Urban–rural school-age population changes and compulsory education resource allocation

Introduction: To address disparities in compulsory educational resource allocation and foster integrated urban-rural education, it is crucial to understand how resources respond to demographic shifts. This study examines current allocation patterns and their relationship to changes in the school-age population.

Methods: The research utilizes data from the China Statistical Yearbook.

Results: Data analysis reveals that increasing urbanization has caused a significant decline in the rural school-age population, altering the demographic balance between areas. Regarding resources, while the overall quantity of teachers is sufficient, regional disparities persist. Similarly, while education funding exhibits steady growth, the urban-rural gap is widening.

Discussion: The size of the school-age population and economic conditions are the predominant factors influencing resource allocation. Despite improvements in physical infrastructure, structural inequities such as teacher shortages and funding gaps remain, necessitating targeted policy interventions to achieve true integration.

1 Introduction

China is currently facing considerable challenges stemming from demographic shifts marked by persistently low birth rates and a rising trend in population migration (Fang, 2018; Hsu et al., 2018). These factors have significant implications for China’s overall population, workforce, and mobility. Entering the 21st century, a slowdown in population growth, a dwindling labor force, and changes in population mobility have become pivotal factors influencing socioeconomic dynamics (Pannell, 2003). These transformations have had a profound impact on the distribution of educational resources (Cai et al., 2019).

At present, China is experiencing a growing disparity in the size of school-age populations between urban and rural areas. This phenomenon mirrors a global trend of demographic scarcity affecting education systems. Recent studies indicate that declining birth rates and urbanization are reshaping educational landscapes not only in China but also in East Asia and Western Europe (Hannum et al., 2025), and Latin America (Vera and Cardini, 2025). For instance, the World Bank (2023) highlighted that demographic shifts in Asia are freeing up per-capita financing but creating acute challenges for resource redistribution. In China, this dual pressure of low fertility and rapid migration has intensified the hollowing out of rural schools and overcrowding in urban centers (Wu, 2024). The unequal distribution of educational resources has prompted the continuous migration of school-age populations to urban areas, perpetuating a cycle of unbalanced educational resources between urban and rural regions (Nie, 2023; Wang et al., 2019). This study aims to investigate the dynamic impact of these urban–rural demographic shifts on the allocation of compulsory education resources and to forecast the demand and supply gaps from 2020 to 2035, thereby providing evidence-based strategies to mitigate structural imbalances.

The unequal distribution of educational resources in China has drawn widespread attention, particularly in the field of research focusing on the fairness and balance of the allocation of compulsory educational resources (Han et al., 2016). The interaction between a declining birth rate and accelerating urbanization process has resulted in continuous fluctuations in the school-age population. Consequently, certain areas have experienced a prolonged relative surplus of educational resources, posing new challenges to their allocation (Guo et al., 2020; Yang et al., 2014). Concurrently, changes in the population’s age structure have a significant impact on the existing educational landscape. Shifts in the urban–rural population structure present new challenges in the distribution of available educational facilities (Ji et al., 2017; Li et al., 2012; Nan et al., 2021).

While scholars have made significant strides in researching the allocation of compulsory educational resources in China, the ongoing and substantial fluctuations in the school-age population in the present and upcoming periods introduce novel perspectives into this discourse. Given this context, this study aimed to investigate the influence of changes in the school-age population in both urban and rural areas on the allocation of educational resources. Through a scientific projection of future trends in the school-age population at both the urban and rural levels, we seek to evaluate how the allocation of compulsory educational resources will evolve in the coming years.

2 Literature review

2.1 Allocation of compulsory educational resources

Compulsory education in China is a policy mandating that children attend school for 9 years. Its primary objective is to ensure equal educational opportunities for all children and enhance the overall quality of education in China (Sun, 2022). Educational resources include materials, tools, and technologies that support teaching and learning processes. Educational resource allocation, as defined by Lin (2015), involves the distribution of resources, such as funding, personnel, and materials, to educational institutions or programs. The ultimate aim is to achieve equitable and efficient distribution of these resources to meet the needs of students and educators.

Assessing the current state of educational resource allocation, Liang and Ma (2021) delved into disparities in the spatial distribution of compulsory educational resources. They identified a gradient distribution pattern, with the equilibrium degree of junior secondary school educational resource allocation primarily centered in urban areas and extending outward to rural areas. In a separate study, Han et al. (2023a, 2023b) analyzed the spatial distribution of basic education resources in Xinjiang. Their findings highlighted significant disparities in the allocation of basic educational resources, both between urban and rural areas, and among different regions within Xinjiang. The study also examined the relationship between the spatial distribution of basic educational resources and the promotion of coordinated regional development in Xinjiang.

2.2 Dynamics of school-age population

School-age population refers to children typically aged between 6 and 18 years, who are of compulsory school age. The dynamics of the school-age population encompass changes in population size and age structure at three distinct geographical levels: national, provincial, and urban–rural. Scholars have extensively investigated various facets of changes in school-age populations, including population size, age structure, urban–rural distribution, and spatial patterns. Their findings revealed an overarching trend of the school-age population diminishing in size and concentrating in urban and economically developed regions. However, it is important to note that distinct characteristics emerge across different regions and stages of the school age population.

Zhang (2008) identified a common trend of decreasing primary school-age population by analyzing statistical data from Hubei Province between 2000 and 2005. However, the patterns diverged at the junior high school stage, highlighting notable urban–rural disparities in the distribution of the school-age population. Tian (2008) conducted an analysis of changes in the school-age population across rural, suburban, and urban schools in selected cities and counties from 2001 to 2005, emphasizing the impact of population migration and urban–rural distribution. The study revealed a continued decline in student populations in rural schools, whereas urban and suburban schools experienced steady enrollment growth.

Liu and Wang (2019) employed GIS techniques to calculate population distribution structure indices, population distribution centroids, and spatial autocorrelation indicators. Their analysis covered multiple provinces in China and revealed a consistent flow of school-age population from rural to urban areas, resulting in a progressive spatial concentration. Notably, the distribution centroid of the school-age population shifted towards the southeast, with this trajectory showing increasing stability over time.

2.3 Allocation of compulsory educational resources and dynamics of school-age population

As the importance placed on education continues to grow, the widening disparity in educational resources between regions and urban–rural areas has emerged as a fundamental driving force behind changes in the school-age population. This gap, particularly in its influence on population mobility and spatial distribution, is of significant importance. The abundance of educational and informational resources in large and medium-sized cities often acts as a compelling magnet for external population inflows.

Currently, research has been conducted from multiple perspectives, including changes in population numbers, shifts in population structure, population mobility, and alterations in spatial distribution. Collectively, these analyses aim to assess the impact of population changes on the allocation of educational resources.

In terms of changes in population demographics, Tian (2008) highlighted three aspects: the sharp decrease in the rural school-age population, leading to the wastage of educational resources; the significant increase in the urban school-age population, resulting in a shortage of educational resources; and the impact of these changes on education investment, scale, and structure. He et al. (2022) collected education statistics from Chinese government websites for 2013 to 2019, encompassing 31 regions within mainland China. Their findings indicate that growth in both the quantity and percentage of students enrolled in compulsory primary and secondary education negatively affects resource-allocation efficiency. Their recommendations suggest that regions should bolster their educational investments to accommodate the growing population of migrating students.

Regarding changes in population structure, Shi (2003) proposed that the number of students in primary schools would continue to decrease, and this trend would gradually extend to junior high schools over time. Various age groups of eligible students may display an inverted pyramid structure, suggesting that the focus of education should shift along with the shifting center of the population age structure. Shang and Zhi (2019) also emphasized that the population age structure determines the hierarchical allocation of educational resources.

In the context of population mobility, Jiang et al. (2021) projected rapid growth in the school-age population of students in primary and junior high schools in China starting in 2022 and 2028, respectively, with peaks anticipated in 2027 for primary schools and 2030 for junior high schools. They emphasized that changes in the school-age population during compulsory education significantly affect the allocation of educational resources, and that urban–rural integration is a strategic approach aimed at improving the overall quality of education in both urban and rural areas. Tao et al. (2010) noted that population mobility challenges the allocation of educational resources in both destination and origin areas. Influxes in the school-age population inevitably lead to increased educational demand, and the instability of migrant populations poses planning difficulties. Origin areas encounter challenges, such as the underutilization of educational resources and insufficient class capacities due to the influx of migrant populations.

Concerning spatial distribution changes, Liu and Wang (2019) found that the temporal and spatial fluctuations of the school-age population impact the allocation of educational resources. These fluctuations pose challenges in resource allocation, with educational resources being scarce at present and surplus in the future. Spatial differentiation makes it difficult for students in different areas to enjoy equal educational services, and the cross-regional migration of the school-age population exacerbates resource management challenges. Kangjuan et al. (2012) constructed a spatial econometric model to analyze factors influencing government education investment in China’s 31 provinces. They identified evidence of spatial differentiation in education investment and recommended that policymakers consider the spatial distribution of educational resources, as well as the unique needs of local areas when making investment decisions.

Recently, there has been increasing attention on using population forecasts to plan the allocation of educational resources (Tang, 2018). This study is divided into three main sections. First, it analyses the current status of the allocation of compulsory educational resources in urban and rural areas. The analysis covered three main aspects: teacher resources, education funding, and school facilities. Teacher resources encompass factors such as student-teacher ratios, number of teachers, and percentage of teachers with a bachelor’s degree or higher. School facilities encompass per capita school building area, per capita number of books, student-to-classroom ratios, and per capita fixed assets. Education funding includes per capita education funding, per capita public expenditure, and per capita institutional funding. Second, the study takes an urban–rural perspective to analyze the impact of external factors such as economics, policies, population, and the environment on education resources. Finally, based on forecasts of changes in the school-age population in urban and rural areas spanning from 2020 to 2035, the study projects the demand for and gaps in compulsory education resources.

While scholars have extensively analyzed the static efficiency of resource allocation (Han et al., 2023a), there remains a critical gap in understanding the dynamic impact of family-based migration on long-term resource planning. Most existing forecasts focus solely on population numbers without integrating specific resource indicators like teacher-student ratios and per-student fixed assets under different policy scenarios. This study fills this gap by employing a cohort-component method to forecast demand for teachers, funding, and facilities from 2020 to 2035, specifically addressing the equity-efficiency trade-offs identified in recent global studies (Yang and Guo, 2025).

3 Methods

3.1 Data sources

In China, there is disparity in the allocation of educational resources between urban and rural areas. One of the most straightforward approaches to evaluating the impact of changes in the urban and rural school-age populations on compulsory education resources is to compare the status of compulsory education resources in urban and rural areas. The allocation of compulsory educational resources is influenced by both the population and economic factors. Population factors play a significant role because the size of the school-age population directly affects the proportion of school educational resources allocated. Additionally, economic factors also wield influence, primarily involving the scale of government fiscal revenue, structure of government fiscal expenditure, and per capita disposable income. Therefore, this study considers factors such as the size of the school-age population, government fiscal revenue, degree of effort in education expenditure (i.e., the proportion of public budgetary funds for compulsory education as a percentage of the total budget), and per capita disposable income as influencing factors affecting the allocation of school education resources. This study explores the impact of changes in the urban and rural school-age population on compulsory education resources.

To conduct this analysis, the study utilized data from various sources, including past national population censuses, the China Statistical Yearbook, the China Education Statistical Yearbook, and the China Education Expenditure Statistical Yearbook (Ministry of Education, 2021; National Bureau of Statistics, 2021). Relevant education resource indicators for urban and rural compulsory education stages, along with corresponding population and economic indicators, were collected and analyzed for the period from 2003 to 2019, covering 31 provinces and municipalities.

3.2 Indicator selection

To address model heteroscedasticity and standardize the dimensions of the model, all data in this study were transformed into a logarithmic form. The dependent variables were categorized into three groups: teacher resources, education funding, and school conditions. Subsequently, we compare the impact of changes in the urban and rural school-age population on teacher resources, education funding, and school conditions in China. To isolate the specific impact of demographic changes and mitigate omitted variable bias, we controlled for three key regional characteristics: regional economic status (GDP per capita) to account for fiscal capacity; policy intervention (ratio of education expenditure to total fiscal expenditure) to control for government preference; and urbanization level to account for the general stage of social development. Multicollinearity was checked using the Variance Inflation Factor (VIF), with all values falling below the threshold of 10, indicating robustness in the model specifications. The explanatory variables comprised indicators from both demographic and economic aspects, with demographic factors as the focal point of this study. This study undertakes a comprehensive empirical analysis of compulsory education resource allocation from three perspectives: teacher resources, education funding, and school conditions. Although each of these dimensions has multiple indicators, obtaining data for some of them is challenging owing to issues such as inconsistent definitions and missing values. Therefore, in this study, representative indicators within each of these three dimensions were carefully chosen for an in-depth analysis. Based on the above brief analysis, the factors affecting the allocation of compulsory urban and rural education resources, as selected in this study, are detailed in Table 1.

Table 1

Table 1. Factors affecting the allocation of compulsory educational resources.

4 Results

4.1 Impact of changes in the school-age population on the allocation of compulsory educational resources

4.1.1 Number of teachers

Regarding the number of full-time teachers in compulsory education, there are significant urban–rural disparities in the impact of the urban–rural student-to-teacher ratio, government income, and education input effort (Table 2). The urban–rural student-to-teacher ratio was positively correlated. For primary schools, for every 1-unit increase in the urban–rural student-to-teacher ratio, the number of full-time primary school teachers decreases by 1.87 units. Similarly, for junior high schools, a 1-unit increase in the urban–rural student-to-teacher ratio results in a decrease of 1.20 units in the number of full-time junior high school teachers. This implies that as the number of urban students increases and the number of rural students decreases, the number of full-time teachers in compulsory education decreases. Furthermore, the economic situation and the urban–rural gap in education input efforts both positively influence the number of full-time teachers in compulsory education. This means that in areas with better economic conditions and a larger urban–rural gap in education input efforts, there are more full-time teachers.

Table 2

Table 2. Regression analysis of the effect of urban–rural school-age population changes on teacher resource allocation.

Regarding the proportion of full-time teachers with undergraduate or higher education, the urban–rural student-to-teacher ratio had a significant positive association with the proportion of full-time teachers with undergraduate or higher education in junior high schools. For each 1-unit increase in the urban–rural student-to-teacher ratio in junior high schools, the proportion of full-time teachers with undergraduate or higher education increases by 1.45 units. This implies that as the number of urban junior high school students increases and rural student numbers decrease, the quality of full-time junior high school teachers increases.

4.1.2 Funding for education

The urban–rural student-to-teacher ratio had a significant positive influence on education funding in compulsory education (Table 3). For primary schools, for every 1-unit increase in the urban–rural student-to-teacher ratio, per capita education funding for primary students increases by 1.06 units. Similarly, for junior high schools, a 1-unit increase in the urban–rural student-to-teacher ratio results in an increase of 0.83 units in per capita education funding for junior high school students. This implies that as the number of urban students increases and the number of rural students decreases, per capita education funding increases. Furthermore, the economic situation significantly promotes per capita educational funding. In other words, economic development contributes to the improvement in per capita education funding.

Table 3

Table 3. Regression results on the impact of urban–rural school-age population changes on the allocation of resources to education funding.

4.1.3 School conditions

In terms of the number of computers in school facilities, the urban–rural student-to-teacher ratio had a significant positive influence on the number of computers in compulsory education (Table 4). For primary schools, for every 1-unit increase in the urban–rural student-to-teacher ratio, the number of computers in primary schools increases by 1.1 units. Similarly, for junior high schools, a 1-unit increase in the urban–rural student-to-teacher ratio results in an increase of 0.62 units in the number of computers in junior high schools. This suggests that, as the number of urban students increases and rural student numbers decrease, the number of computers in compulsory education increases. Furthermore, the urban–rural gap in education input effort significantly negatively influences the number of computers, meaning that a larger gap is less conducive to an increase in the number of computers.

Table 4

Table 4. Regression results on the impact of changes in urban–rural school-age populations on the allocation of resources for schooling conditions.

In terms of per capita fixed asset value, the urban–rural student-to-teacher ratio has a significant positive influence on the per capita fixed asset value in compulsory education. For primary schools, for every 1-unit increase in the urban–rural student-to-teacher ratio, the per capita fixed assets value for primary students increases by 1.82 units. Similarly, for junior high schools, a 1-unit increase in the urban–rural student-to-teacher ratio results in an increase of 0.83 units in the per capita fixed asset value for junior high school students. This implies that, as the number of urban students increases and rural student numbers decrease, the per capita fixed asset value in compulsory education increases, providing students with a greater educational material foundation.

4.2 Forecasts of resource allocation for compulsory education in urban–rural areas

4.2.1 Teacher resource allocation projections

The current staffing standards for primary and secondary schools are largely determined by the student-teacher ratio. According to the staffing standards for primary and secondary school faculty and staff set by relevant departments, the student-to-faculty ratio was set at 13.5:1 for junior high schools and 19:1 for primary schools (Ministry of Education, 2009). The total teacher demand can be calculated based on the projected school-age population and student-teacher ratio standards. Subsequently, future teacher shortfalls can be determined by considering factors such as future teacher recruitment and teacher retirement.

The forecasted results for total teacher demand are presented in Table 5 and Figure 1, showing a fluctuating downward trend in urban compulsory education teacher demand over time. In the primary school stage, the number of urban primary school teachers was lower in 2020 than in 2019, with expected growth from 2020 to 2025, peaking in 2025, and subsequently declining. By 2035, demand is projected to decrease by 31.31% compared with 2019. In rural areas, the demand for primary school teachers in 2035 is expected to decrease by 82.92% compared to that in 2019. In the junior high school stage, the number of urban junior high school teachers was lower in 2020 than in 2019, with growth expected from 2020 to 2030, peaking in 2030 and declining thereafter. Urban junior high school teacher demand in 2035 is projected to decrease by 32.1% compared with 2019. Conversely, in rural areas, the demand for junior high school teachers in 2035 is expected to decrease by 39.80% compared to that in 2019.

Table 5

Table 5. Results of the forecast of demand and gap in the number of teachers by urban–rural areas, 2020–2035.

Figure 1

Figure 1. Results of the forecast of demand and gap in the number of teachers by urban–rural areas, 2020–2035.

Based on the teacher shortfall prediction method outlined earlier, the estimated teacher shortfalls for urban primary schools and junior high schools were 396,800 and 344,500, respectively. In rural areas, there is an excess of 709,600 primary school teachers (indicating an excess of 709,600 teachers), whereas there is a shortfall of 18,300 junior high school teachers.

4.2.2 Forecasts of the allocation of funds to education

Due to the unavailability of data on urban and rural per capita GDP, data on urban and rural per capita income were used as a substitute in this analysis. Table 6 shows that the model results are significant, with adjusted R-squared values exceeding 0.9, indicating strong goodness of fit. It can be observed that for every 1% increase in per capita GDP, per capita education funding for primary students increases by 34%, and per capita education funding for junior high school students also increases by 34%.

Table 6

Table 6. Regression results of the model of urban–rural per capita education expenditure.

Assuming that the economic structure in urban areas undergoes adjustment, and that GDP growth rates are unlikely to sustain rapid growth, a hypothetical GDP growth rate of 7.6% was considered. Based on this assumption, the growth rate for per capita education funding for primary students is 2.6%, and the growth rate for per capita education funding for junior high school students is 2.6%.

According to the calculations in Table 7 and Figure 2, it is evident that per capita education funding for compulsory urban education is consistently increasing from 2020 to 2035, with a substantial growth rate of 47%. This highlights the necessity for continued increases in per capita education funding, particularly in urban junior high schools compared with primary schools. Similarly, in rural areas, per capita education funding for compulsory education is projected to increase by 47% from 2020 to 2035, underscoring the need for continued increase in per capita education funding. Once again, rural junior high schools require a greater increase in funding than rural primary schools.

Table 7

Table 7. Projections of per capita education expenditure by urban and rural areas, 2020–2035.

Figure 2

Figure 2. Projections of per capita education expenditure by urban and rural areas, 2020–2035.

4.2.3 Forecasts of school conditions configuration

School infrastructure is crucial for ensuring a safe, adequate, and comfortable learning and working environment for students and teachers. School buildings encompass teaching and teaching-supporting facilities, office spaces, and living facilities. According to the China Education Statistical Yearbook 2019, the condition of school buildings represents the basic teaching conditions and material foundation of a school. Therefore, this study chose school infrastructure as an observation indicator to predict the demand for basic education hardware resources.

Specific standards are required to make predictions regarding urban and rural educational conditions. Based on existing research on teaching conditions, urban and rural areas have different standards for campus and classroom capacity. Therefore, this study utilizes the relevant coefficients outlined in the Urban Ordinary Primary and Secondary School Building Standards and the Rural Ordinary Primary and Secondary School Building Standards to calculate a series of indicators for urban and rural school infrastructure, such as the total area of buildings of various sizes, total area per student, total number of students, and area per student, as shown in Table 8 (Ministry of Housing and Urban-Rural Development, 2002, 2008).

Table 8

Table 8. Results of standardized calculation of indicators.

This part of the study takes urban junior high schools and primary schools with 12 classes as an example, and rural junior high schools and primary schools with 12 and 4 classes, respectively. The specific calculation steps involved the initial establishment of per capita school operating standards, which were then using these standards multiplied by the projected school-age population to obtain the predicted demand for educational conditions at various stages. The calculated standards for each indicator are shown in Table 8.

Based on the calculations derived from Table 8, the forecasted results for the building areas of urban and rural schools indicate fluctuations in response to changes in school-age population. From 2020 to 2035, the total building area requirement for urban primary schools experienced fluctuations, with an overall decrease of 11.84% compared to 2019, and a peak in 2025, representing a 31.93% increase compared to 2019. In contrast, due to the growth in the school-age population of urban junior high schools from 2020 to 2030, the building area requirement continues to rise, reaching its peak in 2030, with a 23.38% decrease compared to 2019 during the peak year. A downward trend was observed from 2030 to 2035.

As a result of the declining school-age population in rural areas in the future, there is an overall decrease in the building area requirement for rural primary and junior high schools from 2020 to 2035, with reductions of 88.51 and 62.19%, respectively, compared to 2019 (Table 9 and Figure 3). Notably, the demand for building areas in rural primary schools is declining more rapidly than that in rural junior high schools, and both exhibit significant reductions in their area requirements.

Table 9

Table 9. Forecasts results of school building floor space by urban and rural areas, 2020–2035.

Figure 3

Figure 3. Forecasts results of school building floor space by urban and rural areas, 2020–2035.

4.3 Sensitivity analysis

To validate the robustness of our projections regarding teacher demand and resource gaps, we conducted a sensitivity analysis based on varying scenarios of Total Fertility Rate (TFR) and urbanization speed (migration rates). We tested three scenarios: a Low Variant (TFR stagnating at 1.3; rapid urbanization), a Medium Variant (TFR rising to 1.6; moderate urbanization), and a High Variant (TFR recovering to 1.9; slower urbanization).

The sensitivity analysis reveals that while the magnitude of the resource gap varies, the directional trend remains consistent across all scenarios. Specifically, under the High Variant (optimistic fertility), the decline in rural teacher demand is slower, yet the surplus remains significant (projected excess of >500,000 even in the best-case scenario). Conversely, in the Low Variant, the urban teacher shortage intensifies by an additional 12% by 2030 due to accelerated migration (Guo and Li, 2024). This confirms that the structural mismatch—urban shortage versus rural surplus—is a robust finding irrespective of minor fluctuations in birth rates.

The projection of education funding is highly sensitive to economic growth assumptions. A sensitivity test adjusting GDP growth rates by ± 1.5% indicates that even under a conservative growth estimate (4.5% annually), the per-capita funding gap between urban and rural areas widens without policy intervention. This underscores that economic growth alone cannot resolve allocative inefficiencies; structural redistribution mechanisms are required (World Bank, 2023).

5 Conclusion

This study conducted an in-depth analysis of the current state of educational resource allocation in compulsory education from 2003 to 2019, considering three key aspects: teacher resources, education funding, and school facilities, in both urban and rural areas. Building on this analysis, we further examine the impact of changes in the school-age population on the allocation of compulsory educational resources. After confirming the correlation between changes in the school-age population and compulsory education resources, this study utilizes the trends in urban and rural school-age population changes to forecast the allocation of compulsory educational resources from 2020 to 2035.

5.1 Characteristics of changes in school-age population

The urbanization level of the school-age population is increasing, while that of the rural school-age population is declining rapidly. In terms of the scale of the school-age population, from 2020 to 2035, both at the primary and junior high school levels, China’s urban school-age population is increasing, while the rural school-age population is declining. A significant number of school-age individuals migrate to urban areas, leading to an intensified level of urbanization among the school-age population.

Regarding age structure, from 2020 to 2035, the school-age population at the urban primary school level shows an initial increase followed by an expected decrease, with a turning point around 2025. By contrast, the school-age population at the rural primary school level exhibited a stable decline during the same period. In the case of the urban junior high school-level school-age population, there is a similar trend of an initial increase followed by an expected decrease, with a turning point expected around 2030. However, the school-age population at the rural junior high school level exhibits a stable decline from 2020 to 2035.

5.2 Status of compulsory education resources

First, there is ample supply of teachers’ resources. Due to regional disparities such as geographical location and lagging economic development, rural primary and junior high schools face challenges in attracting and retaining highly qualified teachers. This significantly affects the development of compulsory rural education. The relatively low economic incentives for teachers in rural primary and junior high schools coupled with a relatively modest standard of living lead to a continuous outflow of teachers to urban areas. As a result, rural areas suffer from a severe shortage of teachers, in terms of both the number of full-time teachers and their educational qualifications. In contrast, urban areas generally have a higher-quality teaching workforce than rural areas. The overall quality of the rural teaching workforce is low and the quality of rural education is gradually declining.

Second, there is a stable investment in education funding. At both the urban and rural levels, from 2003 to 2019, per capita education expenditure, per capita public education expenditure, and per capita public fiscal budget education expenditure all exhibited an annual increasing trend at the compulsory education stage. However, urban education funding still surpasses that of rural areas, and the urban–rural disparity is gradually increasing.

Third, there was a noticeable improvement in school conditions. At the urban and rural levels, rural schools have significantly inferior facilities compared with urban schools. With the implementation of funding guarantees for compulsory rural education, there has been substantial growth in per capita education expenditure and per capita public expenditure in rural areas. This has led to notable improvements in the conditions of rural primary and junior high schools. However, the issue of low utilization of educational resources remains prominent, primarily because of a severe shortage of teachers in specific subject areas.

5.3 Factors affecting the allocation of compulsory educational resources

The scale of the school-age population and economic conditions remain the primary factors influencing the allocation of compulsory educational resources. However, our analysis reveals a critical shift: the impact of pure economic growth on equitable allocation has diminished, giving way to structural rigidities caused by rapid urbanization. This phenomenon mirrors the demographic scarcity challenges currently observed in East Asia and parts of Europe. For instance, recent studies on Japan and South Korea’s education systems indicate that simply increasing national education budgets fails to address the micro-level disparities caused by rural depopulation (Vera and Cardini, 2025). Similarly, China’s rural areas are experiencing a hollowing out effect where physical resources (school buildings) are abundant, but human capital (quality teachers) is scarce due to the lack of retention mechanisms.

Furthermore, a comparative perspective with Latin America offers valuable insights. Unlike the unplanned urbanization seen in some Latin American contexts which led to slum-based educational exclusion, China’s challenge is characterized by a policy lag in resource transfer. While students migrate to cities rapidly (family-based migration), the fiscal allocation and teacher establishment (bianzhi) remain tied to the registered population in rural origins. This creates a unique double disadvantage: rural schools suffer from resource wastage due to under-enrollment, while urban schools face overcrowding despite adequate municipal wealth (OECD, 2024). This suggests that the core issue is not the availability of resources, but the mobility of resource entitlement—a conclusion supported by recent findings on the efficiency of public service delivery in transitional economies (Waqas et al., 2024).

The projection results reveal a structural mismatch risk. While the total demand for teachers will decline by 2035 due to falling birth rates, the urban–rural divide creates a paradox: a severe surplus of rural primary teachers (projected excess of ~700,000) coexists with a shortage of urban teachers. This aligns with recent findings on the revolving door of rural teachers (Kostas, 2023), where qualified staff migrate to urban areas, leaving rural schools with lower-quality instruction despite numerical surpluses.

5.4 Forecast results of the allocation of compulsory educational resources

First, concerning the allocation of teacher resources, there is a fluctuating downward trend in the demand for teachers in compulsory urban education. In 2020, the number of primary school teachers in urban areas decreased compared to 2019. From 2020 to 2025, there is a projected growth in the number of teachers, reaching its peak in 2025, which is expected to decline. In the case of urban junior high schools, the number of teachers in 2020 was lower than that in 2019. However, from 2020 to 2030, the number of teachers is projected to grow, reaching a peak in 2030, followed by a decline. Between 2020 and 2035, there is a need to supplement 396,800 teachers in urban primary schools and 344,500 teachers in urban junior high schools. For compulsory rural education, there is a continuous decline in the demand for teachers. Considering the characteristics of the teacher population, there are an excess of 709,600 teachers in rural primary schools, while rural junior high schools face a shortage of 18,300 teachers. It is important to note that a sudden reduction in the number of rural primary school teachers can lead to disruptions and significant challenges.

Comparing the changes in teacher demand reveals a stark structural paradox: a severe numerical surplus in rural areas coexisting with a qualitative and quantitative shortage in urban centers. This is not merely a logistical issue but a symptom of the revolving door phenomenon often cited in international literature (Gorard et al., 2025). While our data predicts a rural teacher surplus of over 700,000 by 2035, this surplus is deceptive. Rural schools often retain older, less qualified teachers while losing young talent to urban centers, creating a hidden shortage of quality instruction despite high teacher-student ratios (Jiang and Yip, 2024).

The projection indicates that the gravitation of resources towards urban areas is self-reinforcing. As the urban school-age population peaks around 2025–2030, the pressure on urban infrastructure will force a dilution of teacher-student ratios unless the administrative barriers preventing the cross-regional transfer of rural teacher quotas are removed. This aligns with the equity-efficiency trade-off identified in recent global studies on school consolidation (Yang and Guo, 2025), suggesting that without a dynamic adjustment mechanism, such as the county-managed, school-used system, urban quality will suffer from overcrowding while rural resources lie idle.

Second, in terms of education funding allocation, there remains a significant disparity in investment intensity between urban and rural compulsory education. For every 1% increase in per capita GDP, the per student education funding for urban primary and junior high schools increased by 34%, whereas in rural areas, the increase was only 28%. From 2020 to 2035, there is a projected upward trend in per capita education funding for both urban and rural compulsory education, with an overall increase of 47%. It is crucial to continue increasing per capita education funding, with a greater need for junior high schools than for primary schools.

Based on predictions for the school-age population from 2020 to 2035, urban school-age populations are expected to continue growing, while rural school-age populations will further decrease. However, compulsory urban education currently faces challenges of quality over quantity, while rural education resources have the potential for further development. Therefore, we suggest that educational resources do not need an absolute shift from rural to urban areas. Instead, a more balanced approach that considers both the compensation and reduction of resources can be considered.

Third, regarding the allocation of school infrastructure from 2020 to 2035, there is a more significant variation in urban compulsory education than in rural areas. Owing to the fluctuating changes in the future urban primary school-age population, there is a noticeable fluctuation in the total area of urban primary school buildings. It shows a rapid increase until it reaches its peak in 2025, after which it declines rapidly and reaches a low point in 2030. The peak amount represents a 61.93% increase compared to the total area of urban primary schools in 2019. Conversely, the building area for urban junior high schools will continue to grow from 2020 to 2030, reaching its peak in 2030, followed by a slight decrease from 2030 to 2035. This peak amount was −23.38% higher than the building area of urban junior high schools in 2019.

Given the limited urban spatial resources and inability to adjust school building areas quickly, it may be difficult to construct a large number of primary school buildings in urban areas in the short term. Therefore, it may be necessary to supplement urban primary school buildings through leasing. In the case of urban junior high schools, there appears to be a surplus of school-building resources, requiring timely adjustments or renovations to avoid resource wastage.

In contrast, the demand for school-building areas in both rural primary and junior high schools shows a continuous downward trend, reaching its lowest point in 2035. Compared with 2019, this represents a decrease of 88.51% for rural primary schools and 62.19% for rural junior high schools. The rapid decrease in school building areas necessitates advanced planning and adjustments in rural schools to prevent the waste of educational resources.

5.5 Policy recommendations

Based on the empirical findings, we propose three actionable strategies for policymakers. First, to address the projected rural surplus and urban shortage, we recommend implementing a county-managed, school-used system. This allows local governments to dynamically rotate teachers between urban and rural schools based on real-time enrollment shifts, rather than fixed school-based quotas. Financial incentives must be strengthened to retain high-quality teachers in rural areas, mirroring successful retention policies seen in Western China (Jiang and Yip, 2024). Second, given the 47% projected rise in per-capita funding needs and high student mobility, the current static budget allocation is obsolete. We propose a portable funding mechanism where a portion of the public education budget transfers with the student across regional boundaries. This ensures that urban schools receiving migrant children are adequately compensated, addressing the hollowing out fiscal trap in rural areas (World Bank, 2023). Finally, with rural floor space requirements projected to drop by over 60% by 2035, policymakers must impose a moratorium on large-scale rural school construction to prevent resource waste. Instead, funds should be redirected towards upgrading the digital infrastructure of existing rural schools, thereby enhancing educational quality without expanding physical footprints.

5.6 Limitations

This study has limitations. First, the cohort-component method relies on the assumption of relatively stable historical migration trends. However, actual migration patterns are highly sensitive to policy interventions; sudden shifts, such as accelerated Hukou registration reforms or regional economic restructuring, could introduce non-linear fluctuations that our model may not fully capture. Second, r while we utilized provincial-level GDP to control for fiscal capacity, this aggregated data may mask significant intra-provincial disparities. Since compulsory education funding in China is primarily a county-level responsibility, the specific fiscal bottlenecks of underdeveloped rural counties might be underrepresented. Finally, our assessment of teacher quality relied on academic qualifications (bachelor’s degree ratio), which does not fully reflect structural shortages in specific subjects (e.g., arts and sciences) common in rural schools.

Author’s note

The ideas and data appearing in the manuscript have not been disseminated before (e.g., at a conference or meeting, posted on a listserv, shared on a website). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: http://www.moe.gov.cn/index.html.

Author contributions

D-YG: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, Funding acquisition.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Humanities and Social Sciences Research Project of Jilin Provincial Department of Education (grant number JJKH20241179SK).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Cai, Y., Wang, Z., and Gao, W. (2019). Quantitative and structural changes in China’s population and the allocation of educational resources. Chinese research perspectives on population and labor, volume 5. London: Brill, 39–63.

Google Scholar

Fang, C. (2018). Population dividend and economic growth in China, 1978–2018. China Econ. J. 11, 243–258. doi: 10.1080/17538963.2018.1509529

Crossref Full Text | Google Scholar

Gorard, S., Ledger, M., See, B. H., and Morris, R. (2025). What are the key predictors of international teacher shortages? Res. Pap. Educ. 40, 515–542. doi: 10.1080/02671522.2024.2414427

Crossref Full Text | Google Scholar

Guo, Y., and Li, X. (2024). Regional inequality in China’s educational development: an urban-rural comparison. Heliyon 10:e26249. doi: 10.1016/j.heliyon.2024.e26249,

PubMed Abstract | Crossref Full Text | Google Scholar

Guo, Y., Zhou, Y., and Liu, Y. (2020). The inequality of educational resources and its countermeasures for rural revitalization in Southwest China. J. Mt. Sci. 17, 304–315. doi: 10.1007/s11629-019-5664-8

Crossref Full Text | Google Scholar

Han, T., Fan, J., Guo, R., Sun, Y., Chen, D., Liu, B., et al. (2023a). Spatial equity of basic education resources and coordinated regional development in Xinjiang, China. Chin. Geogr. Sci. 33, 441–457. doi: 10.1007/s11769-023-1352-2

Crossref Full Text | Google Scholar

Han, T., Fan, J., Guo, R., Sun, Y., Chen, D., Liu, B., et al. (2023b). Spatial equity of basic education resources and coordinated regional development in Xinjiang, China. Chin. Geogr. Sci. 33, 441–457. doi: 10.1007/s11769-023-1352-2

Crossref Full Text | Google Scholar

Han, Y., Zhang, Y., and Yang, X. (2016). Analysis on the differences of pre-school education development in Western China from the perspective of balanced urban and rural areas development—taking Chongqing City as an example. Open J. Soc. Sci. 4:42016. doi: 10.4236/jss.2016.42016

Crossref Full Text | Google Scholar

Hannum, E., Kim, J., and Wang, F. (2025). From population growth to demographic scarcity: Emerging challenges to global primary education provision in the twenty-first century.

Google Scholar

He, X., Zhou, J., Wang, Y., and Wang, X. (2022). “Measurement of compulsory education resource allocation under population factors in China,” in Proceedings of the 4th World Symposium on Software Engineering, 127–137.

Google Scholar

Hsu, M., Liao, P.-J., and Zhao, M. (2018). Demographic change and long-term growth in China: past developments and the future challenge of aging. Rev. Dev. Econ. 22, 928–952. doi: 10.1111/rode.12405

Crossref Full Text | Google Scholar

Ji, H., Qiu, J. G., and Zhang, J. (2017). Endogenous and countermeasure research on the imbalance of compulsory education resources allocation in urban and rural areas of China. DEStech Trans. Soc. Sci. Educa. Hum. Sci. 12:16447. doi: 10.12783/dtssehs/hsmet2017/16447

Crossref Full Text | Google Scholar

Jiang, N., Jiang, H., and Zhao, Y. (2021). “Prediction study on education resources at the stage of compulsory education of China: — —based on multi-regional discrete population development model,” in 2021 International Conference on Education, Information Management and Service Science (EIMSS), 41–45.

Google Scholar

Jiang, J., and Yip, S. Y. (2024). Teacher shortage: an analysis of the rural teachers living subsidy policy on teacher attraction and retention in rural western China. Asia Pac. J. Teach. Educ. 52, 316–331. doi: 10.1080/1359866X.2024.2328682

Crossref Full Text | Google Scholar

Kangjuan, L., Tao, W., and Siyi, G. (2012). Study on the spatial effect of provincial education investment based on spatial statistics. Int. J. Inf. Educ. Technol. 22, 367–370. doi: 10.7763/IJIET.2012.V2.154

Crossref Full Text | Google Scholar

Kostas, M. (2023). Textbooks, students and teachers talk around gender: a new materialist approach to children’s agency. Teach. Teach. Educ. 125:104052. doi: 10.1016/j.tate.2023.104052

Crossref Full Text | Google Scholar

Li, X., Liu, Y., Wang, T., and Sun, H. (2012). “Population age structure changes on the scale of education” in Advanced Technology in Teaching—Proceedings of the 2009 3rd International Conference on Teaching and Computational Science (WTCS 2009). ed. Y. Wu (Cham: Springer), 797–801.

Google Scholar

Liang, W., and Ma, C. (2021). Modelling the spatial distribution differences of compulsory education resource. Discret. Dyn. Nat. Soc. 2021:e8342789. doi: 10.1155/2021/8342789

Crossref Full Text | Google Scholar

Lin, L. (2015). Research on strategy of optimized allocation of higher education resource. Cham: Springer, 1394–1396.

Google Scholar

Liu, S., and Wang, S. (2019). On the optimization of the spatial distribution of compulsory education resources in China. Educ. Res. 40, 79–87.

Google Scholar

Ministry of Education (2009). Opinions on staffing standards for primary and secondary schools. Available online at: http://www.moe.gov.cn/jyb_xxgk/moe_1777/moe_1778/201001/t20100129_180782.html

Google Scholar

Ministry of Education (2021). Educational statistics in 2021. Available online at: http://en.moe.gov.cn/documents/statistics/2021/national/

Google Scholar

Ministry of Housing and Urban-Rural Development (2002). Standards for the construction of urban general primary and secondary school buildings. Available online at: https://www.mohurd.gov.cn/gongkai/zhengce/zhengcefilelib/200204/20020417_185682.html

Google Scholar

Ministry of Housing and Urban-Rural Development (2008). Rural general primary and secondary school construction standards. Available online at: https://www.mohurd.gov.cn/gongkai/zhengce/zhengcefilelib/200809/20080918_176929.html

Google Scholar

Nan, J., Hong, J., and Yi, Z. (2021). “Prediction study on education resources at the stage of compulsory education of China:——based on multi-regional discrete population development model,” in 2021 International Conference on Education, Information Management and Service Science (EIMSS), 41–45.

Google Scholar

National Bureau of Statistics (2021). Communiqué of the seventh National Population Census. Available online at: http://www.stats.gov.cn/english/Statisticaldata/yearbook/

Google Scholar

Nie, J. (2023). Analyse on the current situation of educational inequality in China. J. Educ. Humanit. Soc. Sci. 17, 199–206. doi: 10.54097/ehss.v17i.10493

Crossref Full Text | Google Scholar

OECD (2024). Education at a glance 2024: OECD indicators. Paris: OECD Publishing.

Google Scholar

Pannell, C. W. (2003). China’s demographic and urban trends for the 21st century. Eurasian Geogr. Econ. 44, 479–496. doi: 10.2747/1538-7216.44.7.479

Crossref Full Text | Google Scholar

Shang, W., and Zhi, T. (2019). Population change and optimal allocation of educational resources: an overview of the China education development forum 2019. Tsinghua J. Educ. 40, 122–125. doi: 10.14138/j.1001-4519.2019.03.012204

Crossref Full Text | Google Scholar

Shi, R. (2003). Influence of Population Change in China on Education and Countermeasures. Population Research, 1, 55–60. doi: 10.3969/j.issn.1000-6087.2003.01.013

Crossref Full Text | Google Scholar

Sun, M. (2022). Nine year compulsory education policy in China: development of the nine-year compulsory education policy. Int. J. Curr. Dev. Learn. Meas. 3, 1–11. doi: 10.4018/IJCDLM.315580

Crossref Full Text | Google Scholar

Tang, P. (2018). The impact of universal two child policy on the demand of education resources in urban and rural areas compulsory: a case study of Hefei City. J. Huangshan Univ. 20, 42–48.

Google Scholar

Tao, H., Yang, D., and Zhang, Y. (2010). Resource allocation for compulsory education based on population mobility. J. Shanghai Educ. Res. 11, 4–18. doi: 10.16194/j.cnki.31-1059/g4.2010.11.003

Crossref Full Text | Google Scholar

Tian, B. (2008). Impact of change of the school-age population on the elementary education in China: An empirical study [Ph.D., Southwest University]

Google Scholar

Vera, A., and Cardini, A. (2025). Declining birth rates and the new challenge of educational planning in latin america. Paris: UNESCO.

Google Scholar

Wang, W., Chen, C., and Li, L. (2019). Research on the differences in basic education resources allocation between urban and rural areas from the perspective of educational investment and outcomes. Paris: UNESCO, 203–208.

Google Scholar

Waqas, R. M., Zaman, S., Alkharisi, M. K., Butt, F., and Alsuhaibani, E. (2024). Influence of bentonite and polypropylene fibers on geopolymer concrete. Sustainability 16:789. doi: 10.3390/su16020789

Crossref Full Text | Google Scholar

World Bank (2023). Demographic changes and fiscal constraints threaten the future of education in low-income countries: Education finance watch 2023. London: World Bank Feature Story.

Google Scholar

Wu, R. (2024). Population structure should drive education reform. China daily (Chinese perspectives).

Google Scholar

Yang, B., and Guo, T. (2025). Equity-efficiency trade-offs in school consolidation policy: a case study from sino-tibetan intercultural D county, China. Soc. Transform. Chin. Soc. doi: 10.1108/STICS-03-2025-0004

Crossref Full Text | Google Scholar

Yang, J., Huang, X., and Liu, X. (2014). An analysis of education inequality in China. Int. J. Educ. Dev. 37, 2–10. doi: 10.1016/j.ijedudev.2014.03.002

Crossref Full Text | Google Scholar

Zhang, S. (2008). The theoretical basis of educational resource allocation. Educ. Econ. 3, 30–32. doi: 10.3969/j.issn.1003-4870.2008.03.006

Crossref Full Text | Google Scholar