Abstract

Background

Rearranging school layouts in rural areas has been one of the most important changes to basic education in China since the early 2000s. The layout adjustment has increased the service radius of rural primary schools and increased the distance for students to go to school. While numerous studies have revealed that reducing commuting time would improve positive emotions, limited attention has been given to examining the relationship between commuting time, academic performance, and mental health in China.

Methods

Using survey data from 12,394 students in 249 rural primary schools in 18 counties, this study examines the association of commuting time on academic performance and mental health among rural students in China. Instrumental variable (IV) estimation techniques are utilized to address potential endogeneity issues and provide robust estimates of the relationships under investigation. Academic performance was measured using the Trends in International Mathematics and Science Study (TIMSS) test, and mental health status was measured using the Mental Health Test (MHT).

Results

.Lengthy commutes are negatively associated with students鈥� academic performance and mental health. Each additional hour of commuting time is associated with a 0.148 SD decrease in academic performance and a 3.039 increase in mental health test scores for students, indicating a higher risk of mental health problems. Heterogeneity analysis revealed that the effect of commuting time is more pronounced for female students, senior students, students with migrant parents, and students whose parents have lower education levels. Mechanism analysis demonstrates that sleep duration and after-school outdoor activities mediate the relationship between commuting time, academic achievement, and mental health.

Conclusion

This study contributes new evidence regarding the association between commuting time, academic performance, and mental health among rural students in China. The findings offer valuable insights for education policymakers in optimizing school location and layout strategies to support students鈥� academic success and mental well-being.

Trial registration

Permission was received from local Boards of Education in each region and the principals of all schools. The principles of the Declaration of Helsinki were followed throughout. Oral informed consent was obtained from at least one parent for all child participants. Ethical committee approval for this study were obtained from Sun Yat-Sen University (Registration number: 2013MEKY018) and Stanford University (Registration number: 24847). The trial registration number is ISRCTN03252665 (registered at 25/09/2012).

Background

Child development is widely recognized as a pivotal factor in regional economic growth, enhancing income levels, and improving overall quality of life [1,2,3]. Consequently, governments prioritize educational development to foster students鈥� academic and psychological well-being. Recent research has investigated various factors influencing students鈥� academic and psychological well-being, including but not limited to teacher experience, class size, teacher absenteeism, and class start time [4,5,6,7].

Moreover, academic performance and mental health are crucial for the individual child development and social well-being [8]. Academic performance influences educational attainment, job performance, income levels, and overall quality of life [9,10,11].Similarly, mental health issues can affect executive functioning, engagement in risky behaviors, and health-related quality of life, with implications lasting into adulthood [12,13,14]. In this study, we focus on the mental health of students, specifically addressing student鈥檚 general anxiety (as measured by the Mental Health Test scale, described below).

Empirical research findings indicate long commuting time has significant implications for both academic performance and mental health [15,16,17]. The increase in commuting time not only imposes financial and time costs but also potentially diminishes educational investment, thereby negatively impacting students鈥� academic performance [18]. Additionally, research indicates that commuting is closely linked to decreased psychological well-being, underscoring the importance of minimizing travel time to foster positive emotions [19,20,21].

Transitioning to theoretical insights, the time utilization theory, proposed by John P. Robinson in 1980, offers valuable insights into how individuals effectively manage and utilize their time. It highlights the consequential effects of time allocation on behavioral patterns and psychological health [22]. It is frequently employed to examine how individuals prioritize and allocate their time across different activities, such as work, leisure, and personal obligations [23]. In the context of prolonged commuting, individuals may encounter time constraints and challenges in time management, resulting in inefficient time utilization, reduced learning efficacy, and heightened psychological stress.

However, previous research on the impact of commuting time on academic performance and mental health has yielded inconsistent findings. While some studies have reported a significant negative association between commuting time and student outcomes, others have found little to no effect [24,25,26,27,28]. This discrepancy may be attributed to methodological limitations, including small sample sizes, cross-sectional designs, and insufficient consideration of confounding variables, such as road quality, the traffic condition around the school area. Moreover, most studies have primarily focused on the adult population, with limited research specifically examining the effects of commuting time on children and adolescents [16, 29], particularly in the context of rural China. Additionally, there is a gap in the literature regarding the mechanistic analysis of the impact of commuting on academic and psychological well-being. Thus, there is a critical need for rigorous empirical research to analyze the impact of commuting time on rural students鈥� academic performance and mental health, addressing a theoretical gap in the existing literature by examining this influence through the lens of the Time Utilization Theory.

This study aims to investigate the associations between commuting time, academic performance, and mental health among rural children in northwest China. Specifically, it aims to (1) describe commuting time patterns, (2) examine the influence of commuting time on academic performance and mental health, and (3) analyze variations among different groups. Furthermore, the study will explore the mechanisms underlying these associations, particularly focusing on how commuting time affects time allocation patterns and subsequently influences academic performance and mental health outcomes among rural children.

Method

Sampling

The data used in this study were acquired during a randomized controlled trial conducted in the fall of 2012, which evaluated the effects of providing spectacles to 19,977 children at 253 primary schools in Northwest China, as reported by Ma et al. (2014) [30]. The sample schools, located in Tianshui Prefecture in Gansu Province and Yulin Prefecture in Shaanxi Province, provide a regional context for our study. Shaanxi had the 14th-highest gross domestic output per capita among China鈥檚 31 provincial administrative regions in 2012, at USD 6,108, comparable to the country鈥檚 average of USD 6,091 for the same year. With a gross domestic product (GDP) per capita of USD 3,100, Gansu is the second-poorest province in China, according to the 2012 China National Statistical Yearbook. Therefore, the survey鈥檚 findings provide a fair representation of the underdeveloped rural areas in northwest China.

A list of every rural primary school in each prefecture was provided to choose the sample. Firstly, a list of all nineteen counties in two prefectures, Tianshui Prefecture in Gansu Province and Yulin Prefecture in Shaanxi Province, was obtained. Due to limitations in the research budget, the research team deliberately selected the most populous eighteen counties for inclusion in the sample, with one county being excluded due to its significantly lower population size. Subsequently, a representative sample of primary schools was selected. A comprehensive list of all primary schools in the aforementioned eighteen counties, totaling 435 primary schools, was obtained from the respective local education bureaus. Using this list as a reference, a random sampling approach was employed to select one primary school from each township across all eighteen counties within the sample. The number of schools was limited to those with between 50 and 150 children in grades 4 and 5 for implementation efficiency and logistical considerations. Each grade comprised a single class at each school, and if there were multiple classes in any grade, one class was randomly selected. A sample of 19,977 fourth and fifth graders from 253 primary schools in northwest China was obtained. Finally, exclusion criteria were applied to these 19,977 children. Boarders were excluded as the study focused on the relationship between commuting time and academic performance and mental health. Following these six stages, 12,426 students from 249 schools in Northwest China constituted the final sample. The flowchart of the sample selection criteria is in Fig.听1.

Fig. 1

The flowchart of the sample selection criteria

Data collection

Data collection occurred in two phases: baseline (September 2012, the start of the school year) and assessment (May-June 2013, towards the end of the school year). Both baseline and assessment surveys followed the same protocol, which included a basic questionnaire survey, a mathematics achievement test, and a mental health assessment.

The initial section of the survey collected socio-economic information and commuting habits. Comprehensive questionnaires were administered to all eligible fourth and fifth-grade students in the sample. The questionnaire package comprised two parts: a student questionnaire and a household questionnaire. The student questionnaire, distributed directly to sampled students, gathered data on characteristics such as commuting time, age, gender, and mode of transportation to school. The household questionnaire, given to caregivers, aimed to collect essential information on household wealth, education-related expenditures, household registration type, sibling presence, parental migration for work, homework supervision, parental education level, and parental occupation.

The dependent variables of interest were academic performance and mental health. Separate mathematics tests appropriate for sampled students were administered on printed paper by research staff. Local educators assisted with the selection of questions from items developed for the Trends in International Mathematics and Science Study ( TP_About.pdf). The TIMSS test serves as a global benchmark for assessing primary school students鈥� proficiency in science and mathematics, which aims to evaluate students鈥� proficiency in mathematics and their understanding of key concepts [31]. The TIMSS test has been widely adopted and accepted as a reliable measure for assessing academic performance. Numerous studies have utilized the TIMSS test to evaluate students鈥� mathematical abilities [30, 32, 33]. Its extensive usage and acceptance underscore its credibility and validity in educational research [34, 35] and several studies have demonstrated the high reliability of the mathematics test within the TIMSS framework [36, 37]. To maintain consistency, the duration of the math test was strictly controlled to 25听min by the enumerators in the sample classes. Mathematics was chosen for testing to reduce the effect of home learning on performance and to better focus on classroom learning [33].

Mental Health Test (MHT) comprises 100 yes/no questions, including 10 reliability questions designed to assess whether the students answered honestly. If a student answered 鈥測es鈥� to more than 7 of these reliability questions, their test results were considered invalid. In this study, a total of 544 students answered 鈥測es鈥� to more than 7 reliability questions, and the results from these students were excluded in the final analysis. In fact, these samples were removed during the initial data cleaning phase and thus are not part of the analysis sample presented in this paper. This approach is in line with standard procedures for mental health assessments and helps to enhance the accuracy of the research findings [35, 38,39,40]. The remaining 90 questions contribute to the student鈥檚 MHT score, where a lower score indicates a lower risk for mental health problems. A total score of 65 or higher suggests a high risk for mental health issues, signaling the need for professional intervention [35].

The test results are further categorized into eight subcategories, each representing a specific aspect of anxiety: learning anxiety, personal anxiety, loneliness, self-blaming tendency, sensitivity tendency, body anxiety, phobia anxiety, and impulsiveness. A score exceeding 7 on any subsection indicates clinical anxiety. If a student scores 8 or higher on any subsection, it suggests the necessity for assessment and potential treatment by a clinical psychologist. 鈥淟earning anxiety鈥� was selected for robustness checking due to its significant impact on hindrances to mental well-being associated with education [41].

The MHT, a variant of the Children鈥檚 Manifest Anxiety Scale (CMAS), has been established in the literature as a reliable measure of general anxiety. Developed by Professor Zhou Bucheng of East China Normal University [42], this test scale has been extensively utilized across China to assess the mental health of grade school students [41,42,43,44,45]. The reliability of the total scale (Cronbach鈥檚 Alpha) is 0.9, with each subscale ranging from 0.84 to 0.88. Moreover, the retest reliability of both the total scale and subscales ranges from 0.78 to 0.86 [20], indicating the stability of MHT measurements over time.

Statistics analysis

Descriptive statistics were used to describe the sample. To estimate the relationship between students鈥� commuting time and academic performance / mental health, we use the adjusted ordinary least squares (OLS) regression model with covariates. The basic specification of the adjusted model is as follows:

$${Y_i} = \alpha \, + \,\beta C{T_i}\, + \,\gamma {X_i}\, + \,{ \in _i}$$

(1)

Where $Y$ is a standardized continuous indicator for the academic performance or mental health of participant $\text{i}$. ${CT}_{I}$ is a variable that measures the one-way trip commuting time of the student from home to school. The coefficient $\beta$ represents the effect of commuting time on the outcome $Y$. The vector ${X}_{i}$ is a vector of control variables, including standardized math score and MHT score, age, gender, commuting tool, household wealth, family spending on education, types of household registration, whether there are brothers and sisters, whether both parents migrated for work, whether parents supervise homework, parental education level, and whether parents do farm work. ${\epsilon}_{i}$ is a random error term. Here, $\text{i}$ represent each of the observations.

Our empirical objective is to disentangle the specific influence of commuting time to school on students鈥� academic performance and mental health. This task presents challenges due to the presence of unobservable variables that could potentially impact both commuting time and the outcomes of academic performance and mental health. Commuting time is an endogenous variable, and it is critical to address its endophytism to generate unbiased and consistent estimates of the impact of commuting time on academic performance and mental health.

Based on the literature [25, 27, 46], our approach is to use IV estimation. It is important to discuss how we choose the IV. The IV approach necessitates the utilization of variables that impact commuting time but have no direct influence on academic performance and mental health, taking into account the control variables included in the analysis. In this study, we employ two instruments for commuting time: square kilometers per inhabitant in the county and the proportion of the population residing in urban areas, as Falch et al. (2013) suggested. Our rationale for selecting these instruments lies in the fact that the number of primary schools in a district is contingent upon the settlement pattern of the county. It is unlikely for schools to be established in districts with dispersed populations. Nonetheless, we acknowledge potential concerns regarding the validity of our county-based IV approach within the context of rural China. One concern is the possibility that the population density of the county in which elementary school students attend may be associated with factors that influence academic performance and student mental health. However, by incorporating parental education and household wealth as covariates in the model, we aim to mitigate this potential issue. Data on county-level square kilometers per inhabitant and share of the population living in urban areas were collected from the 2012 China National Statistical Yearbooks, as shown in Table听1.

We employed a mediation analysis approach to examine potential mechanisms through which commuting time influences academic performance and mental health. This method explores the impact of commuting time on mediating variables such as sleep time, outdoor activity time, and post-school study time, which subsequently affect academic performance and mental health. The path diagram illustrating the possible mechanisms is depicted in Fig.听2. Following the same regression model as Eq.听(1), where ${CT}_{I}$ represents commuting time and $Y$ represents the respective mediating variable (i.e., time allocation variables, including sleep time, outdoor activity time, and post-school study time), other control variables remain consistent.

Fig. 2

The path diagram of potential mechanisms

To maintain consistency and ensure the reliability of our analysis, all of our robustness analyses and examinations of heterogeneity are conducted using the same instrumental variable (IV) estimations. All analysis are performed using Stata17 (Stata Corp., Texas, USA).

Results

Background characteristics

Table听1 presents descriptive statistics of the standardized math test results, mental health test outcomes, and background characteristics of the respondents. The standardized math test results (TIMSS) are normalized into scores approximating a mean of zero and a variation of one, with the mental health test outcomes serving as dependent variables.

The average travel time for commuting to school is 0.27听h (16听min), with a standard deviation of 0.23听h. The sample comprises an equal number of males and females, with an average age of 10.49 +/- 0.97 years among the 12,394 respondents. Majority of students (93%) commute on foot, 2% on bikes, and only 4% utilize motorized or other forms of transportation (cars, motorcycles, etc.).

Parental and family information is presented in Table听1, including expenditure on respondents鈥� education, rural residency status (95%), presence of siblings (91%), and the proportion of left-behind children (13%, a left-behind child is defined as one whose parents have migrated for work [47]). Furthermore, 62% of respondents received parental homework assistance. Approximately 14% had fathers with at least a high school diploma, compared to only 8% with mothers at the same educational level. Regarding parental occupation, 87% of fathers and 86% of mothers engaged in farm work.

County characteristics, such as the percentage of urban population and square kilometers per resident, are also displayed. The average square kilometer per resident is 61.12, with a standard deviation of 49.95. Additionally, 41% of the county鈥檚 population resides in urban areas, reflecting the average percentage.

Table听1 further includes mediator features as continuous variables, comprising sleep duration (mean of 9.03听h), after-school outdoor activity time (mean of 0.39听h or 23听min), and extracurricular study time (mean of 0.88听h or 53听min). These factors exhibit considerable variation, as indicated in Table听1.

Table 1 Summary statistics of student individual and family characteristics

OLS regression results

The relationship between commute duration and academic performance (Columns 1 and 2) and mental health (Columns 3 and 4) was assessed using multiple linear regressions, as presented in Table听2. This analysis employed adjusted (Eq.听(1)) models, considering individual and family characteristics.

Our findings suggest that prolonged commutes adversely impact both academic performance and mental health. Estimates from the adjusted model (Columns 1 and 3) indicate that each additional hour of commuting time correlates with a 0.143 standard deviation decrease in academic performance and a 2.50 standard deviation increase in mental health test scores, signifying a heightened risk of mental health issues. These results exhibit statistical significance at the 1% level.

Moreover, our analysis reveals that baseline standardized math scores from the previous semester strongly predict academic performance during the assessment period. Specifically, for every standard deviation increase in baseline scores, math performance improves by 0.553 standard deviations. Additionally, younger students, male students, those commuting by car, and students from households with middle-tier wealth demonstrate better academic performance. Furthermore, students whose fathers hold a high school degree or higher, and those receiving parental homework supervision, exhibit enhanced academic performance. Conversely, being a left-behind child and having mothers engaged in farm work are associated with lower academic achievement.

In Column 3 of Table听2, among the controlled variables, students with higher baseline math scores, female students, younger students, those commuting by car, and students with mothers having at least a high school education report better mental health status. Conversely, urban hukou status, being a left-behind child, and having parents supervise homework are negatively correlated with children鈥檚 mental health.

Table 2 Multiple linear regression model for the effect of commute time on school performance and mental health

IV-estimates

In this section, we delve into the instrumental variable (IV) estimation approach to address potential endogeneity concerns associated with commuting time. Table听3 presents the IV results pertaining to both academic success and mental health. Column (1) of Table听3 illustrates the first-stage equation for commuting time, providing insights into the relationship between instrumental variables and the endogenous variable. Subsequently, column (2) showcases the IV estimates for academic success, while columns (3) and (4) present IV estimates for mental health.

Our preliminary findings underscore the insignificance of weak IV concerns, bolstering the validity of our instrumental variables. With an F-value of 46.129 and 45.688, our instruments demonstrate a substantial impact on commuting time. Notably, the estimates in column (2) and column (4) surpass those obtained from multiple linear regression (-1.433 vs. -0.143; 15.838 vs. 2.500), signifying a more pronounced effect of commuting time on academic success and mental health. Furthermore, these estimates exhibit statistical significance at the 1% level. Additionally, the outcomes of the over-identification tests suggest that we cannot refute the validity of our instruments, as they fall substantially below critical values, reaffirming the robustness of our IV estimation strategy.

Expanding upon the results, our IV estimation approach provides nuanced insights into the relationship between commute time and academic performance, as well as mental health, within the context of rural Chinese students. By addressing potential endogeneity concerns inherent in commute duration, we ensure the integrity and reliability of our findings. These results underscore the significant impact of prolonged commutes on both academic success and mental well-being.

Table 3 Instrumental variable estimates

Robustness check

In order to validate the robustness of our model results, which have thus far demonstrated a significant negative impact of commuting time on children鈥檚 academic performance and mental health status, we conduct additional analyses. These robustness assessments involve examining subgroup variations and alternative dependent variables. The outcomes of these tests are presented in Table听4.

Three robustness assessments are carried out to explore the relationships between commuting time, academic performance, and mental health. Firstly, we analyze the subgroup of students who commute to school by walking. Secondly, we investigate the relationships among students鈥� academic performance and mental health within the commuting time range spanning from the first to the ninth deciles. Furthermore, we assess robustness by substituting mental health test (MHT) scores with learning anxiety, recognizing its significance as a component of mental health and a crucial indicator of students鈥� psychological well-being in academic settings [48].

Table听4 demonstrates that among students who walk to school, every hour increase in commuting time corresponds to a decrease of 1.483 standard deviations in math scores, aligning with findings from the overall sample analysis. Additionally, the impact of commuting time on academic performance within the subgroup whose commuting time falls between the first and ninth deciles slightly differs from the earlier findings observed across the entire sample. Despite this variation (with increased coefficients), these results still validate the robustness of the observed relationship in the overall sample, underscoring the consistency and reliability of our analytical outcomes across different subsets.

Moreover, the results in column (3) indicate that when learning anxiety is used as the dependent variable, the coefficient exhibits similar estimates to those obtained when MHT scores are utilized, albeit slightly increased, and remains statistically significant at the 1% level. These findings collectively affirm the robustness of the empirical analysis conducted in this study.

Table 4 Robustness checks the impact of commuting time on school performance and mental health

Mechanisms analysis

To delve deeper into the potential pathways through which commuting time influences students鈥� academic performance and mental health, we conducted additional regressions and tests. Recognizing that students鈥� time allocation is a crucial factor in the educational process [49], we investigated how commuting time may affect activities associated with improved academic performance and mental health, such as studying, physical activities, and sleep [50,51,52]. Specifically, our analysis focused on three channels through which commuting time misappropriates students鈥� time: time spent sleeping, time spent on outdoor activities, and time spent studying after school. The results are presented in Table听5.

Firstly, we examined the impact of commuting time on sleep duration. In the survey, students were asked about the average time spent throughout the day on sleeping. As shown in column (1) of Table听5, the findings indicate a significant negative effect of commuting time on students鈥� sleep duration, with sleep time decreasing by 0.588听h for every additional hour of commuting. This result aligns with recent health sciences literature [53, 54], suggesting a plausible mechanism through which commuting time impacts students鈥� well-being.

The second potential channel explored is the time spent on outdoor activities after school. Prolonged commuting time may curtail children鈥檚 opportunities for outdoor activities, which are vital for their physical and mental development [55, 56]. The estimation results, presented in column (2) of Table听5, affirm a detrimental and statistically significant impact of commuting time on students鈥� engagement in after-school outdoor activities. This implies that commuting time diminishes opportunities for outdoor activities among school-age children, potentially hindering their physical and mental development [57].

Lastly, we investigated the impact of commuting time on after-school study sessions, as outlined in column (3) of Table听5. Our analysis reveals a negative but statistically negligible impact of commuting time on students鈥� after-school study time, suggesting that this channel is unlikely to be the primary mechanism through which commuting time affects studying time.

These findings shed light on the intricate pathways through which commuting time influences students鈥� daily routines and well-being, underscoring the multifaceted nature of the relationship between commute duration, academic performance, and mental health.

These findings indicate that commuting time significantly diminishes students鈥� sleep duration and reduces their engagement in outdoor activities after school. These findings suggest potential mechanisms through which commuting time adversely affects students鈥� mental health and academic performance. Inadequate sleep has been linked to decreased cognitive function, attention deficits, and impaired academic performance [53, 54]. Similarly, limited opportunities for outdoor activities may deprive students of essential physical exercise and social interactions, which are crucial for their overall well-being and academic success [55,56,57].

Moreover, while commuting time appears to have a negligible impact on after-school study sessions, it is essential to consider the quality and effectiveness of study time rather than solely focusing on its duration. Further investigation into the specific activities undertaken during study sessions and their effectiveness in promoting academic success could provide valuable insights into the nuanced relationship between commuting time, time allocation, and academic performance.

In summary, our analysis underscores the complex relationship between commuting time, time allocation, and academic performance. These findings emphasize the importance of understanding how commuting challenges may impact students鈥� time management and subsequently affect their academic outcomes. Efforts to support students鈥� well-being and academic success should consider interventions aimed at promoting healthy time management practices, as well as providing opportunities for physical activity and social interaction.

Table 5 Effects of commuting time on student time allocation

Effect heterogeneity

Table听6 presents the effects of commuting time on academic performance and mental health across different demographic groups, including gender, grade level, household wealth, left-behind status, and parents鈥� education. The results are delineated in Panels A and B of the tables, respectively.

To explore potential variations in the impact of commuting time, separate estimations were conducted for boys and girls. Table听6 showcases these results. Notably, columns (1) and (2) reveal a significant negative effect of commuting time for girls, which may be due to the fact that they experience a greater reduction in outdoor time, which may influence their academic performance and mental health [58,59,60]. Gender disparities in sleep patterns during adolescence may also contribute to differences in outdoor time, underscoring the complex interplay between commuting time, time allocation, and gender-specific outcomes [61].

Additionally, we examine whether the effect of commuting time varies across grade levels. Columns (3) and (4) of Table听6 split the sample by grade, revealing significant impacts on academic performance and mental health, particularly among 5th graders. This may be attributed to the heavier academic workload and increased pressure associated with advancing to higher grades, potentially resulting in shorter sleep and outdoor activity times [60, 62].

Furthermore, columns (5)-(6) in Table听6 disaggregate the sample by household wealth, indicating a substantial impact of commuting time across different socioeconomic backgrounds. However, no significant differences or heterogeneity in the effect of commuting time were observed at the household wealth level.

Regarding the effects of parental migration on children鈥檚 academic performance and mental health, our findings in columns (7) and (8) of Table听6 suggest a larger impact of commuting time on the academic performance of left-behind children. This underscores the need for further investigation into the specific mechanisms through which parental migration and commuting time interact to influence academic outcomes and mental health among left-behind children.

Lastly, we explore whether the impact of commuting time varies based on parental education levels. Columns (9) to (10) of Table听6 reveal that children with lower parental education levels experience a stronger negative impact on their academic performance and mental health from long commutes. This may be because Higher levels of knowledge drive parents to take better care of their children, and students are likelier to have high-quality sleep and outdoor activities [63, 64]. This underscores the role of parental education in shaping students鈥� well-being and academic success. However, the lack of significance in the effect of commuting time on academic performance among students with highly educated parents may be influenced by the small sample size in this category.

Table 6 Heterogeneity analysis: gender dimension, grade dimension, household wealth, left-behind child, and parental education dimension

Discussion

This study used data from a distinct randomized trial conducted in rural areas of Tianshui Prefecture in Gansu Province and Yulin Prefecture in Shaanxi Province, Northwest China. Our comprehensive analysis explored the association between commuting time and children鈥檚 academic performance and mental health status. Our findings reveal a significant negative correlation between commuting time and academic performance and mental health status among school-age children, consistent with numerous prior research studies [16, 21, 27, 46, 54, 65, 66].

We employed various identification strategies, including multiple linear regressions and instrumental variable (IV) models, to examine the robustness of our results. Despite the diversity observed in the findings of IV models compared to multiple linear regression, our estimates remained consistent across different model assumptions and subsample sizes. Notably, we found that the negative relationship of commuting time on academic performance was more pronounced among students from affluent families. Furthermore, left-behind children experienced a more significant negative effect on academic performance but a less negative effect on mental health status compared to non-left-behind children.

Our investigation also incorporates a mechanical analysis to offer deeper insights into the relationship between commuting time and academic performance. We have observed that this association is partially mediated by variations in daily time allocation, particularly concerning sleep duration and engagement in after-school outdoor activities. This approach is grounded in Time Utilization Theory, which suggests that how individuals allocate time across activities significantly impacts their overall well-being and productivity. Our findings align with studies conducted in the United States by Talen (2001) and Voulgaris (2019), revealing a negative correlation between commuting time, sleep duration, and outdoor activity participation. In line with Time Utilization Theory, longer commutes reduce the time available for activities, such as sleep and outdoor engagement, which can, in turn, affect students鈥� mental health and academic outcomes. Previous research [67,68,69,70,71] has consistently linked adequate sleep duration and outdoor activities to increased happiness. Sufficient sleep is crucial for regulating mental health and fostering well-being [72,73,74]. Moreover, exposure to outdoor environments holds significant potential for protecting children鈥檚 mental health [55, 56, 75].

Using a sizable dataset of 19,934 children gathered from 249 rural primary schools in two northwest Chinese provinces (Gansu and Shaanxi), our study fills a gap in the existing literature. To the best of our knowledge, few studies have ventured beyond the boundaries of well-being domains related to commuting, with even fewer focusing on the student鈥檚 academic performance, which holds significant implications for long-term development [66]. By examining the effects of commuting time on both academic achievement and mental health, our study contributes to recent scholarly efforts in this area.

Despite our commitment to addressing potential confounding effects, our study possesses inherent limitations. Notably, the data were collected in 2012, rendering them relatively old. However, it is essential to emphasize that despite the age of the data, they remain valuable for characterizing the relationship between commuting time, academic performance, and mental health. While more recent data would be ideal, the fundamental dynamics of commuting and its impact on student outcomes will likely persist over time, suggesting that our findings still hold relevance and contribute to understanding this critical issue. Additionally, the exclusive focus on rural northwest China restricts the external validity of our findings to other demographic contexts. These limitations underscore the need for cautious interpretation of our results and suggest avenues for future research to explore our findings鈥� temporal and geographical generalizability.

Nevertheless, despite these limitations, our study aims to provide valuable insights that can inform policy-making and decision-making processes. The observed negative effects of prolonged student commuting times on academic performance and mental health have significant implications for governmental policies. Furthermore, the relevance of our study extends beyond rural China, highlighting the importance of pursuing 鈥渟chool district鈥� reforms in urban areas to address the unequal distribution of high-quality compulsory education resources. These policy implications underscore the urgency of addressing the challenges posed by lengthy student commuting times to ensure equitable access to education and promote the well-being of school-age children.

In addition to the findings presented future research may focus on designing intervention strategies to address the negative effects of lengthy commuting times on students in specific regions. These strategies, such as optimizing public transportation systems and providing school bus services, could be evaluated for their effectiveness in improving student well-being and academic performance. Furthermore, while existing research highlights the adverse effects of commuting, further investigation into policies decentralizing school layouts is warranted. Large-scale studies with updated data and diverse student populations, coupled with rigorous causal inference and cost-benefit analyses, would provide valuable insights into the implications of such policies on student outcomes.

Conclusion

Based on data from 19,934 children in 249 rural primary schools in Shaanxi Province, Northwest China, we found a significant negative correlation between commuting time and children鈥檚 academic performance and mental health. This was consistent across various analytical approaches and highlighted the pronounced impact on academic performance among affluent families and left-behind children. Mechanistically, variations in daily time allocation, such as sleep duration and outdoor activities, partially mediated this relationship. These findings emphasize the importance of policies aimed at reducing student commuting times, particularly in developing nations, to ensure equitable access to education and promote the well-being of school-age children.