Were there long-term health effects of exposure to parental migration on adult children? Evidence from rural China

Were there long-term health effects of exposure to parental migration on adult children? 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Evidence from rural China Qundi Feng, Fancun Meng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5787718/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 Little is known about the role of parental rural-to-urban migration during childhood in shaping individuals’ health conditions. Using data from the China Health and Nutrition Survey, this study explores the long-term effect of parental migration during childhood on adult health outcomes. The extended regression model is employed to address the potential endogeneity of parental migration with an instrument variable. The results indicate that exposure to parental migration in childhood has a significant negative impact on adult height. Robustness checks using Body Mass Index and self-rated health status validate our findings. Mechanism analysis shows that parental migration significantly reduces left-behind children’s dietary quality in terms of food consumption patterns and dietary diversity. Given the insufficient protections related to left-behind children, there is a need for preventive intervention to mitigate the health disparity in the long term caused by parental migration. JEL classification I15, J13, O12 Parental rural-to-urban migration dietary quality health China Ⅰ. Introduction It is well known that the large-scale rural-to-urban migration in China contributes to its economic growth and poverty reduction (Cai, 2018). However, a substantial number of children were left behind in rural areas because the migrants could not have entitlements to local health and education systems subject to institutional obstacles. Although the number of left-behind children dropped from over 60 million in 2010 to over 40 million in 2020, more than one in three rural children is being left behind. Parental migration improves family income and promotes investments into children’s living conditions, education, nutrition, and healthcare (e.g., Antman, 2012; Dong et al., 2021; Wang et al., 2019), but the left-behind children are adversely affected by the absence of parental care and guardianship (Zhou et al., 2015). Previous studies suggest a detrimental impact of parental migration on children’s outcomes, such as cognitive ability (Xie et al., 2019; Yue et al., 2020; Zhang et al., 2014), non-cognitive skills (H Liu et al., 2021; Zhou et al., 2022), health (De Brauw & Mu, 2011; Lei et al., 2018; Shi et al., 2016), and academic performance (Bai et al., 2020; Chang et al., 2011; Zhao et al., 2014). Despite the short-term effect of parental migration on left-behind children has received considerable attention in previous studies, it lacks attention to the long-term effects. Extant researches provide evidence that early life events in the fetal period or childhood have long-lasting consequences on the subsequent adult outcomes and explain a great deal of the variation in human capital attainment (Abiona, 2017; Almond et al., 2018; Brugiavini et al., 2023). In particular, early life disadvantages, such as exposure to air pollution (Balietti et al., 2022; Isen et al., 2017), hunger in famine (Cui et al., 2020; Yao & Zhang, 2023), arms conflict (Bharati, 2022; Singhal, 2019), natural disasters (Cornwell & Inder, 2015; de Oliveira et al., 2023; Karbownik & Wray, 2019), etc., can cast a long shadow on human capital and labor market outcomes across the entire life course. Such shock during the critical periods of children’s development would be a health insult that hinders access to human capital accumulation(Currie & Almond, 2011). Previous studies have used natural experiences to investigate the long-term effects of early nutritional deprivation on adulthood caused by exogenous shocks (Caruso & Miller, 2015; Rosales-Rueda, 2018). In addition, early interventions and nutrition programs, such as the student nutrition improvement program in China (Fang & Zhu, 2022), a free school breakfast program in Norwegian cities (Bütikofer et al., 2018) reinforces the conclusion that under undernutrition in childhood has significant effects on labor market performance, socioeconomic status and health in adulthood. Although parental migration may reduce malnutrition and food insecurity through increasing family income (Karamba et al., 2011), alternative caregivers, especially grandparents, consistently fail to provide enough caregiving and nurturing for left-behind children due to their physical weakness and lower educational level (Biao, 2007; Ye & Lu, 2011). In this case, the worse health status of left-behind children may compromise their health outcomes in adulthood. In this paper, we examine the effect of parental migration in childhood on adult health outcomes in adulthood using data from the 1991-2015 China Health and Nutrition Survey (CHNS). Several waves of survey data allow for obtaining parental migration status in childhood and adult health outcomes. We use individual height as the measure of adult health and estimate the causal effect with the extended regression model (ERM) with an instrument variable (IV), which is a more suitable approach for binary endogenous variables. 1 We also investigate the effect on the individual’s self‐esteem in health and BMI as a robust check. Furthermore, we explore the possible heterogeneity of effects by gender, sibship structure, household income, and region. Finally, we test the channels through which adult health outcome is affected by examining the impact of parental migration on dietary quality. The main results reveal that exposure to parental migration in childhood has a negative effect on adult height in adulthood. Our results show the same pattern when using the individual’s BMI and self-rated health as the proxy of health outcomes or replacing tracking data with retrospective data. Furthermore, the effect is stronger for those who are male, have siblings, are born at the bottom quartile of household income, or are from China's central and western regions. Moreover, parental migration reduces children’s dietary quality, such as decreasing intake of nutritious foods and lower dietary diversity, which is the possible channel. Our findings suggest that improving the nutrition status of left-behind children may effectively cut off the persistent negative effect of parental migration on health. This paper contributes to understanding the long-term consequences of parental migration. A small of articles have conducted the cumulative impact of parental migration and found a detrimental impact on labor market outcomes, such as the probability of finding a job, employment quality, and wages(Liu et al., 2020; Lyu & Chen, 2019; Wang et al., 2021). Few studies except Liang and Sun (2020) and Zheng et al. (2022) are concerned about the health outcome of left-behind children in adulthood with retrospective data. Liang and Sun (2020) consider the relationship between parental migration and children’s self-reported health and mental health but do not address the potential endogeneity problem. Zheng et al. (2022) find that left-behind children are more likely to report chronic diseases, be underweight, and have lower levels of perceived health when they become adults. However, neither of these studies further explores the possible channel. Our studies represent the effort to comprehensively identify the long-term impact of parental migration. Our study enhances the economic literature regarding health inequity. Previous studies show that the inequalities of family income and health care access are the most crucial reasons for health disparities (Deaton, 2003; Goode & Mavromaras, 2014; Huang & Liu, 2023). Particularly, childhood nutritional status has been an important indicator of child health status (Fang & Zhu, 2022; Liu et al., 2013). The role of childhood nutrition on economic, educational, and health outcomes throughout life has been well documented in developed countries (Lundborg et al., 2022), but relatively limited concern in developing countries. In this study, we discuss the effects of exposure on the diet quality of left-behind children, including nutritional intake and dietary diversity, adding a novel dimension to our understanding of health inequity caused by parental migration. The rest of the paper is structured as follows. Section II describes the dataset and introduces the methodology. Section III presents the results of the analysis. Section IV concludes the paper and provides some policy suggestions. Ⅱ. Data, Variable and Methodology 1. Data The principal data used in this study is from the 1991-2015 CHNS, 2 an ongoing international collaborative project between the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. The survey covers about 7200 households from communities with different socioeconomic backgrounds in 15 provinces and major cities. 3 The Chinese provinces and major cities vary substantially in geography and economic development. The CHNS data provide detailed economic, demographic, and health information in ten waves during 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015. The CHNS data fit our study very well because it allows us to trace individuals from the early stages of life to late adulthood, which provides the best information possible to conduct a microanalysis of how early-life adversities translate into adverse outcomes late in life. This is critical for exploring the effect of parental migration in childhood on individuals’ health and testing the channels through which individuals’ health outcomes are affected from the perspective of nutrition intake. This data has been widely used in previous research to study the intergenerational effect, such as Du et al. (2014). Our identification of the impact of exposure to parental migration in childhood on individual’s health in adulthood is based on panel data of individual-level occurrences of parental migration at the age of 0-18. 4 Since internal migration mainly occurs in rural China, we restricted the sample to those exposes to parental migration in rural areas. Two questions in CHNS questionnaires are generated to identify parental migration experience in childhood. One is regarding the reasons for family member absence. If the family member has left home to seek employment at the time of the survey, we consider this family member to be a migrant, and then their children are exposed to parental migration. Another is that respondents report whether their fathers and mothers lived at home. In that case, such as a child’s father or mother did not live with him/her, the child is regarded as a left-behind child with exposure to parental migration (Guo, 2019), where those children whose parents were separated, divorced, or widowed were excluded in our sample. Our final sample contains 4250 individuals, of which 323 are left-behind in childhood, accounting for 7.6 percent. Among the final sample, the average age of individuals is 25 years old. To construct a sample for mechanism analysis, we trace individuals’ information back to them aged 2-12 at the time of survey. 5 During their growing up period, the average ages of their fathers and mothers are 35 and 36 years old, respectively. In the empirical analysis, all of the data are pooled to identify the effect of parental migration in childhood on individual’s health in adulthood. 2. Variable In this paper, adult height is the primary dependent variable. Height has been an even longer tradition to measure population health (e.g., Fogel, 2004; Steckel, 2008). In general, adult height is determined by childhood height and an adolescent growth spurt. Childhood height is often thought to be an index reflecting health status, such as nutrition intake and expenditure, some illnesses, particularly from infectious diseases (Martorell & Habicht, 1986). Furthermore, childhood height is consistent with adult cognitive ability (Case & Paxson, 2008). Consequently, adult height is believed to be a good measure of levels of childhood health and some components of childhood health during early childhood. At the same time, adult height will, in part, reflect the difference in the quality of childcare. Parental migration is the primary independent variable in this paper. It is defined as a dummy variable indicating whether an individual is exposed to parental migration at ages 0-18. Since CHNS was conducted every three or four years, we could not identify parental migration at each age in childhood. Generally, people always have persistent migrative behaviors because they need to increase income from off-farm work (Villalobos & Riquelme, 2023). therefore, we consider an individual exposed to parental migration if at least one parent migrated for work or did not live with at a specific time in 0-18 years old, instead of at each age. We assess children’s dietary quality in the growth period according to their food consumption patterns and dietary diversity, which are the primary dependent variables in the mechanism analysis. The CHNS collected three-day records of respondents’ food consumption, including meals per person per day and daily food intake. Based on the China Food Composition Vol.2, we first obtain the average three-day food consumption collected by CHNS. For our purpose, we classify food consumption for each individual into four categories 6 and calculate the percentage deviation for each food category from the Reference Daily Intake provided by the 2016 Chinese Food Pagoda (C Liu et al., 2021). 7 Specifically, a negative value of the percentage deviation indicates inadequate intake, while a positive value shows the excess intake for each food category. Furthermore, we compute the children’s dietary diversity score based on food consumption. Following Swindale and Bilinsky (2006), we categorized the food items into eight major food groups. 8 Based on children’s food consumption, we calculate the score for each food group. If food intake in a group is below the minimum limit value, 9 the score is zero; otherwise, it is one. The group scores are then summed to obtain the dietary diversity score, which ranges from one to eight. The higher value of the dietary diversity score indicates a more diversified diet. Following Cui et al. (2020), we control individuals' and parents' basic socioeconomic and demographic characteristics, which may directly affect adult health. By controlling them, we can measure the impact of exposure to parental migration in childhood on adult health more effectively. Specifically, individual characteristics include gender, age, education, occupational and marital status, and the number of siblings. Similarly, the parental demographic characteristics include age and height. Furthermore, we also control parental education and income as the relevant measure of family socioeconomic status in the growth period. The parental education level is the greater one of parental schooling years, as does parental income. Finally, we also control for wave and province dummy variables. The specific definitions and descriptions of the critical variables are shown in Table 1. Table 1. Definition and description of key variables Variable name Definition Mean S.D. N Adulthood 4250 Parental migration At least one parent migrated in childhood 0.076 0.266 Height Individual’s height (cm) 165.0 8.567 Age Age in years 24.85 5.577 Gender Male=1, female=0 0.688 0.463 Education Years of education 9.760 2.733 Siblings Number of siblings 2.312 0.950 Employment Participate in labor market 0.747 0.435 Married Married=1, single=0 0.344 0.475 Parental education level The greater one of parental schooling years 7.409 3.297 Log of parental income The greater one of parental income 8.695 0.779 Mother's age Mother's age in years 51.40 7.331 Father's age Father's age in years 53.01 7.643 Mother's height Mother's height (cm) 154.1 6.571 Father's height Father's height (cm) 164.9 6.378 BMI Body Mass Index (kg/m^2) 22.089 0.282 3071 Mother's BMI Mother's Body Mass Index 23.736 4.016 Father's BMI Father's Body Mass Index 23.100 3.520 Cereals, mixed beans, tubers, and starches The percentage deviation (deficiency/excess) from the Reference Daily Intake for each food category 0.551 0.767 1579 Vegetables and fruits -0.362 0.387 1577 Meat, poultry, eggs, fish, and shellfish -0.013 0.642 1460 Soybean, nuts, and dairy products -0.547 0.357 1159 Dietary diversity score Children’s dietary diversity score (ranges from one to eight) 3.316 0.890 1722 Note: Two samples are employed in empirical analysis. One sample is composed of adults, which was used to examine the effect of parental migration in childhood on individual’s height in adulthood. Another is composed of children, which was used to investigate the effect of parental migration on children’s food consumptions and dietary diversity. 10 Children’s sample includes some children excluded from the adults’ sample because they have not grown up so that the sample size is different in Table 1. 3. Empirical strategy To investigate the long-term impact of parental migration in childhood on individual’s health outcome in adulthood, we estimate the following specification: An endogeneity problem may exists in the estimation of Equation (1) because of children’s and parents’ unobservable characteristics and selection into parental migration based on children’s health status (Cui et al., 2020). For instance, parental preference and genetic potentially affect both parental migration and adult height. To address this potential endogeneity, we first control the height of the father and mother, which could be used as a measure of genetic. Second, we employ the IV approach, which is widely adopted in existing research. According to Du et al. (2005), the migration rate at the community level can serve as the IV for parental migration. Specifically, we instrument the average proportion of those who migrated for work at childhood ages of the individual in the same community as the IV for the dummy variable of parental migration. 11 The community migration rate, as a proxy for migration social networks, could affect an individual's migration decision. In contrast, the migration rate in early life does not directly relate to an individual health outcome. Since parental migration is a binary endogenous variable, we use ERM to estimate Equation (1) to tackle the endogeneity problem. To check the robustness of the results, we also use the ordered logit model to re-estimate the parameters by measuring adult health with BMI. In addition, Equation (1) is also applied in the mechanistic analysis, where the dependent variable is food consumption and dietary diversity. According to the characteristics of the dependent variables in the mechanistic analysis, the ERM is employed to access the estimations. Ⅲ. Results 1. Effect of parental migration in childhood on individual’s health Table 2 represents the estimation results for the effect of parental migration in childhood on adult height. Model (1) is based on simple OLS regression, treating parental migration as an exogenous variable. The results show that adult height will decrease by 0.393 cm if an individual is left behind in childhood. The Durbin-Wu-Hausman statistic in Column (3) is 0.015 and insignificant, indicating that the endogeneity problem is not obvious. The result of a weak IV test shows no presence of a weak IV. The first stage regression, a Probit model, is used to predict the probability of parents going out to work in childhood. The IV coefficient is significant and positive after controlling for variables such as age, gender, education, siblings, parental height, education, and income. The peer effect always exists in a small community, which affects a parental migration decision by providing employment information and decreasing migration costs and uncertainty (Su et al., 2018). In addition, families in a community also experience a similar economic and political environment. Therefore, parents living in a community with a higher migration rate are more likely to go out to work. The magnitude of the coefficient of parental migration from the ERM is more substantial than that from OLS. Exposure to parental migration in childhood significantly reduces the individual’s height in adulthood by 0.692cm at the 5 percent significance level. The results are consistent with the previous findings in China that the childhood left-behind experience are negatively associated with individuals’ life-cycle outcome in educational attainment, employment quality, wages, and self-rated and mental health. In addition, the IV adopted in this article may correlate with factors beyond parental migration, such as local economic development and culture, violating the exclusion restrictions. To enhance the robustness of the research results, we replaced the province fixed effects with regional dummy variables to absorb the interference common to all households and individuals in the same district and county, and obtained similar results shown in Column (3). The results are consistent with the previous findings that parental migration is negatively associated with an individual’s life-cycle outcome, such as educational achievements (Dong et al., 2021), labor market outcomes (Feng et al., 2022; Wang et al., 2021), self-reported health and mental health (Liang & Sun, 2020). Notably, the results are also in line with the findings of Zheng et al. (2022) that left-behind children are more likely to report chronic diseases, be underweight, and have lower levels of perceived health when they become adults. However, Zheng et al. (2022) did not explore the possible channel. Among the control variables, the height of the father and mother, as the proxy for genetics, is strongly associated with adult height. Parental income in the growth period significantly positively affects an adult’s height, indicating the importance of socioeconomic status on offspring outcomes. Moreover, personal education attainment and gender could also account for an individual’s income. In addition, individuals with more siblings tend to have lower height outcomes, which is consistent with quality-quantity trade-off theory (e.g., Rosenzweig & Wolpin, 1980). Table 2. Effect of parental migration in childhood on individuals’ height in adulthood Variables (1) (2) (3) OLS ERM ERM 1st stage 2nd stage Height Any parent migrated Height Height Parental migration -0.393** -0.692** -0.510*** (0.174) (0.333) (0.105) IV 9.193*** (0.479) Age -0.0837 -0.106*** -0.0884*** -0.0817* (0.0472) (0.0240) (0.0247) (0.0423) Gender(male) 11.31*** -0.134 11.31*** 11.25*** (0.272) (0.147) (0.194) (0.0567) Schooling years 0.208*** 0.0202 0.207*** 0.218*** (0.0585) (0.0187) (0.0668) (0.0658) Siblings -0.327** -0.0990 -0.328*** -0.347*** (0.112) (0.174) (0.0905) (0.124) Employment -0.160 0.126 -0.158 -0.202 (0.257) (0.113) (0.170) (0.319) Married -0.0963 0.0836 -0.0908 -0.128 (0.253) (0.0871) (0.127) (0.178) Parental education -0.0303 0.00464 -0.0307 -0.0451 (0.0425) (0.0191) (0.0513) (0.0372) Log of parent's income 0.293 0.0420 0.292** 0.181 (0.319) (0.0743) (0.119) (0.250) Mother's age 0.0134 -0.0162 0.0123 0.0304 (0.0419) (0.0235) (0.0248) (0.0472) Father's age 0.0694 0.0144 0.0700** 0.0501 (0.0502) (0.0235) (0.0283) (0.0474) Mother's height 0.262*** 0.0448*** 0.262*** 0.281*** (0.0438) (0.0154) (0.0262) (0.00123) Father's height 0.340*** 0.0115* 0.340*** 0.353*** (0.0218) (0.00691) (0.0186) (0.0512) Constant 52.72*** -221.0*** 52.04*** -219.7*** (9.893) (16.65) (3.690) (14.22) Observations 4,250 4,250 4,250 4,250 R-squared 0.594 Control of wave dummies YES YES YES YES Control of province dummies YES YES YES Control of community dummies YES DWH test 0.015 0.675 Weak identification test 545.078*** 712.316*** Notes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. 2. Heterogeneous results Table 3 shows the heterogeneity in the effect of parental migration in childhood on individual’s height by gender, sibship structure, household income, and region. In panel A, we stratify the sample by gender. The results show that parental migration in childhood has a more substantial adverse influence on male’s adult height. On the one hand, males are more vulnerable to parental migration in terms of health than females (Meng & Yamauchi, 2017). On the other hand, females usually leave the natal house after marriage in rural China to weaken the effect of being left behind in childhood. In panel B, we subdivide our sample into two samples by the sibship structure. The results suggest that parental migration in childhood is significantly negative for individuals with siblings while invalid for those without siblings. The potential reason could be that resource constraints faced by families with more children are more stringent, negatively affecting children’s human capital investment (Lee, 2012). Then it exacerbates the adverse effects of being left behind in childhood. In panel C, two sets of estimates are constructed to investigate the impact of exposure to parental migration in childhood on individual’s height when parents are at bottom quartile and top quartile of income. We find that given an individual born to parents at bottom quartile of income, parental migration in childhood has significantly effects on adult height, while it is valid when parents at top quartile. The possible interpretation is that parents at bottom quartile of income could not provide enough support for their children’s nutrition and health, which impedes the long-term healthy development of left-behind children (Currie & Stabile, 2003). In panel D, we conduct separate estimates for individuals from Eastern China and Central China or Western China as China’s provinces are varied substantially in economic development. 12 We find that the exposure to parental migration in childhood is particularly pronounced for individuals from Central China or Western China, suggesting that the left-behind children from these regions suffer from worse development as parental migration. The possible interpretation is that the public service in health and education is relatively poor for left-behind children in the less-developed region of China. Table 3. Effect of parental migration on adult height by gender, sibship structure, household income, and region Panel A: Stratified by gender Parental migration Male Female -0.681*** -0.314 (0.234) (0.608) 2,924 1,326 Panel B: Stratified by sibship structure Without sibling With sibling Parental migration 0.535 -0.837*** (0.602) (0.277) Observations 809 3,441 Panel C: Stratified by household income Bottom quartile Top quartile Parental migration -2.468*** -6.419 (0.944) (0) Observations 679 518 Panel D: Stratified by region Eastern China Central China and Western China Parental migration 0.190 -0.978*** (0.762) (0.292) Observations 1,294 3.188 Notes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. The additional control here is the individual and parental characteristics, such as age, gender, siblings, education, income, et al., and the fixed effects of province and wave. All the results above are estimated by ERM using an IV. The female sample size is smaller than the males because some females leaving the natal house after marriage could not be surveyed. 3. Mechanism results Some empirical evidence highlights the long-term effect of child nutritional status on individuals' human capital accumulation (e.g., Fang & Zhu, 2022; Maluccio et al., 2009). In this study, we test the channel by examining the impact of parental migration on food consumption patterns and dietary diversity. Table 4 presents that the estimates for the four food categories and dietary diversity. The results in Column 1-4 show that the left-behind children with migrant parents consume more cereals, mixed beans, tubers, starches, and vegetables and fruits. However, the difference is negative and significant for the other two categories, such as meat, poultry, eggs, fish, shellfish, and soybean, nuts, and dairy products. The results indicate that the traditional cereal and vegetable consumption for left-behind children is considerably higher than the recommended quantity suggested by the 2016 Chinese Food Pagoda, while meat, eggs, and dairy products are lower than the reference daily intake. In other words, left-behind children with parental migration consume more food in high carbohydrates but less food in high fat and protein. Notably, deficiency or excess food consumption is not beneficial for children’s nutrition status. Furthermore, the result in Column 5 indicates that parental migration has consistently negative and significant effects on the dietary diversity score of left-behind children. The findings are consistent with the result in Column 1-4, pointing out that the left-behind children tend to consume more staple food products, and their dietary patterns are relatively single. Our findings also imply that the absence of parental care can be harmful to children’s diet quality and, thus, health, which is expected to hurt their adult health in the long term (Smith et al., 2012). Table 4. Effect of parental migration on food consumption patterns and dietary diversity in childhood Variables (1) (2) (3) (4) (5) cereals, mixed beans, tubers, and starches vegetable and fruits meat, poultry, eggs, fish and shellfish soybean, nuts and dairy products Dietary diversity score Parental migration 1.082*** 0.551*** -0.294*** -0.179*** -1.036*** (0.188) (0.0726) (0.0866) (0.0656) (0.231) Gender(male) -0.222** 0.0440 0.0610 -0.0163 0.0534 (0.0940) (0.0580) (0.0498) (0.0391) (0.0992) Square of age 1.590*** 0.0114 -0.230 0.133 -0.386 (0.432) (0.340) (0.272) (0.188) (0.486) Gender 0.171** 0.0555** 0.0527* -0.00175 -0.0801 (0.0667) (0.0244) (0.0300) (0.0178) (0.0634) Schooling years 0.00732 -0.0161 0.00587 0.00656 0.0246 (0.0270) (0.0141) (0.0130) (0.0105) (0.0298) Siblings -0.0224 -0.0222 -0.0509** -0.0494** -0.0456 (0.0502) (0.0229) (0.0224) (0.0212) (0.0670) Parental education 0.000978 0.00727 0.00281 0.00605 0.0279* (0.0132) (0.00779) (0.0132) (0.00586) (0.0152) Log of parent's income 0.0222 0.0154 0.0822*** 0.0324** 0.0723** (0.0303) (0.0104) (0.0212) (0.0137) (0.0334) Mother's age 0.000877 -0.00514 -0.000939 0.00233 -0.00929 (0.0120) (0.00827) (0.00605) (0.00420) (0.0117) Father's age 0.00626 0.00728 -0.000220 0.00224 -0.00663 (0.0104) (0.00645) (0.00852) (0.00450) (0.0136) Constant 0.185 -1.312*** -1.431*** -0.894*** 2.370*** (0.584) (0.406) (0.268) (0.212) (0.668) Observations 1,579 1,577 1,460 1,159 1,579 Control of wave dummies YES YES YES YES YES Control of province dummies YES YES YES YES YES DWH test 0.40 0.002 21.41*** 10.10*** 8.32** Weak identification test 11.56*** 11.41*** 13.54*** 13.27*** 11.56*** Notes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. All the results above are estimated by ERM using an IV. 4. Robustness analysis In order to better understand whether parental migration in childhood increases the odds of an individual being underweight or thinness, we also estimated an ordered logit model with BMI categories. The model specification defines the dependent variables based on the Standard of World Health Organization (WHO). 13 In the first model of Table 6, the dependent variable reflecting BMI categories is defined by -2 for moderate and severe thinness, -1 for underweight, 0 for normal weight, 1 for overweight, and 2 for obesity. In the second model of Table 6, the dependent variable reflecting BMI categories is defined by 1 for thinness and underweight, 2 for normal weight, and 3 for overweight and obesity. Table 6 illustrates the estimation results of the ordered logit model, and the results from the two model specifications are similar. We find that parental migration in childhood significantly increases an individual's likelihood of being underweight and going from normal weight to underweight. In addition, one attractive feature of the ordered logit model is that one can use it to predict the probabilities of each BMI categories to which an individual belongs. For example, on average, the probabilities of an individual being normal weight are 0.71, and an individual being underweight or thinness are 0.10 or 0.03, respectively. Table 6. Ordered logit model with BMI categories for individuals Variables (1) (2) BMI category BMI category Parental migration -0.178* -0.203* (0.0930) (0.119) Age 0.0365*** 0.0409*** (0.00860) (0.00893) Gender 0.596*** 0.604*** (0.152) (0.138) Education -0.0203 -0.0157 (0.0543) (0.0111) Siblings -0.00736 -0.00331 (0.0220) (0.0625) Employment 0.193*** 0.202*** (0.00836) (0.0654) Married 0.190 0.193*** (0.177) (0.0707) Parental education 0.00861 0.00696 (0.0404) (0.0189) Log of parent's income -0.00933 -0.0302 (0.0924) (0.0472) Mother's age 0.0239*** 0.0222 (0.00385) (0.0256) Father's age -0.00358 -0.00283 (0.00964) (0.0257) MBMI 0.0909*** 0.0903** (0.0249) (0.0397) FBMI 0.138*** 0.137*** (0.00214) (0.0399) Observations 3,071 3,071 Control of wave dummies YES YES Control of province dummies YES YES Notes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. In Model 1, the dependent variable reflecting BMI categories is defined by -2 for moderate and severe thinness, -1 for underweight, 0 for normal weight, 1 for overweight, and 2 for obesity, based on the standard of the World Health Organization. In Model 2, the dependent variable reflecting weight categories is defined by 1 for thinness and underweight, 2 for normal weight, and 3 for overweight and obesity, based on the standard of the World Health Organization. The results above are based on CHNS data, which track individuals from early stages of life to late adulthood. In this section, we use retrospective data collected by China Family Panel Studies (CFPS) to re-estimate the long-term effect of parental migration. Table 7 presents the estimation results of ERM with an IV. 14 The result in Column 1 shows that exposure to parental migration at ages 0-12 significantly reduces adult height by 0.798 cm, which is generally consistent with our baseline estimates. The result suggests that selection bias is not an issue of concern in this study. Furthermore, following Liang and Sun (2020), we use individuals' self-rated health to measure a different dimension of adult health. The self-evaluation is a standard five-point scale (very good, good, fair, poor, or very poor) of the general state of one’s health. The result in Column 2 of Table 7 indicates that exposure to parental migration at ages 0-12 is more likely to have poor self-rated health, which is concordant with our main findings. Table 7. Effect of parental migration in childhood on height and self-rated health (1) (2) VARIABLES Height Self-rated health Any parent migrating -0.798* 0.108** (0.460) (0.0520) Age -0.170*** 0.0187*** (0.0119) (0.00463) Gender 11.31*** -0.0353* (0.304) (0.0184) rural -0.861*** 0.00489 (0.0313) (0.0216) Education 0.0911*** -0.00978*** (0.00864) (0.00362) Number of siblings -0.545*** -0.0182* (0.0149) (0.0105) Married 0.288 -0.00617 (0.402) (0.0279) Employment -0.148 -0.0488** (0.217) (0.0197) Parental education -0.0447 -0.00153 (0.0427) (0.00248) Mother's age 0.0153 0.000271 (0.0453) (0.00370) Father's age 0.0833*** 0.00160 (0.00776) (0.00332) Mother's height 0.273*** (0.0218) Father's height 0.283*** (0.0375) Constant 71.55*** 0.892*** (8.540) (0.139) Observations 2,410 4,663 Control of province dummies YES YES DWH test 3.33* 0.005 Weak identification test 8.93*** 39.79*** Notes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. All the results above are estimated by ERM using an IV, based on China Family Panel Survey (CFPS). High scores for self-rated health refer to worse health status. Ⅳ. Conclusions This paper estimates the long-term effect of parental migration in childhood on adult health, and it reveals the channels by which adult health is affected. We address the endogeneity of exposure to parental migration using instrumental variable estimations in the ERM based on the data from 1991–2015 CHNS. Our results show that exposure to parental migration in childhood significantly affects adult height and self-rated health. The effect is more substantial for males, those with siblings, born to parents in the bottom quartile of income, or from Central China and Western China. Furthermore, dietary quality is an important contributing channel that likely underlies the long-term effects of parental migration in childhood. More specifically, parental migration would decrease children’s dietary diversity and food intake in high fat and protein. The robustness checks confirm our general findings and strengthen our arguments. With limited data sources, research about the long-term consequences of the childhood left-behind experience in rural China on individuals’ life-cycle outcomes is largely absent. The findings of this study contribute to the literature in several ways. First, we provide new evidence that parental migration has adversely affected their children’s food consumption and subsequent adult height. Second, this research accounts for health inequality from the perspective of the intergenerational effect of parental migration on individuals’ health. Finally, these results may help create policies to mitigate the long-term consequences of exposure to parental migration in other developing countries, such as Bangladesh and Vietnam. The findings of this study have important policy implications. To avoid the potential long-term effect of parental migration, the government should put more effort into preventive intervention. Specifically, the government can provide more employment placements to reduce the migration rate in rural areas or improve welfare to allow more children to migrate with their parents. Moreover, the government should initiate various nutrition programs to promote the dietary quality of left-behind children and provide training programs to increase rural residents’ knowledge of nutrition and health. Besides, limited by data availability, we could not identify the length and the time of being left behind, which may have a very different impact on adult health. Declarations Competing interests The authors declare no competing interests. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Additional information Correspondence and requests for materials should be addressed to Fancun Meng. Funding Project of the start-up fund for scientific research of Shantou University [NTF22006]; Project of Guangdong Medical Science and Technology Research Fund Program [B2023470]; Project of the Liberal Arts and Social Sciences Foundation for Youths from Ministry of Education of China [22YJC790078]. Author Contribution Qundi Feng: Formal analysis, Writing - original draft, Conceptualization. Fancun Meng: Writing - Review & Editing, Funding acquisition. Data Availability Data will be made available on request. References Abiona, O. (2017). Adverse effects of early life extreme precipitation shocks on short‐term health and adulthood welfare outcomes. Review of Development Economics, 21 (4), 1229-1254. Almond, D., Currie, J., & Duque, V. (2018). Childhood circumstances and adult outcomes: Act II. Journal of Economic Literature, 56 (4), 1360-1446. Antman, F. M. (2012). Gender, educational attainment, and the impact of parental migration on children left behind. 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When left-behind children become adults and parents: The long-term human capital consequences of parental absence in China. China Economic Review, 74 , 101821. Zhou, C., Sylvia, S., Zhang, L., Luo, R., Yi, H., Liu, C., Shi, Y., Loyalka, P., Chu, J., & Medina, A. (2015). China’s left-behind children: impact of parental migration on health, nutrition, and educational outcomes. Health affairs, 34 (11), 1964-1971. Zhou, Y., Chen, S., Chen, Y., & Vollan, B. (2022). Does parental migration impede the development of the cooperative preferences in their left-behind children? Evidence from a large-scale field experiment in China. China Economic Review, 74 , 101826. Footnotes 1. As for tackling the endogeneity problem, an ERM fits a linear regression model suitable for any endogenous covariates, such as binary, continuous, and ordinal endogenous covariates, while the two-stage least square (2SLS) regression is only accommodated continuous endogenous variables. 2. https://www.cpc.unc.edu/projects/china 3. Data from Chongqing, Beijing, Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Liaoning, Jiangsu, Shanxi, Shandong, Shanghai, Yunnan, and Zhejiang were collected. 4. Parental migration is always throughout children’s childhood between the ages of 0-18, and the testing results are available on request. 5. Following He et al. (2018), We do not include older children because puberty begins at around 13 years old, and growth spurts during puberty and after that reduces anthropometric methods' reliability. Another reason is that children over 13 years of age may have more control over their food choices, and their health is less affected by family care. 6. The four categories are (1) cereals, mixed beans, tubers, and starches; (2) vegetables and fruits; (3) meat, poultry, eggs, fish, and shellfish products; and (4) soybean, nuts, and dairy products. 7. 8. The eight major food groups are (1) grains, roots, and tubers; (2) vegetables; (3) fruits; (4) dairy products; (5) legumes and nuts; (6) flesh foods (meat, poultry); (7) seafood; (8) egg. 9. The minimum limit value of soybean food is 5g, and others are 25g. 10. We only access to individual’s food consumption pattern and dietary diversity for 1997–2011 in our sample because the China Food Composition before 1997 and the information of food consumption in the 2015 CHNS data are not available. 11. A community means an entire village or a few blocks in a city. The CHNS sample communities come from cities, suburbs, towns, or villages in China, where all entities are legally recognized by the National Bureau of Statistics of China. In the survey data, we do not know the exact name of the community, but we can see the community code of each household. Consequently, the households are from the same community if their community codes are the same; the proportion is based on the sample over 18 years old in a community, and it excludes the family of the child . 12. Eastern China, Central China, and Western China are defined based on the Seventh Five-Year Plan (1986–1990), which grouped the different provinces of China into three economic zones. Eastern China is more developed than others are. 13. BMI <17: moderate and severe thinness; BMI <18.5: underweight; BMI 18.5-24.9: normal weight; BMI ≥25: overweight; BMI ≥30: obesity. 14. The county migration rate excluding the family of the child, as a proxy for migration social networks, is used to serve as the IV. Additional Declarations No competing interests reported. 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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-5787718","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399879906,"identity":"47dfb327-5bb5-4504-b413-232f23532158","order_by":0,"name":"Qundi Feng","email":"","orcid":"","institution":"Southwest University of Political Science \u0026 Law","correspondingAuthor":false,"prefix":"","firstName":"Qundi","middleName":"","lastName":"Feng","suffix":""},{"id":399879907,"identity":"14ae6665-fcab-4573-84e9-9c1709976374","order_by":1,"name":"Fancun Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIie3QsQrCMBCA4QuBdCnOBcU+gRARnBx8lOtip+5ZXdKlz+BjOKcImfIGCmbyGTKJQXDudXO4f7rhvuEOgOP+MekAUL/XqugdkSgEiMbtFmVAOhExuOZSHTVNbKyM0NhHaytASOY6TfZe6UxenV2enRjCnUCeA2QiO7tyKIWlEF9+Sasq1DMIhhvOIfkWNKetzU8eabd4GUXSh7ru+zEmQyC5Iv0mR9rnOI7jpvsA53U7/uievZwAAAAASUVORK5CYII=","orcid":"","institution":"Shantou University","correspondingAuthor":true,"prefix":"","firstName":"Fancun","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2025-01-08 09:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5787718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5787718/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75944385,"identity":"a9fba049-c76e-411b-9b94-eb74676f8a9a","added_by":"auto","created_at":"2025-02-10 20:01:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5787718/v1/63ae9b56-8c71-438c-b50c-4908836cc889.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Were there long-term health effects of exposure to parental migration on adult children? Evidence from rural China","fulltext":[{"header":"Ⅰ. Introduction","content":"\u003cp\u003eIt is well known that the large-scale rural-to-urban migration in China contributes to its economic growth and poverty reduction (Cai, 2018). However, a substantial number of children were left behind in rural areas because the migrants could not have entitlements to local health and education systems subject to institutional obstacles. Although the number of left-behind children dropped from over 60 million in 2010 to over 40 million in 2020, more than one in three rural children is being left behind. Parental migration improves family income and promotes investments into children\u0026rsquo;s living conditions, education, nutrition, and healthcare (e.g., Antman, 2012; Dong et al., 2021; Wang et al., 2019), but the left-behind children are adversely affected by the absence of parental care and guardianship (Zhou et al., 2015). Previous studies suggest a detrimental impact of parental migration on children\u0026rsquo;s outcomes, such as cognitive ability (Xie et al., 2019; Yue et al., 2020; Zhang et al., 2014), non-cognitive skills (H Liu et al., 2021; Zhou et al., 2022), health (De Brauw \u0026amp; Mu, 2011; Lei et al., 2018; Shi et al., 2016), and academic performance (Bai et al., 2020; Chang et al., 2011; Zhao et al., 2014). Despite the short-term effect of parental migration on left-behind children has received considerable attention in previous studies, it lacks attention to the long-term effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExtant researches provide evidence that early life events in the fetal period or childhood have long-lasting consequences on the subsequent adult outcomes and explain a great deal of the variation in human capital attainment (Abiona, 2017; Almond et al., 2018; Brugiavini et al., 2023). In particular, early life disadvantages, such as exposure to air pollution (Balietti et al., 2022; Isen et al., 2017), hunger in famine (Cui et al., 2020; Yao \u0026amp; Zhang, 2023), arms conflict (Bharati, 2022; Singhal, 2019), natural disasters (Cornwell \u0026amp; Inder, 2015; de Oliveira et al., 2023; Karbownik \u0026amp; Wray, 2019), etc., can cast a long shadow on human capital and labor market outcomes across the entire life course. Such shock during the critical periods of children\u0026rsquo;s development would be a health insult that hinders access to human capital accumulation(Currie \u0026amp; Almond, 2011). Previous studies have used natural experiences to investigate the long-term effects of early nutritional deprivation on adulthood caused by exogenous shocks (Caruso \u0026amp; Miller, 2015; Rosales-Rueda, 2018). In addition, early interventions and nutrition programs, such as the student nutrition improvement program in China (Fang \u0026amp; Zhu, 2022), a free school breakfast program in Norwegian cities (B\u0026uuml;tikofer et al., 2018) reinforces the conclusion that under undernutrition in childhood has significant effects on labor market performance, socioeconomic status and health in adulthood. Although parental migration may reduce malnutrition and food insecurity through increasing family income (Karamba et al., 2011), alternative caregivers, especially grandparents, consistently fail to provide enough caregiving and nurturing for left-behind children due to their physical weakness and lower educational level (Biao, 2007; Ye \u0026amp; Lu, 2011). In this case, the worse health status of left-behind children may compromise their health outcomes in adulthood.\u003c/p\u003e\n\u003cp\u003eIn this paper, we examine the effect of parental migration in childhood on adult health outcomes in adulthood using data from the 1991-2015 China Health and Nutrition Survey (CHNS). Several waves of survey data allow for obtaining parental migration status in childhood and adult health outcomes. We use individual height as the measure of adult health and estimate the causal effect with the extended regression model (ERM) with an instrument variable (IV), which is a more suitable approach for binary endogenous variables.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e We also investigate the effect on the individual\u0026rsquo;s self‐esteem in health and BMI as a robust check. Furthermore, we explore the possible heterogeneity of effects by gender, sibship structure, household income, and region. Finally, we test the channels through which adult health outcome is affected by examining the impact of parental migration on dietary quality.\u003c/p\u003e\n\u003cp\u003eThe main results reveal that exposure to parental migration in childhood has a negative effect on adult height in adulthood. Our results show the same pattern when using the individual\u0026rsquo;s BMI and self-rated health as the proxy of health outcomes or replacing tracking data with retrospective data. Furthermore, the effect is stronger for those who are male, have siblings, are born at the bottom quartile of household income, or are from China\u0026apos;s central and western regions. Moreover, parental migration reduces children\u0026rsquo;s dietary quality, such as decreasing intake of nutritious foods and lower dietary diversity, which is the possible channel. Our findings suggest that improving the nutrition status of left-behind children may effectively cut off the persistent negative effect of parental migration on health.\u003c/p\u003e\n\u003cp\u003eThis paper contributes to understanding the long-term consequences of parental migration. A small of articles have conducted the cumulative impact of parental migration and found a detrimental impact on labor market outcomes, such as the probability of finding a job, employment quality, and wages(Liu et al., 2020; Lyu \u0026amp; Chen, 2019; Wang et al., 2021). Few studies except Liang and Sun (2020) and Zheng et al. (2022) are concerned about the health outcome of left-behind children in adulthood with retrospective data. Liang and Sun (2020) consider the relationship between parental migration and children\u0026rsquo;s self-reported health and mental health but do not address the potential endogeneity problem. Zheng et al. (2022) find that left-behind children are more likely to report chronic diseases, be underweight, and have lower levels of perceived health when they become adults. However, neither of these studies further explores the possible channel. Our studies represent the effort to comprehensively identify the long-term impact of parental migration.\u003c/p\u003e\n\u003cp\u003eOur study enhances the economic literature regarding health inequity. Previous studies show that the inequalities of family income and health care access are the most crucial reasons for health disparities (Deaton, 2003; Goode \u0026amp; Mavromaras, 2014; Huang \u0026amp; Liu, 2023). Particularly, childhood nutritional status has been an important indicator of child health status (Fang \u0026amp; Zhu, 2022; Liu et al., 2013). The role of childhood nutrition on economic, educational, and health outcomes throughout life has been well documented in developed countries (Lundborg et al., 2022), but relatively limited concern in developing countries. In this study, we discuss the effects of exposure on the diet quality of left-behind children, including nutritional intake and dietary diversity, adding a novel dimension to our understanding of health inequity caused by parental migration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rest of the paper is structured as follows. Section II describes the dataset and introduces the methodology. Section III presents the results of the analysis. Section IV concludes the paper and provides some policy suggestions.\u003c/p\u003e"},{"header":"Ⅱ. Data, Variable and Methodology ","content":"\u003cp\u003e\u003cstrong\u003e1. Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal data used in this study is from the 1991-2015 CHNS,\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e an ongoing international collaborative project between the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. The survey covers about 7200 households from communities with different socioeconomic backgrounds in 15 provinces and major cities.\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e The Chinese provinces and major cities vary substantially in geography and economic development. The CHNS data provide detailed economic, demographic, and health information in ten waves during 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015. The CHNS data fit our study very well because it allows us to trace individuals from the early stages of life to late adulthood, which provides the best information possible to conduct a microanalysis of how early-life adversities translate into adverse outcomes late in life. This is critical for exploring the effect of parental migration in childhood on individuals\u0026rsquo; health and testing the channels through which individuals\u0026rsquo; health outcomes are affected from the perspective of nutrition intake. This data has been widely used in previous research to study the intergenerational effect, such as Du et al. (2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur identification of the impact of exposure to parental migration in childhood on individual\u0026rsquo;s health in adulthood is based on panel data of individual-level occurrences of parental migration at the age of 0-18.\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e Since internal migration mainly occurs in rural China, we restricted the sample to those exposes to parental migration in rural areas. Two questions in CHNS questionnaires are generated to identify parental migration experience in childhood. One is regarding the reasons for family member absence. If the family member has left home to seek employment at the time of the survey, we consider this family member to be a migrant, and then their children are exposed to parental migration. Another is that respondents report whether their fathers and mothers lived at home. In that case, such as a child\u0026rsquo;s father or mother did not live with him/her, the child is regarded as a left-behind child with exposure to parental migration (Guo, 2019), where those children whose parents were separated, divorced, or widowed were excluded in our sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur final sample contains 4250 individuals, of which 323 are left-behind in childhood, accounting for 7.6 percent. Among the final sample, the average age of individuals is 25 years old. To construct a sample for mechanism analysis, we trace individuals\u0026rsquo; information back to them aged 2-12 at the time of survey.\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e5\u003c/sup\u003e During their growing up period, the average ages of their fathers and mothers are 35 and 36 years old, respectively. In the empirical analysis, all of the data are pooled to identify the effect of parental migration in childhood on individual\u0026rsquo;s health in adulthood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this paper, adult height is the primary dependent variable. Height has been an even longer tradition to measure population health (e.g., Fogel, 2004; Steckel, 2008). In general, adult height is determined by childhood height and an adolescent growth spurt. Childhood height is often thought to be an index reflecting health status, such as nutrition intake and expenditure, some illnesses, particularly from infectious diseases (Martorell \u0026amp; Habicht, 1986). Furthermore, childhood height is consistent with adult cognitive ability (Case \u0026amp; Paxson, 2008). Consequently, adult height is believed to be a good measure of levels of childhood health and some components of childhood health during early childhood. At the same time, adult height will, in part, reflect the difference in the quality of childcare.\u003c/p\u003e\n\u003cp\u003eParental migration is the primary independent variable in this paper. It is defined as a dummy variable indicating whether an individual is exposed to parental migration at ages 0-18. Since CHNS was conducted every three or four years, we could not identify parental migration at each age in childhood. Generally, people always have persistent migrative behaviors because they need to increase income from off-farm work (Villalobos \u0026amp; Riquelme, 2023). therefore, we consider an individual exposed to parental migration if at least one parent migrated for work or did not live with at a specific time in 0-18 years old, instead of at each age.\u003c/p\u003e\n\u003cp\u003eWe assess children\u0026rsquo;s dietary quality in the growth period according to their food consumption patterns and dietary diversity, which are the primary dependent variables in the mechanism analysis. The CHNS collected three-day records of respondents\u0026rsquo; food consumption, including meals per person per day and daily food intake.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBased on the China Food Composition Vol.2, we first obtain the average three-day\u0026nbsp;food consumption\u0026nbsp;collected by CHNS. For our purpose, we classify food consumption for each individual into four categories\u003ca href=\"#_ftn5\" name=\"_ftnref5\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e6\u003c/sup\u003e and calculate the percentage deviation for each food category from the Reference Daily Intake provided by the 2016 Chinese Food Pagoda (C Liu et al., 2021).\u003ca href=\"#_ftn6\" name=\"_ftnref6\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e7\u003c/sup\u003e Specifically, a negative value of the percentage deviation indicates inadequate intake, while a positive value shows the excess intake for each food category.\u003c/p\u003e\n\u003cp\u003eFurthermore, we compute the children\u0026rsquo;s dietary diversity score based on food consumption. Following Swindale and Bilinsky (2006), we categorized the food items into eight major food groups.\u003ca href=\"#_ftn7\" name=\"_ftnref7\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e8\u003c/sup\u003e Based on children\u0026rsquo;s food consumption, we calculate the score for each food group. If food intake in a group is below the minimum limit value,\u003ca href=\"#_ftn8\" name=\"_ftnref8\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e9\u003c/sup\u003e the score is zero; otherwise, it is one. The group scores are then summed to obtain the dietary diversity score, which ranges from one to eight. The higher value of the dietary diversity score indicates a more diversified diet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing Cui et al. (2020), we control individuals\u0026apos; and parents\u0026apos; basic socioeconomic and demographic characteristics, which may directly affect adult health. By controlling them, we can measure the impact of exposure to parental migration in childhood on adult health more effectively. Specifically, individual characteristics include gender, age, education, occupational and marital status, and the number of siblings. Similarly, the parental demographic characteristics include age and height. Furthermore, we also control parental education and income as the relevant measure of family socioeconomic status in the growth period. The parental education level is the greater one of parental schooling years, as does parental income. Finally, we also control for wave and province dummy variables. The specific definitions and descriptions of the critical variables are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDefinition and description of key variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eVariable name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eS.D.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAdulthood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"15\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4250\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAt least one parent migrated in childhood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eIndividual\u0026rsquo;s height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e165.0 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAge in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e24.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e5.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eGender \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMale=1, female=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eYears of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e9.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSiblings\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNumber of siblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.312 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eParticipate in labor market\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMarried=1, single=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eParental education level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eThe greater one of parental schooling years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e7.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eLog of parental income \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eThe greater one of parental income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMother\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMother\u0026apos;s age in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e51.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e7.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFather\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eFather\u0026apos;s age in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e53.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e7.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMother\u0026apos;s height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMother\u0026apos;s height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e154.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFather\u0026apos;s height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eFather\u0026apos;s height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e164.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eBody Mass Index (kg/m^2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e22.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMother\u0026apos;s BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMother\u0026apos;s Body Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e23.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFather\u0026apos;s BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eFather\u0026apos;s Body Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e23.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCereals, mixed beans, tubers, and starches\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 40px;\"\u003e\n \u003cp\u003eThe percentage deviation (deficiency/excess) from the Reference Daily Intake for each food category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eVegetables and fruits\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMeat, poultry, eggs, fish, and shellfish\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSoybean, nuts, and dairy products\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.547 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eDietary diversity score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eChildren\u0026rsquo;s dietary diversity score (ranges from one to eight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Two samples are employed in empirical analysis. One sample is composed of adults, which was used to examine the effect of parental migration in childhood on individual\u0026rsquo;s height in adulthood. Another is composed of children, which was used to investigate the effect of parental migration on children\u0026rsquo;s food consumptions and dietary diversity.\u003ca href=\"#_ftn9\" name=\"_ftnref9\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e10\u003c/sup\u003e Children\u0026rsquo;s sample includes some children excluded from the adults\u0026rsquo; sample because they have not grown up so that the sample size is different in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Empirical strategy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the long-term impact of parental migration in childhood on individual\u0026rsquo;s health outcome in adulthood, we estimate the following specification:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"953\" height=\"350\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAn endogeneity problem may exists in the estimation of Equation (1) because of children\u0026rsquo;s and parents\u0026rsquo; unobservable characteristics and selection into parental migration based on children\u0026rsquo;s health status (Cui et al., 2020). For instance, parental preference and genetic potentially affect both parental migration and adult height. To address this potential endogeneity, we first control the height of the father and mother, which could be used as a measure of genetic. Second, we employ the IV approach, which is widely adopted in existing research. According to Du et al. (2005), the migration rate at the community level can serve as the IV for parental migration. Specifically, we instrument the average proportion of those who migrated for work at childhood ages of the individual \u0026nbsp; in the same community as the IV for the dummy variable of parental migration.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e11\u003c/sup\u003e The community migration rate, as a proxy for migration social networks, could affect an individual\u0026apos;s migration decision. In contrast, the migration rate in early life does not directly relate to an individual health outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince parental migration is a binary endogenous variable, we use ERM to estimate Equation (1) to tackle the endogeneity problem. To check the robustness of the results, we also use the ordered logit model to re-estimate the parameters by measuring adult health with BMI. In addition, Equation (1) is also applied in the mechanistic analysis, where the dependent variable is food consumption and dietary diversity. According to the characteristics of the dependent variables in the mechanistic analysis, the ERM is employed to access the estimations.\u0026nbsp;\u003c/p\u003e"},{"header":"Ⅲ. Results","content":"\u003cp\u003e\u003cstrong\u003e1. Effect of parental migration in childhood on individual\u0026rsquo;s health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 represents the estimation results for the effect of parental migration in childhood on adult height. Model (1) is based on simple OLS regression, treating parental migration as an exogenous variable. The results show that adult height will decrease by 0.393 cm if an individual is left behind in childhood. The Durbin-Wu-Hausman statistic in Column (3) is 0.015 and insignificant, indicating that the endogeneity problem is not obvious. The result of a weak IV test shows no presence of a weak IV.\u003c/p\u003e\n\u003cp\u003eThe first stage regression, a Probit model, is used to predict the probability of parents going out to work in childhood. The IV coefficient is significant and positive after controlling for variables such as age, gender, education, siblings, parental height, education, and income. The peer effect always exists in a small community, which affects a parental migration decision by providing employment information and decreasing migration costs and uncertainty (Su et al., 2018). In addition, families in a community also experience a similar economic and political environment. Therefore, parents living in a community with a higher migration rate are more likely to go out to work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe magnitude of the coefficient of parental migration from the ERM is more substantial than that from OLS. Exposure to parental migration in childhood significantly reduces the individual\u0026rsquo;s height in adulthood by 0.692cm at the 5 percent significance level. The results are consistent with the previous findings in China that the childhood left-behind experience are negatively associated with individuals\u0026rsquo; life-cycle outcome in educational attainment, employment quality, wages, and self-rated and mental health. In addition, the IV adopted in this article may correlate with factors beyond parental migration, such as local economic development and culture, violating the exclusion restrictions. To enhance the robustness of the research results, we replaced the province fixed effects with regional dummy variables to absorb the interference common to all households and individuals in the same district and county, and obtained similar results shown in Column (3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results are consistent with the previous findings that parental migration is negatively associated with an individual\u0026rsquo;s life-cycle outcome, such as educational achievements (Dong et al., 2021), labor market outcomes\u0026nbsp;(Feng et al., 2022; Wang et al., 2021), self-reported health and mental health\u0026nbsp;(Liang \u0026amp; Sun, 2020). Notably, the results are also in line with the findings of\u0026nbsp;Zheng et al. (2022)\u0026nbsp;that left-behind children are more likely to report chronic diseases, be underweight, and have lower levels of perceived health when they become adults. However,\u0026nbsp;Zheng et al. (2022)\u0026nbsp;did not explore the possible channel.\u003c/p\u003e\n\u003cp\u003eAmong the control variables, the height of the father and mother, as the proxy for genetics, is strongly associated with adult height. Parental income in the growth period significantly positively affects an adult\u0026rsquo;s height, indicating the importance of socioeconomic status on offspring outcomes. Moreover, personal education attainment and gender could also account for an individual\u0026rsquo;s income. In addition, individuals with more siblings tend to have lower height outcomes, which is consistent with quality-quantity trade-off theory (e.g., Rosenzweig \u0026amp; Wolpin, 1980).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Effect of parental migration in childhood on individuals\u0026rsquo; height in adulthood\u003c/strong\u003e\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 rowspan=\"4\" style=\"width: 37px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eERM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eERM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1st stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2nd stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eAny parent\u003c/p\u003e\n \u003cp\u003emigrated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.393**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.692**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.510***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e9.193***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.479)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.0837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.106***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0884***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0817*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0423)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eGender(male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e11.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e11.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0567)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eSchooling years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.208***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.218***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0668)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0658)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eSiblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.327**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.328***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.347***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.0963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eParental education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.0303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.00464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0425)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0513)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0372)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eLog of parent\u0026apos;s income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.292**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMother\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0472)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eFather\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0700**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0474)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMother\u0026apos;s height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.262***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0448***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.262***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.281***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.00123)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eFather\u0026apos;s height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.340***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.0115*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.340***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.353***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.00691)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0512)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e52.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-221.0***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e52.04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-219.7***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(9.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(16.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(3.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e(14.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4,250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eControl of wave dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eControl of province dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eControl of community dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eDWH test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eWeak identification test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e545.078***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e712.316***\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\u003eNotes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Heterogeneous results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows the\u0026nbsp;heterogeneity in the effect of parental migration in childhood on individual\u0026rsquo;s height by gender, sibship structure, household income, and region. In panel A, we stratify the sample by gender. The results show that parental migration in childhood has a more substantial adverse influence on male\u0026rsquo;s adult height. On the one hand, males are more vulnerable to parental migration in terms of health than females (Meng \u0026amp; Yamauchi, 2017). On the other hand, females usually leave the natal house after marriage in rural China to weaken the effect of being left behind in childhood.\u003c/p\u003e\n\u003cp\u003eIn panel B, we subdivide our sample into two samples by the sibship structure. The results suggest that parental migration in childhood is significantly negative for individuals with siblings while invalid for those without siblings. The potential reason could be that resource constraints faced by families with more children are more stringent, negatively affecting children\u0026rsquo;s human capital investment (Lee, 2012). Then it exacerbates the adverse effects of being left behind in childhood.\u003c/p\u003e\n\u003cp\u003eIn panel C, two sets of estimates are\u0026nbsp;constructed to investigate the impact of exposure to parental migration\u0026nbsp;in childhood on individual\u0026rsquo;s height when parents are at bottom quartile and top quartile of income. We find that\u0026nbsp;given an individual born to parents at\u0026nbsp;bottom quartile of income, parental migration in childhood has significantly effects on adult height, while it is valid when\u0026nbsp;parents at\u0026nbsp;top quartile.\u0026nbsp;The possible\u0026nbsp;interpretation\u0026nbsp;is that parents at bottom quartile of income could not provide enough support for their children\u0026rsquo;s nutrition and health, which impedes the long-term healthy development of left-behind children (Currie \u0026amp; Stabile, 2003).\u003c/p\u003e\n\u003cp\u003eIn panel D, we conduct separate estimates for individuals from Eastern China and Central China or Western China as China\u0026rsquo;s provinces are varied substantially in economic development.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e12\u0026nbsp;\u003c/sup\u003eWe find that the exposure to parental migration in childhood is particularly pronounced for individuals from Central China or Western China, suggesting that the left-behind children from these regions suffer from worse development as parental migration. The possible interpretation is that the public service in health and education is relatively poor for left-behind children in the less-developed region of China.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Effect of parental migration on adult height\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eby gender, sibship structure, household income, and region\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePanel A: Stratified by gender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e-0.681***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e(0.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e(0.608)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e2,924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1,326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePanel B: Stratified by sibship structure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eWithout sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWith sibling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.837***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e(0.602)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e(0.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e3,441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePanel C: Stratified by household income\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eBottom quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e-2.468***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-6.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e(0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePanel D: Stratified by region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eEastern China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCentral China and Western China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.978***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e(0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e(0.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e1,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e3.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. The additional control here is the individual and parental characteristics, such as age, gender, siblings, education, income, et al., and the fixed effects of province and wave. All the results above are estimated by ERM using an IV. The female sample size is smaller than the males because some females leaving the natal house after marriage could not be surveyed.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Mechanism results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome empirical evidence highlights the long-term effect of child nutritional status on individuals\u0026apos; human capital accumulation (e.g., Fang \u0026amp; Zhu, 2022; Maluccio et al., 2009). In this study, we test the channel by examining the impact of parental migration on food consumption patterns and dietary diversity.\u003c/p\u003e\n\u003cp\u003eTable 4 presents that the estimates for the four food categories and dietary diversity. The results in Column 1-4 show that the left-behind children with migrant parents consume more cereals, mixed beans, tubers, starches, and vegetables and fruits. However, the difference is negative and significant for the other two categories, such as meat, poultry, eggs, fish, shellfish, and soybean, nuts, and dairy products. The results indicate that the traditional cereal and vegetable consumption for left-behind children is considerably higher than the recommended quantity suggested by the 2016 Chinese Food Pagoda, while meat, eggs, and dairy products are lower than the reference daily intake. In other words, left-behind children with parental migration consume more food in high carbohydrates but less food in high fat and protein. Notably, deficiency or excess food consumption is not beneficial for children\u0026rsquo;s nutrition status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the result in Column 5 indicates that parental migration has consistently negative and significant effects on the dietary diversity score of left-behind children. The findings are consistent with the result in Column 1-4, pointing out that the left-behind children tend to consume more staple food products, and their dietary patterns are relatively single. Our findings also imply that the absence of parental care can be harmful to children\u0026rsquo;s diet quality and, thus, health, which is expected to hurt their adult health in the long term (Smith et al., 2012). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Effect of parental migration on food consumption patterns and dietary diversity in childhood\u003c/strong\u003e\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 rowspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ecereals, mixed beans, tubers, and starches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003evegetable and fruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003emeat, poultry,\u003c/p\u003e\n \u003cp\u003eeggs, fish and shellfish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003esoybean, nuts and dairy products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eDietary diversity score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.082***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.551***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.294***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.179***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-1.036***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0866)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eGender(male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.222**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0992)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eSquare of age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.590***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.171**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0555**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0527*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.00175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0667)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0244)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0634)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eSchooling years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.0161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0298)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eSiblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.0224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.0222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0509**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0494**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0670)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eParental education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0279*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.00779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.00586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eLog of parent\u0026apos;s income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0822***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0324**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0723**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0334)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eMother\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.00514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.000939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.00929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.00827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.00605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.00420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0117)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eFather\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.000220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.00663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.0104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.00645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.00852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.00450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0136)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.312***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-1.431***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.894***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.370***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.668)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1,579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1,577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1,460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1,159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1,579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eControl of wave dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eControl of province dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eDWH test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e21.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eWeak identification test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11.56***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e11.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e13.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e13.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e11.56***\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\u003eNotes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. All the results above are estimated by ERM using an IV.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Robustness analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to better understand whether parental migration in childhood increases the odds of an individual being underweight or thinness, we also estimated an ordered logit model with BMI categories. The model specification defines the dependent variables based on the Standard of World Health Organization (WHO).\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e13\u003c/sup\u003e In the first model of Table 6, the dependent variable reflecting BMI categories is defined by -2 for moderate and severe thinness, -1 for underweight, 0 for normal weight, 1 for overweight, and 2 for obesity. In the second model of Table 6, the dependent variable reflecting BMI categories is defined by 1 for thinness and underweight, 2 for normal weight, and 3 for overweight and obesity. Table 6 illustrates the estimation results of the ordered logit model, and the results from the two model specifications are similar. We find that parental migration in childhood significantly increases an individual\u0026apos;s likelihood of being underweight and going from normal weight to underweight.\u003c/p\u003e\n\u003cp\u003eIn addition, one attractive feature of the ordered logit model is that one can use it to predict the probabilities of each BMI categories to which an individual belongs. For example, on average, the probabilities of an individual being normal weight are 0.71, and an individual being underweight or thinness are 0.10 or 0.03, respectively.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Ordered logit model with BMI categories for individuals\u003c/strong\u003e\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 rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eBMI category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eBMI category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eParental migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.178*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.203*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.0365***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.0409***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.00860)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.00893)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.596***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.604***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.0203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.0157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0111)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eSiblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.00736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.00331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0625)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.193***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.202***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.00836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0654)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.193***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0707)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eParental education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eLog of parent\u0026apos;s income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.00933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.0302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0472)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMother\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.0239***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.0222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.00385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0256)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eFather\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.00358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.00283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.00964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0257)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.0909***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.0903**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0397)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eFBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.138***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.137***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.00214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.0399)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3,071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3,071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eControl of wave dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eControl of province dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eYES\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\u003eNotes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. In Model 1, the dependent variable reflecting BMI categories is defined by -2 for moderate and severe thinness, -1 for underweight, 0 for normal weight, 1 for overweight, and 2 for obesity, based on the standard of the World Health Organization. In Model 2, the dependent variable reflecting weight categories is defined by 1 for thinness and underweight, 2 for normal weight, and 3 for overweight and obesity, based on the standard of the World Health Organization.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results above are based on CHNS data, which track individuals from early stages of life to late adulthood. In this section, we use retrospective data collected by China Family Panel Studies (CFPS) to re-estimate the long-term effect of parental migration. Table 7 presents the estimation results of ERM with an IV.\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e14\u003c/sup\u003e The result in Column 1 shows that exposure to parental migration at ages 0-12 significantly reduces adult height by 0.798 cm, which is generally consistent with our baseline estimates. The result suggests that selection bias is not an issue of concern in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, following Liang and Sun (2020), we use individuals\u0026apos; self-rated health to measure a different dimension of adult health. The self-evaluation is a standard five-point scale (very good, good, fair, poor, or very poor) of the general state of one\u0026rsquo;s health. The result in Column 2 of Table 7 indicates that exposure to parental migration at ages 0-12 is more likely to have poor self-rated health, which is concordant with our main findings. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Effect of parental migration in childhood on height and self-rated health\u0026nbsp;\u003c/strong\u003e\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 valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSelf-rated health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eAny parent migrating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.798*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.108**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.0520)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.170***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.0187***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.00463)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e11.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.0353*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.0184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003erural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.861***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.00489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.0216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0911***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.00978***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.00864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.00362)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eNumber of siblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.545***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.0182*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.0105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.00617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.402)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.0279)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.0488**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.0197)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eParental education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.0447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.00153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.00248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eMother\u0026apos;s \u0026nbsp;age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.000271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.00370)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eFather\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0833***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.00160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.00776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.00332)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eMother\u0026apos;s height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.273***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eFather\u0026apos;s height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.283***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.0375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e71.55***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.892***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(8.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e(0.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2,410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e4,663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eControl of province dummies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eDWH test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.33*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eWeak identification test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.93***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e39.79***\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\u003eNotes: *, ** and *** represent significance at the 10%, 5% and 1% levels, respectively. Robust standard errors are in parentheses. All the results above are estimated by ERM using an IV, based on China Family Panel Survey (CFPS). High scores for self-rated health refer to worse health status.\u003c/p\u003e"},{"header":"Ⅳ. Conclusions","content":"\u003cp\u003eThis paper estimates the long-term effect of parental migration in childhood on adult health, and it reveals the channels by which adult health is affected. We address the endogeneity of exposure to parental migration using instrumental variable estimations in the ERM based on the data from 1991\u0026ndash;2015 CHNS.\u003c/p\u003e \u003cp\u003eOur results show that exposure to parental migration in childhood significantly affects adult height and self-rated health. The effect is more substantial for males, those with siblings, born to parents in the bottom quartile of income, or from Central China and Western China. Furthermore, dietary quality is an important contributing channel that likely underlies the long-term effects of parental migration in childhood. More specifically, parental migration would decrease children\u0026rsquo;s dietary diversity and food intake in high fat and protein. The robustness checks confirm our general findings and strengthen our arguments.\u003c/p\u003e \u003cp\u003eWith limited data sources, research about the long-term consequences of the childhood left-behind experience in rural China on individuals\u0026rsquo; life-cycle outcomes is largely absent. The findings of this study contribute to the literature in several ways. First, we provide new evidence that parental migration has adversely affected their children\u0026rsquo;s food consumption and subsequent adult height. Second, this research accounts for health inequality from the perspective of the intergenerational effect of parental migration on individuals\u0026rsquo; health. Finally, these results may help create policies to mitigate the long-term consequences of exposure to parental migration in other developing countries, such as Bangladesh and Vietnam.\u003c/p\u003e \u003cp\u003eThe findings of this study have important policy implications. To avoid the potential long-term effect of parental migration, the government should put more effort into preventive intervention. Specifically, the government can provide more employment placements to reduce the migration rate in rural areas or improve welfare to allow more children to migrate with their parents. Moreover, the government should initiate various nutrition programs to promote the dietary quality of left-behind children and provide training programs to increase rural residents\u0026rsquo; knowledge of nutrition and health. Besides, limited by data availability, we could not identify the length and the time of being left behind, which may have a very different impact on adult health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Fancun Meng.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eProject of the start-up fund for scientific research of Shantou University [NTF22006]; Project of Guangdong Medical Science and Technology Research Fund Program [B2023470]; Project of the Liberal Arts and Social Sciences Foundation for Youths from Ministry of Education of China [22YJC790078].\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eQundi Feng: Formal analysis, Writing - original draft, Conceptualization. Fancun Meng: Writing - Review \u0026amp; Editing, Funding acquisition.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbiona, O. (2017). Adverse effects of early life extreme precipitation shocks on short‐term health and adulthood welfare outcomes. \u003cem\u003eReview of Development Economics, 21\u003c/em\u003e(4), 1229-1254.\u003c/li\u003e\n \u003cli\u003eAlmond, D., Currie, J., \u0026amp; Duque, V. (2018). Childhood circumstances and adult outcomes: Act II. \u003cem\u003eJournal of Economic Literature, 56\u003c/em\u003e(4), 1360-1446.\u003c/li\u003e\n \u003cli\u003eAntman, F. M. (2012). 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As for tackling the endogeneity problem, an ERM fits a linear regression model suitable for any endogenous covariates, such as binary, continuous, and ordinal endogenous covariates, while the two-stage least square (2SLS) regression is only accommodated continuous endogenous variables.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2. https://www.cpc.unc.edu/projects/china\u003c/p\u003e\n\u003cp\u003e3. Data from Chongqing, Beijing, Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Liaoning, Jiangsu, Shanxi, Shandong, Shanghai, Yunnan, and Zhejiang were collected.\u003c/p\u003e\n\u003cp\u003e4. Parental migration is always throughout children\u0026rsquo;s childhood between the ages of 0-18, and the testing results are available on request.\u003c/p\u003e\n\u003cp\u003e5. Following He et al. (2018), We do not include older children because puberty begins at around 13 years old, and growth spurts during puberty and after that reduces anthropometric methods\u0026apos; reliability. Another reason is that children over 13 years of age may have more control over their food choices, and their health is less affected by family care.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003csup\u003e\u003c/sup\u003e\u003c/sup\u003e6. The four categories are (1) cereals, mixed beans, tubers, and starches; (2) vegetables and fruits; (3) meat, poultry, eggs, fish, and shellfish products; and (4) soybean, nuts, and dairy products.\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" style=\"width: 873px;\" width=\"873\" height=\"98\"\u003e\u003c/p\u003e\n\u003cp\u003e8. The eight major food groups are (1) grains, roots, and tubers; (2) vegetables; (3) fruits; (4) dairy products; (5) legumes and nuts; (6) flesh foods (meat, poultry); (7) seafood; (8) egg.\u003c/p\u003e\n\u003cp\u003e9. The minimum limit value of soybean food is 5g, and others are 25g.\u003c/p\u003e\n\u003cp\u003e10. We only access to individual\u0026rsquo;s food consumption pattern and dietary diversity for 1997\u0026ndash;2011 in our sample because the China Food Composition before 1997 and the information of food consumption in the 2015 CHNS data are not available.\u003c/p\u003e\n\u003cp\u003e11. A community means an entire village or a few blocks in a city. The CHNS sample communities come from cities, suburbs, towns, or villages in China, where all entities are legally recognized by the National Bureau of Statistics of China. In the survey data, we do not know the exact name of the community, but we can see the community code of each household. Consequently, the households are from the same community if their community codes are the same; the proportion is based on the sample over 18 years old in a community, and it excludes the family of the child .\u003c/p\u003e\n\u003cp\u003e12. Eastern China, Central China, and Western China are defined based on the Seventh Five-Year Plan (1986\u0026ndash;1990), which grouped the different provinces of China into three economic zones. Eastern China is more developed than others are.\u003c/p\u003e\n\u003cp\u003e13. BMI \u0026lt;17: moderate and severe thinness; BMI \u0026lt;18.5: underweight; BMI 18.5-24.9: normal weight; BMI \u0026ge;25: overweight; BMI \u0026ge;30: obesity.\u003c/p\u003e\n\u003cp\u003e14. The county migration rate excluding the family of the child, as a proxy for migration social networks, is used to serve as the IV.\u003c/p\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":"Parental rural-to-urban migration, dietary quality, health, China","lastPublishedDoi":"10.21203/rs.3.rs-5787718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5787718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLittle is known about the role of parental rural-to-urban migration during childhood in shaping individuals’ health conditions. Using data from the China Health and Nutrition Survey, this study explores the long-term effect of parental migration during childhood on adult health outcomes. The extended regression model is employed to address the potential endogeneity of parental migration with an instrument variable. The results indicate that exposure to parental migration in childhood has a significant negative impact on adult height. Robustness checks using Body Mass Index and self-rated health status validate our findings. Mechanism analysis shows that parental migration significantly reduces left-behind children’s dietary quality in terms of food consumption patterns and dietary diversity. Given the insufficient protections related to left-behind children, there is a need for preventive intervention to mitigate the health disparity in the long term caused by parental migration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI15, J13, O12\u003c/p\u003e","manuscriptTitle":"Were there long-term health effects of exposure to parental migration on adult children? Evidence from rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 08:47:31","doi":"10.21203/rs.3.rs-5787718/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":"9d7f2651-f0b2-41b6-9548-bf84142321d3","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T19:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 08:47:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5787718","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5787718","identity":"rs-5787718","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}