The Impact of Rural–Urban Student Mobility on the Efficiency of Resource Allocation in China’s Rural Households: Optimization or Distortion?

Abstract

The impact of rural-urban student mobility on the efficiency of resource allocation in China’s rural households is crucial for refining policies related to rural migrant settlement and the balanced allocation of urban and rural educational resources. Drawing on theoretical insights, this study employs a mixed methods approach—primarily qualitative, with quantitative support—to empirically test the impact of rural-urban student mobility on the resource allocation efficiency of rural households in China. Utilizing micro-survey data from China Family Panel Studies (CFPS), this study applies ordinary least squares (OLS) models, propensity score matching difference-in-differences (PSM-DID) models, and endogenous switching regression (ESR) models to ascertain the direction of this impact. Furthermore, this study incorporates in-depth interview data gathered from field research in D County to explore the dynamic mechanisms of resource allocation adjustments within these households. The results show that rural-urban student mobility significantly reduces the efficiency of labor resource allocation and agricultural production in China’s rural households. The impact is heterogeneous across different groups, most negatively affecting households with limited labor resources, lower educational levels, lower incomes, more productive fixed assets, and less self-owned land. Mobility leads to suboptimal occupational choices, resulting in decreased labor resource efficiency. Additionally, it distorts agricultural production by leading to labor loss, reduced investments in agricultural productivity, and misaligned land allocation decisions.

1. Introduction

Urban–rural relationships are among the most fundamental and significant aspects of human society, profoundly influencing the modernization process of a country or region [1,2]. Observing global population development patterns, the urbanization rates of developed countries tend to stabilize after exceeding 80% [3]. Over the past four decades, amid ongoing economic and social progress, China has also undergone a rapid urbanization process. China’s rural-urban population mobility can generally be divided into two stages: The first stage involves rural laborers individually moving to cities, creating a “migrant worker flow”, which refers to a massive group of rural migrant workers. The second stage involves a shift towards family-based migration, gradually leading to a “student flow”, which consists of an increasing number of migrant children [4]. Since Lewis (1954) proposed the dual-sector model, rural-urban labor mobility has attracted extensive attention from scholars, resulting in a well-developed theoretical framework and analytical model [5,6]. However, studies focusing on rural-urban student mobility during the compulsory education phase (hereafter referred to as “RUSM”) have been somewhat insufficient.

As the urbanization process accelerates, policies for the schooling of migrant workers’ children have improved, and rural families have pursued better educational opportunities. China’s RUSM rate has been increasing year by year, rising from 16.05% in 2012 to 30.22% in 2020 [7]. RUSM in China shares many similarities with that in other countries, but it also exhibits some peculiarities due to China’s unique household registration (hukou) system [8]. For instance, rural-urban population mobility in European countries was significantly tied to industrialization during the mid-20th century, with RUSM closely linked to rural-urban labor mobility [9]. However, RUSM in China shows a trend that is not synchronized with rural-urban labor mobility. This asynchronous phenomenon is a unique feature of the Chinese context. In such cases, as China’s rural households recognize the value of investing in their children’s education, RUSM has become a crucial factor influencing their resource allocation decisions. For instance, with RUSM, some rural households have changed their labor model from “part-farming, part-working” to “part-working, part-accompanying”, leading many rural parents to relocate to cities for their children’s education [10]. In addition to labor, RUSM also affects land and capital allocation, as well as vital material resources in rural households. Thus, it is evident that RUSM comprehensively impacts the allocation of the three core household resources of labor, land, and capital in rural households (Figure 1).

Classic theories of population migration identify income disparities as a primary driver of population movements [5,6]. However, beyond seeking higher incomes, migrants can also be motivated by the pursuit of better public services. Tiebout’s “voting with their feet” theory was among the first to incorporate local public services into the utility model of residential choice [11]. In recent years, research has increasingly focused on the importance of children’s needs in family migration decisions [12]. For instance, a study by Bushin (2009) on rural-urban migration among families in the United Kingdom highlighted that children’s developmental prospects are a key driving factor in parental and familial migration decisions, including considerations for providing better opportunities for their children [13]. Building on Becker’s framework of family economics, the fundamental principle of family educational investment is that parents consider their children’s utility while maximizing their own, aiming to optimize their overall family utility [14]. Studies in these areas often discuss how migration for educational and familial reasons can lead to shifts in labor market participation and occupational changes [15,16].

Therefore, what impact have resource allocation adjustments, prompted by RUSM, had on the efficiency of resource allocation in rural households? A review of the literature reveals that the impact of RUSM on the efficiency of resource allocation in rural households is a double-edged sword [17]. On the one hand, several studies show that population mobility driven by disparities in public services or comfort levels between regions can lead to negative consequences. For instance, it may redirect labor from high-productivity, low-comfort areas to low-productivity, high-comfort ones, with regional productivity deviating from its optimal level and rural welfare diminishing [18]. RUSM could result in hidden unemployment and higher developmental risks [19]. On the other hand, for developing countries, given the productivity gap between the urban and rural sectors, encouraging rural-urban population mobility can still enhance overall levels of productivity [20,21,22]. In China, where the land and hukou systems constrain rural-urban labor mobility, optimal cross-sector labor allocation has not yet been achieved [23,24]. RUSM can be viewed as a mechanism to alleviate the barriers to rural-urban labor mobility. The labor mobility induced by student mobility, referred to as “mobility for education” [25,26], can potentially optimize labor resource allocation in rural households, improving agricultural production efficiency and overall productivity. Notably, empirical evidence is lacking on whether this mobility has optimized or distorted the efficiency of resource allocation in rural households.

Our research background and literature review show the theoretical and practical significance of studying the impact of RUSM on the efficiency of resource allocation in China’s rural households. Such research is also crucial for refining policies related to rural migrant settlement and the balanced allocation of urban and rural educational resources [27]. If the current policy on RUSM results in a loss of efficiency in family resource allocation, this highlights the potential for policy refinement. This necessity points to a critical reassessment of existing frameworks to promote sustainable resource management, thereby improving overall wellbeing and advancing sustainable development objectives. Therefore, this paper specifically addresses the following three questions: Does RUSM optimize or distort the efficiency of resource allocation in China’s rural households? Does this impact vary among rural household groups with differing resource endowments? How do China’s rural households dynamically adjust their resource allocation after moving? To explore these questions, this study first developed an analytical framework based on the New Economics of Labor Migration and family strategy concepts, aiming to delineate the theoretical mechanisms through which RUSM affects resource allocation efficiency. This study then utilized nationally representative data from China Family Panel Studies (CFPS) and applied ordinary least squares (OLS), propensity score matching difference-in-differences (PSM-DID), and endogenous switching regression (ESR) models to examine the impact of RUSM on the efficiency of both labor resource allocation and agricultural production in China’s rural households. A heterogeneity analysis was subsequently conducted to identify which rural households, based on specific factor endowment characteristics, experience greater losses in resource allocation efficiency due to RUSM. Moreover, in-depth field interviews were conducted to analyze the dynamic adjustment mechanism of resource allocation among these households. Finally, the findings of this study are juxtaposed with those from existing research, its contributions are highlighted, and policy recommendations that are directly informed by the key research findings are formulated.

2. Theoretical Analysis and Research Hypothesis

2.1. Theoretical Basis

The New Economics of Labor Migration (NELM) situates individual migration events within a family context, underscoring the significance of the family as a cohesive decision-making unit [28,29]. In China, RUSM often transcends individual benefits, with children living under the arrangements made by their parents until adulthood. Thus, the choice of educational location across urban and rural areas is typically made in the interest of the entire family. Building on the NELM as a theoretical foundation, this study considers the family as a decision-making unit and adopts a family strategy perspective to analyze the impact of RUSM on the efficiency of rural family resource allocation. The family strategy approach is characterized by its dynamic nature, interconnectedness, and blend of individuality and commonality within family units [30]. Firstly, the formulation and implementation of family strategies are dynamic processes. Secondly, significant decisions within a family are interconnected, each serving as a means to achieve the strategic goals of the family, continually giving rise to new decisions and forming a series of interconnected decisions. Finally, while the family strategy of each unit has unique aspects, the strategies of families sharing similar traits or circumstances exhibit commonalities [31].

Family strategy focuses on the timing, process, and outcomes of decisions made by families and their members. The goal of these strategies is to consolidate or improve the economic status of the family and its descendants, involving the generation and distribution of resources [32]. Rural families, possessing limited resources, must strategically allocate these resources to maximize their value and economic returns to accumulate within the family. Concurrently, rural families must consider how to distribute these resources across generations to invest in their children’s human capital, thereby creating new familial human capital. In this context, education plays a critical role, as it is crucial to enhancing the human capital of the next generation [33]. Therefore, under the strategic goal of “maximizing comprehensive utility centered on education”, China’s rural households implement decision-making pathways for RUSM (Figure 2). (1) Decision timing: The critical decision-making time for RUSM coincides with the generation of offspring. Before the birth of children, the strategic goal of rural households is to maximize the family’s income within the constraints of the family’s resources and to make decisions on resource allocation accordingly. After the birth of children, the focus shifts to maximizing current consumption and the human capital of the offspring within the constraints of the family’s income. (2) Decision process: Upon the birth of children, rural families face educational decisions (e.g., decisions about RUSM). Such mobility disrupts the equilibrium of resource allocation within rural households, leading to dynamic post-mobility adjustments in the family’s resources. (3) Decision outcomes: Differentiated decisions on RUSM and resource allocation adjustments among various rural households lead to divergences in the efficiency of resource allocation. Consequently, RUSM significantly impacts the resource allocation efficiency of China’s rural households.

Resource allocation efficiency in rural households denotes the ratio of actual to maximum potential output under optimal resource allocation [34,35]. The key resource allocation decisions faced by China’s rural households encompass choices regarding labor force occupations and agricultural production strategies [36,37]. These decisions impact both the labor resource allocation efficiency (LRAE) and agricultural production efficiency (APE). Optimizing both the LRAE and APE not only affects the agricultural performance of households but also influences their welfare within broader economic activities, thereby reflecting the comprehensive efficiency of resource allocation [38]. Correspondingly, in our theoretical analysis, this paper discusses how RUSM affects both the LRAE and APE of China’s rural households (Figure 3 and Figure 4).

2.2. RUSM and Labor Resource Allocation Efficiency

2.2.1. RUSM → Household Division-of-Labor Adjustment → Labor Resource Allocation Efficiency

RUSM catalyzes labor resource allocation efficiency by prompting rural households to adjust their labor division based on comparative advantages. Becker (1965) stated that if all members of an efficient family have different comparative advantages, then no one would want to allocate their time between the market and household sectors [39]. In the market sector, each person with a greater comparative advantage would fully specialize their market activities, while in the household sector, each person with a greater comparative advantage would specialize their household activities. Consequently, RUSM may cause laborers with a household sector advantage to reduce or even cease their participation in remunerated market labor. Similarly, those with a market sector advantage are likely to increase their involvement in paid labor activities. In this scenario, if the family member with a comparative advantage in the household sector engages in child care and other domestic work full-time, this will reduce the unpaid labor burden on other family members, allowing them more time and energy to focus on market sector labor, thereby increasing the overall wage compensation for rural households [40], i.e., enhancing their labor resource allocation efficiency. However, in scenarios in which a rural household’s labor resources are constrained, the disengagement of certain family members from the market sector may not improve the family’s labor resource allocation efficiency [41,42].

2.2.2. RUSM → Occupational Choice Adjustment → Labor Resource Allocation Efficiency

RUSM reduces labor resource allocation efficiency by prompting rural households to adjust their occupational choices based on comparative advantages. In a free market, rural households typically allocate labor to higher-paying sectors to maximize their household income, thereby achieving optimal labor resource allocation efficiency [43]. However, after moving on account of students in their family, laborers moving to cities for childcare can generate gains in human capital investment utility. Consequently, rural households may shift laborers from rural to urban employment, especially those with a comparative advantage in the household sector, which can reduce the family’s labor resource allocation efficiency [44]. It is important to note that original occupational choices are influenced by households’ endowments, including land and capital, which also affect the likelihood of occupational changes. These elements can affect the degree of the negative impact of RUSM on labor resource allocation efficiency.

2.3. RUSM and Agricultural Production Efficiency

2.3.1. RUSM → Labor Resource Allocation Adjustment → Agricultural Production Efficiency

RUSM influences agricultural production efficiency via two primary changes: the loss of the agricultural labor force and increased access to social services. However, the specific direction of this impact remains ambiguous. From one perspective, RUSM, by leading to the loss of agricultural labor, not only results in a more extensive agricultural production approach but also decreases the technical efficiency of agricultural production [17,20]. It is essential to note that the levels of labor force and capital abundance significantly influence how labor loss impacts agricultural production efficiency. From another perspective, while RUSM leads to the loss of agricultural labor, such loss also creates opportunities to acquire more social services for agricultural production [12]. In practice, social service entities offer specialized labor divisions and economies of scale in segmented services, thereby enhancing agricultural production efficiency [14]. It is essential to note that the degree of capital constraint will influence the optimizing effect of access to social services on agricultural production efficiency.

2.3.2. RUSM → Capital Allocation Adjustment → Agricultural Production Efficiency

RUSM can reduce agricultural production efficiency in rural households by diminishing agricultural productive investments. In addition to essential daily spending, rural households’ capital expenditures mainly include two items: productive investments and educational expenditures. Productive investments boost agricultural operational income, while educational expenditures improve the human capital of the family’s offspring [45]. As per the Pareto principle, optimal capital allocation occurs when the marginal benefits of productive investments and educational expenditures are equal [46]. After moving on account of students in their family, the expected returns on educational spending increase for rural households, leading to a shift in capital allocation towards more educational expenditures. Consequently, the additional educational expenses brought by RUSM may crowd out investments in agricultural productivity, thereby reducing agricultural production efficiency. Notably, the abundance of capital will influence the distorting effect of capital allocation adjustments on agricultural production efficiency.

2.3.3. RUSM → Land Allocation Adjustment → Agricultural Production Efficiency

The reallocation of labor and capital not only directly affects agricultural production efficiency but also indirectly impacts it by influencing land allocation. In rural households, land allocation methods predominantly encompass self-cultivation and land transfer. As per the Pareto principle, optimal land allocation occurs when the marginal benefits of land transfer and self-cultivation are equal [47], with any deviation signifying inefficiency. RUSM may externally shock optimal land allocation, leading to two scenarios. In the first scenario, the marginal benefit of self-cultivation surpasses that of land transfer, indicating an over-transfer of land [48]. Here, if RUSM leads to a loss of agricultural labor or diverts funds from agricultural productivity investments, forcing land to be transferred, this will reduce agricultural production efficiency.

In the second scenario, the marginal benefit of self-cultivation is less than that of land transfer, indicating a deficiency in land transfer [49]. In rural areas with underdeveloped land transaction markets, peasants often rely on word of mouth for land transfer opportunities, lacking access to wider information and trading platforms [50]. Given the unique nature of rural social relations, greater trust among relatives and friends often leads to a preference for land transfers within familiar networks due to trust and risk concerns. Consequently, land transfers are frequently limited to within villages or among relatives and friends, resulting in an insufficient level of land transfer when these parties are not engaged in transferring land, thereby diminishing agricultural production efficiency. It is crucial to note that the abundance of land influences how adjustments in land allocation affect agricultural production efficiency.

Based on the above analysis, the following research hypotheses (H) are proposed:

H1. 

RUSM significantly affects the resource allocation efficiency of China’s rural households.

H2. 

There is heterogeneity in the impact of RUSM on the resource allocation efficiency of China’s rural households with different resource endowments.

H3. 

RUSM affects the LRAE in rural households by changing their labor distribution and occupational choices, and it affects APE through the reallocation of labor, capital, and land resources.

3. Data, Models, and Variables

3.1. Data Source

This study’s baseline estimation analysis utilized data collected as part of CFPS in 2012. CFPS is a comprehensive longitudinal survey that tracks and collects micro-level data across the individual, family, and community levels to reflect changes in society, the economy, the population, and education in China. The survey encompasses samples from 25 provinces, representing approximately 95% of the national population, ensuring its national representativeness. The empirical analysis did not include post-2014 data in the baseline estimation due to the absence of questions on agricultural land transfer areas in later questionnaires. This omission renders it impossible to accurately identify the actual cultivated land area of rural households and thus to estimate the agricultural production efficiency of rural households [51]. This paper focused on rural household samples with students in the compulsory education stage (5 to 17 years old). After processing the original data, a total of 2990 valid samples were obtained.

The in-depth interview materials used in this study were sourced from D County, X Province, China, where this paper conducted a field survey for 20 days from 13 July to 1 August 2022. In line with academic ethical norms and specific contractual agreements, the identity of the sample county remains undisclosed. This confidentiality, mandated by an agreement signed with the county-level government before the commencement of the fieldwork, is critical for accessing detailed county data and securing the trust of the participants involved in the study. Ensuring their anonymity helps maintain the integrity of the research process, as all interactions with the study subjects were conducted through established local government networks. County D, nestled in the mid-western region of X Province in China, is the epitome of a typical agricultural county. As of the end of 2021, 28.79% of the county’s registered population was classified under urban household registration, contrasting with the 71.21% classified under rural registration. The demographic composition included 45.54% urban and 54.46% rural permanent residents. Over the decade spanning from 2010 to 2021, the county’s urbanization rate increased from 16.99% to 46.62%. Situated in a mountainous area with challenging natural conditions, County D’s rural populace faces substantial hurdles in achieving sustainable income growth through traditional agriculture. The county’s strategic proximity to City N, the provincial capital and its adjacency to the economically advanced Province D has catalyzed substantial migration. As of the end of June 2022, the total number of rural migrant workers from the county reached 277,100, with 61,700 working within the county, 97,200 working elsewhere within the province, and 118,200 working outside the province. The majority of the young workforce migrated across other provinces to Province D to work in the electronics manufacturing industry, while middle-aged and older workers mostly migrated to City N to work in construction. Some of the workforce was also engaged in entrepreneurial activities. By the end of 2021, there were 326 primary schools in the county, including 4 urban and 322 rural schools, and there were 25 middle schools, including 4 urban and 21 rural schools.

D County was selected as the sample county due to its typical and representative nature for researching RUSM issues in China. Geographically, D County is neither adjacent to the urban districts of its affiliated prefecture-level city nor is it the most remote county within the city. Regarding urban–rural income disparities, in 2021, the per capita disposable income ratio between urban and rural residents in D County was 2.6:1, which is close to the national average of 2.5:1 in China. In terms of educational development, D County met the national assessment criteria for balanced compulsory education development in 2020 and ranked mid-tier in provincial educational development. In terms of RUSM, the rates in D County for the years 2012, 2014, 2018, and 2020 were 14.79%, 19.98%, 19.55%, and 21.55%, respectively. These figures are comparable to the national rates measured through the CFPS database, which recorded mobility rates of 11.57%, 16.99%, 19.77%, and 22.61% for the same years. Overall, the situation of RUSM in D County and its impact on the efficiency of rural household resource allocation are indicative of broader national trends.

Participants for case interviews were randomly selected from rural household samples, with 40 parents of rural students who experienced urban–rural student mobility interviewed. Each interviewee received a one-to-one in-depth interview lasting no less than 30 min (the interview outline is provided in Appendix A). The occurrence of RUSM among the interviewed families spanned from 2010 to 2022, aligning well with the CFPS data used in the qualitative analysis section. This consistency ensured that the findings and trends observed were reflective of broader data insights and representative of the period studied. The coding method for the interview records was XXYY-P (or N), in which XX represents the interviewee’s place of household registration (located township), YY represents the interviewee’s coding in the township, and P and N represent the identity of the interviewee as a parent of the student or another family member (mostly grandparents), respectively.

3.2. Model Setting

3.2.1. Basic Model: OLS Model

This study first used the ordinary least squares (OLS) estimation method to construct the following basic regression model:

$${EFF}_i={\alpha +\beta Migrate}_i+\lambda Z_i+{\mu }_i$$

(1)

where the subscript i denotes the sample number among the rural households; ${EFF}_i$ denotes the resource allocation efficiency of rural household i; ${Migrate}_i$ denotes whether rural household i has experienced RUSM; $Z_i$ is a set of control variables; $\alpha$, $\beta$, and $\lambda$ are coefficients to be estimated; and ${\mu }_i$ is the random disturbance term.

3.2.2. Robust Models: ESR Model and PSM-DID Model

The difficulty in the empirical analysis of this study lies in addressing the issues of self-selection and endogeneity. If RUSM decisions were random, making the groups comparable, the OLS model could yield valid conclusions. However, the choice of urban versus rural schooling, influenced by household resources, leads to self-selection issues. Alternatively, there is an endogenous problem of mutual causality between RUSM decisions and resource allocation efficiency. Therefore, this paper employed an endogenous switching regression (ESR) model and propensity score matching difference-in-differences (PSM-DID) estimation to deal with self-selection and endogeneity issues.

ESR Model

The ESR model simultaneously estimates the following three equations:

$${Migrate}_i={\xi }_i{Z}_i+{\mu }_i$$

(2)

$${EFF}_{1i}={\eta }_{1i}{X}_i+v_{1i}$$

(3)

$${EFF}_{2i}={\eta }_{2i}{X}_i+v_{2i}$$

(4)

Equation (2) is the behavior equation, where ${\mathit{Migrate}}_i$ indicates whether RUSM occurs for rural household i, $Z_i$ denotes a series of control variables, ${\xi }_i$ is the coefficient to be estimated, and ${\mu }_i$ denotes the random disturbance term. Equations (3) and (4) are the outcome equations for the treatment group and the control group, respectively, where ${\mathit{EFF}}_{1i}$ and ${\mathit{EFF}}_{2i}$ denote the resource allocation efficiency for rural households that move or do not move, respectively, $X_i$ denotes a series of control variables, ${\eta }_{1i}$ and ${\eta }_{2i}$ are the coefficients to be estimated, and $v_{1i}$ and $v_{2i}$ are the random disturbance terms. In addition to at least one instrumental variable to ensure model identification, the variables in $Z_i$ are generally consistent with those included in $X_i$.

After estimating the coefficients of the ESR model, this paper estimated the factual and counterfactual levels of resource allocation efficiency for rural households that move or do not move, thereby calculating the average treatment effect of RUSM on resource allocation efficiency.

PSM-DID Model

The PSM-DID model is set as follows:

(1)

PSM stage: The probit model is specified as follows:

$$P_r\left({treat}_i=1|X_i\right)=\mathsf{\Phi }\left\{h\left(X_i\right)\right\}$$

(5)

where $P_r$ denotes the probability of engaging in RUSM, and $\mathsf{\Phi }$ is the normal cumulative distribution. $X_i$ denotes the matching variables that influence the decision-making process of RUSM, and $h\left(X_i\right)$ denotes the matching rule for the characteristic variables, which, in this paper, is five-to-one nearest-neighbor matching.

(2)

DID Stage: Subsequently, the DID model is established as follows:

$${EFF}_{it}={\beta }_0+{\beta }_1{treat}_{it}+{\gamma }_i{Z}_i+{\rho }_{it}{Z}_{it}+v_i+v_t+{\epsilon }_{it}$$

(6)

where ${EFF}_{it}$ denotes the resource allocation efficiency of rural household i in year t, ${treat}_{it}$ indicates whether RUSM is experienced, $Z_i$ and $Z_{it}$ are vectors of control variables that are constant over time and those that vary over time, respectively, $v_i$ is the individual fixed effect, $v_t$ is the time-fixed effect, $\gamma$ and $\rho$ are the parameters to be estimated for the control variables, ${\epsilon }_{it}$ is the random disturbance term, and ${\beta }_0$ is the constant term.

3.3. Variable Selection

3.3.1. Dependent Variable: Resource Allocation Efficiency of Rural Households

Labor Resource Allocation Efficiency

Following the identification method of Bryan and Morten (2019), this paper used the average labor compensation of the household to reflect the labor resource allocation efficiency of rural households. The compensation per unit of labor in rural households was determined by dividing the sum of the household’s annual wage income and operating income by the number of labor force members in the household.

Agricultural Production Efficiency

Referencing the settings of [52], this paper selected a stochastic frontier production function to measure the loss in agricultural production efficiency and then calculated the agricultural production efficiency. The measurement index can be seen in

Table 1. Measurement index and definitions of variables in agricultural production efficiency.

Variable		Definition	Mean
Output indicator	Agricultural output	The total value of agricultural and forestry products produced by the household (including the part sold in the market and the part consumed by the household). Unit: CNY	10,572.1
Input indicator	Land input	The actual cultivated land area of the household = collective allocated land area—land area transferred out + land area transferred in. Unit: Mu (1 mu = 666.67 m2)	10.17
	Labor input	The number of labor force members involved in the household’s own agricultural production. Unit: person	2.74
	Capital input	The total input used by the household for agricultural and forestry production = seed, fertilizer, and pesticide cost + hired labor cost + machine rental and irrigation cost + other costs. Unit: CNY	5969.54

.

3.3.2. Focus Variable: RUSM

RUSM was ascertained through the question “where is the child’s school located?” A response indicating the school’s location as “rural” was coded as zero, and selections of “county town”, “general city”, or “provincial capital city” were coded as one.

3.3.3. Instrumental Variable: School Consolidation Intensity

This paper selected “School Consolidation Intensity” as the instrumental variable for RUSM. The “School Consolidation Policy” is a significant initiative within China’s education system, specifically applied to schools at the compulsory education stage in rural areas. This policy focuses on merging small-scale schools that lack sufficient resources into larger educational hubs. The policy aims to enhance the efficiency of resource allocation and improve the quality of education, ensuring that educational facilities are more sustainable and better equipped to meet the needs of rural communities [53]. The reason for this is that “school consolidation” is an exogenous policy that can directly change rural households’ choice of school location (their decision making in the context of RUSM), and such RUSM is “passive”, as opposed to being influenced by the household’s resource allocation. For instance, if the school in Family A’s area is closed, the children from Family A must move to school elsewhere, and this decision is not affected by the initial resource allocation of the household. Referencing the settings of Liang and Wang (2020), this paper measured the school consolidation intensity in different cities based on the change in the number of schools per student from 2000 to 2012. The city names in CFPS are restricted data, and the use of this variable has been officially authorized by the CFPS project office. The specific formula is as follows:

$$\begin{array}{c}SchoolConsolidationIntensity\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=\left(\frac{Numberofprimaryschoolsin2000}{Primaryschoolenrollmentin2000}\right)-\left(\frac{Numberofprimaryschoolsin2012}{Primaryschoolenrollmentin2012}\right)\hfill \end{array}$$

3.3.4. Control Variables

Drawing from existing research, the control variables selected for this study included the following: (1) Offspring characteristics, including their gender, age, and number of siblings. (2) Parental characteristics, including their age, educational level, and health status. (3) Family characteristics, including the family’s party membership status, religious beliefs, land area, productive assets, non-productive assets, financial assets, and non-mortgage financial liabilities. (4) External environmental characteristics, including village topographical features and locational attributes (

Table 2. Variable definitions and descriptive statistics.

Variable	Definition	Mean	SD	Min	Max
Labor resource allocation efficiency	Labor average work reward (CNY/person); logarithm taken after 1% tail treatment	8.7361	1.787	0	12.1585
Agricultural production efficiency	Agricultural production efficiency of rural households engaged in agricultural production activities	0.5553	0.1683	0.0001	0.9192
Rural-urban student mobility	Location of the school where the student in the compulsory education stage of the family is studying: county/city/provincial capital = 1; rural = 0	0.1605	0.3672	0	1
School merging intensity	The number of primary schools dropped per 10,000 primary school students in the sample city from 2000 to 2012	15.2688	14.305	−96.8273	87.8894
Offspring gender	Gender of rural students: male = 1; female = 0	0.5251	0.4995	0	1
Offspring age	Age of rural students (years)	10.7482	2.71	5	17
Number of siblings of offspring	Number of living siblings of rural students (people)	1.1234	0.9429	0	8
Parental age	Average age of parents of rural students (years)	38.2141	5.3941	22	70
Parental education level	Average years of education of parents of rural students (years)	5.3528	3.4039	0	16
Parental health status	Average health status of parents of rural students: unhealthy = 5; general = 4; relatively healthy = 3; very healthy = 2; extremely healthy = 1	2.9919	0.9934	1	5
Party member household	Yes = 1; no = 0	0.0468	0.2113	0	1
Religious household	Yes = 1; no = 0	0.0525	0.2231	0	1
Land area	Per capita collective allocated land area (Mu/person)	1.7387	3.7047	0	71.4286
Productive assets	Per capita original value of productive fixed assets (10,000 CNY/person)	0.2182	1.4276	0	44.45778
Non-productive assets	Per capita original value of housing and durable goods assets (10,000 CNY/person)	2.6245	4.3081	0	93.8591
Financial assets	Per capita year-end financial asset amount (10,000 CNY/person)	0.3869	0.9623	0	20
Non-mortgage financial liabilities	Per capita cumulative amount borrowed within the year (10,000 CNY/person)	0.2251	0.7203	0	15
Hilly and mountainous area	Village terrain is hilly and mountainous: yes = 1; no = 0	0.3381	0.4732	0	1
Tall mountain	Village is on a tall mountain: yes = 1; no = 0	0.1789	0.3834	0	1
Plateau	Village is on a plateau: yes = 1; No = 0	0.0538	0.2258	0	1
Plains	Village terrain is plains: yes = 1; No = 0	0.4104	0.492	0	1
Grassland	Village terrain is grassland: yes = 1; No = 0	0.0027	0.0517	0	1
Fishing village	Village is a fishing village: yes = 1; No = 0	0.0067	0.0815	0	1
Other	Village terrain is other: yes = 1; no = 0	0.0094	0.0963	0	1
Eastern region	Located in the eastern region: yes = 1; no = 0	0.3438	0.4751	0	1
Central region	Located in the central region: yes = 1; no = 0	0.2923	0.4549	0	1
Western region	Located in the western region: yes = 1; no = 0	0.3639	0.4812	0	1

Note(s): SD = standard deviation; same below.

).

4. Empirical Analysis

4.1. Baseline Estimation Results

Table 3. Ordinary least squares (OLS) estimation of the impact of RUSM on resource allocation efficiency.

	(1)	(2)
	LRAE	APE
RUSM	−0.3143 ***	−0.0285 **
	(0.0940)	(0.0113)
Control variables	Yes	Yes
Constant	9.5526 ***	0.5335 ***
	(0.2947)	(0.0352)
Obs	2354	2024
R2	0.0700	0.0835

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses.

shows the OLS estimation results of the impact of RUSM on the resource allocation efficiency of rural households. The regression results indicate that RUSM specifically decreases their labor resource allocation efficiency and agricultural production efficiency. This suggests that the negative inhibitory effect of RUSM on the resource allocation efficiency of rural households is stronger than its positive promotional effect. Thus, H1 is supported. From an economic standpoint, this indicates that the loss of resource allocation efficiency in rural households caused by RUSM essentially represents a hidden cost generated by such mobility.

4.2. Robustness Test

4.2.1. Using “School Consolidation Intensity” as an Instrumental Variable for ESR Estimation

Both panels in

Table 4. Robustness test: estimation results of the endogenous switching regression (ESR) model.

Panel A: LRAE
	Studying in Urban Area	Studying in Rural Area	ATT	ATU	ATE	T Value
Treatment group	8.7118	8.7955	−0.0837 *			−1.7591
	(0.9656)	(0.4148)	(0.9223)
Control group	8.3627	8.9096		−0.5469 ***		−25.0638
	(1.0184)	(0.3952)		(0.9675)
Full sample	8.4156	8.8955			−0.4799 ***	−25.3063
	(0.9991)	(0.3862)			(0.9426)
Panel B: APE
	Studying in Urban Area	Studying in Rural Area	ATT	ATU	ATE	T Value
Treatment group	0.5328	0.6025	−0.0697 ***			−17.164
	(0.0700)	(0.0473)	(0.0728)
Control group	0.4761	0.5597		−0.0836 ***		−53.5321
	(0.0720)	(0.0509)		(0.0658)
Full sample	0.4851	0.5672			−0.0821 ***	−58.6389
	(0.0821)	(0.0514)			(0.0658)

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses.

show that the average treatment effect of RUSM on the labor resource allocation efficiency and agricultural production efficiency of rural households is significantly negative across the treatment group, control group, and the entire sample. These findings further validate H1.

4.2.2. PSM-DID Estimation

This paper used three time periods of fully traceable panel data from CFPS 2012, 2014, and 2018 for the PSM-DID estimation. The sample used in this section adhered to the following criteria: at the baseline (2012), none of the responders had experienced RUSM; during the follow-up period (2014 and 2018), a subset of the sample underwent RUSM (the treatment group), while the rest did not experience such mobility (the control group). Since only the CFPS 2012 data contained the area of self-cultivated land, this section used the unit labor agricultural output value and unit capital agricultural output value as proxy variables for the agricultural production efficiency. As shown in

Table 5. Robustness test: estimation results of the PSM-DID model.

	(1)	(2)	(3)	(4)	(5)	(6)
	LRAE		Unit Labor Agricultural Output Value		Unit Capital Agricultural Output Value
	DID	PSM-DID	DID	PSM-DID	DID	PSM-DID
RUSM	−0.4006 ***	−0.3397 *	−0.3263 *	−0.3321 *	−0.5408 **	−0.5130 *
	(0.1536)	(0.1767)	(0.176)	(0.1899)	(0.2534)	(0.2805)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Constant	8.9739 ***	6.4679	−4.7651	−0.1982	−1.454	0.9147
	(3.281)	(5.5854)	(3.5158)	(5.5253)	(5.3809)	(8.6665)
Obs	1242	1027	1179	992	1277	1061
R2	0.0611	0.0078	0.0247	0.0464	0.0058	0.0065

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors are presented in parentheses.

, after the issues of self-selection and endogeneity were addressed using the DID method and the PSM-DID method, the impact of RUSM on the resource allocation efficiency of rural households remained significantly negative, further validating H1.

4.3. Heterogeneity Effects

In this section, this paper conducted a heterogeneity analysis to identify which rural households with certain factor endowment characteristics suffered more from the loss of resource allocation efficiency due to RUSM.

4.3.1. Labor Heterogeneity

Grouped by Labor Force Quantity

In

Table 6. Heterogeneity analysis: labor heterogeneity.

Panel A: Estimates Grouped by Labor Force Quantity
	(1)	(2)		(3)	(4)
	LRAE			APE
	Lower Labor Quantity	Higher Labor Quantity		Lower Labor Quantity	Higher Labor Quantity
RUSM	−0.4742 ***	−0.3546 ***		−0.0410 ***	0.0042
	−0.1213	−0.1023		−0.0141	−0.0183
Control variables	Yes	Yes		Yes	Yes
Obs	1547	1978		1204	820
R2	0.0882	0.0713		0.1007	0.0813
Panel B: Estimates grouped by labor force quality
	(1)	(2)	(3)	(4)	(5)	(6)
	LRAE			APE
	Lower education level	Medium education level	Higher education level	Lower education level	Medium education level	Higher education level
RUSM	−0.5997 **	−0.2637 **	0.1301	0.0121	−0.0275 **	−0.0345
	−0.2641	−0.1046	−0.373	−0.0315	−0.0125	−0.0437
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Obs	375	1827	152	337	1568	119
R2	0.2613	0.0517	0.1874	0.1681	0.0913	0.2818

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses.

, Panel A indicates that rural households with fewer laborers experienced greater losses in resource allocation efficiency due to RUSM. One possible reason for this phenomenon is that the scarcity of labor endowments leads to the entire burden of maintaining the family livelihood falling to one or two family members. In order to extend their working hours, they are forced to seek part-time activities that do not match their labor skills, thereby weakening their labor resource allocation efficiency. Another possible reason is that the scarcity of labor endowments results in a severe shortage of labor for agricultural production, intensifying the distorting effect of agricultural labor loss on agricultural production efficiency.

Grouped by Labor Force Quality

In Table 6, Panel B reveals that rural households whose labor forces had lower-to-medium levels of education incurred greater losses in resource allocation efficiency due to RUSM. One possible reason for this phenomenon is that the insufficient quality of labor endowments restricts the employment opportunities of family members in the non-agricultural sector, thereby intensifying the distorting effect of occupational changes on labor resource allocation efficiency. Another possible reason is that the insufficient quality of labor endowments leads to lower skill levels among the labor force members who continue to stay in rural areas for non-agricultural work, thereby intensifying the distorting effect of the loss of agricultural labor force on agricultural production efficiency.

4.3.2. Capital Heterogeneity

Grouped by Annual Household Income

In

Table 7. Heterogeneity analysis: capital heterogeneity.

Panel A: Estimates Grouped by Annual Household Income
	(1)	(2)		(3)	(4)	(5)		(6)
	LRAE				APE
	Lower Income	Medium Income		Higher Income	Lower Income	Medium Income		Higher Income
RUSM	−2.8046 ***	−0.2741 ***		−0.5733 ***	−0.036	−0.0266 **		−0.0352
	−0.3074	−0.0891		−0.1842	−0.0333	−0.0131		−0.028
Control variables	Yes	Yes		Yes	Yes	Yes		Yes
Obs	202	1876		246	171	1584		269
R2	0.4275	0.0693		0.1736	0.2215	0.0748		0.1806
Panel B: Estimates grouped by productive fixed assets
	(1)		(2)		(3)		(4)
	LRAE				APE
	Less productive fixed assets		More productive fixed assets		Less productive fixed assets		More productive fixed assets
RUSM	−0.2238 *		−0.4210 ***		−0.0091		−0.0383 ***
	−0.1296		−0.1349		−0.0166		−0.0148
Control variables	Yes		Yes		Yes		Yes
Obs	1130		1224		965		1059
R2	0.1033		0.075		0.0672		0.1329

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses.

, Panel A reveals that RUSM leads to larger losses in resource allocation efficiency for households with low–medium annual incomes. One possible reason for this phenomenon is that the high degree of capital constraints keeps the household’s income at a subsistence level, leading households to invest more in their children’s human capital to generate future income, thereby intensifying the distorting effect of occupational changes on labor resource allocation efficiency. Another possible reason is that the high degree of capital constraints leads to educational expenses crowding out productive investments, limiting funds for essential agricultural inputs and access to social services, further distorting capital allocation in agriculture and reducing the positive impact of labor allocation adjustments on agricultural production efficiency.

Grouped by Productive Fixed Assets

In Table 7, Panel B indicates that rural households with more productive fixed assets incur greater losses in resource allocation efficiency due to RUSM. One possible reason for this phenomenon is that high levels of productive fixed assets imply they have stronger agricultural skills and they were fully engaged in farming prior to moving, intensifying the impact of occupational changes on labor resource allocation efficiency. Another possible reason is that high levels of productive fixed assets result in agricultural machinery lying idle after labor losses, further distorting the agricultural production efficiency.

4.3.3. Land Heterogeneity

Table 8. Heterogeneity analysis: land heterogeneity.

	(1)	(2)	(3)	(4)
	LRAE		APE
	Less Self-Owned Land Area	More Self-Owned Land Area	Less Self-Owned Land Area	More Self-Owned Land Area
RUSM	−0.6236 ***	0.067	−0.0587 ***	0.0078
	(0.1458)	(0.1099)	(0.0175)	(0.0141)
Control variables	Yes	Yes	Yes	Yes
Obs	1255	1099	952	1072
R2	0.1	0.1407	0.0794	0.147

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses.

reveals that rural households that own less land experience greater losses in resource allocation efficiency due to RUSM. One possible reason for this phenomenon is that the insufficient abundance of land resources implies poor agricultural livelihoods, increasing the utility of investing in their children’s human capital, thereby intensifying the distorting effect of occupational changes on labor resource allocation efficiency. Another possible reason is that the insufficient abundance of land resources reduces households’ understanding and trust in the land transaction market, further affecting agricultural production efficiency through land allocation adjustments.

In summary, the impact of RUSM on the resource allocation efficiency of rural households with different resource endowments shows significant differences. Thus, H2 is confirmed. Specifically, rural households with lower-to-medium annual incomes, more productive fixed assets, smaller labor forces, lower-to-medium education levels, and smaller land areas suffer greater losses in resource allocation efficiency after moving on account of students in their family.

5. Analysis of the Dynamic Adjustment Mechanisms of Resource Allocation

Previous analyses have revealed that despite RUSM being a rational choice made by rural households based on cost–benefit considerations, the internal resource reallocation that it leads to has resulted in losses in resource allocation efficiency. In this section, in conjunction with publicly available data from CFPS and firsthand information obtained from in-depth interviews conducted in D County, X Province, China, this paper further discusses the dynamic adjustment mechanisms of resource allocation in China’s rural households.

5.1. RUSM → Labor Resource Allocation Adjustment → Resource Allocation Efficiency Loss

5.1.1. RUSM Triggers Adjustments in the Division of the Household’s Labor Force

The optimal allocation of labor resources between urban and rural areas is a critical economic strategy for rural households. Typically, rational decision making, considering overall development and income, leads rural households to a “part-time work, part-time farming” model, maximizing labor value and resource acquisition, thereby ensuring sustainable household reproduction [54]. However, since at least one family member must stay in an urban area for the child’s care, RUSM disrupts this labor model, adversely affecting the arrangement of the household’s livelihood [55]. “Previously, my parents farmed and took care of the kids back home, while my wife (the child’s mother) and I worked in Guangdong. We all came back when the child went to school in the county town. My wife takes care of the child at home full-time and does some wicker weaving when she has time, while I do odd jobs in the county town” (LL01-F).

5.1.2. The Labor Efficiency of Household Members with Comparative Advantages in the Household Sector Further Decreases

After moving on account of students in their family, laborers who modify their occupations often possess comparative advantages in the household sector. Accompanying their children to urban areas for education, they frequently abandon their initially optimal job choices, which manifests into two primary scenarios:

The first scenario involves rural laborers, initially in farming, switching to support their children’s education in urban areas. These family members, more efficient in agriculture than in non-agricultural work, are compelled to take non-agricultural jobs, affecting their choice between the agricultural and non-agricultural sectors [56]. “Previously, both of us (the child’s parents) were farming at home, planting corn, raising pigs and sheep. Now that the child is in school in the county town, my wife has gone to work in an electronics factory in Guangdong, earning 4000 yuan a month, while I stay at home and take care of the child, earning 1500 yuan a month. The baby is too young for both of us to work outside the home” (DX01-F).

In the second scenario, laborers initially employed in higher-productivity non-agricultural jobs in big cities return to their county hometowns for their children’s education, switching to lower-productivity non-agricultural work, thereby affecting their choice between urban non-agricultural sectors [57]. “My wife (the child’s mother) used to earn 7000–8000 yuan a month working in Dongguan. When she first returned to the county in 2013, she skipped her first semester of work to care for the child full-time. She then worked part-time in insurance and helped out in hotels and restaurants, earning 3000–4000 yuan a month” (BA05-F). Parents staying with their children in county towns face limited employment opportunities, often restricted to low-wage service jobs. In fact, in many central and western regions, employment opportunities are scarce, leading most accompanying parents to withdraw completely from the market sector’s paid labor.

5.2. RUSM → Adjustment in Capital Allocation → Resource Allocation Efficiency Loss

5.2.1. RUSM Leads to Increased Household Educational Expenditure

The CFPS2012 data indicate that rural households with RUSM have significantly higher educational expenditures for their children (CNY 4071.39) than those without such mobility (CNY 1348.85) (

Table 9. Household educational expenditure of three types of students.

Educational Expenditure Category	Rural Students Studying in Rural Areas		Rural Students Studying in Cities		Urban Students Studying in Cities
	Mean	SD	Mean	SD	Mean	SD
Total education expenditure	1348.85	1815.11	4071.39	3877.32	5453.96	7510.89
Tuition and miscellaneous fees	436.53	929.08	1749.39	2671.88	996.42	2702.55
Book fees	159.85	222.32	290.75	492.69	435.23	560.55
Accommodation fees	45.57	278.04	155.63	533.16	37.39	736.44
Meal fees	435.28	823.54	1049.54	1645.54	689.95	1178.24
Transportation fees	101.09	300.61	232.38	475.35	227.14	655.13
Childcare fees	14.65	185.38	43.54	420.51	151.78	977.93
Nanny fees	1.36	35.98	12.5	273.86	1.48	38.49
Software fees	14.46	147.16	30.27	221.75	122.82	528.39
Tutoring fees	70.5	332.69	257.6	774.05	2296.49	4728.56
School selection fees	9.65	225.21	136.79	753.57	242.24	2050.2
Other fees	59.9	305.57	112.99	482.33	253.03	1094

Note(s): Data from CFPS2012. The educational expenditure in this table refers to the expenditure of a household on a single child, while the cultural, educational, and entertainment expenditure in the following table refers to the total expenditure of a household on culture, education, and entertainment, with a different statistical caliber.

). Tuition, accommodation, and meal expenses for these students not only surpass those of rural students without such mobility, they also exceed the costs incurred by urban students.

On the one hand, additional expenses, such as housing purchases or rentals, school choice fees, and tuition fees for non-local residents, contribute to a “passive” increase in household educational spending. To facilitate RUSM, rural households must first satisfy the prerequisites for urban areas’ school admissions [25]. Four methods facilitate RUSM: proximity enrollment, selection by payment, influence, and academic performance (Figure 5). Proximity enrollment is the most common means of admission and the only legal way to attend urban public schools under the “Compulsory Education Law”, which means residing within an urban public school district, usually via purchasing or renting a home, thereby increasing the expenditure on housing costs or rental fees. Selection by payment or influence involves entering urban public or private schools through patronage payments, non-local tuition fees, or personal connections, all reliant on a household’s socioeconomic resources. For instance, a principal from a rural primary school made the following observation: “Last year, a total of 13 students from the school attended the county town middle school, among which 5 were admitted through connections with acquaintances”. Selection by academic performance allows rural students to enter urban schools based on high grades or talents; however, these schools—primarily private—demand substantial tuition fees. A case in point is a rural household that moved their child to Dongguan for schooling from another province: “I (the child’s father) went to Dongguan to work in 1993, later my wife and I did business in electronic wires and cables there, and the child has been with us since birth. Without a Dongguan hukou, we can only attend private schools, with a tuition fee of 4000 yuan per semester” (BA05-F). Consequently, due to the steep fees charged by private schools, when their eldest son finished the second grade of primary school and their younger son was about to enter the first grade, the parents opted to return their children to a county town primary school in D County to continue their education.

On the other hand, after moving on account of students in their family, rural households often “actively” escalate their educational expenditures. The aim of this mobility is to access superior educational resources, enhance intergenerational mobility, and alter parents’ perspectives on education, thereby driving increased educational spending [58]. “We encourage our children’s interests and talents. Our eldest and third child have always been passionate about painting, and attending related classes, while our second child is interested in programming, so we purchase books he enjoys” (YA01-F). “I feel that the educational attitudes of parents in the county town are very different from those of rural parents. Previously, I thought it was enough for a child to study for self-support, now I think parents should provide more educational investment” (GL01-F).

5.2.2. Increased Educational Investment Crowds Out Productive Investment

RUSM has reduced the proportion of productive investments in total household expenditures and increased the share of educational investments. According to CFPS data, the educational expenses of rural households that move on account of students in their family are significantly higher than those without, in both absolute and relative terms. Relatively, the shares of cultural, educational, and recreational expenses in total family expenditures are 10.04%, 16.70%, and 12.23%, respectively, for the three family types. Educational expenditure in rural households with student mobility is 6.66% higher than that in those without and 2.29% higher than that in urban households. This additional educational expenditure crowds out productive investments in rural households. The expenditures on agricultural production and operations for rural households, with and without student mobility, are CNY 9325.86 and CNY 8555.99, respectively (

Table 10. Three categories of household expenditure.

	Rural Households				Urban Households
	No RUSM		RUSM
	Mean	SD	Mean	SD	Mean	SD
Cultural, educational, and recreational expenditure (CNY)	3644.81	5809.99	7442.45	8671.55	11,982.87	19,299.35
Cultural, educational, and recreational expenditure as a proportion of total household expenditure	0.1004	0.1311	0.167	0.1652	0.1233	0.1164
Agricultural production expenditure (CNY)	8555.99	21,231.61	9325.86	29,723.58	——	——

); the corresponding ratios of these expenditures to total household expenses are 19.28% and 22.04%. These statistics suggest that RUSM not only decreases the share of productive investments in total household expenditures but also increases the share of educational investments. Consequently, the crowding out effect on productive investments by increased educational spending results in over-allocation in education and under-allocation in agricultural production for rural households.

5.3. RUSM → Land Allocation Adjustment → Resource Allocation Efficiency Loss

5.3.1. Scenario I: The Marginal Benefit of Self-Cultivation Is Higher Than That of Land Transfer

The scenario in which the marginal benefit of self-cultivation exceeds that of land transfer arises from an over-extension of land transfer activities. After moving on account of students in their family, rural parents often seek employment in urban areas to bolster their household income, subsequently allocating funds for their children’s education. Due to land circulation reducing agricultural productive investments while increasing land rental income, which is favorable for education savings, RUSM has encouraged excessive land circulation [10]. According to the 2012 CFPS data, among rural households that move on account of students in their family, the average land rental income is CNY 338.28 per mu, significantly lower than the CNY 1404.60 per mu yield of self-farmed land.

This trend was also evident in our in-depth interviews conducted in D County. One respondent mentioned “We had 20 mu of land, mainly for self-cultivating sugarcane. When our child started school in the county, we cultivated only 8 mu with soybeans, corn, and rice, transferring the rest for orchard cultivation at a contract fee of 800 yuan per mu” (DS01-F). Another stated “Our family had 4 mu of arable land. I (the child’s mom) farmed rice and sugarcane, consuming the rice and selling the sugarcane. When my child began school in 2013, I moved to the county for work, and all our farmland was subsequently transferred at 800 yuan per mu, and we ceased self-farming” (DS02-F). The survey revealed that sugarcane cultivation in D County typically yields ~2000 yuan/mu. Compared to this, renting out sugarcane-cultivated land for 800 CNY/mu reduces the land’s marginal benefit.

5.3.2. Scenario II: The Marginal Benefit of Self-Cultivation Is Lower Than That of Land Transfer

The scenario in which the marginal benefit of land transfer surpasses that of self-cultivation stems from insufficient land transfers. In such cases, the agricultural production efficiency of these households is initially lower than their labor productivity in the non-agricultural sector. Thus, without RUSM, a household’s optimal choice would be transferring the family’s employment to non-agricultural sectors and land. RUSM has led some rural households to shift from individual to family migration, moving their primary members to urban areas. Despite this, many households do not transfer all of their cultivated land to fellow villagers, and some even abandon their land [23]. In D County, poor soil quality leads to a very low agricultural efficiency. A significant portion of the county’s rural young and middle-aged labor force has moved to the county town or beyond for work, with whole-family migration being a common phenomenon. However, many retain land for crops such as rice and corn, generating minimal income and resulting in significant land allocation inefficiencies.

6. Discussion

This study examines the effects of RUSM decisions on subsequent resource allocation within China’s rural households, adopting a theoretical perspective rooted in “family strategy”. Previous research typically regards RUSM decisions as discrete subjects for analysis, examining factors that influence these decisions [4], the living conditions of mobile and left-behind children [8], the challenges faced, and opportunities for institutional improvements [7] from a static viewpoint. This approach often overlooks the dynamic interactions between RUSM and the broader family decision-making process. This study argues that RUSM should be understood as a dynamic element of family strategy, reflecting rational choices made by China’s rural households based on their available resource conditions. These decisions profoundly influence subsequent family planning and resource allocation. The study finds that RUSM is not merely an isolated decision, but is an integral part of family dynamics, significantly impacting consumption patterns, production, and lifestyle choices within a family. This shift in resource allocation often results in inefficiencies, which affect economically vulnerable families in particular, undermining their capacity for economic growth and weakening their survival and development prospects. By focusing on family strategy, this research offers a nuanced view of the complexities of RUSM and its implications for China’s “urban–rural amphibious” families, thereby enriching the understanding of sustainable development challenges and resilience in rural China.

Second, this study highlights losses in resource allocation efficiency stemming from RUSM, a subject not extensively covered in previous research. While prior studies have primarily celebrated the benefits of RUSM—boosting urbanization rates [12], decreasing the number of left-behind children [19], and improving rural human capital [33]—scant attention has been paid to its adverse impacts. Typically, discussions focus on direct costs, such as increased living and educational expenses [27]. However, the literature from contexts outside of China indicates that population movement driven by regional disparities in public services may result in resource mismatches and reduce the efficiency of resource allocation in rural households [18]; however, detailed empirical evidence for China remains sparse. This paper significantly enriches this discourse by shedding light on the hidden costs—namely the losses in resource allocation efficiency—associated with RUSM, supported by observational data from China. Our findings confirm that RUSM markedly diminishes the resource allocation efficiency of China’s rural households. While these inefficiencies appear at the micro level as rational economic decisions aimed at optimizing family utility, at the macro level, they manifest as regional production inefficiencies. Thus, addressing these inefficiencies through targeted policy intervention and optimization is imperative for enhancing sustainable development in rural areas.

Third, this study advances our understanding of losses in resource allocation efficiency among China’s rural households by employing a hypothesis of heterogeneity. Unlike the majority of existing research, which is anchored upon an assumption of homogeneity and neglects the varied resource constraints faced by rural households across different contexts [10], this paper recognizes that these households might adopt diverse resource allocation strategies in response to RUSM. Such strategies can lead to varied impacts on resource allocation efficiency. By conducting a nuanced analysis of heterogeneity, this study precisely pinpoints the traits of rural households experiencing the most significant losses in resource allocation efficiency. This insight is crucial, as it equips China’s rural households with the knowledge to more accurately assess the phenomenon of RUSM and to make informed, rational decisions about student mobility and resource management. This approach not only enhances the sustainability of rural development but also supports the strategic planning and resilience of rural communities facing socio-economic transitions.

7. Conclusions and Policy Recommendations

Our conclusions can be summarized as follows: First, using large-scale, nationally representative data, this paper found that RUSM, a strategy employed by rural households to access quality urban educational resources, exerts a substantial negative impact on both the LRAE and APE of China’s rural households. This finding suggests that RUSM, chosen by China’s rural households to access superior urban educational resources, incurs a cost that has been previously underemphasized in the literature—the loss of resource allocation efficiency in rural households. Therefore, this paper constitutes a significant addition to the academic research on the relationship between population mobility and resource mismatch. Second, our heterogeneity analysis showed that China’s rural households characterized by lower-to-medium annual incomes, substantial productive fixed assets, limited labor force, lower-to-medium educational levels in their labor force, and smaller land holdings face heightened losses in resource allocation efficiency after moving. Therefore, this study precisely identifies groups experiencing greater losses in resource allocation efficiency, helping rural households to make informed decisions about RUSM and resource allocation. Third, by detailing how RUSM affects resource allocation in China’s rural households, this study shows that RUSM prompts a shift in the rural labor force away from sectors with optimal labor productivity, thereby diminishing LRAE. RUSM also impairs APE through agricultural labor force loss, the crowding out of productive agricultural investments, and disruptions in decisions about land cultivation and transfer.

These findings provide insights for China’s policymakers to improve strategies such as rural migrant settlement and the balanced allocation of urban and rural educational resources: Firstly, governments should focus on the development of rural education to mitigate the expenses incurred by rural households in accessing quality education. Secondly, considering that China’s resource-scarce rural households experience greater losses in resource allocation efficiency after moving on account of students in their family, governments should advise such households to make “careful” decisions regarding their RUSM. At the same time, governments could introduce differentiated educational support policies, such as providing additional educational aid, subsidies, or financial support to low-income migrant workers’ children, thereby helping these households to better cope with the challenges brought by RUSM. Thirdly, to address the adverse effects of RUSM on the occupational choices of the rural household labor force and agricultural production, governments could improve the skill levels and employment competitiveness of rural accompanying parents through employment training programs. By promoting rural industrial upgrading, providing social services, and improving the rural land market, governments can help rural households to better utilize and allocate their household resources after the loss of their agricultural labor force, thereby promoting the sustainable development of rural households.

Given the historical dual structure of urban and rural areas in China and the characteristics of progressive reforms, as well as the ongoing process of urbanization, it is crucial to acknowledge that the impact of RUSM on resource allocation in China’s rural households may vary across different institutional contexts. Therefore, it is important to note that this paper focuses on the phenomenon of RUSM from 2012 to 2022, and, thus, the conclusions and perspectives presented are primarily applicable to China within this timeframe. A limitation of this study is that it primarily explains the dynamic adjustment mechanisms of resource allocation based on observations from a single case study, without rigorous statistical validation. Consequently, the representativeness of these findings at a statistical level remains uncertain. Future research could build on the theoretical framework constructed in this paper, gather data on a larger scale, and conduct more rigorous testing of the dynamic adjustment mechanisms.