The Long-Time Consequences of Parental Early Left-Behind Event on the Human Capital of Rural Children in China

The Long-Time Consequences of Parental Early Left-Behind Event on the Human Capital of Rural Children in China | 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 The Long-Time Consequences of Parental Early Left-Behind Event on the Human Capital of Rural Children in China Xiaotong Sun, Mi Zhou, Li Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3833421/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 Background Speedy urban development over the preceding years has been with the migration of laborers in rural China. The essential inquiry that has arisen pertains to whether the experience of workers’ movement has a long-term mixed influence on the human capital accumulation among rural offspring. The goal of current study is to address how parental early left-behind event relates to long-time development outcomes in rural offspring. Methods This paper uses a nationally representative dataset from China Family Panel Studies to investigate whether parental early left-behind event impacts the prevalence of human capital among rural children. To do so, this paper uses econometric models to analyze the causality between parental early left-behind event and the offspring’s human capital accumulation, and then uses sensitivity analysis to test robustness. Results We find evidence that rural children whose parents have left-behind event in early life have fewer human capital. These findings also differ markedly by the heterogeneity of parental left-behind event. Further, mothers who have experienced being left behind are more likely to allocate increased time to their offspring. Conversely, fathers who have experienced being left behind tend to exhibit lower socioeconomic outcomes within homes and put fewer investments in children’s education. Conclusions Our study proposes that there is strong correlation between parental early left-behind event and children’s development. Based on our findings, it is recommended that the Chinese government should take measures to minimize instances of involuntary separation between parents and children caused by institutional limitations. This action is crucial for enhancing the human capital outcomes among rural offspring. Rural children Parental early left-behind event Generational continuity Human capital outcomes Background Since 1980s, China has experienced a rapid process of urbanization, characterized by a substantial influx of migrants from rural to urban areas. This migration phenomenon played an important part in the urban development of China. In 2021, an estimated 292.5 million individuals migrated from rural areas to urban centers in pursuit of improved livelihood prospects. The rising trend of population mobility has led to a significant multitude of rural left-behind offspring whose parents move to urban for employment opportunities. Extensive research has provided convincing demonstration about the contemporary impacts of parental migration on the human capital accumulation of stay-at-home children. These impacts have mixed positive, negative, or neutral. Specifically, research has verified the influences on various domains, including educational achievement [ 1 , 2 ], cognitive or noncognitive skills [ 3 – 5 ], nutrition and physical health [ 6 , 7 ], along with psychological well-being [ 8 , 9 ]. Meanwhile, the literature has rapidly accumulated substantial evidence that highlights the critical connections between childhood event and a wide array of growth consequences in offspring throughout lives [ 10 , 11 ]. On the one hand, parents’ early left-behind event will positively impact offspring’s human capital accumulation. When parents have experience of being left behind, they tend to allocate a greater amount of time to housework and childcare duties upon becoming parents. This reduces the likelihood of their own children becoming left-behind and enhances parent-child interactions [ 11 ]. On the other hand, the parents’ event of stay-at-home in early life will hinder the human capital accumulation, consequently obstructing the future employment quality and wage levels [ 12 , 13 ]. Therefore, the left-behind event during early life may result in a significant reduction in both household earnings and monetary allocation directed to offspring. With the presence of conflicting findings, this study aims to ascertain whether parental early left-behind event has beneficial or detrimental influences on the human capital outcomes of rural Chinese offspring. To achieve the article objective, our research inspects the comprehensive impacts of parental early left-behind event on the children’s human capital, while also exploring the underlying mechanisms through which the effects manifest. Specifically, the mechanisms involve monetary allocation, and time allocation, which may vary depending on the role of father or mother with early left-behind event. To do so, this paper uses econometric models to analyze the causal influence of parental early left-behind event on the offspring’s human capital accumulation. Method Data collection and sample choice The dataset employed in this research is sourced from the China Family Panel Studies (CFPS) data, encompassing the years 2010 and 2018. The CFPS dataset encompasses distinct modules designed for the analysis of adults and children. Individuals whose age is 16 years or older autonomously accomplished the research survey designated for adults, whereas data pertaining to children below the age of 15 were provided by their parents or guardians. Furthermore, a self-report questionnaire was provided to children between the ages of 10 and 15 in order to supplement the information obtained from parents’ answers. CFPS dataset in 2010 includes evaluations of parental absence during early life, while the 2018 CFPS survey includes a range of human capital indicators of children, offering a distinct possibility to investigate the long-time outcomes of human capital intergenerational transmission. Overall, 2206 children aged 0 to 15, along with their parents, were included in the sample to investigate the association between parents’ left-behind event during childhood and the human capital skills of offspring. Parental early left-behind event To assess the condition of parental early left-behind event, our study constructed a binary categorical variable that assigned 1 to individuals who had been abandoned by father or mother for a minimum duration of six months by age 12. To investigate various aspects of early left-behind event, this article created three distinct categories of binary variables to represent the model, age, and period of parental early left-behind event. Different models of parental early left-behind event contained early left-behind by father, and early left-behind by mother during ages 0–12. For the age of parental early left-behind event, we created three dummies to compare the influence of parental early left-behind event occurring between the ages of 0–3, 4–12, and during both of these periods. Considering that prolonged left-behind experience is commonly defined as separation for three years and above [ 12 ], our study categorized the period of parental early left-behind event into four time periods: 0.5-1 year, 1–2 years, 2–3 years, and 3 years or longer. The descriptive statistics of parental early left-behind event is presented in Table S1 . Human capital outcomes of children Human capital pertains to the unique asset which is inherent in individuals [ 14 ]. There is a growing recognition that, in addition to education, other pertinent characteristics (such as health, cognitive, and noncognitive abilities) are equally vital components of human capital, as they contribute to the generation of income and other forms of returns [ 15 , 16 ]. Consequently, children’s human capital indicators comprised of physical health, mental health, cognitive skills, and noncognitive skills in this research. The measurement of physical health was conducted through a self-reported health issue, employing an answer that ranged from 1 (poor health) to 5 (excellent health). And then, the evaluation of physical health also incorporated two variables, newborn weight and height-for-age z-score (HAZ), which have been established as influential indicators of future health consequences [ 17 , 18 ]. Greater levels of newborn weight or HAZ typically correspond to superior physical health in children. The mental health questionnaire employed in this study primarily comprises inquiries derived from the Center for Epidemiologic Studies Depression Scale (CES-D). According to Table S2, the CES-D scale is composed of 20 items, with scores varying between 0 and 60. We establish the threshold according to the 80th and 95th percentile: CES-D scores ranging from 16 to 27 indicate “depressive symptoms”, while scores equal to or exceeding 28 indicate “depression” [ 19 , 20 ]. The CES-D scale in its Chinese version has been extensively employed in previous studies, demonstrating its reliability and validity within Chinese population [ 21 , 22 ]. This study measured cognitive abilities through the test of word and math. noncognitive measures were measured using internalizing problem behavior and externalizing problem behavior (see Table S3). To minimize estimation bias caused by confounding heterogeneity, we included numerous control variables in our article. Table S4 presents the descriptive statistics for control variables. Results Parental early left-behind event and offspring’s human capital consequences The primary findings about the influence of parental early left-behind event on offspring’s human capital consequences are shown in Table 1. Children’s health outcomes are displayed in Panel A of Table 1. The findings indicated that parental childhood left-behind event was noticeably related to a decrease in the levels of children’s self-reported health. Panel B of Table 1 presents the estimated values of cognitive and noncognitive abilities. Column (7) demonstrated that parental early left-behind event was significantly and negatively related to children’s word test. Results in column (8) indicated that parental early left-behind event was not significantly linked to offspring’s math test score. In terms of noncognitive skills, it is noteworthy that parental early left-behind event was positively relevant to offspring’s internalizing or externalizing problem behavior index. In general, the findings from baseline regression revealed that parental early left-behind event adversely predicted most human capital consequences of offspring throughout their lives. Table 1 Impact of parental early left-behind event on children’s human capital Panel A Explained Variables: Health consequences Variables Physical health Mental health Self-reported health Newborn weight HAZ CES-D Depressive symptoms Depression Oprobit OLS OLS OLS Logit Logit (1) (2) (3) (4) (5) (6) Parental early -0.067*** -0.017** -0.122*** -0.119 0.006** 0.041*** left-behind event (0.005) (0.003) (0.019) (0.529) (0.003) (0.002) Control variables No No No No No No Parental early -0.053*** -0.048*** -0.066** -0.004 0.095*** 0.024*** left-behind event (0.005) (0.003) (0.013) (0.524) (0.004) (0.000) Control variables c Yes Yes Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Yes Yes R-squared 0.088 0.141 0.023 Observations 1,014 2,206 2,100 1,014 1,014 1,014 Panel B Explained variables: Cognitive and noncognitive abilities Variables Cognitive abilities Noncognitive abilities Word test Math test Internalizing problem behavior Externalizing problem behavior OLS OLS OLS OLS (7) (8) (9) (10) Parental early -0.116* 0.002 0.268*** 0.263** left-behind event (0.015) (0.362) (0.002) (0.004) Control variables No No No No Parental early -0.053** 0.121 0.427*** 0.363*** left-behind event (0.008) (0.247) (0.001) (0.002) Control variables c Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes R-squared 0.437 0.500 0.027 0.057 Observations 909 909 1,004 1,003 Source: China Family Panel Studies (2010 and 2018). Note: a Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; b Robust standard errors are presented within parentheses; c Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother. IV regression and UCI approach This paper utilized historical community-level migration rate as instrument and performed IV estimation. The first-stage estimation of IV analysis indicated a notable connection between the community-level migration rate and the likelihood of parental early left-behind event. The F statistics of IV estimation exhibited significantly higher values compared to the commonly accepted boundary of 10 for identifying weak instruments [23]. Hence, the results obtained through the IV results indicated no evidence of the weak-IV issue in this study 1 . Table 2 displays the secondary IV regression about the human capital consequences of children, utilizing community-level of migration rate as IV. The findings from IV analysis were normally in line with the Table 1 regression. Parental early left-behind event negatively affected offspring’s self-reported health, newborn weight, word and math test scores. It also significantly increased children’s internalizing problem behavior index, externalizing problem behavior index, and the probability of being depression. Table 2 Impact of parental early left-behind event on children’s human capital (Instrumental Variable regression) Panel A Explained variables: Health consequences Variables Physical health Mental health Self-reported health Newborn weight HAZ CES-D Depressive symptoms Depression CMP 2SLS 2SLS 2SLS IVprobit IVprobit (1) (2) (3) (4) (5) (6) Parental early -0.069*** -0.234*** 0.291 2.568*** -0.280 1.485*** left-behind event (0.001) (0.009) (0.341) (0.020) (0.639) (0.000) Control variables c Yes Yes Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Yes Yes Observations 1,014 2,178 2,073 1,002 1,002 1,002 Panel B Explained variables: Cognitive and noncognitive abilities Variables Cognitive abilities Noncognitive abilities Word test Math test Internalizing problem behavior Externalizing problem behavior 2SLS 2SLS 2SLS 2SLS (7) (8) (9) (10) Parental early -3.982*** -0.205*** 2.915*** 1.024*** left-behind event (1.517) (0.069) (1.021) (0.249) Control variables c Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Observations 906 906 992 991 Source: China Family Panel Studies (2010 and 2018). Note: a Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; b Robust standard errors are presented within parentheses; c Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother. In order to alleviate the exclusion restriction associated with the instrumental variable utilized in the research, we adopted Conley’s UCI method to test plausibly exogenous inference. Table S5 presents the thresholds at which the direct impacts of IV on various children’s human capital consequences become statistically insignificant in second-stage. The most immediate impacts of community-level migration proportion on children’s health consequences exhibited a percentage range of 25.86% to 57.09%. Only when the direct effect of community-level migration rate on noncognitive abilities constitutes a minimum of 14.03% of its overall impact do the IV results become insignificant. Overall, the plausibly exogenous inference of sensitivity analysis confirmed the robustness of IV used in this paper. Robustness test about sensitivity analysis of unobservables In addition to using the IV method, a robustness test was conducted to assess the potential effects of unobservables. To be specific, we utilized the approach developed by Altonji et al. [24] and Oster [25] to compute the range for the predicted value of parents’ early experience in regression models. This analysis aimed to assess the sensitivity to the choosing of control variables. This approach relies on the assumption of selection ratio, which posits that unobserved factors are proportional to the observable factors selected at random from all influence variables of children’s human capital consequences. Based on the premise, the robustness of the coefficient could be assessed by the informativeness of the chosen control variables. Based on the study of Botha et al. [26], our study adopted the assumption =1.3(), where denotes the goodness of fit when accounting for all potential determinants associated with children’s human capital outcomes, and is derived from the baseline regressions using observables. Table S6 revealed that the coefficient range of the connections between parental early left-behind event and the majority of children’s human capital consequences did not involve zero. Moreover, the minimum ratio of choosing on unobservables to choosing on observables () was found to be 2.554 for offspring’s human capital consequences, with the exception of mental health and math scores. This value signifies that only when the choosing on unobservables was 2.554 times or higher than that on observables, unobserved variables will entirely eliminate the observed impacts. According to these findings, we cannot dismiss the possibility of the causality between parental early left-behind event and offspring’s human capital outcomes. Heterogenous: model, age, and period of parental early left-behind event Table 3 reports the associations between varying models of parental early left-behind event and children’s human capital accumulation. These findings revealed that father’s early left-behind event was significantly and adversely connected to offspring’s health outcomes and cognitive abilities (involving word and math performance), and was positively correlated with internalizing problem behavior. On the contrary, mother early left-behind event was more beneficial to offspring’s health outcomes and cognitive skills, but mother being left behind had a stronger effect on children’s externalizing problem behavior index. The variation in findings based on parental gender may be attributed to the traditional gender roles that assign distinct spheres to men as breadwinners and women as homemakers [27]. The human capital of parents and the long-time investments of family, including financial and spiritual aspects, play an important part in the outcomes of offspring’s human capital. Moreover, early left-behind event has a detrimental influence on the consequence of human capital in adulthood, consequently hindering both the level of occupation and the degree of salary in subsequent stages of career [12, 13]. As a result, the early left-behind event, particularly for the household sustainer who is conventionally males, could result in a more significant reduction in household earnings and monetary allocation of offspring. Additionally, females, who generally assume the role of housewives, are more inclined to allocate time to children if they have experienced being left behind in early life. This is because they possess a deeper understanding of the potential consequences associated with the absence of parental care, in contrast to mothers who did not experience such separation. Table 3 Estimation of children’s human capital: models of parental early left-behind event Panel A Explained variables: Health consequences Variables Physical health Mental health Self-reported health Newborn weight HAZ CES-D Depressive symptoms Depression (1) (2) (3) (4) (5) (6) Father early -0.013*** -0.184** -0.475*** -0.499 -0.330 0.281*** left-behind event (0.004) (0.079) (0.120) (0.674) (0.376) (0.108) Mother early -0.188 0.142* 0.363*** 0.102 0.157 -0.624*** left-behind event (0.132) (0.074) (0.115) (0.627) (0.313) (0.207) Control variables c Yes Yes Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Yes Yes Observations 1,014 2,206 2,100 1,014 1,014 1,014 Panel B Explained variables: Cognitive and noncognitive abilities Variables Cognitive abilities Noncognitive abilities Word test Math test Internalizing problem behavior Externalizing problem behavior (7) (8) (9) (10) Father early -0.563** -0.093* 0.118*** -0.392 left-behind event (0.028) (0.008) (0.015) (0.408) Mother early 0.903*** 0.458** 0.636 0.844* left-behind event (0.001) (0.019) (0.514) (0.476) Control variables c Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Observations 909 909 1,004 1,003 Source: China Family Panel Studies (2010 and 2018). Note: a Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; b Robust standard errors are presented within parentheses; c Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother. Table 4 shows the results for the various ages of parental early left-behind event. The findings indicated that parental experience of being left behind in the ages of 4 and 12, was linked to a higher likelihood of physical health and mental well-being issues among children. Meanwhile, the experience of being left behind during the period of 4-12 years old had a more adverse impact on the offspring’s cognitive skills and noncognitive skills. Table 4 Estimation of children’s human capital: ages of parental early left-behind event Panel A Explained variables: Health consequences Variables Physical health Mental health Self-reported health Newborn weight HAZ CES-D Depressive symptoms Depression (1) (2) (3) (4) (5) (6) c Parental childhood left-behind -0.007 -0.063 0.108 -0.320 0.187 - experience at 0-3 years only (0.141) (0.077) (0.115) (0.660) (0.354) - Parental childhood left-behind -0.078*** -0.039*** -0.090* -0.149 0.035** 0.131*** experience at 4-12 years only (0.006) (0.000) (0.013) (0.551) (0.016) (0.003) Parental childhood left-behind -0.055*** -0.074** 0.098 -0.744 0.077*** - experience at 0-3 and 4-12 years (0.017) (0.003) (0.126) (0.728) (0.001) - Control variables d Yes Yes Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Yes Yes Observations 1,014 2,206 2,100 1,014 1,014 1,014 Panel B Explained variables: Cognitive and noncognitive abilities Variables Cognitive abilities Noncognitive abilities Word test Math test Internalizing problem behavior Externalizing problem behavior (7) (8) (9) (10) Parental childhood left-behind 0.536 0.281 -0.198 0.156 experience at 0-3 years only (0.503) (0.296) (0.710) (0.539) Parental childhood left-behind -0.104*** -0.062** 0.566*** 0.395*** experience at 4-12 years only (0.010) (0.009) (0.018) (0.013) Parental childhood left-behind 0.837 0.495 -0.023 0.192*** experience at 0-3 and 4-12 years (0.590) (0.303) (0.821) (0.011) Control variables d Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Observations 909 909 1,004 1,003 Source: China Family Panel Studies (2010 and 2018). Note: a Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; b Robust standard errors are presented within parentheses; c When the value of “parental early left-behind event at 0-3 years only” and “parental early left-behind event at 0-3 and 4-12 years” variables are 1, the value of children’s depression has always been 0. We therefore do not consider these two results; d Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother. Table 5 estimates the findings for various periods of parental early left-behind event. In general, the findings indicated that longer-term parental early left-behind event was linked to more significant changes in offspring’s human capital consequences. Table 5 Estimation of children’s human capital: periods of parental early left-behind event Panel A Explained variables: Health consequences Variables Physical health Mental health Self-reported health Newborn weight HAZ CES-D Depressive symptoms Depression (1) (2) (3) (4) (5) (6) c Years of parental early -0.309* -0.064 -0.031 0.784 -0.270 0.892*** left-behind event: [0.5,1) (0.170) (0.091) (0.152) (1.116) (0.542) (0.269) Years of parental early 0.061 0.072 0.232 -0.456 -0.171 - left-behind event: [1,2) (0.171) (0.091) (0.144) (0.765) (0.472) - Years of parental early -0.065*** 0.069 0.128 -0.588 0.690*** - left-behind event: [2,3) (0.012) (0.170) (0.227) (1.212) (0.120) - Years of parental early -0.004*** -0.104*** -0.204** -0.474 0.129*** -0.360 left-behind event: [3,+∞) (0.001) (0.001) (0.014) (0.681) (0.002) (1.073) Control variables c Yes Yes Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Yes Yes Observations 1,014 2,206 2,100 1,014 1,014 1,014 Panel B Explained variables: Cognitive and noncognitive abilities Variables Cognitive abilities Noncognitive abilities Word test Math test Internalizing problem behavior Externalizing problem behavior (7) (8) (9) (10) Years of parental early -0.664 0.057 -0.031 0.119 left-behind event: [0.5,1) (0.840) (0.424) (0.987) (0.698) Years of parental early -0.136 0.493 -0.247 0.344 left-behind event: [1,2) (0.704) (0.300) (0.773) (0.546) Years of parental early 1.177 -0.061*** 1.151*** 0.936*** left-behind event: [2,3) (1.206) (0.006) (0.028) (0.010) Years of parental early -0.454** -0.010* 0.418** 0.261** left-behind event: [3,+∞) (0.033) (0.001) (0.008) (0.007) Control variables c Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Observations 909 909 1,004 1,003 Source: China Family Panel Studies (2010 and 2018). Note: a Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; b Robust standard errors are presented within parentheses; c When the value of “Years of parental early left-behind event: [1,2)” and “Years of parental early left-behind event: [2,3)” variables are 1, the value of children’s depression has always been 0. We therefore do not consider these two results; d Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother. Possible mechanisms To elaborate the intergenerational effect of parental early left-behind event on human capital, we conducted a comprehensive investigation into several potential mechanisms. Based on the extant research, there exist three possible paths that connect parental early left-behind event with the human capital accumulation of offspring, namely hereditary factors, monetary allocation, and time allocation. Firstly, early left-behind event exhibited a negative association with various indicators of adult human capital, such as health and well-being. These outcomes could potentially be transmitted to the left-behind children’s descendants [17, 28], thereby leading to diminished degree of human capital outcomes in the subsequent generation. Secondly, previous studies have demonstrated the critical factor of human capital in determining the socioeconomic status of persons and families [29, 30]. Consequently, the early left-behind event could have a detrimental impact on the adult human capital consequences, thereby leading to reduce the degree of household earnings and monetary allocation in subsequent generations. The limited monetary allocation may hinder the human capital accumulation in the descendants of left-behind children. Thirdly, left-behind children are more inclined to prioritize avoiding separation from children and dedicating additional time to parenting in order to compensate for the absence of parents during their early years. Due to the absence of hereditary data, this research was limited to examining monetary and time investments. In Panel A, we investigated the correlation between parental left-behind event in early life and the family socioeconomic status in adulthood, which encompassed household earnings, consumption, and educational funding. In Panel B, we proceeded to examine the daily time distribution of individuals, exploring whether adults with childhood left-behind event allocated longer duration to childminding when they grow into parents than individuals without left-behind event. We operationalized housework and family care as surrogate indicators for assessing childcare activities. In Panel C, we directly examined the influences of parental early left-behind event on children’s left-behind situation, parental involvement in offspring’s education, and caregiver-child interaction 2 . Table 6 shows the mechanism outcomes. In Panel A, the results revealed that father left-behind event in early life has negatively impact on the household earnings, consumption, and educational funding, which could be a result of lower socioeconomic status in adulthood. Further, we found that mother with early left-behind event only adversely predicted educational funding, suggesting that mother with early left-behind event has less impact on family socioeconomic status than fathers. The findings in Panel B indicated that whether father or mother early left-behind event was positively related to the time allocated to housework and family caregiving, particularly on weekends. The findings indicated that, to some extent, left-behind children were more inclined to avoid separation from their offspring when they became parents. Moreover, the findings from Panel C indicated that offspring whose father had experienced early left-behind were comparatively less likely to be left-behind by father than those whose father had no left-behind event during early years, so was mother. Furthermore, our analysis revealed positive correlations between the mother’s early left-behind event and parents’ engagement in offspring’s education, as well as parent-child interaction, whereas father early left-behind event did not show statistically significant. In summary, the findings from Panel B and Panel C demonstrated that descendant of left-behind children were less inclined to being left-behind by contrast with descendant of children without left-behind event. Additionally, mothers with left-behind event showed increased involvement in children’s education and parent-child communication. Table 6 Possible mechanisms of parental early left-behind event on the intergenerational human capital consequence Panel A: Family socioeconomic status and educational funding Variables Household earnings Household consumption Educational funding OLS OLS OLS (1) (2) (3) Father early left-behind event -0.012** -0.039* -0.059** (0.000) (0.003) (0.001) Mother early left-behind event 0.051 0.102 -0.053*** (0.052) (0.043) (0.000) Control variables d Yes Yes Yes Regional fixed effect Yes Yes Yes Observations 2,206 2,205 2,206 Panel B: Time distribution for housework and family caregiving Variables Housework (weekdays) Family caregiving (weekdays) Housework (weekends) Family caregiving (weekends) OLS OLS OLS OLS (4) (5) (6) (7) Father early left-behind event 0.300*** 0.243* 0.296*** 0.346** (0.094) (0.142) (0.091) (0.165) Mother early left-behind event 0.117* 0.117** 0.134** 0.051** (0.011) (0.007) (0.008) (0.002) Control variables d Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Observations 2,206 2,206 2,206 2,206 Panel C: Children’s left-behind situation and parent-child communication Variables Children left behind by father Children left behind by mother Parental care for children’s education Parent-child communication Logit Logit Oprobit Oprobit (8) (9) (10) (11) Father early left-behind event -0.101** 0.342 -0.059 -0.094 (0.051) (0.288) (0.090) (0.091) Mother early left-behind event 0.005 -0.080*** 0.245*** 0.209** (0.186) (0.013) (0.087) (0.084) Control variables d Yes Yes Yes Yes Regional fixed effect Yes Yes Yes Yes Observations 2,206 2,206 1,877 1,799 Source: China Family Panel Studies (2010 and 2018). Note: a Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; b Robust standard errors are presented within parentheses; c Measures of family socioeconomic status, and educational funding, were transformed using logarithms to enhance the interpretation of estimation results. d Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother. Discussion Based on nationally representative data from the CFPS 2010 and 2018, this study demonstrates that parental left-behind event in early life significantly decreased the human capital outcomes among offspring in rural areas of China. In particular, the self-reported health of those children with parental early left-behind event is, on average, 0.053 lower than that of offspring with none parental early left-behind event in baseline estimations, and 9.5% higher in the depressive symptoms. In terms of cognitive skills, the offspring with parental early left-behind event had a significantly lower word score. With respect to non-cognitive skills, parental early left-behind event increases both internalizing problem behavior and externalizing problem behavior index. Furthermore, we employed the instrumental variable method using Conley et al.’s [ 31 ] sensitivity analysis method to ensure plausible exogenous inference, and a range of robustness checks, to mitigate potential endogeneity concerns. These results demonstrate that measures should be taken to improve the left-behind status, thereby improving the human capital of rural children. Although it seems that rural children as a whole are affected by parental early left-behind event and, therefore, see reduced human capital accumulation, our results also demonstrate that certain sub-groups experience a particularly detrimental impact. This phenomenon may be attributed to the varying roles that parents play in the lives of different groups. For instance, fathers being left-behind in early life exerts a greater impact on household earnings, consumption, and educational funding, while mother early left-behind event significantly increased attention towards children’s education and parent-child interactions. In Chinese culture, women tend to have family-oriented values, rather than self-oriented ones [ 32 ]. Therefore, women tend to exhibit a higher level of concern regarding the development of children. Moreover, the influences of parental early left-behind event were particularly noticeable among children whose parents experienced separation during later childhood years (between the ages of 4 and 12) and had long-time encounter with parental absence. While there is a lack of comprehensive articles on the influence of parental early left-behind event on offspring’s human capital accumulation among subgroups in rural areas of China, the findings of our study align with existing literature regarding the relationship between parental early adversities and negative outcomes in children. For instance, previous studies have demonstrated a negative correlation between maternal socioeconomic situation in early life and the newborn weight of offspring and that mother unhealthy lifestyle, including drug misuse, and succeeding unhealthy condition as significant mechanism that contribute to the association [ 10 , 33 ]. These suggest that the transmission of human capital across generations may serve as a crucial pathway of the intergenerational impact of parental early experiences. Prior research has indicated that the intergenerational transmission of human capital primarily arises from heredity and surrounding factors [ 34 , 35 ]. From one perspective, parents with superior human capital are more prone to pass down advantageous genetic traits to children, which can facilitate the acquisition of skills and abilities. From other perspective, parents with elevated degrees of human capital possess the capacity to create a more conducive surrounding and increased chances for children’s growth via improved economic condition [ 17 , 36 ]. Simultaneously, environmental elements can interact with genetic endowments of children, shaping their potential for optimal development [ 37 ]. Hence, childhood adversities significantly impact the development of human capital in the subsequent generation. The research possesses several notable advantages. Firstly, the research framework captures 95% of the Chinese population, thereby establishing a high degree of national representativeness. Additionally, CFPS data is an annual longitudinal survey, which means it can be used to build intergenerational data for analysis. Secondly, the substantial dataset size (2,206) employed in this study ensures robust statistical power and enhances the external validity. Thirdly, all data used in our study come from the Institute of Social Science Survey (ISSS) of Peking University utilizing a uniform sampling strategy. Lastly, we concentrate on the influence of parental early left-behind event on offspring’s human capital accumulation among various sub-categories. The contrast among various sub-groups of early left-behind event can give us more insight into the impacts of parental early left-behind event on offspring’s human capital accumulation. Although this research possesses notable strengths, it is not exempt from certain limitations. The collection of self-reported information in the CFPS constrained our ability to thoroughly investigate the depressive status and problem behavior of rural children with medical evaluation. Subsequent investigations examining causality between parental early left-behind event and offspring’s human capital in rural China would gain from concentrating on these subject matters. In terms of policy, this study indicated that the comprehensive effects of parental migration on offspring’s growth may have been significantly underrated if the long-time consequences on human capital are not taken into account. The negative correlations between parental early left-behind event and the development of offspring’s human capital further suggest that parental migration could serve as a potential factor resulting in the intergenerational transmission of penury and disparity. Our results not only provide insight into safeguarding and fostering Chinese rural children, but also hold significant policy effects for some developing countries with substantial population migration, including Pakistan, India, and Nigeria. Hence, efforts need to be made to shorten nonvoluntary division between parents and children caused by institutional constraints. Meanwhile, to promote the long-time development of human capital among rural children being left behind, it is crucial to implement research-driven prevention, like parenting projects for guardians and the equitable distribution of educational materials. Conclusion Using nationally representative data, this study explored the relationship between parental left-behind event in early life and human capital outcomes among offspring in rural areas. In addition, we examined the mechanism between parental childhood left-behind event and human capital from the perspectives of heterogeneity. Our results showed that mothers who have experienced being left behind are more likely to allocate increased time to their offspring. Conversely, fathers who have experienced being left behind tend to exhibit lower socioeconomic outcomes within homes and put fewer investments in children’s education. Abbreviations CFPS China Family Panel Studies HAZ Height-for-Age Z-Score CES-D Center for Epidemiologic Studies Depression ISSS Institute of Social Science Survey Declarations Ethics approval and consent to participate: Research has been performed in accordance with the Declaration of Helsinki. China Family Panel Studies indicates that informed consent was obtained from all subjects and their legal guardians. “Peking University Biomedical Ethics Committee” Ethics Review Number: IRB00001052-14010, approved the study protocol. Consent for publication: Not applicable. Availability of data and materials: The data of the studies is publicly available and could be accessible via website: China Family Panel Studies (CFPS). The datasets analysed during the current study are available in the [CFPS] repository, [http://www.isss.pku.edu.cn/cfps/download/logout]. We can enter the username and password, and then download the data. Username: [email protected] ; Password: o2jsouu9 Competing interests: The authors declare that they have no competing interests. Funding: We would like to acknowledge the support of the National Natural Science Foundation of China (Grants Nos. 71973100). Authors’ contributions: Conceptualization, MZ, and LH; Data curation, XS; Formal analysis, XS; Methodology, MZ; Writing—original draft, XS; Writing—review and editing, LH. Acknowledgements: We sincerely thank all the interviewers and respondents who participated in the field survey. Thanks to the people who have supported this research work. Authors’ information (optional): Xiaotong Sun 1* Mi Zhou 1* Li Huang 1 1 College of Economics and Management, Shenyang Agricultural University, Shenyang, 110866, Liaoning, China. Email: [email protected] ; [email protected] ; [email protected] References Zhao Q, Yu X, Wang X, Glauben T. The impact of parental migration on children’s school performance in rural China. China Economic Review. 2014;31:43-54. https://doi.org/10.1016/j.chieco.2014.07.013. Wang S, Yang Y, Wen YY, Cui LJ. Self-compassion promoted educational flow through increased future orientation in left-behind children groups. International Journal of Psychology. 2023;58(4):351-359. https://doi.org/10.1002/ijop.12904. Zheng XD, Zhang Y, Jiang WY. Migrating with parents or left-behind: Associations of internal migration with cognitive and noncognitive outcomes among Chinese children. Current Psychology. 2022b;1-22. https://doi.org/10.1007/s12144-022-03095-x. Chen C. Left-behind children’s cognitive development in China: Gain in financial capital versus loss in parental capital. Sociological Perspectives. 2023;66(3):523-542. https://doi.org/10.1177/07311214221145059. Abbasi BN, Luo Z, Sohail A. Effect of parental migration on the noncognitive abilities of left-behind school-going children in rural China. Humanities and Social Sciences Communications. 2023;10(11):1-14. https://doi.org/10.1057/s41599-022-01496-8. Wu YC, Guo ZL. An analysis of the nutritional status of left-behind children in rural China and the impact mechanisms of child malnutrition. Children and Youth Services Review. 2020;119:105598. https://doi.org/10.1016/j.childyouth.2020.105598. Jiang Y, Xiao H, Yang F. Accompanying your children: Living without parents at different stages of pre-adulthood and individual physical and mental health in adulthood. Front. Public Health. 2023;11:992539. https://doi.org/10.3389/fpubh.2023.992539. Zhang H, Chi P, Long H, Ren X. Bullying victimization and depression among left-behind children in rural China: roles of self-compassion and hope. Child Abuse & Neglect. 2019;96(1):104072. https://doi.org/10.1016/j.chiabu.2019.104072. Tan DQ, Xie RB, Song SC, Ding W, Wu W, Li WJ. How does parent-child attachment influence left-behind children’s loneliness and depression: The mediating roles of peer attachment and teacher-student relationship. Child: Care, Health and Development. 2023;1-11. https://doi.org/10.1111/cch.13118. Gavin AR, Thompson E, Rue T, Guo Y. Maternal early life risk factors for offspring birth weight: Findings from the add health study. Prevention Science. 2012;13(2):162-172. https://doi.org/10.1007/s11121-011-0253-2. Zheng XD. Fang ZY, Wang YJ, Fang XM. When left-behind children become adults and parents: The long-term human capital consequences of parental absence in China. China Economic Review. 2022a;74:101821. https://doi.org/10.1016/j.chieco.2022.101821. Liu J, Zheng X, Parker M, Fang X. Childhood left-behind experience and employment quality of new-generation migrants in China. Population Research and Policy Review. 2020;39(4):691-718. https://doi.org/10.1007/s11113-020-09568-w. Wang H, Cheng Z, Zhe WB, Chen Y. Childhood left-behind experience and labour market outcomes in China. Journal of Business Research. 2021;132:196-207. https://doi.org/10.1016/j.jbusres.2021.04.014. Currie J. Child health as human capital. Health Economics (United Kingdom). 2020;29(4):452-463. https://doi.org/10.1002/hec.3995. Almond D, Currie J. Human capital development before age five. In, vol. 4. Handbook of labor economics, Elsevier B.V. Issue PART B; 2011. https://doi.org/10.1016/S0169-7218(11)02413-0. Heckman JJ, Mosso S. The economics of human development and social mobility. Annual Review of Economics. 2014;6(1):689-733. https://doi.org/10.1146/annurev-economics-080213-040753. Aizer A, Currie J. The intergenerational transmission of inequality: Maternal disadvantage and health at birth. Science. 2014;344(6186):856-861. https://doi.org/10.1126/science.1251872. Zheng XD, Fang XM, Brown DS. Social pensions and child health in rural China. Journal of Development Studies. 2020;56(3):545-559. https://doi.org/10.1080/00220388.2019.1577387. Huang L, Sun XT, Zhou M. Depressive symptoms in Chinese laborers: Prevalence and correlated factors among subgroups. Journal of Affective Disorders. 2020;268:141-149. https://doi.org/10.1016/j.jad.2020.03.013. Sun XT, Zhou M, Huang L, Nuse B. Depressive costs: medical expenditures on depression and depressive symptoms among rural elderly in China. Public Health. 2020;181:141-150. https://doi.org/10.1016/j.puhe.2019.12.011. Rankin SH, Galbraith ME. Reliability and validity data for a Chinese translation of the center for epidemiological studies-depression. Psychological Reports. 1993;73(3):1291-1298. https://doi.org/10.2466/pr0.1993.73.3f.1291. Greenberger E, Chena C, Tallya S, Dong Q. Family, peer, and individual correlates of depressive symptomatology among U.S. and Chinese adolescents. Journal of Consulting & Clinical Psychology. 2000;68:209-219. https://doi.org/10.1037//0022-006X.68.2.209. Stock J, Yogo M. Testing for weak instruments in linear IV regression. NBER Technical Working Papers. 2002;284. https://doi.org/10.3386/t0284. Altonji JG, Elder TE, Taber CR. Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. Journal of Political Economy. 2005;113(1):151-184. https://doi.org/10.1086/426036. Oster E. Unobservable selection and coefficient stability: Theory and evidence. Journal of Business and Economic Statistics. 2019;37(2):187-204. https://doi.org/10.1080/07350015.2016.1227711. Botha F, de New JP, de New SC, Ribar DC, Salamanca N. Implications of COVID-19 labour market shocks for inequality in financial wellbeing. Journal of Population Economics. 2021;34(2):655-689. https://doi.org/10.1007/s00148-020-00821-2. Yu Y. The male breadwinner/female homemaker model and perceived marital stability: A comparison of Chinese wives in the United States and urban China. Journal of Family and Economic Issues. 2015;36(1):34-47. https://doi.org/10.1007/s10834-014-9417-0. Almond D, Currie J, Duque V. Childhood circumstances and adult outcomes: Act II. Journal of Economic Literature. 2018;56:1360-1446. https://doi.org/10.1257/jel.20171164. Heckman JJ, Stixrud J, Urzua S. The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics. 2006;24(3):411-482. https://doi.org/10.1086/504455. Manuelli RE, Seshadri A. Human capital and the wealth of nations. American Economic Review. 2014;104(9):2736-2762. https://doi.org/10.1257/aer.104.9.2736. Conley TG, Hansen CB, Rossi PE. Plausibly exogenous. Review of Economics and Statistics. 2012;94(1):260-272. https://doi.org/10.1162/REST_a_00139. Xiong H, Bairner A, Tang Z. Embracing city life: physical activities and the social integration of the new generation of female migrant workers in urban China. Leisure Studies. 2020;39(6):782-796. https://doi.org/10.1080/02614367.2020.1800802. Kane JB, Harris KM, Siega-Riz AM. Intergenerational pathways linking maternal early life adversity to offspring birthweight. Social Science and Medicine. 2018;207:89-96. https://doi.org/10.1016/j.socscimed.2018.04.049. Black SE, Devereux PJ, Salvanes KG. Why the apple doesn’t fall far: Understanding intergenerational transmission of human capital. American Economic Review. 2005;95(1):437-449. https://doi.org/10.1257/0002828053828635. Dong Y, Luo R, Zhang L, Liu C, Bai Y. Intergenerational transmission of education: The case of rural China. China Economic Review. 2019;53:311-323. https://doi.org/10.1016/j.chieco.2018.09.011. Carneiro P, Meghir C, Parey M. Maternal education, home environments, and the development of children and adolescents. Journal of the European Economic Association. 2013;11(Suppl.1):123-160. https://doi.org/10.1111/j.1542-4774.2012.01096.x. Thompson O. Genetic mechanisms in the intergenerational transmission of health. Journal of Health Economics. 2014;35(1):132-146. https://doi.org/10.1016/j.jhealeco.2014.02.003 Footnotes Due to space limitations, the results of the first stage are not listed. Interested parties can request them from the author. We measured the left-behind status of children using two binary variables, suggesting whether the offspring was left-behind by father or mother, based on pertinent issues in the children’s database of the 2018 CFPS. “Children left behind by father” was assigned 1 if a child had lived with their father for below 6 months in the previous year. At the same time, “children left behind by mother” was assigned 1 if the time duration for which child lived with their mother in previous 12 months were below 6 months. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.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-3833421","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266840389,"identity":"77fc22a3-2697-4c7d-8fdd-8b5df433be53","order_by":0,"name":"Xiaotong Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2SsUrEQBCGNxwkzUIsJxx6r7ByEITkYWY52G3WAxFE4ZBUSaftCb6EbxAIpIqtBK6JjZWFNkdsDndZRIscsRTcrxn4mY/5iyHE4firoB1e9yObjCpgttiXPK4Qq/jwK2W2Pq2hW6XX4bR4uvroK50sXoBcJjwLHsshhbXLBcNaQHTfXGwoVjoRMZBG8owucVABddyhXwFrldgQowDG4OUVz4Cy4WKKlbizylmPppjcgrfbr5BWX+G5UWRNTDGd6CvZfoU1r3PGb0R0t1aTKRWS6uT8BGs5z6kaLlaoOOq3aRiCfH7v0+RoVsiH9m2VHN4GzXCxb2wNSg7Q/oM/sq8JOjvDcnzX4XA4/hWfCnVdH6htFpQAAAAASUVORK5CYII=","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Sun","suffix":""},{"id":266840390,"identity":"caa4585a-6f6a-49aa-b6d2-7c3112d59f77","order_by":1,"name":"Mi Zhou","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mi","middleName":"","lastName":"Zhou","suffix":""},{"id":266840391,"identity":"c7972517-efe8-4bb9-aea3-5559bc203a7c","order_by":2,"name":"Li Huang","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-01-04 03:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3833421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3833421/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60152360,"identity":"71a2e32f-ccfa-44df-98dc-aa37cd0a1323","added_by":"auto","created_at":"2024-07-12 11:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":923485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3833421/v1/df1ced3d-c04d-40c3-8c28-933eabb0a77e.pdf"},{"id":49669441,"identity":"15ca8807-cc50-4724-b065-f795e816b433","added_by":"auto","created_at":"2024-01-16 08:21:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41859,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3833421/v1/980ec858e102de3b1f436852.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Long-Time Consequences of Parental Early Left-Behind Event on the Human Capital of Rural Children in China","fulltext":[{"header":"Background","content":"\u003cp\u003eSince 1980s, China has experienced a rapid process of urbanization, characterized by a substantial influx of migrants from rural to urban areas. This migration phenomenon played an important part in the urban development of China. In 2021, an estimated 292.5\u0026nbsp;million individuals migrated from rural areas to urban centers in pursuit of improved livelihood prospects. The rising trend of population mobility has led to a significant multitude of rural left-behind offspring whose parents move to urban for employment opportunities. Extensive research has provided convincing demonstration about the contemporary impacts of parental migration on the human capital accumulation of stay-at-home children. These impacts have mixed positive, negative, or neutral. Specifically, research has verified the influences on various domains, including educational achievement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], cognitive or noncognitive skills [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], nutrition and physical health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], along with psychological well-being [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, the literature has rapidly accumulated substantial evidence that highlights the critical connections between childhood event and a wide array of growth consequences in offspring throughout lives [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On the one hand, parents\u0026rsquo; early left-behind event will positively impact offspring\u0026rsquo;s human capital accumulation. When parents have experience of being left behind, they tend to allocate a greater amount of time to housework and childcare duties upon becoming parents. This reduces the likelihood of their own children becoming left-behind and enhances parent-child interactions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On the other hand, the parents\u0026rsquo; event of stay-at-home in early life will hinder the human capital accumulation, consequently obstructing the future employment quality and wage levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, the left-behind event during early life may result in a significant reduction in both household earnings and monetary allocation directed to offspring.\u003c/p\u003e \u003cp\u003eWith the presence of conflicting findings, this study aims to ascertain whether parental early left-behind event has beneficial or detrimental influences on the human capital outcomes of rural Chinese offspring. To achieve the article objective, our research inspects the comprehensive impacts of parental early left-behind event on the children\u0026rsquo;s human capital, while also exploring the underlying mechanisms through which the effects manifest. Specifically, the mechanisms involve monetary allocation, and time allocation, which may vary depending on the role of father or mother with early left-behind event. To do so, this paper uses econometric models to analyze the causal influence of parental early left-behind event on the offspring\u0026rsquo;s human capital accumulation.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and sample choice\u003c/h2\u003e \u003cp\u003eThe dataset employed in this research is sourced from the China Family Panel Studies (CFPS) data, encompassing the years 2010 and 2018. The CFPS dataset encompasses distinct modules designed for the analysis of adults and children. Individuals whose age is 16 years or older autonomously accomplished the research survey designated for adults, whereas data pertaining to children below the age of 15 were provided by their parents or guardians. Furthermore, a self-report questionnaire was provided to children between the ages of 10 and 15 in order to supplement the information obtained from parents\u0026rsquo; answers. CFPS dataset in 2010 includes evaluations of parental absence during early life, while the 2018 CFPS survey includes a range of human capital indicators of children, offering a distinct possibility to investigate the long-time outcomes of human capital intergenerational transmission. Overall, 2206 children aged 0 to 15, along with their parents, were included in the sample to investigate the association between parents\u0026rsquo; left-behind event during childhood and the human capital skills of offspring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParental early left-behind event\u003c/h2\u003e \u003cp\u003eTo assess the condition of parental early left-behind event, our study constructed a binary categorical variable that assigned 1 to individuals who had been abandoned by father or mother for a minimum duration of six months by age 12. To investigate various aspects of early left-behind event, this article created three distinct categories of binary variables to represent the model, age, and period of parental early left-behind event. Different models of parental early left-behind event contained early left-behind by father, and early left-behind by mother during ages 0\u0026ndash;12. For the age of parental early left-behind event, we created three dummies to compare the influence of parental early left-behind event occurring between the ages of 0\u0026ndash;3, 4\u0026ndash;12, and during both of these periods. Considering that prolonged left-behind experience is commonly defined as separation for three years and above [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], our study categorized the period of parental early left-behind event into four time periods: 0.5-1 year, 1\u0026ndash;2 years, 2\u0026ndash;3 years, and 3 years or longer. The descriptive statistics of parental early left-behind event is presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHuman capital outcomes of children\u003c/h2\u003e \u003cp\u003eHuman capital pertains to the unique asset which is inherent in individuals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There is a growing recognition that, in addition to education, other pertinent characteristics (such as health, cognitive, and noncognitive abilities) are equally vital components of human capital, as they contribute to the generation of income and other forms of returns [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, children\u0026rsquo;s human capital indicators comprised of physical health, mental health, cognitive skills, and noncognitive skills in this research.\u003c/p\u003e \u003cp\u003eThe measurement of physical health was conducted through a self-reported health issue, employing an answer that ranged from 1 (poor health) to 5 (excellent health). And then, the evaluation of physical health also incorporated two variables, newborn weight and height-for-age z-score (HAZ), which have been established as influential indicators of future health consequences [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Greater levels of newborn weight or HAZ typically correspond to superior physical health in children.\u003c/p\u003e \u003cp\u003eThe mental health questionnaire employed in this study primarily comprises inquiries derived from the Center for Epidemiologic Studies Depression Scale (CES-D). According to Table S2, the CES-D scale is composed of 20 items, with scores varying between 0 and 60. We establish the threshold according to the 80th and 95th percentile: CES-D scores ranging from 16 to 27 indicate \u0026ldquo;depressive symptoms\u0026rdquo;, while scores equal to or exceeding 28 indicate \u0026ldquo;depression\u0026rdquo; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The CES-D scale in its Chinese version has been extensively employed in previous studies, demonstrating its reliability and validity within Chinese population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study measured cognitive abilities through the test of word and math. noncognitive measures were measured using internalizing problem behavior and externalizing problem behavior (see Table S3).\u003c/p\u003e \u003cp\u003eTo minimize estimation bias caused by confounding heterogeneity, we included numerous control variables in our article. Table S4 presents the descriptive statistics for control variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParental early left-behind event and offspring\u0026rsquo;s human capital consequences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary findings about the influence of parental early left-behind event on offspring\u0026rsquo;s human capital consequences are shown in Table 1. Children\u0026rsquo;s health outcomes are displayed in Panel A of Table 1. The findings indicated that parental childhood left-behind event was noticeably related to a decrease in the levels of children\u0026rsquo;s self-reported health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePanel B of Table 1 presents the estimated values of cognitive and noncognitive abilities. Column (7) demonstrated that parental early left-behind event was significantly and negatively related to children\u0026rsquo;s word test. Results in column (8) indicated that parental early left-behind event was not significantly linked to offspring\u0026rsquo;s math test score. In terms of noncognitive skills, it is noteworthy that parental early left-behind event was positively relevant to offspring\u0026rsquo;s internalizing or externalizing problem behavior index. In general, the findings from baseline regression revealed that parental early left-behind event adversely predicted most human capital consequences of offspring throughout their lives.\u003c/p\u003e\n\u003cp\u003eTable 1 Impact of parental early left-behind event on children\u0026rsquo;s human capital\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003ePanel A Explained Variables: Health consequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.21212121212121%\" rowspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" colspan=\"4\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" colspan=\"3\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.584415584415584%\"\u003e\n \u003cp\u003eSelf-reported health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.48051948051948%\" colspan=\"2\"\u003e\n \u003cp\u003eNewborn weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.07792207792208%\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eDepressive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.584415584415584%\"\u003e\n \u003cp\u003eOprobit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.48051948051948%\" colspan=\"2\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.07792207792208%\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eLogit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eLogit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.584415584415584%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.48051948051948%\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.07792207792208%\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eParental early\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e-0.067***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.017**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e-0.122***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e0.041***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(0.529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eParental early\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e-0.053***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.048***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e-0.066**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e0.095***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e0.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(0.524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e2,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003ePanel B Explained variables: Cognitive and noncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.21212121212121%\" rowspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" colspan=\"4\"\u003e\n \u003cp\u003eCognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" colspan=\"3\"\u003e\n \u003cp\u003eNoncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.77922077922078%\" colspan=\"2\"\u003e\n \u003cp\u003eWord test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003eMath test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.07792207792208%\"\u003e\n \u003cp\u003eInternalizing problem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003eExternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.77922077922078%\" colspan=\"2\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.07792207792208%\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.77922077922078%\" colspan=\"2\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.07792207792208%\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eParental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.116*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e0.268***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e0.263**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eParental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e0.427***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e0.363***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e1,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e1,003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: China Family Panel Studies (2010 and 2018). Note: \u003csup\u003ea\u003c/sup\u003e Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; \u003csup\u003eb\u003c/sup\u003e Robust standard errors are presented within parentheses; \u003csup\u003ec\u003c/sup\u003e Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIV regression and UCI approach\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper utilized historical community-level migration rate as instrument and performed IV estimation. The first-stage estimation of IV analysis indicated a notable connection between the community-level migration rate and the likelihood of parental early left-behind event. The F statistics of IV estimation exhibited significantly higher values compared to the commonly accepted boundary of 10 for identifying weak instruments [23]. Hence, the results obtained through the IV results indicated no evidence of the weak-IV issue in this study\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTable 2 displays the secondary IV regression about the human capital consequences of children, utilizing community-level of migration rate as IV. The findings from IV analysis were normally in line with the Table 1 regression. Parental early left-behind event negatively affected offspring\u0026rsquo;s self-reported health, newborn weight, word and math test scores. It also significantly increased children\u0026rsquo;s internalizing problem behavior index, externalizing problem behavior index, and the probability of being depression.\u003c/p\u003e\n\u003cp\u003eTable 2 Impact of parental early left-behind event on children\u0026rsquo;s human capital (Instrumental Variable regression)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel A Explained variables: Health consequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\" rowspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"4\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"4\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003eSelf-reported\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\" colspan=\"2\"\u003e\n \u003cp\u003eNewborn weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32876712328767%\"\u003e\n \u003cp\u003eHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003eDepressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003eCMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\" colspan=\"2\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32876712328767%\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003eIVprobit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003eIVprobit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32876712328767%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003eParental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.069***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.234***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e2.568***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e1.485***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e(0.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e2,178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e2,073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e1,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\"\u003e\n \u003cp\u003e1,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e1,002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel B Explained variables: Cognitive and noncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\" rowspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"4\"\u003e\n \u003cp\u003eCognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.795918367346935%\" colspan=\"4\"\u003e\n \u003cp\u003eNoncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" colspan=\"2\"\u003e\n \u003cp\u003eWord test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.61111111111111%\" colspan=\"2\"\u003e\n \u003cp\u003eMath test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.38888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003eInternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.38888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003eExternalizing problem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" colspan=\"2\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.61111111111111%\" colspan=\"2\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.38888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.38888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" colspan=\"2\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.61111111111111%\" colspan=\"2\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.38888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.38888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eParental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003e-3.982***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.205***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e2.915***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e1.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003e(1.517)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e(1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003e906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\"\u003e\n \u003cp\u003e906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: China Family Panel Studies (2010 and 2018). Note: \u003csup\u003ea\u003c/sup\u003e Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; \u003csup\u003eb\u003c/sup\u003e Robust standard errors are presented within parentheses; \u003csup\u003ec\u003c/sup\u003e Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother.\u003c/p\u003e\n\u003cp\u003eIn order to alleviate the exclusion restriction associated with the instrumental variable utilized in the research, we adopted Conley\u0026rsquo;s UCI method to test plausibly exogenous inference. Table S5 presents the thresholds at which the direct impacts of IV on various children\u0026rsquo;s human capital consequences become statistically insignificant in second-stage. The most immediate impacts of community-level migration proportion on children\u0026rsquo;s health consequences exhibited a percentage range of 25.86% to 57.09%. Only when the direct effect of community-level migration rate on noncognitive\u0026nbsp;abilities\u0026nbsp;constitutes a minimum of 14.03% of its overall impact do the IV results become insignificant. Overall, the plausibly exogenous inference of sensitivity analysis confirmed the robustness of IV used in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness test about sensitivity analysis of unobservables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to using the IV method, a robustness test was conducted to assess the potential effects of unobservables. To be specific, we utilized the approach developed by Altonji et al. [24] and Oster [25] to compute the range for the predicted value \u0026nbsp;of parents\u0026rsquo; early experience in regression models. This analysis aimed to assess the sensitivity to the choosing of control variables. This approach relies on the assumption of selection ratio, which posits that unobserved factors are proportional to the observable factors selected at random from all influence variables of children\u0026rsquo;s human capital consequences. Based on the premise, the robustness of the coefficient could be assessed by the informativeness of the chosen control variables. Based on the study of Botha et al. [26], our study adopted the assumption =1.3(), where \u0026nbsp;denotes the goodness of fit when accounting for all potential determinants associated with children\u0026rsquo;s human capital outcomes, and \u0026nbsp;is derived from the baseline regressions using observables. Table S6 revealed that the coefficient range of the connections between parental early left-behind event and the majority of children\u0026rsquo;s human capital consequences did not involve zero. Moreover, the minimum ratio of choosing on unobservables to choosing on observables () was found to be 2.554 for offspring\u0026rsquo;s human capital consequences, with the exception of mental health and math scores. This value signifies that only when the choosing on unobservables was 2.554 times or higher than that on observables, unobserved variables will entirely eliminate the observed impacts. According to these findings, we cannot dismiss the possibility of the causality between parental early left-behind event and offspring\u0026rsquo;s human capital outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogenous: model, age, and period of parental early left-behind event\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 reports the associations between varying models of parental early left-behind event and children\u0026rsquo;s human capital accumulation. These findings revealed that father\u0026rsquo;s early left-behind event was significantly and adversely connected to offspring\u0026rsquo;s health outcomes and cognitive abilities (involving word and math performance), and was positively correlated with internalizing problem behavior. On the contrary, mother early left-behind event was more beneficial to offspring\u0026rsquo;s health outcomes and cognitive skills, but mother being left behind had a stronger effect on children\u0026rsquo;s externalizing problem behavior index. The variation in findings based on parental gender may be attributed to the traditional gender roles that assign distinct spheres to men as breadwinners and women as homemakers [27]. The human capital of parents and the long-time investments of family, including financial and spiritual aspects, play an important part in the outcomes of offspring\u0026rsquo;s human capital. Moreover, early left-behind event has a detrimental influence on the consequence of human capital in adulthood, consequently hindering both the level of occupation and the degree of salary in subsequent stages of career [12, 13]. As a result, the early left-behind event, particularly for the household sustainer who is conventionally males, could result in a more significant reduction in household earnings and monetary allocation of offspring. Additionally, females, who generally assume the role of housewives, are more inclined to allocate time to children if they have experienced being left behind in early life. \u0026nbsp;This is because they possess a deeper understanding of the potential consequences associated with the absence of parental care, in contrast to mothers who did not experience such separation.\u003c/p\u003e\n\u003cp\u003eTable 3 Estimation of children\u0026rsquo;s human capital: models of\u0026nbsp;parental early left-behind event\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel A Explained variables: Health consequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" colspan=\"4\" style=\"width: 43.2033%;\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.37373737373738%\" colspan=\"4\" style=\"width: 35.5316%;\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.17808219178082%\"\u003e\n \u003cp\u003eSelf-reported\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003eNewborn weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.068493150684931%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003eHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.698630136986301%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003eDepressive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.17808219178082%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.068493150684931%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.698630136986301%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.80821917808219%\" colspan=\"2\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eFather early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-0.013***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.184**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003e-0.475***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003e-0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.281***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003e(0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.376)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e(0.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eMother early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e0.142*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003e0.363***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e-0.624***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e(0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003e(0.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 13.7282%;\"\u003e\n \u003cp\u003e2,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 12.6513%;\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" colspan=\"2\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel B Explained variables: Cognitive and noncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" colspan=\"4\" style=\"width: 43.2033%;\"\u003e\n \u003cp\u003eCognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.37373737373738%\" colspan=\"4\" style=\"width: 35.5316%;\"\u003e\n \u003cp\u003eNoncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28%\" colspan=\"2\"\u003e\n \u003cp\u003eWord test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003eMath test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003eInternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" colspan=\"2\"\u003e\n \u003cp\u003eExternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28%\" colspan=\"2\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" colspan=\"2\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eFather early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.563**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003e-0.093*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003e0.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eMother early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.903***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003e0.458**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\"\u003e\n \u003cp\u003e0.844*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eleft-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003e(0.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.476)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" valign=\"top\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" valign=\"top\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" valign=\"top\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" valign=\"top\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 19.9193%;\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\" style=\"width: 20.0538%;\"\u003e\n \u003cp\u003e1,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"2\"\u003e\n \u003cp\u003e1,003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: China Family Panel Studies (2010 and 2018). Note: \u003csup\u003ea\u003c/sup\u003e Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; \u003csup\u003eb\u003c/sup\u003e Robust standard errors are presented within parentheses; \u003csup\u003ec\u003c/sup\u003e Control variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother.\u003c/p\u003e\n\u003cp\u003eTable 4 shows the results for the various ages of parental early left-behind event. The findings indicated that parental experience of being left behind in the ages of 4 and 12, was linked to a higher likelihood of physical health and mental well-being issues among children. Meanwhile, the experience of being left behind during the period of 4-12 years old had a more adverse impact on the offspring\u0026rsquo;s cognitive skills and noncognitive skills.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Estimation of children\u0026rsquo;s human capital: ages of\u0026nbsp;parental early left-behind event\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel A Explained variables: Health consequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" colspan=\"4\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"4\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.388059701492537%\"\u003e\n \u003cp\u003eSelf-reported health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91044776119403%\" colspan=\"2\"\u003e\n \u003cp\u003eNewborn weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\"\u003e\n \u003cp\u003eHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91044776119403%\" colspan=\"2\"\u003e\n \u003cp\u003eDepressive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.417910447761194%\"\u003e\n \u003cp\u003eDepression\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.388059701492537%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91044776119403%\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91044776119403%\" colspan=\"2\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.417910447761194%\"\u003e\n \u003cp\u003e(6)\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eParental childhood left-behind\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e-0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eexperience at 0-3 years only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e(0.660)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eParental childhood left-behind\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e-0.078***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.039***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e-0.090*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e0.035**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.131***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eexperience at 4-12 years only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e(0.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eParental childhood left-behind\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e-0.055***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.074**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e-0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e0.077***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eexperience at 0-3 and 4-12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e(0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e(0.728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e2,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" colspan=\"2\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel B Explained variables: Cognitive and noncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" colspan=\"4\"\u003e\n \u003cp\u003eCognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"4\"\u003e\n \u003cp\u003eNoncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.34328358208955%\" colspan=\"2\"\u003e\n \u003cp\u003eWord test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" colspan=\"2\"\u003e\n \u003cp\u003eMath test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.880597014925375%\" colspan=\"2\"\u003e\n \u003cp\u003eInternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.880597014925375%\" colspan=\"2\"\u003e\n \u003cp\u003eExternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ebehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.34328358208955%\" colspan=\"2\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" colspan=\"2\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.880597014925375%\" colspan=\"2\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.880597014925375%\" colspan=\"2\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eParental childhood left-behind\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eexperience at 0-3 years only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.710)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eParental childhood left-behind\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.104***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.062**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.566***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.395***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eexperience at 4-12 years only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eParental childhood left-behind\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.192***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eexperience at 0-3 and 4-12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.590)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e1,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e1,003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: China Family Panel Studies (2010 and 2018). Note: \u003csup\u003ea\u003c/sup\u003e Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; \u003csup\u003eb\u003c/sup\u003e Robust standard errors are presented within parentheses; \u003csup\u003ec\u003c/sup\u003e When the value of \u0026ldquo;parental early left-behind event at 0-3 years only\u0026rdquo; and \u0026ldquo;parental early left-behind event at 0-3 and 4-12 years\u0026rdquo; variables are 1, the value of children\u0026rsquo;s depression has always been 0. We therefore do not consider these two results; \u003csup\u003ed\u0026nbsp;\u003c/sup\u003eControl variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother.\u003c/p\u003e\n\u003cp\u003eTable 5 estimates the findings for various periods of parental early left-behind event. In general, the findings indicated that longer-term parental early left-behind event was linked to more significant changes in offspring\u0026rsquo;s human capital consequences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Estimation of children\u0026rsquo;s human capital: periods of\u0026nbsp;parental early left-behind event\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel A Explained variables: Health consequences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.816326530612244%\" colspan=\"4\"\u003e\n \u003cp\u003ePhysical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"4\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.285714285714285%\"\u003e\n \u003cp\u003eSelf-reported\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" colspan=\"2\"\u003e\n \u003cp\u003eNewborn weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.714285714285714%\" colspan=\"2\"\u003e\n \u003cp\u003eDepressive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.714285714285714%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.285714285714285%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.714285714285714%\" colspan=\"2\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.714285714285714%\"\u003e\n \u003cp\u003e(6)\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e-0.309*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.892***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eleft-behind event: [0.5,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e(0.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e(0.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e(1.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e(0.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e-0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eleft-behind event: [1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e(0.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e(0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e(0.765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e-0.065***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e-0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e0.690***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eleft-behind event: [2,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e(0.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e(1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e-0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.104***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e-0.204**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e-0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e0.129***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e-0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eleft-behind event: [3,+\u0026infin;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e(0.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e(1.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" colspan=\"2\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ePanel B Explained variables: Cognitive and noncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.816326530612244%\" colspan=\"4\"\u003e\n \u003cp\u003eCognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"4\"\u003e\n \u003cp\u003eNoncognitive abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.80281690140845%\" colspan=\"2\"\u003e\n \u003cp\u003eWord test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" colspan=\"2\"\u003e\n \u003cp\u003eMath test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" colspan=\"2\"\u003e\n \u003cp\u003eInternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" colspan=\"2\"\u003e\n \u003cp\u003eExternalizing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproblem\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ebehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.80281690140845%\" colspan=\"2\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" colspan=\"2\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" colspan=\"2\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" colspan=\"2\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eleft-behind event: [0.5,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eleft-behind event: [1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.704)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.061***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e1.151***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.936***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eleft-behind event: [2,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e(1.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eYears of parental early\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.454**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e0.418**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e0.261**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eleft-behind event: [3,+\u0026infin;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"2\"\u003e\n \u003cp\u003e1,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" colspan=\"2\"\u003e\n \u003cp\u003e1,003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: China Family Panel Studies (2010 and 2018). Note: \u003csup\u003ea\u003c/sup\u003e Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; \u003csup\u003eb\u003c/sup\u003e Robust standard errors are presented within parentheses; \u003csup\u003ec\u003c/sup\u003e When the value of \u0026ldquo;Years of parental early left-behind event: [1,2)\u0026rdquo; and \u0026ldquo;Years of parental early left-behind event: [2,3)\u0026rdquo; variables are 1, the value of children\u0026rsquo;s depression has always been 0. We therefore do not consider these two results; \u003csup\u003ed\u0026nbsp;\u003c/sup\u003eControl variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePossible mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elaborate the intergenerational effect of parental early left-behind event on human capital, we conducted a comprehensive investigation into several potential mechanisms. Based on the extant research, there exist three possible paths that connect parental early left-behind event with the human capital accumulation of offspring, namely hereditary factors, monetary allocation, and time allocation. Firstly, early left-behind event exhibited a negative association with various indicators of adult human capital, such as health and well-being. These outcomes could potentially be transmitted to the left-behind children\u0026rsquo;s descendants [17,\u0026nbsp;28], thereby leading to diminished degree of human capital outcomes in the subsequent generation. Secondly, previous studies have demonstrated the critical factor of human capital in determining the socioeconomic status of persons and families [29,\u0026nbsp;30]. Consequently, the early left-behind event could have a detrimental impact on the adult human capital consequences, thereby leading to reduce the degree of household earnings and monetary allocation in subsequent generations. The limited monetary allocation may hinder the human capital accumulation in the descendants of left-behind children. Thirdly, left-behind children are more inclined to prioritize avoiding separation from children and dedicating additional time to parenting in order to compensate for the absence of parents during their early years.\u003c/p\u003e\n\u003cp\u003eDue to the absence of hereditary data, this research was limited to examining monetary and time investments. In Panel A, we investigated the correlation between parental left-behind event in early life and the family socioeconomic status in adulthood, which encompassed household earnings, consumption, and educational funding. In Panel B, we proceeded to examine the daily time distribution of individuals, exploring whether adults with childhood left-behind event allocated longer duration to childminding when they grow into parents than individuals without left-behind event. We operationalized housework and family care as surrogate indicators for assessing childcare activities. In Panel C, we directly examined the influences of parental early left-behind event on children\u0026rsquo;s left-behind situation, parental involvement in offspring\u0026rsquo;s education, and caregiver-child interaction\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 shows the mechanism outcomes. In Panel A, the results revealed that father left-behind event in early life has negatively impact on the household earnings, consumption, and educational funding, which could be a result of lower socioeconomic status in adulthood. Further, we found that mother with early left-behind event only adversely predicted educational funding, suggesting that mother with early left-behind event has less impact on family socioeconomic status than fathers. The findings in Panel B indicated that whether father or mother early left-behind event was positively related to the time allocated to housework and family caregiving, particularly on weekends. The findings indicated that, to some extent, left-behind children were more inclined to avoid separation from their offspring when they became parents. Moreover, the findings from Panel C indicated that offspring whose father had experienced early left-behind were comparatively less likely to be left-behind by father than those whose father had no left-behind event during early years, so was mother. Furthermore, our analysis revealed positive correlations between the mother\u0026rsquo;s early left-behind event and parents\u0026rsquo; engagement in offspring\u0026rsquo;s education, as well as parent-child interaction, whereas father early left-behind event did not show statistically significant. In summary, the findings from Panel B and Panel C demonstrated that descendant of left-behind children were less inclined to being left-behind by contrast with descendant of children without left-behind event. Additionally, mothers with left-behind event showed increased involvement in children\u0026rsquo;s education and parent-child communication.\u003c/p\u003e\n\u003cp\u003eTable 6 Possible mechanisms of parental early left-behind event on the intergenerational human capital consequence\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003ePanel A: Family socioeconomic status and educational funding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eHousehold earnings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003eHousehold\u0026nbsp;\u003c/p\u003e\n \u003cp\u003econsumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eEducational\u0026nbsp;\u003c/p\u003e\n \u003cp\u003efunding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.333333333333332%\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.333333333333332%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eFather early left-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.012**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.059**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMother early left-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.053***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003e2,205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003ePanel B: Time distribution for housework and family caregiving\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eHousework (weekdays)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eFamily caregiving (weekdays)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eHousework (weekends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eFamily\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ecaregiving\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(weekends)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.333333333333332%\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.333333333333332%\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eFather early left-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.300***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.243*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.296***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.346**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMother early left-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.117*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.117**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003e0.051**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003ePanel C: Children\u0026rsquo;s left-behind situation and parent-child communication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eChildren\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eleft behind\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eby father\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eChildren\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eleft behind\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eby mother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eParental care for children\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eParent-child communication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.333333333333332%\"\u003e\n \u003cp\u003eLogit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eLogit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eOprobit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\"\u003e\n \u003cp\u003eOprobit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.333333333333332%\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\"\u003e\n \u003cp\u003e(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eFather early left-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.101**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMother early left-behind event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e-0.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.245***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.209**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e(0.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e(0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e(0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eControl variables \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eRegional fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e2,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e1,877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e1,799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: China Family Panel Studies (2010 and 2018). Note: \u003csup\u003ea\u003c/sup\u003e Statistical significance is indicated by ***/**/* denoting the 1%, 5%, and 10% levels, respectively; \u003csup\u003eb\u003c/sup\u003e Robust standard errors are presented within parentheses; \u003csup\u003ec\u003c/sup\u003e Measures of family socioeconomic status, and educational funding, were transformed using logarithms to enhance the interpretation of estimation results. \u003csup\u003ed\u0026nbsp;\u003c/sup\u003eControl variables involve gender, age, ethnicity, education in years, residency, family size, education of father in years, education of mother in years, political identity of father, and political identity of mother.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on nationally representative data from the CFPS 2010 and 2018, this study demonstrates that parental left-behind event in early life significantly decreased the human capital outcomes among offspring in rural areas of China. In particular, the self-reported health of those children with parental early left-behind event is, on average, 0.053 lower than that of offspring with none parental early left-behind event in baseline estimations, and 9.5% higher in the depressive symptoms. In terms of cognitive skills, the offspring with parental early left-behind event had a significantly lower word score. With respect to non-cognitive skills, parental early left-behind event increases both internalizing problem behavior and externalizing problem behavior index. Furthermore, we employed the instrumental variable method using Conley et al.\u0026rsquo;s [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] sensitivity analysis method to ensure plausible exogenous inference, and a range of robustness checks, to mitigate potential endogeneity concerns. These results demonstrate that measures should be taken to improve the left-behind status, thereby improving the human capital of rural children.\u003c/p\u003e \u003cp\u003eAlthough it seems that rural children as a whole are affected by parental early left-behind event and, therefore, see reduced human capital accumulation, our results also demonstrate that certain sub-groups experience a particularly detrimental impact. This phenomenon may be attributed to the varying roles that parents play in the lives of different groups. For instance, fathers being left-behind in early life exerts a greater impact on household earnings, consumption, and educational funding, while mother early left-behind event significantly increased attention towards children\u0026rsquo;s education and parent-child interactions. In Chinese culture, women tend to have family-oriented values, rather than self-oriented ones [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, women tend to exhibit a higher level of concern regarding the development of children. Moreover, the influences of parental early left-behind event were particularly noticeable among children whose parents experienced separation during later childhood years (between the ages of 4 and 12) and had long-time encounter with parental absence.\u003c/p\u003e \u003cp\u003eWhile there is a lack of comprehensive articles on the influence of parental early left-behind event on offspring\u0026rsquo;s human capital accumulation among subgroups in rural areas of China, the findings of our study align with existing literature regarding the relationship between parental early adversities and negative outcomes in children. For instance, previous studies have demonstrated a negative correlation between maternal socioeconomic situation in early life and the newborn weight of offspring and that mother unhealthy lifestyle, including drug misuse, and succeeding unhealthy condition as significant mechanism that contribute to the association [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These suggest that the transmission of human capital across generations may serve as a crucial pathway of the intergenerational impact of parental early experiences. Prior research has indicated that the intergenerational transmission of human capital primarily arises from heredity and surrounding factors [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. From one perspective, parents with superior human capital are more prone to pass down advantageous genetic traits to children, which can facilitate the acquisition of skills and abilities. From other perspective, parents with elevated degrees of human capital possess the capacity to create a more conducive surrounding and increased chances for children\u0026rsquo;s growth via improved economic condition [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Simultaneously, environmental elements can interact with genetic endowments of children, shaping their potential for optimal development [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Hence, childhood adversities significantly impact the development of human capital in the subsequent generation.\u003c/p\u003e \u003cp\u003eThe research possesses several notable advantages. Firstly, the research framework captures 95% of the Chinese population, thereby establishing a high degree of national representativeness. Additionally, CFPS data is an annual longitudinal survey, which means it can be used to build intergenerational data for analysis. Secondly, the substantial dataset size (2,206) employed in this study ensures robust statistical power and enhances the external validity. Thirdly, all data used in our study come from the Institute of Social Science Survey (ISSS) of Peking University utilizing a uniform sampling strategy. Lastly, we concentrate on the influence of parental early left-behind event on offspring\u0026rsquo;s human capital accumulation among various sub-categories. The contrast among various sub-groups of early left-behind event can give us more insight into the impacts of parental early left-behind event on offspring\u0026rsquo;s human capital accumulation.\u003c/p\u003e \u003cp\u003eAlthough this research possesses notable strengths, it is not exempt from certain limitations. The collection of self-reported information in the CFPS constrained our ability to thoroughly investigate the depressive status and problem behavior of rural children with medical evaluation. Subsequent investigations examining causality between parental early left-behind event and offspring\u0026rsquo;s human capital in rural China would gain from concentrating on these subject matters.\u003c/p\u003e \u003cp\u003eIn terms of policy, this study indicated that the comprehensive effects of parental migration on offspring\u0026rsquo;s growth may have been significantly underrated if the long-time consequences on human capital are not taken into account. The negative correlations between parental early left-behind event and the development of offspring\u0026rsquo;s human capital further suggest that parental migration could serve as a potential factor resulting in the intergenerational transmission of penury and disparity. Our results not only provide insight into safeguarding and fostering Chinese rural children, but also hold significant policy effects for some developing countries with substantial population migration, including Pakistan, India, and Nigeria. Hence, efforts need to be made to shorten nonvoluntary division between parents and children caused by institutional constraints. Meanwhile, to promote the long-time development of human capital among rural children being left behind, it is crucial to implement research-driven prevention, like parenting projects for guardians and the equitable distribution of educational materials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing nationally representative data, this study explored the relationship between parental left-behind event in early life and human capital outcomes among offspring in rural areas. In addition, we examined the mechanism between parental childhood left-behind event and human capital from the perspectives of heterogeneity. Our results showed that mothers who have experienced being left behind are more likely to allocate increased time to their offspring. Conversely, fathers who have experienced being left behind tend to exhibit lower socioeconomic outcomes within homes and put fewer investments in children\u0026rsquo;s education.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCFPS \u0026nbsp; \u0026nbsp;China Family Panel Studies\u003c/p\u003e\n\u003cp\u003eHAZ \u0026nbsp; \u0026nbsp; \u0026nbsp;Height-for-Age Z-Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCES-D \u0026nbsp; \u0026nbsp;Center for Epidemiologic Studies Depression\u003c/p\u003e\n\u003cp\u003eISSS \u0026nbsp; \u0026nbsp; Institute of Social Science Survey\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch has been performed in accordance with the Declaration of Helsinki. China Family Panel Studies indicates that informed consent was obtained from all subjects and their legal guardians. \u0026ldquo;Peking University Biomedical Ethics Committee\u0026rdquo; Ethics Review Number: IRB00001052-14010, approved the study protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of the studies is publicly available and could be accessible via website: China Family Panel Studies (CFPS). The datasets analysed during the current study are available in the [CFPS] repository, [http://www.isss.pku.edu.cn/cfps/download/logout]. We can enter the username and password, and then download the data. Username: [email protected]; Password: o2jsouu9\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the support of the National Natural Science Foundation of China (Grants Nos. 71973100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, MZ, and LH; Data curation, XS; Formal analysis, XS; Methodology, MZ; Writing\u0026mdash;original draft, XS; Writing\u0026mdash;review and editing, LH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the interviewers and respondents who participated in the field survey. Thanks to the people who have supported this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information (optional):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaotong Sun\u003csup\u003e1*\u003c/sup\u003e\u0026nbsp; Mi Zhou\u003csup\u003e1*\u003c/sup\u003e\u0026nbsp; Li Huang\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e College of Economics and Management, Shenyang Agricultural University, Shenyang, 110866, Liaoning, China.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]; [email protected]; [email protected]\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao Q, Yu X, Wang X, Glauben T. The impact of parental migration on children\u0026rsquo;s school performance in rural China. China Economic Review. 2014;31:43-54. https://doi.org/10.1016/j.chieco.2014.07.013.\u003c/li\u003e\n\u003cli\u003eWang S, Yang Y, Wen YY, Cui LJ. Self-compassion promoted educational flow through increased future orientation in left-behind children groups. International Journal of Psychology. 2023;58(4):351-359. https://doi.org/10.1002/ijop.12904.\u003c/li\u003e\n\u003cli\u003eZheng XD, Zhang Y, Jiang WY. Migrating with parents or left-behind: Associations of internal migration with cognitive and noncognitive outcomes among Chinese children. Current Psychology. 2022b;1-22. https://doi.org/10.1007/s12144-022-03095-x.\u003c/li\u003e\n\u003cli\u003eChen C. Left-behind children\u0026rsquo;s cognitive development in China: Gain in financial capital versus loss in parental capital. Sociological Perspectives. 2023;66(3):523-542. https://doi.org/10.1177/07311214221145059.\u003c/li\u003e\n\u003cli\u003eAbbasi BN, Luo Z, Sohail A. Effect of parental migration on the noncognitive abilities of left-behind school-going children in rural China. Humanities and Social Sciences Communications. 2023;10(11):1-14. https://doi.org/10.1057/s41599-022-01496-8.\u003c/li\u003e\n\u003cli\u003eWu YC, Guo ZL. An analysis of the nutritional status of left-behind children in rural China and the impact mechanisms of child malnutrition. Children and Youth Services Review. 2020;119:105598. https://doi.org/10.1016/j.childyouth.2020.105598.\u003c/li\u003e\n\u003cli\u003eJiang Y, Xiao H, Yang F. Accompanying your children: Living without parents at different stages of pre-adulthood and individual physical and mental health in adulthood. Front. Public Health. 2023;11:992539. https://doi.org/10.3389/fpubh.2023.992539.\u003c/li\u003e\n\u003cli\u003eZhang H, Chi P, Long H, Ren X. Bullying victimization and depression among left-behind children in rural China: roles of self-compassion and hope. Child Abuse \u0026amp; Neglect. 2019;96(1):104072. https://doi.org/10.1016/j.chiabu.2019.104072.\u003c/li\u003e\n\u003cli\u003eTan DQ, Xie RB, Song SC, Ding W, Wu W, Li WJ. How does parent-child attachment influence left-behind children\u0026rsquo;s loneliness and depression: The mediating roles of peer attachment and teacher-student relationship. Child: Care, Health and Development. 2023;1-11. https://doi.org/10.1111/cch.13118.\u003c/li\u003e\n\u003cli\u003eGavin AR, Thompson E, Rue T, Guo Y. Maternal early life risk factors for offspring birth weight: Findings from the add health study. Prevention Science. 2012;13(2):162-172. https://doi.org/10.1007/s11121-011-0253-2.\u003c/li\u003e\n\u003cli\u003eZheng XD. Fang ZY, Wang YJ, Fang XM. When left-behind children become adults and parents: The long-term human capital consequences of parental absence in China. China Economic Review. 2022a;74:101821. https://doi.org/10.1016/j.chieco.2022.101821.\u003c/li\u003e\n\u003cli\u003eLiu J, Zheng X, Parker M, Fang X. Childhood left-behind experience and employment quality of new-generation migrants in China. Population Research and Policy Review. 2020;39(4):691-718. https://doi.org/10.1007/s11113-020-09568-w.\u003c/li\u003e\n\u003cli\u003eWang H, Cheng Z, Zhe WB, Chen Y. Childhood left-behind experience and labour market outcomes in China. Journal of Business Research. 2021;132:196-207. https://doi.org/10.1016/j.jbusres.2021.04.014.\u003c/li\u003e\n\u003cli\u003eCurrie J. Child health as human capital. Health Economics (United Kingdom). 2020;29(4):452-463. https://doi.org/10.1002/hec.3995.\u003c/li\u003e\n\u003cli\u003eAlmond D, Currie J. Human capital development before age five. In, vol. 4. Handbook of labor economics, Elsevier B.V. Issue PART B; 2011. https://doi.org/10.1016/S0169-7218(11)02413-0.\u003c/li\u003e\n\u003cli\u003eHeckman JJ, Mosso S. The economics of human development and social mobility. Annual Review of Economics. 2014;6(1):689-733. https://doi.org/10.1146/annurev-economics-080213-040753.\u003c/li\u003e\n\u003cli\u003eAizer A, Currie J. The intergenerational transmission of inequality: Maternal disadvantage and health at birth. Science. 2014;344(6186):856-861. https://doi.org/10.1126/science.1251872.\u003c/li\u003e\n\u003cli\u003eZheng XD, Fang XM, Brown DS. Social pensions and child health in rural China. Journal of Development Studies. 2020;56(3):545-559. https://doi.org/10.1080/00220388.2019.1577387.\u003c/li\u003e\n\u003cli\u003eHuang L, Sun XT, Zhou M. Depressive symptoms in Chinese laborers: Prevalence and correlated factors among subgroups. Journal of Affective Disorders. 2020;268:141-149. https://doi.org/10.1016/j.jad.2020.03.013.\u003c/li\u003e\n\u003cli\u003eSun XT, Zhou M, Huang L, Nuse B. Depressive costs: medical expenditures on depression and depressive symptoms among rural elderly in China. Public Health. 2020;181:141-150. https://doi.org/10.1016/j.puhe.2019.12.011.\u003c/li\u003e\n\u003cli\u003eRankin SH, Galbraith ME. Reliability and validity data for a Chinese translation of the center for epidemiological studies-depression. Psychological Reports. 1993;73(3):1291-1298. https://doi.org/10.2466/pr0.1993.73.3f.1291.\u003c/li\u003e\n\u003cli\u003eGreenberger E, Chena C, Tallya S, Dong Q. Family, peer, and individual correlates of depressive symptomatology among U.S. and Chinese adolescents. Journal of Consulting \u0026amp; Clinical Psychology. 2000;68:209-219. https://doi.org/10.1037//0022-006X.68.2.209.\u003c/li\u003e\n\u003cli\u003eStock J, Yogo M. Testing for weak instruments in linear IV regression. NBER Technical Working Papers. 2002;284. https://doi.org/10.3386/t0284.\u003c/li\u003e\n\u003cli\u003eAltonji JG, Elder TE, Taber CR. Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. Journal of Political Economy. 2005;113(1):151-184. https://doi.org/10.1086/426036.\u003c/li\u003e\n\u003cli\u003eOster E. Unobservable selection and coefficient stability: Theory and evidence. Journal of Business and Economic Statistics. 2019;37(2):187-204. https://doi.org/10.1080/07350015.2016.1227711.\u003c/li\u003e\n\u003cli\u003eBotha F, de New JP, de New SC, Ribar DC, Salamanca N. Implications of COVID-19 labour market shocks for inequality in financial wellbeing. Journal of Population Economics. 2021;34(2):655-689. https://doi.org/10.1007/s00148-020-00821-2.\u003c/li\u003e\n\u003cli\u003eYu Y. The male breadwinner/female homemaker model and perceived marital stability: A comparison of Chinese wives in the United States and urban China. Journal of Family and Economic Issues. 2015;36(1):34-47. https://doi.org/10.1007/s10834-014-9417-0.\u003c/li\u003e\n\u003cli\u003eAlmond D, Currie J, Duque V. Childhood circumstances and adult outcomes: Act II. Journal of Economic Literature. 2018;56:1360-1446. https://doi.org/10.1257/jel.20171164.\u003c/li\u003e\n\u003cli\u003eHeckman JJ, Stixrud J, Urzua S. The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics. 2006;24(3):411-482. https://doi.org/10.1086/504455.\u003c/li\u003e\n\u003cli\u003eManuelli RE, Seshadri A. Human capital and the wealth of nations. American Economic Review. 2014;104(9):2736-2762. https://doi.org/10.1257/aer.104.9.2736.\u003c/li\u003e\n\u003cli\u003eConley TG, Hansen CB, Rossi PE. Plausibly exogenous. Review of Economics and Statistics. 2012;94(1):260-272. https://doi.org/10.1162/REST_a_00139.\u003c/li\u003e\n\u003cli\u003eXiong H, Bairner A, Tang Z. Embracing city life: physical activities and the social integration of the new generation of female migrant workers in urban China. Leisure Studies. 2020;39(6):782-796. https://doi.org/10.1080/02614367.2020.1800802.\u003c/li\u003e\n\u003cli\u003eKane JB, Harris KM, Siega-Riz AM. Intergenerational pathways linking maternal early life adversity to offspring birthweight. Social Science and Medicine. 2018;207:89-96. https://doi.org/10.1016/j.socscimed.2018.04.049.\u003c/li\u003e\n\u003cli\u003eBlack SE, Devereux PJ, Salvanes KG. Why the apple doesn\u0026rsquo;t fall far: Understanding intergenerational transmission of human capital. American Economic Review. 2005;95(1):437-449. https://doi.org/10.1257/0002828053828635.\u003c/li\u003e\n\u003cli\u003eDong Y, Luo R, Zhang L, Liu C, Bai Y. Intergenerational transmission of education: The case of rural China. China Economic Review. 2019;53:311-323. https://doi.org/10.1016/j.chieco.2018.09.011.\u003c/li\u003e\n\u003cli\u003eCarneiro P, Meghir C, Parey M. Maternal education, home environments, and the development of children and adolescents. Journal of the European Economic Association. 2013;11(Suppl.1):123-160. https://doi.org/10.1111/j.1542-4774.2012.01096.x.\u003c/li\u003e\n\u003cli\u003eThompson O. Genetic mechanisms in the intergenerational transmission of health. Journal of Health Economics. 2014;35(1):132-146. https://doi.org/10.1016/j.jhealeco.2014.02.003\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Due to space limitations, the results of the first stage are not listed. Interested parties can request them from the author.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We measured the left-behind status of children using two binary variables, suggesting whether the offspring was left-behind by father or mother, based on pertinent issues in the children\u0026rsquo;s database of the 2018 CFPS. \u0026ldquo;Children left behind by father\u0026rdquo; was assigned 1 if a child had lived with their father for below 6 months in the previous year. At the same time, \u0026ldquo;children left behind by mother\u0026rdquo; was assigned 1 if the time duration for which child lived with their mother in previous 12 months were below 6 months.\u003c/span\u003e\u003c/li\u003e\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":"Rural children, Parental early left-behind event, Generational continuity, Human capital outcomes","lastPublishedDoi":"10.21203/rs.3.rs-3833421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3833421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSpeedy urban development over the preceding years has been with the migration of laborers in rural China. The essential inquiry that has arisen pertains to whether the experience of workers\u0026rsquo; movement has a long-term mixed influence on the human capital accumulation among rural offspring. The goal of current study is to address how parental early left-behind event relates to long-time development outcomes in rural offspring.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis paper uses a nationally representative dataset from China Family Panel Studies to investigate whether parental early left-behind event impacts the prevalence of human capital among rural children. To do so, this paper uses econometric models to analyze the causality between parental early left-behind event and the offspring\u0026rsquo;s human capital accumulation, and then uses sensitivity analysis to test robustness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe find evidence that rural children whose parents have left-behind event in early life have fewer human capital. These findings also differ markedly by the heterogeneity of parental left-behind event. Further, mothers who have experienced being left behind are more likely to allocate increased time to their offspring. Conversely, fathers who have experienced being left behind tend to exhibit lower socioeconomic outcomes within homes and put fewer investments in children\u0026rsquo;s education.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study proposes that there is strong correlation between parental early left-behind event and children\u0026rsquo;s development. Based on our findings, it is recommended that the Chinese government should take measures to minimize instances of involuntary separation between parents and children caused by institutional limitations. This action is crucial for enhancing the human capital outcomes among rural offspring.\u003c/p\u003e","manuscriptTitle":"The Long-Time Consequences of Parental Early Left-Behind Event on the Human Capital of Rural Children in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 08:21:33","doi":"10.21203/rs.3.rs-3833421/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":"cb3cd142-ee4e-4cbe-a351-c28ea4077484","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-12T11:06:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 08:21:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3833421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3833421","identity":"rs-3833421","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}