Intergenerational Caregiving and Adolescent Depression in China: Mechanisms and Risk Factors

Intergenerational Caregiving and Adolescent Depression in China: Mechanisms and Risk Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intergenerational Caregiving and Adolescent Depression in China: Mechanisms and Risk Factors Shuangshuang Lu, Haibin Wei, Wenneng Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6887674/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective:This study aims to investigate the impact of intergenerational caregiving on adolescent mental health and its underlying mechanisms,providing empirical evidence for developing intervention strategies to improve adolescent mental health. Methods: Based on data from the China Family Panel Studies (CFPS) spanning 2010-2020, this study employed individual fixed-effects models to examine the impact of intergenerational caregiving on adolescent mental health (measured by depression risk). Additionally, Probit and Logit models were used for robustness checks. Results: The study found that intergenerational caregiving significantly increases adolescent depression risk (β=0.086, p<0.05). Heterogeneity analysis revealed that intergenerational caregiving has more pronounced effects on depression risk among female adolescents (β=0.076, p<0.05), younger adolescents (β=0.139, p<0.05), and those with rural household registration (β=0.194, p<0.05). Among adolescents with lower frequency of parent-child communication, the negative impact of intergenerational caregiving was also stronger (β=0.084, p<0.05). Mechanism analysis revealed that declining academic performance (β=-0.241, p<0.05) and reduced frequency of parent-child communication (specific indicators β=-0.1319, -0.0228, respectively, p<0.05) serve as important mediating pathways through which intergenerational caregiving increases adolescent depression risk. Conclusion: Intergenerational caregiving constitutes a significant risk factor for adolescent depression. This study not only deepens theoretical understanding of the intrinsic mechanisms through which intergenerational caregiving affects adolescent mental health but also provides evidence for identifying intergenerational caregiving families as priority groups for mental health policies and interventions, contributing to enhanced policy precision and effectiveness. Intergenerational caregiving adolescents mental health impact mechanisms 1. Introduction Adolescent mental health is a crucial component of human capital development, with significant implications for individual economic well-being, national productivity, and long-term economic growth (Heckman, 2006 ), a concern also reflected in national policies like the China Children's Development Program (2021–2030) (State Council of the People’s Republic of China, 2021). Currently, child and adolescent mental health problems constitute a major global public health challenge (WHO, 2021; Kessler et al., 2007). In China, rapid economic growth and significant social transformations, notably large-scale internal migration (Chan, 2010 ), have led to a rise in intergenerational caregiving, where grandparents frequently assume primary child-rearing duties. Simultaneously, the long-term effects of the one-child policy have intensified the caregiving burden on adult children, indirectly increasing the prevalence of intergenerational caregiving (Short et al., 2001 ). Intergenerational caregiving has garnered considerable academic attention. Early research primarily focused on grandparents’ health and well-being (Hayslip & Kaminski, 2005 ; Hughes et al., 2007 ) or parents’ (especially women’s) labor market participation and fertility intentions (Arpino et al., 2014 ; Hank & Buber, 2009 ). Although research on the developmental impacts on children and adolescents has been increasing, existing literature presents complex and sometimes contradictory findings (Ruiz & Silverstein, 2007 ). Some studies suggest that intergenerational caregiving may have positive effects under certain conditions, such as promoting family ethical values or providing emotional support, particularly in contexts of parental absence (Chen & Liu, 2012 ). However, more research reveals potential negative effects, including ambiguous family roles (Goodman & Silverstein, 2006), distant parent-child relationships (Wen & Lin, 2012 ), and intergenerational educational philosophy conflicts, all of which may adversely affect adolescent psychological development. Current research has started to explore mechanisms linking intergenerational caregiving to children's educational outcomes, considering factors like caregivers' educational expectations, the quality of care, and limitations related to grandparents' educational attainment and health (Zeng & Xie, 2014 ). Although existing research has laid a foundation for understanding the multidimensional impacts of intergenerational caregiving, disagreement remains regarding its net effects on adolescent development. Specifically concerning adolescent mental health, there is a lack of systematic empirical investigation and robust theoretical integration regarding the precise pathways and psychosocial mechanisms through which intergenerational caregiving exerts its influence. Therefore, this study investigates the impact of intergenerational caregiving on adolescent depression risk using large-sample longitudinal data. We further explore academic performance and parent-child communication as potential mediating channels, aiming to provide novel empirical evidence and theoretical insights into this complex relationship. Specifically, this study explores: (1) whether and to what extent intergenerational caregiving affects adolescent depression symptom levels; (2) whether academic performance and frequency of parent-child communication play mediating roles in this relationship; (3) whether the impact of intergenerational caregiving exhibits significant group heterogeneity (such as gender, age, household registration). This paper makes several key contributions. First, while prior research has explored intergenerational caregiving, many studies lack granular, individual-level longitudinal data necessary for robust empirical testing of its nuanced effects on adolescents in rapidly changing socioeconomic contexts like China. Using CFPS data from 2010–2020, we construct precise individual-level indicators of intergenerational caregiving for adolescents. This quantitative measurement provides a solid empirical foundation and facilitates more rigorous analysis in this research area. Second, we extend the literature by focusing on adolescent mental health, a critical but under-explored outcome of intergenerational caregiving, thereby testing the applicability of family and developmental theories in this specific domain. Furthermore, we investigate micro-level transmission mechanisms by integrating insights from attachment theory and family systems theory. We specifically examine “academic performance” and “parent-child communication frequency” as mediating psychosocial variables—which have rarely been systematically analyzed in conjunction—to elucidate their roles in the pathway from intergenerational caregiving to mental health, thus shedding light on the “black box” of these complex interactions. Additionally, this paper identifies heterogeneous effects across different subsamples (gender, age, household registration), helping clarify more vulnerable adolescent groups in intergenerational caregiving contexts, thereby enhancing the explanatory power and practical relevance of related theories. Third, this paper has important policy implications. With the normalization of population mobility and continuous changes in family structure, intergenerational caregiving has become a widespread phenomenon in China, and its potential risks to adolescent development cannot be ignored. This paper identifies significant impacts of intergenerational caregiving on adolescent depression risk and its mechanisms, providing theoretical foundation and empirical support for developing more targeted policy interventions. Based on empirical testing of micro-transmission mechanisms, it provides effective empirical evidence support for policymakers. Meanwhile, high-risk groups identified (such as adolescents of specific genders, age groups, or household registration backgrounds) can be prioritized for inclusion in public health services and mental health intervention focus groups, achieving optimal resource allocation and maximized policy intervention effectiveness. 2. Theoretical Framework and Research Hypotheses This study primarily constructs an analytical framework based on Attachment Theory and Family Systems Theory to explain the potential mechanisms through which intergenerational caregiving affects adolescent mental health. Attachment theory, proposed by John Bowlby, emphasizes individuals' intrinsic need to establish emotional connections with primary caregivers to obtain security and protection. The quality of early attachment relationships has lasting impacts on individual mental health development, with the core mechanism being the construction of "Internal Working Models" (IWMs). IWMs are cognitive and emotional representational systems about self (such as self-worth) and others (such as whether others are reliable) that individuals form based on interactive experiences with attachment figures. Secure attachment helps form positive IWMs, promoting individuals' emotional regulation abilities and psychological resilience; while insecure attachment may lead to negative IWMs, increasing susceptibility to psychological problems. In intergenerational caregiving contexts, grandparents may become adolescents' primary attachment figures. If grandparents, owing to their own limitations (e.g., health status, traditional caregiving philosophies, or energy levels), are unable to provide consistent, sensitive, and responsive care, adolescents may develop insecure attachment patterns, consequently impacting their mental health. Additionally, intergenerational caregiving may be accompanied by reduced contact frequency with parents or decreased interaction quality, further weakening adolescents’ opportunities to obtain security and emotional support from parents. Based on the above analysis, this paper proposes the first hypothesis: H1: Intergenerational caregiving is positively correlated with higher levels of depression symptoms in adolescents. Family Systems Theory, particularly Murray Bowen’s perspective, views the family as an interconnected emotional unit where family members’ emotions and behaviors mutually influence each other, forming dynamic equilibrium. This theory emphasizes anxiety transmission within families, intergenerational boundaries, role functions, and the level of differentiation among individuals within the family system. When family systems encounter stress or changes (such as intergenerational caregiving due to parental absence), existing balance may be disrupted. Intergenerational caregiving may lead to family role redistribution, changes in interaction patterns between parent-child and grandparent-grandchild subsystems, and may even trigger intergenerational caregiving philosophy conflicts (Goodman & Silverstein, 2006; Hayslip et al., 2017). These changes may generate chronic anxiety within families and transmit to adolescents through mechanisms such as triangular relationships, making them “symptom bearers” of family stress. Poor family interaction patterns (such as poor communication, lack of emotional support) (Sheeber et al., 2001) and role conflicts may damage adolescents’ self-efficacy and coping abilities, increasing their depression risk. Academic performance is an important stressor and source of achievement for adolescents during development (Eccles & Roeser, 2011 ). Intergenerational caregiving may indirectly affect adolescents’ academic engagement and achievement due to grandparents’ insufficient tutoring abilities, educational expectation differences, or inadequate family environment support for learning, while academic setbacks are common risk factors for adolescent depression (Fröjd et al., 2008 ; Steinberg, 2001 ). Therefore, based on the above analysis, this paper proposes the second hypothesis: H2a: Declining academic performance mediates the relationship between intergenerational caregiving and adolescent depression symptom levels. Communication with parents is an important pathway for adolescents to obtain emotional support, establish identity, and solve problems (Barnes & Olson, 1985 ; Laursen & Collins, 2009 ). Intergenerational caregiving may lead to reduced direct communication opportunities between adolescents and parents, or decreased communication quality due to physical separation and emotional distance (Jordan & Graham, 2012 ). Lack of effective parent-child communication may make adolescents feel neglected or misunderstood, thereby increasing their loneliness and depressive emotions (Allen et al., 2006). H2b: Reduced frequency of parent-child communication impacts on the relationship between intergenerational caregiving and adolescent depression symptom levels. Furthermore, this paper will deeply examine potential heterogeneous manifestations of these impacts across different sample groups (such as gender, age groups, urban-rural household registration) (Conger & Donnellan, 2007 ). 3. Data and Methods 3.1.Data Sources The data used in this paper comes from the China Family Panel Studies (CFPS). Specifically, this paper selected variables from child questionnaires and family economic questionnaires from 2010 to 2020 for family-level control and explanatory variables. The data processing procedure was as follows: (1) retained samples aged 6–18 years; (2) removed samples with severe missing data; (3) to eliminate the influence of outliers, all continuous variables were winsorized at the 1% and 99% percentile levels. It is particularly noteworthy that this data survey was approved by the Biomedical Ethics Committee of Peking University (approval number: IRB00001052-14010) and conducted with research subjects signing informed consent forms. 3.2 Variable Construction 3.2.1. Independent Variable: Intergenerational Caregiving (IC) Intergenerational caregiving can be divided into narrow and broad definitions. Narrow intergenerational caregiving refers to adult parents completely abandoning child-rearing responsibilities, with grandparents assuming “full caregiving responsibility”; broad intergenerational caregiving refers to grandparents participating in the upbringing and education of the third generation, assuming “partial caregiving responsibility.” This paper follows the approach of Lu Hongyou et al., based on the question “Who takes care of the child at night” in the CFPS child self-report questionnaire, which has 7 options: child’s father, child’s mother, child’s maternal grandparents, child’s paternal grandparents, self-care, nanny, daycare/kindergarten/preschool. Based on this questionnaire item, we construct a dummy variable for intergenerational caregiving. When a child is cared for by grandparents or other elderly relatives, the variable is assigned a value of 1, otherwise 0. 3.2.2 Dependent Variable: Adolescent Mental Health Status (AMHS) The dependent variable of mental health status in this paper is constructed as follows: using the depression scale from the child self-report questionnaire, where scales in different periods contain 8 or 20 questions, comprehensively scoring based on annual conditions, with samples scoring above the sample median assigned a value of 1, otherwise 0. Existing research shows that the CES-D scale has good reliability and validity with a Cronbach α value of 0.809. 3.2.3 Control Variables Following the existing literature, we select control variables from two dimensions: regional characteristics and household characteristics. The regional characteristic variables are as follows: (1) Regional economic development level (GDP), measured by GDP per capita at the regional level; (2) Local government expenditure level (LGE), measured by the natural logarithm of regional government fiscal expenditure. The household characteristic variables are as follows: (1) Number of household members (NHM), measured by the natural logarithm of the number of core household members; (2) Household size (HS), measured by the natural logarithm of the total number of household members; (3) Household expenditure (HE), measured by the natural logarithm of household expenditure level; (4) Education expenditure (EE), measured by the natural logarithm of household education expenditure; (5) Education, culture, and recreation expenditure (ECRE), measured by the natural logarithm of household expenditure on education, culture, and recreation; (6) Household income (HI), measured by the natural logarithm of household income. 3.3. Empirical Model Construction To accurately identify the impact of intergenerational caregiving on adolescent mental health status, this paper establishes a two-way fixed effects model as follows: Y = β₀ + β₁ICₚ,ₜ + β₂Xₚ,ₜ + year + ε (1) In Eq. (1), the dependent variable Y represents the individual adolescent’s mental health status. IC is the main explanatory variable, indicating whether an adolescent experiences intergenerational caregiving, taking a value of 1 if present, otherwise 0. Xₚ,ₜ represents a series of control variables included in the baseline regression. Additionally, to further ensure accuracy and reliability of research results, this paper includes individual fixed effects (pid) for adolescents and clusters at the individual level. Since the explanatory variable does not vary by year, to avoid variable collinearity, this paper does not include time fixed effects. ε represents the error term, the part unexplained by the model. 4. Results 4.1. Descriptive Statistical Analysis Results Table 1 presents descriptive statistics for all variables employed in the baseline regression, following listwise deletion of observations with missing values. The depression status variable, coded 0–1, has a mean of 0.548 (standard deviation = 0.498) and a median of 1.000, implying that 54.8% of the sample individuals report depression symptoms. The primary explanatory variable, intergenerational caregiving (a binary indicator), has a mean of 0.251 (standard deviation = 0.433) and a median of 0.000, indicating that 25.1% of the sample individuals are in intergenerational caregiving arrangements. Table 1 Descriptive statistical Variables Obs MEAN SD P50 P25 P75 MIN MAX AMHS 24809 0.548 0.498 1.000 0.000 1.000 0.000 1.000 IC 24809 0.251 0.433 0.000 0.000 1.000 0.000 1.000 HI 23171 10.200 1.218 10.396 9.616 11.010 5.890 12.409 NHM 24809 4.756 1.728 5.000 4.000 6.000 1.000 10.000 HS 24809 3.007 1.320 3.000 2.000 4.000 1.000 7.000 GDP 24809 10.522 0.448 10.491 10.218 10.785 9.464 11.707 HE 23358 10.539 0.859 10.532 9.954 11.092 8.468 12.725 EE 24558 5.916 3.484 7.314 4.615 8.517 0.000 10.309 ECRE 24289 6.408 3.244 7.601 5.707 8.613 0.000 10.556 LGE 24809 0.200 0.0700 0.185 0.154 0.222 0.109 0.376 Source from: CFPS (2010,2012,2014,2016,2018,2020). 4.2 Regression Results Table 2 reports the baseline regression results. Column (1) presents a bivariate regression without control variables or fixed effects. Column (2) incorporates individual fixed effects, and Column (3) further includes a full set of control variables. The results in columns (1) and (2) show a statistically significant positive association between intergenerational caregiving and adolescent depression risk. The estimated coefficients are 0.046 and 0.081, respectively, both significant at the 1% level. These initial findings suggest that intergenerational caregiving is associated with an increase in adolescent mental health distress. Upon inclusion of covariates capturing regional and family characteristics (Column 3), the coefficient on intergenerational caregiving remains positive and statistically significant at the 1% level, lending further support to this association. Table 2 Baseline regression results Variables (1) (2) (3) AMHS AMHS AMHS IC 0.046*** 0.081*** 0.086*** (0.007) (0.016) (0.017) HI 0.049*** (0.005) NHM -0.043*** (0.006) HS -0.002 (0.007) GDP -0.115*** (0.024) HE -0.022*** (0.008) EE 0.003 (0.003) ECRE 0.002 (0.004) LGE 4.997*** (0.311) Constant 0.536*** 0.503*** 0.639*** (0.003) (0.004) (0.214) Individual effect No Yes Yes Observations 24,809 20,39 17,535 R-squared 0.002 0.203 0.256 Note: Table reports the baseline regression results based on the Two-Way Fixed Effects (TWFE) model.The key explanatory variable is Intergenerational Care (IC), and the dependent variable is Adolescent Mental Health Status (AMHS). A series of household-level control variables are included in the regression. To mitigate potential bias from omitted variables, individual fixed effects is also controlled. Standard errors are clustered at the individual level and reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 4.3 Robustness Checks 4.3.1 Alternative Baseline Models To assess the robustness of our baseline findings, particularly given the binary nature of the dependent variable (depression status), we re-estimate the model using Probit and Logit specifications. These two models are more commonly used in addressing binary choice problems and differ from OLS in their assumptions about the error term distribution, thus allowing us to test whether our conclusions are sensitive to specific model assumptions. As shown in Table 3 , columns (1) and (2) present Probit model regression results, where (2) includes control variables, with the key explanatory variable coefficient of 0.112, which is positive and statistically significant, confirming estimation robustness. Columns (3) and (4) show Logit model regression results, also displaying significance, further verifying result reliability. Table 3 Robust checks: changing baseline model Variables (1) (2) (3) (4) AMHS AMHS AMHS AMHS IC 0.116*** 0.112*** 0.185*** 0.181*** (0.019) (0.020) (0.030) (0.032) HI 0.060*** 0.097*** (0.008) (0.013) NHM -0.028*** -0.044*** (0.007) (0.012) HS -0.027*** -0.045*** (0.010) (0.016) GDP 0.145*** 0.227*** (0.025) (0.040) HE -0.042*** -0.068*** (0.012) (0.020) EE 0.013** 0.021** (0.006) (0.009) ECRE 0.000 0.001 (0.006) (0.010) LGE 1.543*** 2.448*** (0.147) (0.235) Constant 0.091*** -1.716*** 0.145*** -2.690*** (0.009) (0.263) (0.015) (0.419) Observations 24,809 22,070 24,809 22,070 Note: This table reports the results of robustness checks using alternative baseline models. Specifically, we replace the original model with Probit and Logit specifications to re-estimate. Standard errors are clustered at the individual level and reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 4.3.2 Alternative Model Clustering Levels In this study, to ensure the robustness of our estimates regarding the relationship between intergenerational caregiving and adolescent depression risk, we consider incorporating standard error clustering at the time and regional levels separately, as well as implementing two-way clustering simultaneously at both the regional and time dimensions. The rationale for this approach is that unobserved factors affecting adolescent depression risk may exhibit correlation both within geographic regions (for example, adolescents within a community or county may share similar socioeconomic environments, educational resources, or local stressful events) and at specific time points (such as common shocks or major social events faced nationally or regionally in a particular year). By adjusting the clustering levels in our baseline regressions, we obtain more reliable standard error estimates, thereby enabling more accurate assessment of the statistical significance of the intergenerational caregiving effect. Consequently, this enhances the rigor of our research conclusions. Table 4 reports estimates using clustering at a higher level. The results indicate that the positive and statistically significant association between intergenerational caregiving and depression status persists under these alternative clustering methods. This suggests our main conclusions are robust to different assumptions about the error structure. Table 4 Robust checks: employing fixed effects interaction term and a higher cluster level Variables (1) (2) (3) AMHS AMHS AMHS IC 0.086*** 0.086*** 0.086* (0.017) (0.031) (0.039) HI 0.049*** 0.049*** 0.049* (0.005) (0.010) (0.023) NHM -0.043*** -0.043*** -0.043 (0.006) (0.014) (0.048) HS -0.002 -0.002 -0.002 (0.007) (0.020) (0.068) GDP -0.115*** -0.115 -0.115 (0.026) (0.123) (0.693) HE -0.022*** -0.022 -0.022 (0.008) (0.016) (0.021) EE 0.003 0.003 0.003 (0.003) (0.006) (0.010) ECRE 0.002 0.002 0.002 (0.004) (0.006) (0.006) LGE 4.997*** 4.997* 4.997 (0.319) (2.469) (3.638) Constant 0.639*** 0.639 0.639 (0.238) (0.937) (7.955) Individual fixed effect YES YES YES Individual # Time Clustering YES NO NO Clustered by region NO YES YES Clustered by time NO NO YES Observations 17,535 17,535 17,535 R-squared 0.2557 0.2557 0.2557 Note: The table reports the results of robustness checks with alternative clustering levels. We separately incorporate individual-time clustering interactions, regional-level clustering, and two-way clustering at both time and regional levels simultaneously. Standard errors are clustered at the individual level and reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 4.4 Further Analysis We next explore potential heterogeneous associations between intergenerational caregiving and mental health across different adolescent subgroups. Specifically, we conduct subgroup analyses by splitting the sample based on gender, age, and urban-rural household registration (hukou) to examine variations in the estimated association. First, gender-based heterogeneity analysis shows that intergenerational caregiving’s impact on male adolescent depression risk (approximately 47.3% of the sample, coefficient = 0.064, p < 0.05) is significantly lower than that on female adolescents (approximately 52.7% of the sample, coefficient = 0.076, p < 0.05). This indicates that females have higher depression risk under intergenerational caregiving. Our results confirm that female adolescents indeed face higher depression risk in intergenerational caregiving environments. This may be related to societal expectations of female roles, women’s stronger empathetic capacity, greater social pressures experienced by women, or the possibility that they bear more invisible caregiving responsibilities within intergenerational households. Second, based on sample age median, the groups are divided into (1) older group (approximately 47.6%) and (2) younger group (approximately 52.4%). Regression results show that intergenerational caregiving has significant positive impact on depression risk for younger adolescents. This may be because younger adolescents have stronger emotional and daily dependence on their primary caregivers, making the adaptation process more difficult when the primary caregiver changes to grandparents. Additionally, urban-rural difference analysis reveals that rural household registration adolescents (81.4% of the sample) show higher depression risk in intergenerational caregiving environments, with significantly positive coefficients; while urban household registration adolescents’regression coefficients (18.6% of the sample) are not significant. The results show substantial differences between urban and rural contexts. This may reflect the reality that intergenerational caregiving families in rural areas face greater economic pressures, relatively weak social support networks, and lower accessibility to quality educational and medical resources. Finally, frequency of parent-child communication also affects how intergenerational caregiving differentially impacts mental health. Results show that in samples with lower communication frequency (approximately 50.5%), intergenerational caregiving’s impact on adolescent mental health is more severe. Therefore, these results may reflect differences in urban-rural environments, family structures, and social support networks. Table 5 Heterogeneity Analysis: The role of gender Variables (1) (2) AMHS Male sample Female sample IC 0.064*** 0.076** HI 0.051*** 0.032*** NHM -0.039*** -0.030*** HS -0.039** -0.030** GDP -0.252*** 0.239** HE -0.015 -0.023 EE 0.003 0.004 ECRE -0.003 0.000 LGE 3.668** 10.757*** Constant 2.266*** -4.044*** Individual fixed effect YES YES Observations 8,117 5,907 R-squared 0.286 0.393 Note: This table reports regression results concerning gender heterogeneity. We measure gender characteristics using a dummy variable that takes the value of 1 for males and 0 for females. Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Table 6 Heterogeneity Analysis: The role of age Variables (1) (2) AMHS Older group Younger group IC 0.074** 0.139*** HI 0.048*** 0.051*** NHM -0.059*** -0.005 HS 0.032*** -0.105*** GDP -0.106** -0.606*** HE -0.036*** -0.014 EE -0.003 0.009* ECRE 0.011** 0.002 LGE 4.905*** 3.385*** Individual fixed effect YES YES Constant 0.679 6.030*** Observations 8,001 7,499 R-squared 0.358 0.300 Note: This table reports regression results concerning age heterogeneity. We partition the sample into older and younger cohorts based on the median age of individuals. Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Table 7 Heterogeneity Analysis: urban-rural distribution Variables (1) (2) AMHS Rural sample Urban sample IC 0.047** 0.194*** HI 0.047*** 0.068*** NHM -0.030*** -0.102*** HS -0.016* 0.056*** GDP -0.058** -0.653*** HE -0.019** -0.017 EE -0.001 0.011* ECRE 0.005 -0.006 LGE 4.474*** 11.996*** Individual fixed effect YES YES Constant 0.110 4.854*** Observations 13,099 3,589 R-squared 0.264 0.278 Note: This table reports regression results concerning heterogeneity in the urban-rural distribution of the sample. We partition the sample into urban and rural subsamples based on individuals’ household registration status (hukou). Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Table 8 Heterogeneity Analysis: Frequency of Parental Communication Variables (1) (2) AMHS Frequent communication Infrequent communication IC 0.063** 0.084*** HI 0.040** 0.050*** NHM -0.026*** -0.021* HS -0.000 -0.051*** GDP 0.150*** -0.571*** HE -0.001 -0.034** EE 0.004 -0.002 ECRE -0.006 0.009 LGE 3.289*** 4.874*** Individual fixed effect YES YES Constant -1.920*** 5.567*** Observations 6,771 6,902 R-squared 0.427 0.278 Note: This table reports regression results concerning heterogeneity in communication frequency with parents. We partition the sample into high communication frequency and low communication frequency subsamples based on the frequency of communication with parents. Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 4.5 Mechanism Testing Following Jiang Ting (2022)’s suggestions for improving mediation effect testing, this study employs a two-step method to construct mediation effect models. Results show that intergenerational caregiving’s coefficient on academic performance is -0.129, statistically significant at the 5% level, indicating that intergenerational caregiving significantly affects academic performance and exacerbates depressive emotions through impact on student academic performance. Intergenerational caregiving is significantly negative for both heart-to-heart talks with parents and trust in parents, significant at the 1% level. Given this, intergenerational caregiving significantly reduces the foundation and performance of parent-child communication within families. Family systems theory suggests that poor academic performance and insufficient grandparent trust reflect family interaction imbalance, reducing children’s self-efficacy and increasing depression risk. Lack of parental support and encouragement forms negative feedback, further exacerbating depressive emotions. Table 9 Mediating Analysis: Communication Ability and Achievement Variables (1) (2) (3) AMHS Academic performance Intimate communication with parents Trust toward parents IC -0.129** -0.132*** -0.023*** HI 0.026* 0.011 -0.002 NHM -0.000 -0.039*** -0.002 HS 0.046** 0.004 0.003 GDP 2.827*** 0.060 0.000 HE 0.045* 0.044** -0.001 EE 0.000 -0.006 0.000 ECRE 0.006 0.019* 0.001 LGE 15.663*** 0.141 -0.037 Individual fixed effect YES YES YES Constant -31.207*** -0.449 2.350*** Observations 17,535 4,970 3,824 R-squared 0.738 0.013 0.005 Note: This table reports regression results for the mechanism analysis. We control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 5. Conclusion The findings of this study indicate that intergenerational caregiving is significantly associated with an increased risk of psychological depression among adolescents. Interpreted through the lens of family systems theory, this elevated depression risk may stem from mechanisms including weakened parent-child attachment, disrupted family communication, and heightened adolescent role confusion or functional conflict. This is consistent with research results from domestic scholars such as Chen Jianhua, further confirming the applicability of attachment theory and family systems theory in explaining adolescent mental health problems. Related theories indicate that lack of emotional exchange and family interaction imbalance not only weaken satisfaction of adolescent emotional needs but also reduce self-efficacy, significantly increasing depressive tendencies and affecting overall mental health levels. Further analysis found significant differences in depression risk faced by adolescents across different genders, ages, and urban-rural household registration backgrounds in intergenerational caregiving contexts. Specifically, female adolescents are more likely to exhibit depressive emotions compared to males, possibly related to their higher emotional dependency and sensitivity to caregiver behavioral patterns. Regarding age, younger adolescents, due to immature nervous system development, have relatively weak emotional regulation abilities. With limited life experience, they often lack effective coping strategies when facing academic pressure, family conflicts, and interpersonal setbacks, leading to significantly increased depression risk. Regarding urban-rural differences, rural household registration adolescents show significantly higher depression levels under intergenerational caregiving backgrounds compared to urban adolescents. Possible reasons include that rural families often have relatively insufficient educational resources and emotional support, while urban families, despite abundant resources, are more prone to intergenerational communication barriers and educational philosophy conflicts. Under multiple pressure accumulation, rural adolescents are more likely to develop depression symptoms, adversely affecting their mental health and individual development. Given intergenerational caregiving’s profound impact on adolescent mental health, coordinated intervention measures from family, school, community, and government levels are urgently needed. At the family level, interventions should aim to enhance grandparents’ caregiving capacities and emotional support awareness, improve family communication dynamics, and address the gender-specific mechanisms influencing mental health. At the school level, mental health education should be strengthened, psychological counseling systems improved, and family-school coordination mechanisms established to alleviate dual pressures on adolescents academically and emotionally. At the community level, support service networks for intergenerational caregiving families need improvement, providing platforms for psychological counseling and parent-child activities to promote adolescent social support system construction. At the government level, specific policies addressing intergenerational caregiving issues should be introduced, providing financial and institutional support, enhancing public awareness of intergenerational caregiving impacts, and promoting establishment of adolescent mental health monitoring and evaluation systems. Meanwhile, basic and applied research should be strengthened to provide scientific evidence for related policy formulation, promoting sustainable development of adolescent mental health initiatives. This study is subject to certain limitations. First, the complexity of factors influencing intergenerational caregiving and adolescent mental health means that unobserved variables related to family environment or broader social support systems may not be fully controlled for, potentially contributing to modest R² values in the models. While a lower R² indicates that a substantial portion of the variance in adolescent depression remains unexplained by the included variables, the primary focus of this study is on the consistent estimation of the ceteris paribus effect of intergenerational caregiving, for which the unbiasedness of the coefficient estimates is paramount, rather than overall predictive power. Future research should further enrich assessment dimensions to comprehensively present the overall impact of intergenerational caregiving on adolescent mental health. Declarations Author Contribution Conceptualization, Lu Shuangshuang and Jiang Wenneng;Methodology, Lu Shuangshuang and Wei Haibin; Software, Lu Shuangshuang and Wei Haibin; Formal analysis, Lu Shuangshuang and Jiang Wenneng; Investigation, Lu Shuangshuang and Wei Haibin; Data curation, Lu Shuangshuang;Writing—original draft, Lu Shuangshuang and Jiang Wenneng; Writing—review&editing, Lu Shuangshuang, Wei Haibin and Jiang Wenneng; Visualization, LuShuangshuang; Supervision, Jiang Wenneng and Wei Haibin; Project administration, Wei Haibin. All authors have read and agreed to the published version of the manuscript. References Allen, J. P., Porter, M. R., McFarland, F. C., Marsh, P., & McElhaney, K. B. (2005). The two faces of adolescents' success with peers: Adolescent popularity, social adaptation, and deviant behavior. Child Development, 76(3), 747–760. Arpino, B., Pronzato, C. D., & Tavares, L. P. (2014). The effect of grandparental support on mothers’ labour market participation: An instrumental variable approach. European Journal of Population, 30, 369–390. Barnes, H. L., & Olson, D. H. (1985). Parent-adolescent communication and the circumplex model. Child Development, 438–447. Chan, K. W. (2010). The global financial crisis and migrant workers in China: ‘There is no future as a labourer; returning to the village has no meaning’. International Journal of Urban and Regional Research, 34(3), 659–677. Chen, F., & Liu, G. (2012). The health implications of grandparents caring for grandchildren in China. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 67(1), 99–112. Conger, R. D., & Donnellan, M. B. (2007). An interactionist perspective on the socioeconomic context of human development. Annual Review of Psychology, 58(1), 175–199. Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1), 225–241. Fröjd, S. A., Nissinen, E. S., Pelkonen, M. U. I., Marttunen, M. J., & Kaltiala-Heino, R. (2008). Depression and school performance in middle adolescent boys and girls. Journal of Adolescence, 31(4), 485–498. Goodman, C., & Silverstein, M. (2002). Grandmothers raising grandchildren: Family structure and well-being in culturally diverse families. The Gerontologist, 42(5), 676–689. Hank, K., & Buber, I. (2009). Grandparents caring for their grandchildren: Findings from the 2004 Survey of Health, Ageing, and Retirement in Europe. Journal of Family Issues, 30(1), 53–73. Hayslip, B. Jr., & Kaminski, P. L. (2005). Grandparents raising their grandchildren: A review of the literature and suggestions for practice. The Gerontologist, 45(2), 262–269. Hayslip, B. Jr., Fruhauf, C. A., & Dolbin-MacNab, M. L. (2019). Grandparents raising grandchildren: What have we learned over the past decade? The Gerontologist, 59(3), e152–e163. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900–1902. Hughes, M. E., Waite, L. J., LaPierre, T. A., & Luo, Y. (2007). All in the family: The impact of caring for grandchildren on grandparents' health. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 62(2), S108–S119. Jordan, L. P., & Graham, E. (2012). Resilience and well-being among children of migrant parents in South-East Asia. Child Development, 83(5), 1672–1688. Kessler, R. C., Aguilar-Gaxiola, S., Alonso, J., Chatterji, S., Lee, S., Ormel, J., ... & Wang, P. S. (2009). The global burden of mental disorders: An update from the WHO World Mental Health (WMH) surveys. Epidemiology and Psychiatric Sciences, 18(1), 23–33. Laursen, B., & Collins, W. A. (2009). Parent-child relationships during adolescence. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (pp. 3–42). Wiley. Model, B. (Year unknown). The Bioecological Model of Human Development. [Journal name missing]. Ruiz, S. A., & Silverstein, M. (2007). Relationships with grandparents and the emotional well-being of late adolescent and young adult grandchildren. Journal of Social Issues, 63(4), 793–808. Sheeber, L., Hops, H., Alpert, A., Davis, B., & Andrews, J. (1997). Family support and conflict: Prospective relations to adolescent depression. Journal of Abnormal Child Psychology, 25, 333–344. Short, S. E., Fengying, Z., Siyuan, X., & Mingliang, Y. (2001). China's one-child policy and the care of children: An analysis of qualitative and quantitative data. Social Forces, 79(3), 913–943. Steinberg, L. (2001). We know some things: Parent–adolescent relationships in retrospect and prospect. Journal of Research on Adolescence, 11(1), 1–19. Wen, M., & Lin, D. (2012). Child development in rural China: Children left behind by their migrant parents and children of nonmigrant families. Child Development, 83(1), 120–136. Zeng, Z., & Xie, Y. (2014). The effects of grandparents on children’s schooling: Evidence from rural China. Demography, 51, 599–617. Additional Declarations No competing interests reported. Supplementary Files highlight.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6887674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483395391,"identity":"6b77fb0b-34e8-46cb-bf4e-50f27cf76592","order_by":0,"name":"Shuangshuang Lu","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Lu","suffix":""},{"id":483395392,"identity":"09101eba-c614-41d4-9867-a706e0c9a35d","order_by":1,"name":"Haibin Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFACxgZmxgYbHn725gMHPvwgXkuanGTPscSDM3uItAeo5bCxwY0c48McbEQoNzje3MBcuIM5seFAzofDDDwM8vxiBwhoOXOwgXnmGbbExoazGw4XWDAYzpydgF+L2Y3EBmbeNp7EZsbeDYdn8DAkGNwmpOX+Q5AWicQ2Zp4Hh3nYiNFygxGkxcCYh42HgTgt9mdADjuTICfBw2YADGQJwn6RbD/+gJl3x38e+/uPH3/48MNGnl+agBYgYEeOcgmCykfBKBgFo2AUEAEAD+BIKVZGTggAAAAASUVORK5CYII=","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Wei","suffix":""},{"id":483395393,"identity":"36a7ed33-a8d3-4b2f-aafa-91a30e2d33b8","order_by":2,"name":"Wenneng Jiang","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenneng","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-06-13 11:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6887674/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6887674/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89927455,"identity":"3d7fac91-dfb3-4333-bf23-3622fea243c5","added_by":"auto","created_at":"2025-08-26 13:47:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1163726,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6887674/v1/23c623f2-a4d6-47f8-8a2e-89577eb8f2ec.pdf"},{"id":86490470,"identity":"9cd23c4b-bfa5-4e49-8934-714b8d6c1543","added_by":"auto","created_at":"2025-07-11 09:01:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10677,"visible":true,"origin":"","legend":"","description":"","filename":"highlight.docx","url":"https://assets-eu.researchsquare.com/files/rs-6887674/v1/4128c815a199ca696cce54cb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intergenerational Caregiving and Adolescent Depression in China: Mechanisms and Risk Factors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAdolescent mental health is a crucial component of human capital development, with significant implications for individual economic well-being, national productivity, and long-term economic growth (Heckman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), a concern also reflected in national policies like the China Children's Development Program (2021\u0026ndash;2030) (State Council of the People\u0026rsquo;s Republic of China, 2021). Currently, child and adolescent mental health problems constitute a major global public health challenge (WHO, 2021; Kessler et al., 2007). In China, rapid economic growth and significant social transformations, notably large-scale internal migration (Chan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), have led to a rise in intergenerational caregiving, where grandparents frequently assume primary child-rearing duties. Simultaneously, the long-term effects of the one-child policy have intensified the caregiving burden on adult children, indirectly increasing the prevalence of intergenerational caregiving (Short et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIntergenerational caregiving has garnered considerable academic attention. Early research primarily focused on grandparents\u0026rsquo; health and well-being (Hayslip \u0026amp; Kaminski, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hughes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or parents\u0026rsquo; (especially women\u0026rsquo;s) labor market participation and fertility intentions (Arpino et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hank \u0026amp; Buber, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Although research on the developmental impacts on children and adolescents has been increasing, existing literature presents complex and sometimes contradictory findings (Ruiz \u0026amp; Silverstein, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Some studies suggest that intergenerational caregiving may have positive effects under certain conditions, such as promoting family ethical values or providing emotional support, particularly in contexts of parental absence (Chen \u0026amp; Liu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, more research reveals potential negative effects, including ambiguous family roles (Goodman \u0026amp; Silverstein, 2006), distant parent-child relationships (Wen \u0026amp; Lin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and intergenerational educational philosophy conflicts, all of which may adversely affect adolescent psychological development. Current research has started to explore mechanisms linking intergenerational caregiving to children's educational outcomes, considering factors like caregivers' educational expectations, the quality of care, and limitations related to grandparents' educational attainment and health (Zeng \u0026amp; Xie, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough existing research has laid a foundation for understanding the multidimensional impacts of intergenerational caregiving, disagreement remains regarding its net effects on adolescent development. Specifically concerning adolescent mental health, there is a lack of systematic empirical investigation and robust theoretical integration regarding the precise pathways and psychosocial mechanisms through which intergenerational caregiving exerts its influence. Therefore, this study investigates the impact of intergenerational caregiving on adolescent depression risk using large-sample longitudinal data. We further explore academic performance and parent-child communication as potential mediating channels, aiming to provide novel empirical evidence and theoretical insights into this complex relationship.\u003c/p\u003e\u003cp\u003eSpecifically, this study explores: (1) whether and to what extent intergenerational caregiving affects adolescent depression symptom levels; (2) whether academic performance and frequency of parent-child communication play mediating roles in this relationship; (3) whether the impact of intergenerational caregiving exhibits significant group heterogeneity (such as gender, age, household registration).\u003c/p\u003e\u003cp\u003eThis paper makes several key contributions. First, while prior research has explored intergenerational caregiving, many studies lack granular, individual-level longitudinal data necessary for robust empirical testing of its nuanced effects on adolescents in rapidly changing socioeconomic contexts like China. Using CFPS data from 2010\u0026ndash;2020, we construct precise individual-level indicators of intergenerational caregiving for adolescents. This quantitative measurement provides a solid empirical foundation and facilitates more rigorous analysis in this research area.\u003c/p\u003e\u003cp\u003eSecond, we extend the literature by focusing on adolescent mental health, a critical but under-explored outcome of intergenerational caregiving, thereby testing the applicability of family and developmental theories in this specific domain. Furthermore, we investigate micro-level transmission mechanisms by integrating insights from attachment theory and family systems theory. We specifically examine \u0026ldquo;academic performance\u0026rdquo; and \u0026ldquo;parent-child communication frequency\u0026rdquo; as mediating psychosocial variables\u0026mdash;which have rarely been systematically analyzed in conjunction\u0026mdash;to elucidate their roles in the pathway from intergenerational caregiving to mental health, thus shedding light on the \u0026ldquo;black box\u0026rdquo; of these complex interactions. Additionally, this paper identifies heterogeneous effects across different subsamples (gender, age, household registration), helping clarify more vulnerable adolescent groups in intergenerational caregiving contexts, thereby enhancing the explanatory power and practical relevance of related theories.\u003c/p\u003e\u003cp\u003eThird, this paper has important policy implications. With the normalization of population mobility and continuous changes in family structure, intergenerational caregiving has become a widespread phenomenon in China, and its potential risks to adolescent development cannot be ignored. This paper identifies significant impacts of intergenerational caregiving on adolescent depression risk and its mechanisms, providing theoretical foundation and empirical support for developing more targeted policy interventions. Based on empirical testing of micro-transmission mechanisms, it provides effective empirical evidence support for policymakers. Meanwhile, high-risk groups identified (such as adolescents of specific genders, age groups, or household registration backgrounds) can be prioritized for inclusion in public health services and mental health intervention focus groups, achieving optimal resource allocation and maximized policy intervention effectiveness.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Theoretical Framework and Research Hypotheses","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study primarily constructs an analytical framework based on Attachment Theory and Family Systems Theory to explain the potential mechanisms through which intergenerational caregiving affects adolescent mental health. Attachment theory, proposed by John Bowlby, emphasizes individuals' intrinsic need to establish emotional connections with primary caregivers to obtain security and protection. The quality of early attachment relationships has lasting impacts on individual mental health development, with the core mechanism being the construction of \"Internal Working Models\" (IWMs). IWMs are cognitive and emotional representational systems about self (such as self-worth) and others (such as whether others are reliable) that individuals form based on interactive experiences with attachment figures. Secure attachment helps form positive IWMs, promoting individuals' emotional regulation abilities and psychological resilience; while insecure attachment may lead to negative IWMs, increasing susceptibility to psychological problems.\u003c/p\u003e\u003cp\u003eIn intergenerational caregiving contexts, grandparents may become adolescents' primary attachment figures. If grandparents, owing to their own limitations (e.g., health status, traditional caregiving philosophies, or energy levels), are unable to provide consistent, sensitive, and responsive care, adolescents may develop insecure attachment patterns, consequently impacting their mental health. Additionally, intergenerational caregiving may be accompanied by reduced contact frequency with parents or decreased interaction quality, further weakening adolescents\u0026rsquo; opportunities to obtain security and emotional support from parents.\u003c/p\u003e\u003cp\u003eBased on the above analysis, this paper proposes the first hypothesis:\u003c/p\u003e\u003cp\u003eH1: Intergenerational caregiving is positively correlated with higher levels of depression symptoms in adolescents.\u003c/p\u003e\u003cp\u003eFamily Systems Theory, particularly Murray Bowen\u0026rsquo;s perspective, views the family as an interconnected emotional unit where family members\u0026rsquo; emotions and behaviors mutually influence each other, forming dynamic equilibrium. This theory emphasizes anxiety transmission within families, intergenerational boundaries, role functions, and the level of differentiation among individuals within the family system. When family systems encounter stress or changes (such as intergenerational caregiving due to parental absence), existing balance may be disrupted. Intergenerational caregiving may lead to family role redistribution, changes in interaction patterns between parent-child and grandparent-grandchild subsystems, and may even trigger intergenerational caregiving philosophy conflicts (Goodman \u0026amp; Silverstein, 2006; Hayslip et al., 2017). These changes may generate chronic anxiety within families and transmit to adolescents through mechanisms such as triangular relationships, making them \u0026ldquo;symptom bearers\u0026rdquo; of family stress. Poor family interaction patterns (such as poor communication, lack of emotional support) (Sheeber et al., 2001) and role conflicts may damage adolescents\u0026rsquo; self-efficacy and coping abilities, increasing their depression risk.\u003c/p\u003e\u003cp\u003eAcademic performance is an important stressor and source of achievement for adolescents during development (Eccles \u0026amp; Roeser, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Intergenerational caregiving may indirectly affect adolescents\u0026rsquo; academic engagement and achievement due to grandparents\u0026rsquo; insufficient tutoring abilities, educational expectation differences, or inadequate family environment support for learning, while academic setbacks are common risk factors for adolescent depression (Fr\u0026ouml;jd et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Steinberg, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, based on the above analysis, this paper proposes the second hypothesis:\u003c/p\u003e\u003cp\u003eH2a: Declining academic performance mediates the relationship between intergenerational caregiving and adolescent depression symptom levels.\u003c/p\u003e\u003cp\u003eCommunication with parents is an important pathway for adolescents to obtain emotional support, establish identity, and solve problems (Barnes \u0026amp; Olson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Laursen \u0026amp; Collins, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Intergenerational caregiving may lead to reduced direct communication opportunities between adolescents and parents, or decreased communication quality due to physical separation and emotional distance (Jordan \u0026amp; Graham, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Lack of effective parent-child communication may make adolescents feel neglected or misunderstood, thereby increasing their loneliness and depressive emotions (Allen et al., 2006).\u003c/p\u003e\u003cp\u003eH2b: Reduced frequency of parent-child communication impacts on the relationship between intergenerational caregiving and adolescent depression symptom levels.\u003c/p\u003e\u003cp\u003eFurthermore, this paper will deeply examine potential heterogeneous manifestations of these impacts across different sample groups (such as gender, age groups, urban-rural household registration) (Conger \u0026amp; Donnellan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Data and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1.Data Sources\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data used in this paper comes from the China Family Panel Studies (CFPS). Specifically, this paper selected variables from child questionnaires and family economic questionnaires from 2010 to 2020 for family-level control and explanatory variables. The data processing procedure was as follows: (1) retained samples aged 6\u0026ndash;18 years; (2) removed samples with severe missing data; (3) to eliminate the influence of outliers, all continuous variables were winsorized at the 1% and 99% percentile levels. It is particularly noteworthy that this data survey was approved by the Biomedical Ethics Committee of Peking University (approval number: IRB00001052-14010) and conducted with research subjects signing informed consent forms.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable Construction\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Independent Variable: Intergenerational Caregiving (IC)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIntergenerational caregiving can be divided into narrow and broad definitions. Narrow intergenerational caregiving refers to adult parents completely abandoning child-rearing responsibilities, with grandparents assuming \u0026ldquo;full caregiving responsibility\u0026rdquo;; broad intergenerational caregiving refers to grandparents participating in the upbringing and education of the third generation, assuming \u0026ldquo;partial caregiving responsibility.\u0026rdquo; This paper follows the approach of Lu Hongyou et al., based on the question \u0026ldquo;Who takes care of the child at night\u0026rdquo; in the CFPS child self-report questionnaire, which has 7 options: child\u0026rsquo;s father, child\u0026rsquo;s mother, child\u0026rsquo;s maternal grandparents, child\u0026rsquo;s paternal grandparents, self-care, nanny, daycare/kindergarten/preschool. Based on this questionnaire item, we construct a dummy variable for intergenerational caregiving. When a child is cared for by grandparents or other elderly relatives, the variable is assigned a value of 1, otherwise 0.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Dependent Variable: Adolescent Mental Health Status (AMHS)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe dependent variable of mental health status in this paper is constructed as follows: using the depression scale from the child self-report questionnaire, where scales in different periods contain 8 or 20 questions, comprehensively scoring based on annual conditions, with samples scoring above the sample median assigned a value of 1, otherwise 0. Existing research shows that the CES-D scale has good reliability and validity with a Cronbach α value of 0.809.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Control Variables\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFollowing the existing literature, we select control variables from two dimensions: regional characteristics and household characteristics. The regional characteristic variables are as follows: (1) Regional economic development level (GDP), measured by GDP per capita at the regional level; (2) Local government expenditure level (LGE), measured by the natural logarithm of regional government fiscal expenditure. The household characteristic variables are as follows: (1) Number of household members (NHM), measured by the natural logarithm of the number of core household members; (2) Household size (HS), measured by the natural logarithm of the total number of household members; (3) Household expenditure (HE), measured by the natural logarithm of household expenditure level; (4) Education expenditure (EE), measured by the natural logarithm of household education expenditure; (5) Education, culture, and recreation expenditure (ECRE), measured by the natural logarithm of household expenditure on education, culture, and recreation; (6) Household income (HI), measured by the natural logarithm of household income.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Empirical Model Construction\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo accurately identify the impact of intergenerational caregiving on adolescent mental health status, this paper establishes a two-way fixed effects model as follows:\u003c/p\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;β₀ + β₁ICₚ,ₜ + β₂Xₚ,ₜ + year\u0026thinsp;+\u0026thinsp;ε (1)\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;(1), the dependent variable Y represents the individual adolescent\u0026rsquo;s mental health status. IC is the main explanatory variable, indicating whether an adolescent experiences intergenerational caregiving, taking a value of 1 if present, otherwise 0. Xₚ,ₜ represents a series of control variables included in the baseline regression. Additionally, to further ensure accuracy and reliability of research results, this paper includes individual fixed effects (pid) for adolescents and clusters at the individual level. Since the explanatory variable does not vary by year, to avoid variable collinearity, this paper does not include time fixed effects. ε represents the error term, the part unexplained by the model.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive Statistical Analysis Results\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for all variables employed in the baseline regression, following listwise deletion of observations with missing values. The depression status variable, coded 0\u0026ndash;1, has a mean of 0.548 (standard deviation\u0026thinsp;=\u0026thinsp;0.498) and a median of 1.000, implying that 54.8% of the sample individuals report depression symptoms. The primary explanatory variable, intergenerational caregiving (a binary indicator), has a mean of 0.251 (standard deviation\u0026thinsp;=\u0026thinsp;0.433) and a median of 0.000, indicating that 25.1% of the sample individuals are in intergenerational caregiving arrangements.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistical\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMEAN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP25\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP75\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMIN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMAX\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSource from: CFPS (2010,2012,2014,2016,2018,2020).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Regression Results\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the baseline regression results. Column (1) presents a bivariate regression without control variables or fixed effects. Column (2) incorporates individual fixed effects, and Column (3) further includes a full set of control variables. The results in columns (1) and (2) show a statistically significant positive association between intergenerational caregiving and adolescent depression risk. The estimated coefficients are 0.046 and 0.081, respectively, both significant at the 1% level. These initial findings suggest that intergenerational caregiving is associated with an increase in adolescent mental health distress. Upon inclusion of covariates capturing regional and family characteristics (Column 3), the coefficient on intergenerational caregiving remains positive and statistically significant at the 1% level, lending further support to this association.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline regression results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.046***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.081***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.086***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.043***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.115***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.022***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.997***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.311)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.536***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.503***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.639***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.214)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20,39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17,535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Table reports the baseline regression results based on the Two-Way Fixed Effects (TWFE) model.The key explanatory variable is Intergenerational Care (IC), and the dependent variable is Adolescent Mental Health Status (AMHS). A series of household-level control variables are included in the regression. To mitigate potential bias from omitted variables, individual fixed effects is also controlled. Standard errors are clustered at the individual level and reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Robustness Checks\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Alternative Baseline Models\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo assess the robustness of our baseline findings, particularly given the binary nature of the dependent variable (depression status), we re-estimate the model using Probit and Logit specifications. These two models are more commonly used in addressing binary choice problems and differ from OLS in their assumptions about the error term distribution, thus allowing us to test whether our conclusions are sensitive to specific model assumptions. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, columns (1) and (2) present Probit model regression results, where (2) includes control variables, with the key explanatory variable coefficient of 0.112, which is positive and statistically significant, confirming estimation robustness. Columns (3) and (4) show Logit model regression results, also displaying significance, further verifying result reliability.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobust checks: changing baseline model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.116***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.112***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.185***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.181***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.097***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.028***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.044***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.027***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.045***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.145***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.227***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.042***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.068***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.543***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.448***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.235)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.091***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.716***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.145***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.690***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.263)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.419)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22,070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24,809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22,070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: This table reports the results of robustness checks using alternative baseline models. Specifically, we replace the original model with Probit and Logit specifications to re-estimate. Standard errors are clustered at the individual level and reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Alternative Model Clustering Levels\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study, to ensure the robustness of our estimates regarding the relationship between intergenerational caregiving and adolescent depression risk, we consider incorporating standard error clustering at the time and regional levels separately, as well as implementing two-way clustering simultaneously at both the regional and time dimensions. The rationale for this approach is that unobserved factors affecting adolescent depression risk may exhibit correlation both within geographic regions (for example, adolescents within a community or county may share similar socioeconomic environments, educational resources, or local stressful events) and at specific time points (such as common shocks or major social events faced nationally or regionally in a particular year). By adjusting the clustering levels in our baseline regressions, we obtain more reliable standard error estimates, thereby enabling more accurate assessment of the statistical significance of the intergenerational caregiving effect. Consequently, this enhances the rigor of our research conclusions.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports estimates using clustering at a higher level. The results indicate that the positive and statistically significant association between intergenerational caregiving and depression status persists under these alternative clustering methods. This suggests our main conclusions are robust to different assumptions about the error structure.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobust checks: employing fixed effects interaction term and a higher cluster level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.086***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.086***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.086*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.043***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.043***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.048)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.068)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.115***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.693)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.022***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.997***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.997*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.469)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.638)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.639***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7.955)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual # Time Clustering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClustered by region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClustered by time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17,535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17,535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17,535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The table reports the results of robustness checks with alternative clustering levels. We separately incorporate individual-time clustering interactions, regional-level clustering, and two-way clustering at both time and regional levels simultaneously. Standard errors are clustered at the individual level and reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Further Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe next explore potential heterogeneous associations between intergenerational caregiving and mental health across different adolescent subgroups. Specifically, we conduct subgroup analyses by splitting the sample based on gender, age, and urban-rural household registration (hukou) to examine variations in the estimated association.\u003c/p\u003e\u003cp\u003eFirst, gender-based heterogeneity analysis shows that intergenerational caregiving\u0026rsquo;s impact on male adolescent depression risk (approximately 47.3% of the sample, coefficient\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) is significantly lower than that on female adolescents (approximately 52.7% of the sample, coefficient\u0026thinsp;=\u0026thinsp;0.076, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that females have higher depression risk under intergenerational caregiving. Our results confirm that female adolescents indeed face higher depression risk in intergenerational caregiving environments. This may be related to societal expectations of female roles, women\u0026rsquo;s stronger empathetic capacity, greater social pressures experienced by women, or the possibility that they bear more invisible caregiving responsibilities within intergenerational households.\u003c/p\u003e\u003cp\u003eSecond, based on sample age median, the groups are divided into (1) older group (approximately 47.6%) and (2) younger group (approximately 52.4%). Regression results show that intergenerational caregiving has significant positive impact on depression risk for younger adolescents. This may be because younger adolescents have stronger emotional and daily dependence on their primary caregivers, making the adaptation process more difficult when the primary caregiver changes to grandparents.\u003c/p\u003e\u003cp\u003eAdditionally, urban-rural difference analysis reveals that rural household registration adolescents (81.4% of the sample) show higher depression risk in intergenerational caregiving environments, with significantly positive coefficients; while urban household registration adolescents\u0026rsquo;regression coefficients (18.6% of the sample) are not significant. The results show substantial differences between urban and rural contexts. This may reflect the reality that intergenerational caregiving families in rural areas face greater economic pressures, relatively weak social support networks, and lower accessibility to quality educational and medical resources.\u003c/p\u003e\u003cp\u003eFinally, frequency of parent-child communication also affects how intergenerational caregiving differentially impacts mental health. Results show that in samples with lower communication frequency (approximately 50.5%), intergenerational caregiving\u0026rsquo;s impact on adolescent mental health is more severe. Therefore, these results may reflect differences in urban-rural environments, family structures, and social support networks.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity Analysis: The role of gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale sample\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.064***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.076**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.051***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.032***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.030***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.030**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.252***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.239**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.668**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.757***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.266***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.044***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: This table reports regression results concerning gender heterogeneity. We measure gender characteristics using a dummy variable that takes the value of 1 for males and 0 for females. Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity Analysis: The role of age\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOlder group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYounger group\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.074**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.139***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.048***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.059***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.032***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.105***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.106**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.606***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.036***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.905***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.385***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.030***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: This table reports regression results concerning age heterogeneity. We partition the sample into older and younger cohorts based on the median age of individuals. Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity Analysis: urban-rural distribution\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban sample\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.194***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.068***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.102***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.016*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.056***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.058**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.653***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.474***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.996***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.854***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,589\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: This table reports regression results concerning heterogeneity in the urban-rural distribution of the sample. We partition the sample into urban and rural subsamples based on individuals\u0026rsquo; household registration status (hukou). Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity Analysis: Frequency of Parental Communication\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequent\u003c/p\u003e\u003cp\u003ecommunication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfrequent communication\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.063**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.084***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.040**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.050***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.026***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.051***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.150***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.571***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.034**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.289***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.874***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.920***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.567***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,902\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: This table reports regression results concerning heterogeneity in communication frequency with parents. We partition the sample into high communication frequency and low communication frequency subsamples based on the frequency of communication with parents. Additionally, we control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Mechanism Testing\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFollowing Jiang Ting (2022)\u0026rsquo;s suggestions for improving mediation effect testing, this study employs a two-step method to construct mediation effect models. Results show that intergenerational caregiving\u0026rsquo;s coefficient on academic performance is -0.129, statistically significant at the 5% level, indicating that intergenerational caregiving significantly affects academic performance and exacerbates depressive emotions through impact on student academic performance.\u003c/p\u003e\u003cp\u003eIntergenerational caregiving is significantly negative for both heart-to-heart talks with parents and trust in parents, significant at the 1% level. Given this, intergenerational caregiving significantly reduces the foundation and performance of parent-child communication within families. Family systems theory suggests that poor academic performance and insufficient grandparent trust reflect family interaction imbalance, reducing children\u0026rsquo;s self-efficacy and increasing depression risk. Lack of parental support and encouragement forms negative feedback, further exacerbating depressive emotions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediating Analysis: Communication Ability and Achievement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAMHS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntimate communication with parents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrust toward parents\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.129**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.132***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.023***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.026*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNHM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.039***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.046**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.827***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.045*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eECRE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLGE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.663***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-31.207***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.350***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17,535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: This table reports regression results for the mechanism analysis. We control for individual-level clustered robust standard errors to address potential heterogeneity across individuals. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe findings of this study indicate that intergenerational caregiving is significantly associated with an increased risk of psychological depression among adolescents. Interpreted through the lens of family systems theory, this elevated depression risk may stem from mechanisms including weakened parent-child attachment, disrupted family communication, and heightened adolescent role confusion or functional conflict. This is consistent with research results from domestic scholars such as Chen Jianhua, further confirming the applicability of attachment theory and family systems theory in explaining adolescent mental health problems. Related theories indicate that lack of emotional exchange and family interaction imbalance not only weaken satisfaction of adolescent emotional needs but also reduce self-efficacy, significantly increasing depressive tendencies and affecting overall mental health levels.\u003c/p\u003e\u003cp\u003eFurther analysis found significant differences in depression risk faced by adolescents across different genders, ages, and urban-rural household registration backgrounds in intergenerational caregiving contexts. Specifically, female adolescents are more likely to exhibit depressive emotions compared to males, possibly related to their higher emotional dependency and sensitivity to caregiver behavioral patterns. Regarding age, younger adolescents, due to immature nervous system development, have relatively weak emotional regulation abilities. With limited life experience, they often lack effective coping strategies when facing academic pressure, family conflicts, and interpersonal setbacks, leading to significantly increased depression risk.\u003c/p\u003e\u003cp\u003eRegarding urban-rural differences, rural household registration adolescents show significantly higher depression levels under intergenerational caregiving backgrounds compared to urban adolescents. Possible reasons include that rural families often have relatively insufficient educational resources and emotional support, while urban families, despite abundant resources, are more prone to intergenerational communication barriers and educational philosophy conflicts. Under multiple pressure accumulation, rural adolescents are more likely to develop depression symptoms, adversely affecting their mental health and individual development.\u003c/p\u003e\u003cp\u003eGiven intergenerational caregiving\u0026rsquo;s profound impact on adolescent mental health, coordinated intervention measures from family, school, community, and government levels are urgently needed. At the family level, interventions should aim to enhance grandparents\u0026rsquo; caregiving capacities and emotional support awareness, improve family communication dynamics, and address the gender-specific mechanisms influencing mental health. At the school level, mental health education should be strengthened, psychological counseling systems improved, and family-school coordination mechanisms established to alleviate dual pressures on adolescents academically and emotionally. At the community level, support service networks for intergenerational caregiving families need improvement, providing platforms for psychological counseling and parent-child activities to promote adolescent social support system construction. At the government level, specific policies addressing intergenerational caregiving issues should be introduced, providing financial and institutional support, enhancing public awareness of intergenerational caregiving impacts, and promoting establishment of adolescent mental health monitoring and evaluation systems. Meanwhile, basic and applied research should be strengthened to provide scientific evidence for related policy formulation, promoting sustainable development of adolescent mental health initiatives.\u003c/p\u003e\u003cp\u003eThis study is subject to certain limitations. First, the complexity of factors influencing intergenerational caregiving and adolescent mental health means that unobserved variables related to family environment or broader social support systems may not be fully controlled for, potentially contributing to modest R\u0026sup2; values in the models. While a lower R\u0026sup2; indicates that a substantial portion of the variance in adolescent depression remains unexplained by the included variables, the primary focus of this study is on the consistent estimation of the ceteris paribus effect of intergenerational caregiving, for which the unbiasedness of the coefficient estimates is paramount, rather than overall predictive power. Future research should further enrich assessment dimensions to comprehensively present the overall impact of intergenerational caregiving on adolescent mental health.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Lu Shuangshuang and Jiang Wenneng;Methodology, Lu Shuangshuang and Wei Haibin; Software, Lu Shuangshuang and Wei Haibin; Formal analysis, Lu Shuangshuang and Jiang Wenneng; Investigation, Lu Shuangshuang and Wei Haibin; Data curation, Lu Shuangshuang;Writing\u0026mdash;original draft, Lu Shuangshuang and Jiang Wenneng; Writing\u0026mdash;review\u0026amp;editing, Lu Shuangshuang, Wei Haibin and Jiang Wenneng; Visualization, LuShuangshuang; Supervision, Jiang Wenneng and Wei Haibin; Project administration, Wei Haibin. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, J. P., Porter, M. R., McFarland, F. C., Marsh, P., \u0026amp; McElhaney, K. B. (2005). The two faces of adolescents\u0026apos; success with peers: Adolescent popularity, social adaptation, and deviant behavior. Child Development, 76(3), 747\u0026ndash;760.\u003c/li\u003e\n\u003cli\u003eArpino, B., Pronzato, C. D., \u0026amp; Tavares, L. P. (2014). The effect of grandparental support on mothers\u0026rsquo; labour market participation: An instrumental variable approach. European Journal of Population, 30, 369\u0026ndash;390.\u003c/li\u003e\n\u003cli\u003eBarnes, H. L., \u0026amp; Olson, D. H. (1985). Parent-adolescent communication and the circumplex model. Child Development, 438\u0026ndash;447.\u003c/li\u003e\n\u003cli\u003eChan, K. W. (2010). The global financial crisis and migrant workers in China: \u0026lsquo;There is no future as a labourer; returning to the village has no meaning\u0026rsquo;. International Journal of Urban and Regional Research, 34(3), 659\u0026ndash;677.\u003c/li\u003e\n\u003cli\u003eChen, F., \u0026amp; Liu, G. (2012). The health implications of grandparents caring for grandchildren in China. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 67(1), 99\u0026ndash;112.\u003c/li\u003e\n\u003cli\u003eConger, R. D., \u0026amp; Donnellan, M. B. (2007). An interactionist perspective on the socioeconomic context of human development. Annual Review of Psychology, 58(1), 175\u0026ndash;199.\u003c/li\u003e\n\u003cli\u003eEccles, J. S., \u0026amp; Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1), 225\u0026ndash;241.\u003c/li\u003e\n\u003cli\u003eFr\u0026ouml;jd, S. A., Nissinen, E. S., Pelkonen, M. U. I., Marttunen, M. J., \u0026amp; Kaltiala-Heino, R. (2008). Depression and school performance in middle adolescent boys and girls. Journal of Adolescence, 31(4), 485\u0026ndash;498.\u003c/li\u003e\n\u003cli\u003eGoodman, C., \u0026amp; Silverstein, M. (2002). Grandmothers raising grandchildren: Family structure and well-being in culturally diverse families. The Gerontologist, 42(5), 676\u0026ndash;689.\u003c/li\u003e\n\u003cli\u003eHank, K., \u0026amp; Buber, I. (2009). Grandparents caring for their grandchildren: Findings from the 2004 Survey of Health, Ageing, and Retirement in Europe. Journal of Family Issues, 30(1), 53\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eHayslip, B. Jr., \u0026amp; Kaminski, P. L. (2005). Grandparents raising their grandchildren: A review of the literature and suggestions for practice. The Gerontologist, 45(2), 262\u0026ndash;269.\u003c/li\u003e\n\u003cli\u003eHayslip, B. Jr., Fruhauf, C. A., \u0026amp; Dolbin-MacNab, M. L. (2019). Grandparents raising grandchildren: What have we learned over the past decade? The Gerontologist, 59(3), e152\u0026ndash;e163.\u003c/li\u003e\n\u003cli\u003eHeckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900\u0026ndash;1902.\u003c/li\u003e\n\u003cli\u003eHughes, M. E., Waite, L. J., LaPierre, T. A., \u0026amp; Luo, Y. (2007). All in the family: The impact of caring for grandchildren on grandparents\u0026apos; health. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 62(2), S108\u0026ndash;S119.\u003c/li\u003e\n\u003cli\u003eJordan, L. P., \u0026amp; Graham, E. (2012). Resilience and well-being among children of migrant parents in South-East Asia. Child Development, 83(5), 1672\u0026ndash;1688.\u003c/li\u003e\n\u003cli\u003eKessler, R. C., Aguilar-Gaxiola, S., Alonso, J., Chatterji, S., Lee, S., Ormel, J., ... \u0026amp; Wang, P. S. (2009). The global burden of mental disorders: An update from the WHO World Mental Health (WMH) surveys. Epidemiology and Psychiatric Sciences, 18(1), 23\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eLaursen, B., \u0026amp; Collins, W. A. (2009). Parent-child relationships during adolescence. In R. M. Lerner \u0026amp; L. Steinberg (Eds.), Handbook of adolescent psychology (pp. 3\u0026ndash;42). Wiley.\u003c/li\u003e\n\u003cli\u003eModel, B. (Year unknown). The Bioecological Model of Human Development. [Journal name missing].\u003c/li\u003e\n\u003cli\u003eRuiz, S. A., \u0026amp; Silverstein, M. (2007). Relationships with grandparents and the emotional well-being of late adolescent and young adult grandchildren. Journal of Social Issues, 63(4), 793\u0026ndash;808.\u003c/li\u003e\n\u003cli\u003eSheeber, L., Hops, H., Alpert, A., Davis, B., \u0026amp; Andrews, J. (1997). Family support and conflict: Prospective relations to adolescent depression. Journal of Abnormal Child Psychology, 25, 333\u0026ndash;344.\u003c/li\u003e\n\u003cli\u003eShort, S. E., Fengying, Z., Siyuan, X., \u0026amp; Mingliang, Y. (2001). China\u0026apos;s one-child policy and the care of children: An analysis of qualitative and quantitative data. Social Forces, 79(3), 913\u0026ndash;943.\u003c/li\u003e\n\u003cli\u003eSteinberg, L. (2001). We know some things: Parent\u0026ndash;adolescent relationships in retrospect and prospect. Journal of Research on Adolescence, 11(1), 1\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eWen, M., \u0026amp; Lin, D. (2012). Child development in rural China: Children left behind by their migrant parents and children of nonmigrant families. Child Development, 83(1), 120\u0026ndash;136.\u003c/li\u003e\n\u003cli\u003eZeng, Z., \u0026amp; Xie, Y. (2014). The effects of grandparents on children\u0026rsquo;s schooling: Evidence from rural China. Demography, 51, 599\u0026ndash;617.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intergenerational caregiving, adolescents, mental health, impact mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-6887674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6887674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective:This study aims to investigate the impact of intergenerational caregiving on adolescent mental health and its underlying mechanisms,providing empirical evidence for developing intervention strategies to improve adolescent mental health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Based on data from the China Family Panel Studies (CFPS) spanning 2010-2020, this study employed individual fixed-effects models to examine the impact of intergenerational caregiving on adolescent mental health (measured by depression risk). Additionally, Probit and Logit models were used for robustness checks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The study found that intergenerational caregiving significantly increases adolescent depression risk (β=0.086, p\u0026lt;0.05). Heterogeneity analysis revealed that intergenerational caregiving has more pronounced effects on depression risk among female adolescents (β=0.076, p\u0026lt;0.05), younger adolescents (β=0.139, p\u0026lt;0.05), and those with rural household registration (β=0.194, p\u0026lt;0.05). Among adolescents with lower frequency of parent-child communication, the negative impact of intergenerational caregiving was also stronger (β=0.084, p\u0026lt;0.05). Mechanism analysis revealed that declining academic performance (β=-0.241, p\u0026lt;0.05) and reduced frequency of parent-child communication (specific indicators β=-0.1319, -0.0228, respectively, p\u0026lt;0.05) serve as important mediating pathways through which intergenerational caregiving increases adolescent depression risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Intergenerational caregiving constitutes a significant risk factor for adolescent depression. This study not only deepens theoretical understanding of the intrinsic mechanisms through which intergenerational caregiving affects adolescent mental health but also provides evidence for identifying intergenerational caregiving families as priority groups for mental health policies and interventions, contributing to enhanced policy precision and effectiveness.\u003c/p\u003e","manuscriptTitle":"Intergenerational Caregiving and Adolescent Depression in China: Mechanisms and Risk Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 09:01:10","doi":"10.21203/rs.3.rs-6887674/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24554802-7910-49b0-9d8c-173301331333","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T13:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 09:01:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6887674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6887674","identity":"rs-6887674","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}