87, no. 2 : 1009 – 1026. https://doi.org/10.1007/s10708‐020‐10290‐5.</bibtext> </blist> <blist> <bibtext> Angrist, J. D., and K. Lang. 2004. " Does School Integration Generate Peer Effects? Evidence From Boston's Metco Program." American Economic Review 94, no. 5 : 1613 – 1634. https://doi.org/10.1257/0002828043052169.</bibtext> </blist> <blist> <bibtext> Beuchert, L., M. K. Humlum, H. S. Nielsen, and N. Smith. 2015. " The Short‐Term Effects of School Consolidation on Student Achievement: Evidence of Disruption? " Economics of Education Review 65 : 31 – 47. https://doi.org/10.1016/j.econedurev.2018.05.004.</bibtext> </blist> <blist> <bibtext> Bhatnagar, A., and N. B. Bolia. 2019. " Improved Governance of Indian School System Through School Consolidation." Journal of Policy Modeling 41, no. 6 : 1160 – 1178. https://doi.org/10.1016/j.jpolmod.2019.05.001.</bibtext> </blist> <blist> <bibtext> Brummet, Q. 2014. " The Effect of School Closings on Student Achievement." Journal of Public Economics 119, no. 11 : 108 – 124. https://doi.org/10.1016/j.jpubeco.2014.06.010.</bibtext> </blist> <blist> <bibtext> Card, D., and A. A. Payne. 2002. " School Finance Reform, the Distribution of School Spending, and the Distribution of Student Test Scores." Journal of Public Economics 83, no. 1 : 49 – 82. https://doi.org/10.1016/S0047‐2727(00)00177‐8.</bibtext> </blist> <blist> <bibtext> De Haan, M., E. Leuven, and H. Oosterbeek. 2016. " School Consolidation and Student Achievement." Journal of Law, Economics, and Organization 32, no. 4 : 816 – 839. https://doi.org/10.1093/jleo/eww006.</bibtext> </blist> <blist> <bibtext> Duflo, E. 2001. " Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence From an Unusual Policy Experiment." American Economic Review 91, no. 4 : 795 – 813. https://doi.org/10.1257/aer.91.4.795.</bibtext> </blist> <blist> <bibtext> Eacott, S., and A. Freeborn. 2019. " Regional and Rural School Consolidation: A Scoping Study of Research Literature." International Journal of Educational Management 34, no. 3 : 477 – 491. https://doi.org/10.1108/IJEM‐08‐2019‐0318.</bibtext> </blist> <blist> <bibtext> Engberg, J., B. Gil, G. Zamarro, and R. Zimmer. 2012. " Closing Schools in a Shrinking District: Do Student Outcomes Depend on Which Schools Are Closed? " Journal of Urban Economics 71, no. 2 : 189 – 203. https://doi.org/10.1016/j.jue.2011.10.001.</bibtext> </blist> <blist> <bibtext> Hannum, E., and F. Wang. 2022. " Fewer, Better Pathways for All? Intersectional Impacts of Rural School Consolidation in China's Minority Regions." World Development 151 : 105734. https://doi.org/10.1016/j.worlddev.2021.105734.</bibtext> </blist> <blist> <bibtext> Hershkovitz, S. 1991. " Socio‐Spatial Aspects of Changes in Educational Services: Tel Aviv and Jerusalem, 1970–88." Service Industries Journal 11, no. 2 : 137 – 153. https://doi.org/10.1080/02642069100000024.</bibtext> </blist> <blist> <bibtext> Huang, Y. 2022. " Family Size and Children's Education: Evidence From the One‐Child Policy in China." Population Research and Policy Review 41, no. 1 : 317 – 342. https://doi.org/10.1007/s11113‐021‐09638‐7.</bibtext> </blist> <blist> <bibtext> Ju, F., S. Wang, and W. Lin. 2022. " Nonlinear Effect of Urbanization on the Gap Between Urban and Rural Elementary Education in China." Mathematical Problems in Engineering 2022 : 7025433. https://doi.org/10.1155/2022/7025433.</bibtext> </blist> <blist> <bibtext> Karakaplan, M. U., and L. Kutlu. 2019. " School District Consolidation Policies: Endogenous Cost Inefficiency and Saving Reversals." Empirical Economics 56, no. 5 : 1729 – 1768. https://doi.org/10.1007/s00181‐017‐1398‐z.</bibtext> </blist> <blist> <bibtext> Larsen, M. F. 2020. " Does Closing Schools Close Doors? The Effect of High School Closings on Achievement and Attainment." Economics of Education Review 76 : 101980. https://doi.org/10.1016/j.econedurev.2020.101980.</bibtext> </blist> <blist> <bibtext> Liang, C. 2017. " School Consolidation Basic Education Supply and Rural Human Capital." Research on Financial and Economic Issues 3 : 82 – 90. https://doi.org/10.3969/j.issn.1000‐176X.2017.03.012.</bibtext> </blist> <blist> <bibtext> Liang, C., and S. Wang. 2020. " The Influence of Public School Allocation on Human Capital: Evidence From the School Consolidation in China." Economic Research Journal 55, no. 9 : 138 – 154.</bibtext> </blist> <blist> <bibtext> Ling, Z., J. Na, S. Yan‐Li, and J. Sriyanto. 2020. " School Culture and Professional Development of School Teachers From Urban and Rural Areas in China." Jurnal Cakrawala Pendidikan 39, no. 3 : 609 – 619. https://doi.org/10.21831/cp.v39i3.31580.</bibtext> </blist> <blist> <bibtext> Long, W., X. Pang, X. Y. Dong, and J. Zeng. 2020. " Is Rented Accommodation a Good Choice for Primary School students' Academic Performance? Evidence From Rural China." China Economic Review 62 : 101459. https://doi.org/10.1016/j.chieco.2020.101459.</bibtext> </blist> <blist> <bibtext> Lugauer, S., J. Ni, and Z. Yin. 2019. " Chinese Household Saving and Dependent Children: Theory and Evidence." China Economic Review 57 : 101091. https://doi.org/10.1016/j.chieco.2017.08.005.</bibtext> </blist> <blist> <bibtext> Luo, C., and X. Meng. 2016. " Inequality in Tertiary Education, Decision to Enroll in Senior High School, and Rural‐Urban Divide." China Economics of Education Review 1, no. 1 : 90 – 111. https://doi.org/10.19512/j.cnki.issn2096‐2088.2016.01.011.</bibtext> </blist> <blist> <bibtext> Luo, R., Y. Shi, L. Zhang, C. Liu, S. Rozelle, and B. Sharbono. 2009. " Malnutrition in China's Rural Boarding Schools: The Case of Primary Schools in Shaanxi Province." Asia Pacific Journal of Education 29, no. 4 : 481 – 501. https://doi.org/10.1080/02188790903312680.</bibtext> </blist> <blist> <bibtext> Lykke Sørensen, J. F., G. L. Haase Svendsen, P. S. Jensen, and T. D. Schmidt. 2021. " Do Rural School Closures Lead to Local Population Decline? " Journal of Rural Studies 87 : 226 – 235. https://doi.org/10.1016/j.jrurstud.2021.09.016.</bibtext> </blist> <blist> <bibtext> Mandic, S., S. Sandretto, E. García Bengoechea, et al. 2017. " Enrolling in the Closest School or Not? Implications of School Choice Decisions for Active Transport to School." Journal of Transport & Health 2017, no. 5 : S16 – S17. https://doi.org/10.1016/j.jth.2017.05.006.</bibtext> </blist> <blist> <bibtext> Mei, H., Q. Jiang, Y. Xiang, and X. Song. 2015. " School Consolidation: Whither China's Rural Education? " Asian Social Work and Policy Review 9, no. 2 : 138 – 150. https://doi.org/10.1111/aswp.12053.</bibtext> </blist> <blist> <bibtext> Nguyen‐Hoang, P. 2019. " Does the Cost‐Cutting Strategy of Closing Public Schools Provide Financial Benefits? Evidence From Ohio." Public Budgeting & Finance 39, no. 3 : 3 – 21. https://doi.org/10.1111/pbaf.12236.</bibtext> </blist> <blist> <bibtext> Sageman, J. 2012. " School Closures and Rural Population Decline." Rural Sociology 87, no. 3 : 960 – 992. https://doi.org/10.1111/ruso.12437.</bibtext> </blist> <blist> <bibtext> Steinberg, M. P., and J. M. Macdonald. 2019. " The Effects of Closing Urban Schools on Students' Academic and Behavioral Outcomes: Evidence From Philadelphia." Economics of Education Review 69 : 25 – 60. https://doi.org/10.1016/j.econedurev.2018.12.005.</bibtext> </blist> <blist> <bibtext> Sunday, O. J., and Y. P. Olatunde. 2011. " School Location and Academic Achievement of Secondary School in Ekiti State, Nigeria." Asian Social Science 7, no. 5 : 170 – 175. https://doi.org/10.5539/ass.v7n5p170.</bibtext> </blist> <blist> <bibtext> United Nations Educational, Scientific and Cultural Organization (UNESCO). 2023. What You Need To Know About the Right to Education. https://<ulink href="http://www.unesco.org/en/right‐education/need‐know">www.unesco.org/en/right‐education/need‐know</ulink>.</bibtext> </blist> <blist> <bibtext> Zhang, X., and S. Rozelle. 2022. " Education Universalization, Rural School Participation, and Population Density." China & World Economy 30, no. 4 : 4 – 30. https://doi.org/10.1111/cwe.12426.</bibtext> </blist> <blist> <bibtext> Zhang, Z., W. Luan, C. Tian, M. Su, and Z. Li. 2021. " Spatial Distribution Equilibrium and Relationship Between Construction Land Expansion and Basic Education Schools in Shanghai Based on POI Data." Land 10, no. 10 : 1059. https://doi.org/10.3390/land10101059.</bibtext> </blist> </ref> <aug> <p>By Yu Li and Bo Gao</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib31" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib12" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib33" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib28" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib11" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib30" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib19" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib14" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib15" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib27" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib32" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib10" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib18" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib17" firstref="ref24"></nolink> <nolink nlid="nl15" bibid="bib25" firstref="ref25"></nolink> <nolink nlid="nl16" bibid="bib16" firstref="ref27"></nolink> <nolink nlid="nl17" bibid="bib23" firstref="ref28"></nolink> <nolink nlid="nl18" bibid="bib20" firstref="ref29"></nolink> <nolink nlid="nl19" bibid="bib29" firstref="ref32"></nolink> <nolink nlid="nl20" bibid="bib26" firstref="ref33"></nolink> <nolink nlid="nl21" bibid="bib24" firstref="ref36"></nolink> <nolink nlid="nl22" bibid="bib22" firstref="ref47"></nolink> <nolink nlid="nl23" bibid="bib13" firstref="ref49"></nolink> <nolink nlid="nl24" bibid="bib21" firstref="ref50"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1450294
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Does School Consolidation in Rural Areas Affect Students' Education? Empirical Evidence from China
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yu+Li%22">Yu Li</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1848-8975">0000-0003-1848-8975</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bo+Gao%22">Bo Gao</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22European+Journal+of+Education%22"><i>European Journal of Education</i></searchLink>. 2024 59(4).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Rural+Areas%22">Rural Areas</searchLink><br /><searchLink fieldCode="DE" term="%22Consolidated+Schools%22">Consolidated Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Change%22">Educational Change</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+History%22">Educational History</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+Schools%22">Urban Schools</searchLink><br /><searchLink fieldCode="DE" term="%22School+Closing%22">School Closing</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Attainment%22">Educational Attainment</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Gap%22">Achievement Gap</searchLink><br /><searchLink fieldCode="DE" term="%22Rural+Youth%22">Rural Youth</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Enrollment+Influences%22">Enrollment Influences</searchLink><br /><searchLink fieldCode="DE" term="%22Government+Role%22">Government Role</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Gains%22">Achievement Gains</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/ejed.12790
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0141-8211<br />1465-3435
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In the late 1990s, an extensive consolidation of schools in rural China led to the amalgamation of numerous primary and secondary schools into urban schools or their discontinuation. The study aimed to investigate whether China's school consolidation movement has influenced the educational attainment of rural students. The data are sourced from the China Household Income Project (CHIP) and regional statistical yearbooks, and the cross-section difference-in-differences (DID) method is used to analyse the differences in high school enrolment of rural students between regions with different degrees of school consolidation. Empirical analysis results indicate that school consolidation substantially enhances the probability of rural students receiving high school education, as well as having a positive impact on rural students taking the college entrance examination and receiving a longer formal education. Moreover, the findings of the empirical analysis remain significant even after controlling for variations in government educational expenditures, substituting the explained variables, as well as eliminating the influence of school-entering age. The adjustment of rural schools, as an inevitable historical trend, is beneficial to improving rural educational attainment.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1450294
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1450294
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/ejed.12790
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Rural Areas
        Type: general
      – SubjectFull: Consolidated Schools
        Type: general
      – SubjectFull: Educational Change
        Type: general
      – SubjectFull: Educational History
        Type: general
      – SubjectFull: Urban Schools
        Type: general
      – SubjectFull: School Closing
        Type: general
      – SubjectFull: Educational Attainment
        Type: general
      – SubjectFull: Achievement Gap
        Type: general
      – SubjectFull: Rural Youth
        Type: general
      – SubjectFull: High School Students
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Enrollment Influences
        Type: general
      – SubjectFull: Government Role
        Type: general
      – SubjectFull: Achievement Gains
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Does School Consolidation in Rural Areas Affect Students' Education? Empirical Evidence from China
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yu Li
      – PersonEntity:
          Name:
            NameFull: Bo Gao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0141-8211
            – Type: issn-electronic
              Value: 1465-3435
          Numbering:
            – Type: volume
              Value: 59
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: European Journal of Education
              Type: main
ResultId 1