behind children in - Wiley Online Library

I N T E R NAT I O NA L J O U R NA L O F SOCIAL WELFARE

DOI: 10.1111/ijsw.12162 Int J Soc Welfare 2016: 25: 58–68

ISSN 1369-6866

Well-being of migrant and left-behind children in China: Education, health, parenting, and personal values Lu S., Lin Y-T., Vikse J.H., Huang C-C. Well-being of migrant and left-behind children in China: Education, health, parenting, and personal values Over the last several decades of urbanization and industrialization, China has encountered mass labor force migration from rural to urban areas. As a result, two-child populations have dramatically increased in number: so-called left-behind children and migrant children. Using data from the 2006 and 2009 China Nutrition and Health Surveys, this study examined the effects of parental migration and residency status on the education, health, parenting, and personal values of children, with particular focus on left-behind and migrant children. The findings suggest that parental migration and residency status play important roles in the educational and health outcomes, parental supervision, and personal values of children. Through analyzing the differences and possible reasons for disparate outcomes among child populations, this study aimed to improve public understanding of migrant and left-behind children’s well-being in China, and explore implications for future studies and welfare policy making. Key Practitioner Message: • Help to identify the impact of migration on individual, family, and the society domestically and internationally; • Provide implications for welfare policy making, program design, and service delivery for migrant populations; • Explore the approaches to addressing migration-related issues in different countries with internal and transnational migrant populations.

Since China instituted its reform and opening policy in the late 1970s, the country has undergone tremendous economic development (Chan & Zhang, 1999; Lin, 2009). As a result of rapid industrialization and urbanization, population migration from rural to urban areas has greatly increased, which has led to the emergence of a new class of migrant workers. This migrant worker population has grown substantially; the 2010 Census in China estimated that about 260 million people were working and living in places that were not their hometowns, accounting for 19.4 percent of the entire population in Mainland China (Chen & Feng, 2012). This migration of the Chinese labor force represents the largest movement of people in modern history (UNICEF, 2010). In the 1950s, the Chinese government began to enforce the Household Registration System, which was primarily intended to achieve political control of population mobility and resource redistribution, keep urban unemployment under control, protect the interests of 58

Shuang Lu1, Yi-Ting Lin2, Juliann H. Vikse1, Chien-Chung Huang1 1

School of Social Work, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA 2 Department of Social Welfare, Taiwan National Chung Cheng University, Chia-yi County, Taiwan

Key words: child well-being, migrant children, left-behind children, education, health, parenting, personal values Shuang Lu, School of Social Work, Rutgers, The State University of New Jersey, 390 George Street, Room 504, New Brunswick, NJ 08901, USA E-mail: [email protected] Accepted for publication January 17, 2015

urban citizens, and develop capital-intensive heavy industries and urban economies (Chan & Zhang, 1999; Wu & Yao, 2003; Xu, Guan, & Yao, 2011). Since then, all citizens have had to obtain household registration booklets, which identify their place of registration and their status as either “agricultural” (predominantly held by rural residents) or “nonagricultural” (predominantly held by urban residents). Subsequent social welfare policies have typically favored urban residents, and official residence status has thereby become a crucial determinant of opportunity (Chan & Zhang, 1999). Consequently, most urban residents have greater access to income, social security, healthcare, and education. To legally migrate between “agricultural” and “nonagricultural” locations, it is first necessary to obtain government permission through a complicated application process (Chan & Zhang, 1999). As a result, many rural residents who try to relocate to urban areas for employment are denied access to government benefits and have fewer opportunities for self-development.

Int J Soc Welfare 2016: 25: 58–68 © 2015 The Author(s). International Journal of Social Welfare © 2015 International Journal of Social Welfare and John Wiley & Sons Ltd

Left-behind children in China

In 1978, China began its transition from a planned to a market economy. Economic reforms since that time, which have included the privatization of many previously state-owned enterprises and the loosening of residency permit restrictions, have increasingly facilitated rural-to-urban migration (Chang, Dong, & MacPhail, 2011; Seeborg, Jin, & Zhu, 2000; Wu & Yao, 2003). To date, a significant portion of the agrarian population has relocated to cities. Migrants seek to improve their economic status and that of their families, typically by working in labor-intensive industries such as manufacturing, construction, and service (Li & Li, 2007). Migrant workers have contributed meaningfully to the urban economy (Cai & Wang, 2010; Li & Li, 2007). Despite their participation in the urban workforce, however, current laws preclude them from enjoying the rights and benefits conferred on urban residents. They often work and live in poor conditions, have access to few public services and benefits, and experience social marginalization. They are also faced with the difficult choice of whether to allow their children to migrate with them or to place their children in the care of extended family members or neighbors. According to UNICEF (2009), children with rural registration status who migrate with their parent(s) to a different county or province are defined as “migrant children.” Children whose parents leave home to work for more than 3 months at a time and whose care is entrusted to others (usually grandparents or other extended family members) are considered “left-behind children.” In this study, we examined the effects of residency status and parental migration on the wellbeing of both migrant and left-behind children. It is clear that these child populations deserve more research attention. In 2008, the number of Chinese children impacted by migration reached 82.4 million, which represented more than one quarter of China’s child population (UNICEF, 2010). As the number of migrant workers in China continues to increase, so do the numbers of migrant and left-behind children. In fact, the number of migrant children rose from 23.6 million in 2000 to approximately 27.3 million, or 9.8 percent of China’s child population, in 2008, and the number of left-behind children rose from 22 million in 2000 to an estimated 55.1 million, or 19.8 percent of China’s child population, in 2008 (UNICEF, 2010).

outcomes. Young children are particularly vulnerable to the disruptive effects of parental migration (Lu, 2012). In one study, left-behind children (defined as rural children whose parents had migrated) had a 37-percentage point lower probability of being enrolled in school than their peers (defined as rural children with no migrant parents) (Lee, 2011). Among adolescents aged 15 to 18 years (the typical age range of Chinese high school students), those with migrant parents had approximately 0.9 fewer years of schooling than their peers. This finding indicates that children with migrant parents are more likely to discontinue their education after middle school (Lee, 2011). Parents’ migration has also been shown to impact the dietary health of left-behind children. De Brauw and Mu (2011) studied rural households with children aged 2–12 and found that in homes where parents or other household members had migrated for work, caretakers spent three fewer hours per week preparing and cooking food and one to two fewer hours buying food than did other caretakers. The study also found that among children aged 7–12, a parent’s migration was associated with a 6.3 percent increase in the probability of being underweight, compared with children whose parents resided in the home. According to the authors, less time spent preparing and buying food may have resulted in lower quality and smaller amounts of food consumed by leftbehind children (de Brauw & Mu, 2011). In rural areas, many left-behind children do not maintain regular contact with their parents, and often feel lonely and unsupported (Jia & Tian, 2010). Some children see their parents only once a year during the Spring Festival holiday when many migrant workers return to their hometowns to visit their families. The caregivers of left-behind children, particularly grandparents, are often unable to provide adequate emotional support, hygiene and nutrition, and homework supervision (UNICEF, 2010). Despite this, left-behind children may require more care and attention from those around them during their parents’ absence. In rural China, leftbehind children were shown to place greater value on social popularity than their peers did (80 vs. 73%) and were more likely to care about school grades and to seek their parents’ praise than were children who resided with their parents (Lee, 2011). These disparate personal values might stem from separation from their parents.

Literature review The effects of parents’ migration on left-behind children’s well-being

The effects of parents’ migration on migrant children’s well-being

Left-behind children are generally concentrated in economically underdeveloped areas in Sichuan, Guangdong, Jiangxi, Jiangsu, Anhui, Hunan, and Hainan provinces (UNICEF, 2010). Migration has been found to significantly impact children’s educational

Given migrant children’s frequent travel with their families, their parents’ low income, and inadequate schooling options, delayed education has become a serious problem (Duan & Liang, 2005; Lu & Zhang, 2004; Wei & Hou, 2010). For example, in Lu and


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Zhang’s survey of schools for migrant children in Beijing, 15 of the 53 third-grade students at Wabian No. 4 Elementary School were over the age of 14 – significantly older than the average 10-year-old third grader. At Zhangbei Elementary School, some second-grade students were aged 18. At the Taiyanggong Migrant School, 42 (or 28.4%) of the total 148 students were deemed “over-age” (Lu & Zhang, 2004). Under the Household Registration System, migrant workers and their children are considered “temporary residents” in the urban areas they migrate to. Many public schools charge migrant students extra fees or require complicated documentation to attend (Duan & Liang, 2005; Wong, Li, & Song, 2007). In effect, many are left with no choice but to attend privately run schools for migrant children (Wang & Holland, 2011). A qualitative study of migrant children in Shanghai found that few migrant children were able to attend public high schools in the city, mainly because of the prohibitively high costs or strict residency requirements (Wang & Holland, 2011). In another study, migrant children were shown to have lower school enrollment rates than urban children, from middle school onward (Duan & Liang, 2005). In the 1990s, privately run migrant schools were established in urban areas to provide education to an increasing population of migrant children. However, the facilities and teaching quality at these migrant schools were often substandard, as their funding was much lower than for government-supported public schools (Dong, 2010; Wang & Holland, 2011). Many schools for migrant children have limited space, poor facilities, and few certified teachers (Dong, 2010; Guo, Yao, & Yang, 2005). Such educational disparities are rooted in the Household Registration System. The structural barriers prevent migrant children from enjoying the same educational benefits as their urban peers. The stress of migration may also lead to unhealthy behavioral and mental health outcomes. A survey of 4,550 adolescents and young adults in urban Vietnam, for instance, showed that the prevalence of cigarette smoking was higher among migrants from rural areas than among urban residents. Studies have also shown a relatively high prevalence of depression among migrant youth (Nguyen, Rahman, Emerson, Nguyen, & Zabin, 2012). Migration also influences children’s personal values and social relationships. In a study of migrant children in Ireland, Devine (2013) found that appraising migrant children differently at school leads to the naturalization of their underachievement. These children also struggled to negotiate belongingness and decipher what was valued in their peer relationships. The study also found that some migrant children were very conscious of their behavior and strove to secure acceptance 60

at school, most likely because of their feelings of vulnerability (Devine, 2013). Policy development and response During the 1970s and early 1980s, China’s social welfare and economic policies discriminated among citizens according to geographical and workplace affiliations. This resulted in stark inequalities between urban (nonagricultural) and rural (agricultural) residents (Smart & Smart, 2001; Solinger, 1999). For the most part, the long-term influx of migrant workers and their children in urban China was unanticipated. Initially, policymakers expected migrant workers to live as temporary residents and eventually return to their hometowns (Dong, 2010). It took time for the government to recognize the need for policies that supported a growing population of migrant children (Xu et al., 2011). Finally, in the late 1990s, the central government began providing social insurance for migrant workers employed by state-owned enterprises and large private companies (with over 50 employees). These programs included maternity benefits for working mothers, basic healthcare, pensions, unemployment insurance, and occupational injury insurance (Xu et al., 2011). However, a 2005 government report from China’s Ministry of Labor and Social Security showed that only 15 percent of migrant workers were participating in the pension program, only 10 percent in the healthcare program, and none were using the maternity-related or occupational injury-related benefits (Xu et al., 2011). In terms of schooling, in 1998, the Chinese central government initiated a regulation that instructed local governments to strictly prevent school-aged children from migrating (except in cases when no legal guardians were available in the children’s hometowns) and permitted urban public schools to charge migrant families additional fees, known as “transient student fees” or “school selection fees” (Dong, 2010). These policies have been changed over the years. The Chinese central government, for instance, stipulated in 2001 that urban public schools would be the major education providers of migrant children and that migrant children should pay the same fees as local children for schooling. Local governments, however, continued to charge additional fees and request extensive application materials from migrant families (Dong, 2010). Overall, policies have not effectively addressed the inequality in accessing welfare benefits and public education for migrant families. Although individuals typically migrate to improve the lives of their families, parents’ migration has been shown to profoundly impact their children’s physical, educational, and psychosocial development. While recent policies and reforms have addressed some of the issues, the challenges facing migrant and left-behind



children still threaten the healthy development of children in China. Using recent cohort data, the aim of the present study was to examine the effects of residency status and parental migration on the education, health, parenting, and personal values of migrant and leftbehind children, compared with those of rural and urban children, and provide implications for programs and policies to address such issues in China and elsewhere. Methods Data The data for this study came from the 2006 and 2009 China Health and Nutrition Survey, a longitudinal survey collaboratively conducted by the Carolina Population Center at the University of North Carolina and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. Using a multistage, random clustered sampling strategy, the study surveyed approximately 4,400 households from 9 provinces in China. The survey collected detailed information on children’s level of education, health status, and personal values, which enabled us to analyze how parents’ migration affects children’s wellbeing. In this study, the sample included children aged 6 to 18, all of whom were grouped into four categories based on whether their parents were absent or living at home, their current place of residence (rural or urban), and their household registration status (rural or urban). There were in all 3,804 children surveyed in 2006 and 2009, and 1,204 of them were younger than 6 at the time of the survey. These children were dropped from the sample, as most key variables were measured by questions for children aged 6 or older (school enrollment, years of education, and parents’ restriction on television watching) and for those aged 12 or older (smoking behavior and personal values). The total sample of children aged 6 to 18 was 2,600, among which 552 provided incomplete data related to key variables. The final sample of the study included 2,048 children whose data relating to key variables were complete. Measures The dependent variables in the study were based on four dimensions of child well-being: education, health, parental supervision, and personal values. Education was measured according to two dimensions: school enrollment, which was measured by asking whether the child was currently enrolled in school (yes = 1, no = 0), and the number of years the child had attended school, which was measured by asking the child how many years of formal education he or she had completed. Health was measured in two areas: weight and

smoking. Weight was measured by three categories: overweight, underweight, or normal weight; these categories were based on the sex- and age-adjusted body mass index cut-off points suggested by Cole, Flegal, Nicholls, and Jackson (2007). Smoking behavior was measured by asking whether the child had ever smoked before – this question was posed only to children aged 12 or older, with answers of “yes” coded as 1 and “never smoked” coded as 0. Parental restriction on television watching, our indicator of parental supervision, was measured by the question “Does your family have rules about how long you can watch television?” Answers were coded as 0 to 4 from “very seldom” to “very often.” Child personal values was measured by the question “How often do you care about this priority?” for children aged 12 or older. Children were asked to selfreport their frequency of caring about being praised by their parents, being liked by friends, and getting good grades in school. Answers were coded as 1 to 4 from “never” to “usually.” Child migration and residency status was the main independent variable. Each child was categorized as left-behind, migrant, rural, or urban, based on parents’ migration, current place of residence, and household registration status. In this study, children were defined as left-behind if both of their parents had migrated. Children who were registered residents of rural areas, but were living in urban areas with at least one of their parents, were defined as migrant children. Rural children referred to those who were living in rural areas with one or both of their parents. Lastly, urban children referred to those who were living in urban areas and also registered urban residents, and who resided with one or both of their parents. In addition, the study also controlled for covariates such as age, gender, household income, family size, number of children within each household, and wave. Age was coded as a continuous variable; gender was coded as “0” for girl and “1” for boy. Household income, which included household market income and income from all types of government transfers, was defined as the household gross income inflated to the currency of the last survey year, 2009. Number of children within each household was coded as a continuous variable, and our analysis included data from two survey waves, 2006 and 2009. Analytic strategy First, descriptive analyses of main variables were conducted. These were followed by bivariate analyses of key variables by child migration and residency status, the main independent variable. Lastly, multivariate regressions were performed to examine the effects of child migration and residency status on education,


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health, parental supervision, and child personal values. Logistic regression was performed to analyze school enrollment and smoking behavior, both dichotomous variables. A multi-logit regression was used to examine child weight. Cumulative logistic regressions were conducted to analyze years of education, parental restriction on television watching, and child personal values. Results Sample description Table 1 presents the descriptive statistics for all variables. Among the 2,048 children in the sample, the majority were rural children (64.9%), followed by urban (15.6%), migrant (11.9%), and left-behind (7.6%) children. The average school enrollment rate was high (93%); children had an average of 5.4 years of education, with a standard deviation of 3.1. Approximately 11 percent of the children were overweight, 83 percent were normal weight, and 6 percent were underweight. About 2.5 percent had smoked cigarettes. On a 0–4-point scale, parents’ average level of restricting children’s television watching time was 2.01, with a standard deviation of 1.28. On a 1–4-point scale, children on average valued parents’ praise at 2.19 points, peer recognition at 2.45 points, and school grades at 2.6 points. The average age of the sample was 11.5 years

Table 1. Descriptive statistics, N = 2,048. Mean or percentage Child migration and residency status (%) Left-behind Rural Migrant Urban Currently in school (%) Years of education Weight (%) Overweight Normal weight Underweight Smoking (%) Parents’ restriction on TV watching (0–4) Personal values (1–4) Parents’ praise Peer recognition Good grade Family size Income Number of children Age Age group 1 (≤12) (%) Age group 2 (12–15) (%) Age group 3 (>15) (%) Boy (%)

SD

7.57 64.89 11.91 15.63 93.11 5.39

3.07

10.56 83.18 6.26 2.47 2.01

1.28

2.19 2.45 2.60 4.44 30,472.43 1.50 11.54 57.13 25.00 17.87 54.15

Bivariate results Table 2 presents findings from bivariate analyses of key variables, by child migration and residency status. The school enrollment rate was highest for left-behind children and lowest for migrant children (96 vs. 91%). On average, urban children had the highest number of years of education (6.2 years), followed by migrant children (5.8 years), and rural children (5.2 years), while leftbehind children had the lowest (4.3 years). With respect to health outcomes, urban children had the highest rate of being overweight (16.6%), followed by migrant children (11.5%), rural children (9.3%), and left-behind children (7.1%). The rates of being underweight did not vary by child migration or residency status. Among children aged 12 and older, about 2 percent of leftbehind, rural, and urban children had smoked cigarettes. Migrant children showed a significantly higher rate of smoking (6.7%). Parents’ restrictions on television watching were highest for urban children (2.4 points over a 0–4-point scale), followed by rural (2.0 points), migrant (1.8 points), and left-behind (1.7 points) children. In terms of children’s personal values, children with different migration and residency statuses showed significant differences in their levels of caring about parents’ praise. Migrant and left-behind children attached the least importance to their parents’ praise (2.10 and 2.12 points, over a 1–4-point scale, respectively), followed by rural children (2.17 points). Urban children attached the greatest importance to parents’ praise (2.32 points). Regression results

0.85 0.89 0.96 1.46 32,334.54 0.69 3.28

Note: Figures in the table are means or percentages and standard deviations (SD).

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old, with a standard deviation of 3.3 years. The gender distribution of the sample was approximately equal; boys accounted for 54 percent.

Table 3 presents regression estimates of educational outcomes. Urban children’s odds of school enrollment were significantly greater than rural children’s. And respectively, migrant children and urban children had 1.38 times and 1.72 times greater odds of getting more years of education than rural children did, holding all other variables constant. Overall, boys had 18 percent lower odds of having additional years of education than girls. Further analyses by age group (6–15-year-olds vs. 16–18-year-olds) were conducted to examine the effects of age on children’s educational outcomes. The results, available on request, showed no difference in school enrollment, based on child migration and residency status, among students who were of compulsory education age (6–15). However, among children older than 15, migrant and rural children had 84 percent lower odds of being enrolled in school than urban children, holding other variables constant. This demonstrated that


Left-behind children in China Table 2. Bivariate statistics by child migration and residency status. Left-behind Currently in school (%) Years of education Weight (%) Overweight Underweight Smoking (%) Parents’ restriction on TV watching (0–4) Personal values (1–4) Parents’ praise Peer recognition Good grades Family size Income Number of children Age Age group 1 (≤12) (%) Age group 2 (12–15) (%) Age group 3 (>15) (%) Boy (%)

Rural

Migrant

Urban

F-test or chi-square test

96.13 4.29

92.40 5.24

91.36 5.82

95.94 6.21

8.40* 17.12***

7.14 3.25 2.00 1.68

9.34 6.85 1.79 2.00

11.48 5.74 6.67 1.80

16.61 5.64 1.86 2.39

16.60*** 3.50 9.89* 65.60***

2.12 2.48 2.54 5.28 21,194.30 1.59 10.31 69.03 23.23 7.74 52.26

2.17 2.38 2.57 4.50 29,084.62 1.58 11.46 58.62 25.35 16.03 55.91

2.10 2.55 2.54 4.33 28,832.33 1.43 11.90 51.23 25.41 23.36 52.46

2.32 2.59 2.73 3.86 41,980.81 1.17 12.18 49.69 24.06 26.25 49.06

20.36* 12.95 14.51 37.52*** 19.30*** 34.32*** 12.84*** 20.87*** 0.52 34.23*** 5.49

Note: Figures in the table are means or percentages. F-test is performed for continuous variables and chi-square test for categorical variables. The sample size is 879 for smoking and personal values on parents’ praise, peer recognition, and good grade (including children aged 12 and above only) and is 2,048 for all other variables in the table. * p < 0.05; *** p < 0.001.

Table 3. Regression estimates of educational outcomes. N = 2,048

Child status Left-behind Migrant Urban Age Boy ln(income) Family size Number of children Wave 2009

Currently enrolled in school

Years of education

OR

95% confidence interval

1.817 0.972 2.21 0.811 0.794 1.023 0.861 1.06 1.198

0.765 0.587 1.198 0.765 0.555 0.921 0.751 0.782 0.839

p

4.316 1.609 4.075 0.86 1.135 1.136 0.987 1.436 1.712

** ***

*

OR


1.211 1.381 1.722 6.774 0.822 1.018 0.926 0.895 1.407

0.879 1.068 1.359 6.231 0.698 0.968 0.865 0.776 1.193

1.668 1.785 2.182 7.364 0.968 1.071 0.992 1.032 1.659

p

** *** *** * * ***

Note: Figures in the table are odds ratios (ORs) and 95% confidence intervals. * p < 0.05; ** p < 0.01; *** p < 0.001.

the most significant differences in school enrollment by child migration and residency existed among children aged above 15. With respect to years of education, left-behind children and rural children aged 15 or younger had 30 and 40 percent lower odds, respectively, of having an additional year of education than their urban peers; among children aged above 15, rural children had 60 percent lower odds of having an additional year of education than urban children. This suggested that there might be a delay in rural and left-behind children’s education, and rural children’s education might end earlier than urban children’s. Table 4 shows regression estimates of child weight. Urban children had 1.97 times greater odds of being overweight than normal weight, and 2.16 times greater

odds of being overweight than underweight, compared with rural children. The results also showed significant differences related to age and gender. Younger children were more likely to be overweight than normal weight compared with older children, and boys were more likely to be overweight than normal weight compared with girls (44% greater odds). Also, boys were less likely to be underweight than normal weight compared with girls (51% lower odds). Table 5 presents regression estimates of smoking behavior for children aged 12 or older. Migrant children had 3.8 times greater odds of having smoked cigarettes than did rural children. Being older was associated with greater odds of smoking, and boys’ odds of smoking were 19.9 times greater than girls’ odds.


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Lu et al. Table 4. Regression estimates of child weight. n = 2,045


Underweight/normal

Overweight/normal

Overweight/underweight

OR


p

OR


0.439 0.858 0.914 0.999 0.492 1.057 1.00 1.23 1.109

0.173 0.477 0.529 0.943 0.338 0.925 0.858 0.915 0.768

+

0.681 1.299 1.97 0.862 1.441 1.029 0.858 0.994 1.198

0.353 0.832 1.36 0.822 1.068 0.936 0.75 0.76 0.895

1.112 1.545 1.576 1.059 0.715 1.206 1.166 1.655 1.601

***

1.314 2.029 2.855 0.904 1.943 1.13 0.981 1.30 1.603

p

*** *** * *

OR


1.55 1.514 2.156 0.863 2.931 0.973 0.858 0.808 1.08

0.51 0.746 1.149 0.803 1.85 0.832 0.705 0.551 0.689

4.709 3.073 4.049 0.927 4.643 1.14 1.043 1.185 1.693

p

* *** ***

Note: Figures in the table are odds ratios (ORs) and 95% confidence intervals. + p < 0.1; * p < 0.05; *** p < 0.001.

Table 5. Regression estimates of smoking behavior. n = 879

Table 6. Regression estimates of parental restriction on TV watching.

Have smoked cigarettes ever n = 2,045 OR


2.271 3.807 0.848 2.015 19.94 1.092 1.297 0.715 1.414


0.292 1.368 0.241 1.451 2.613 0.727 0.905 0.253 0.556

17.64 10.598 2.985 2.797 152.175 1.641 1.856 2.022 3.60

* *** **

Note: Figures in the table are odds ratios (ORs) and 95% confidence intervals. * p < 0.05; ** p < 0.01; *** p < 0.001.

As an indicator of parental supervision, we examined the frequency of parents’ restrictions on their children’s television watching time, by child migration and residency status. As shown in Table 6, urban parents restricted children’s television watching more often than other parents did. Unsurprisingly, given their absence, parents of left-behind children were found to be least likely to restrict their children’s television watching time (35% lower odds than rural parents), followed by migrant parents (26% lower odds than rural parents). Urban parents, in contrast, had 69 percent greater odds of restricting television watching than rural parents had. Additionally, each 1 percent increase in family income was associated with 1.1 percent greater odds of parents restricting child television watching. Every additional number of children in a family was associated with 16 percent lower odds of parents’ restricting television watching. To better understand children’s perspectives, we examined the level to which they valued their parents’ 64

Parents’ restriction on TV watching

p


OR


p

0.652 0.742 1.688 0.932 0.967 1.063 0.937 0.84 0.695

0.48 0.579 1.341 0.909 0.825 1.012 0.878 0.733 0.593

** * *** ***

0.886 0.949 2.124 0.955 1.132 1.117 1.001 0.963 0.814

* + * ***

Note: Figures in the table are odds ratios (ORs) and 95% confidence intervals. + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001.

praise, peer recognition, and academic grades. Table 7 illustrates that, in comparison with rural children, urban children had 1.4 times greater odds of caring about parents’ praise. Migrant children and urban children, respectively, had 1.4 times and 1.5 times greater odds of caring about peer recognition than rural children had. With respect to gender differences, boys had 29 percent lower odds of caring about parents’ praise, 30 percent lower odds of caring about peer recognition, and 36 percent lower odds of caring about school grades, compared with girls. Discussion This study examined the roles that household registration status and parents’ residency play in shaping the educational outcomes, health, parental supervision, and personal values of children aged 6 to 18 in China. The results suggest that significant differences in well-being


Left-behind children in China Table 7. Regression estimates of child personal value. n = 879

Child status Left-behind Migrant Urban Age group Boy ln(income) Family size Number of children Wave 2009

Parents’ praise

Peer recognition

OR


0.69 0.791 1.40 0.846 0.706 1.031 1.103 0.853 1.192

0.393 0.544 0.996 0.784 0.547 0.948 0.987 0.668 0.924

1.213 1.15 1.969 0.913 0.911 1.122 1.233 1.09 1.536

p

+ *** ** +

Good grades

OR


1.027 1.429 1.547 0.894 0.697 0.999 1.061 0.789 1.202

0.593 0.992 1.106 0.83 0.543 0.92 0.951 0.621 0.938

1.778 2.059 2.164 0.963 0.894 1.084 1.183 1.002 1.54

p

+ * ** **

+

OR


0.848 0.968 1.363 0.846 0.641 1.098 1.002 0.957 0.731

0.492 0.674 0.978 0.786 0.501 1.012 0.9 0.755 0.572

1.463 1.388 1.898 0.911 0.821 1.192 1.117 1.213 0.936

p

+ *** *** *

*

Note: Figures in the table are odds ratios (ORs) and 95% confidence intervals. + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001.

exist among children with different migration and residency statuses. We have found that rural children are significantly less likely to enroll in school than urban children, and that rural children have fewer total years of education than migrant and urban children. Rural children aged 15 or older were found to have significantly lower school enrollment rates and fewer years of education than urban children of the same age, and older migrant children to have significantly lower school enrollment rates compared with their urban peers. Consistent with the literature (Duan & Liang, 2005; Wang & Holland, 2011), these findings suggest that children with migrant parents are more likely to discontinue their education after middle school. Possible causes include their failing to pass the high school entrance exam, entering the labor force to support their families, or in many cases, following in parents’ footsteps and becoming migrant workers (Lu & Zhang, 2004; Lu, 2012). The study also finds that health status varies among children with different migration and residency statuses. First, urban children are more likely to be overweight than rural children. Previous studies have found that at higher income levels, food structures often shift toward higher energy and fat intake, and increased consumption of meat and processed food; this may explain the higher incidence of overweightness (Guo, Mroz, Popkin, & Zhai, 2000). In accordance with these findings, our study suggests that overweightness is more prevalent among urban children, who typically come from higher income families than rural children do. Our findings also show that younger children (aged 6 to 12) are more likely to be overweight than older ones (aged 12 or older). Existing research has claimed that younger children are more dependent on their parents and caregivers, and that older children are sometimes asked to take on household chores (de Brauw & Mu,

2011; Monda & Popkin, 2005). For instance, the presence of young children in a household has been shown to increase the time allocated to domestic work, for both boys and girls (Chang et al., 2011). Therefore, the relationships among age, household activity, and weight merit further study. Chang et al. (2011) also identified a gender difference with regard to increased workload among left-behind children: The migration of one parent increased the time allocated to domestic work by 5.1 hours per day for girls aged 7–14 and by only 1.2 hours per day for boys in the same age group. Accordingly, our results show that boys are more likely to be overweight and that girls are more likely to be underweight. Although children’s increased physical work may potentially lower their risk of being overweight, it may also negatively impact their well-being – depending on the intensity of work, the working conditions, and the extent to which it interferes with schooling (Chang et al., 2011). Migrant children show significantly greater odds of having smoked cigarettes compared with rural children. This finding is in line with previous research, including Lee’s (2011) study, which found higher likelihood of smoking among children with migrant parents (Lee, 2011). The risk of smoking initiation is significantly higher among adolescents with more hostility and depressive symptoms (Weiss, Mouttapa, Cen, Johnson, & Unger, 2011). Migrant children may be at such high risk for a number of reasons: the stress of separation from their original communities and friends, inadequate emotional connection with parents, negative feelings toward new environments, stressors associated with adjustment, being deprived of many privileges that urban children enjoy, and discrimination (Chen, Wang, & Wang, 2009; Lee, 2011; Zhan, Sun, & Dong, 2005). Thus, programs that target migrant children should help them build skills and coping strategies for adjusting to their new environments.


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With regard to parents’ restrictions on hours children spend on television watching, our indicator of parental supervision, left-behind children’s parents restrict television watching on the least frequent basis, followed by migrant parents, and rural parents. Urban parents, parents with higher incomes, and parents with fewer children tend to restrict child television watching more often. These findings may reflect the fact that migrated parents have less time to spend with, and supervise, their children. Even in cases when migrant parents relocate with their children, parents’ long work hours often limit the time they are able to spend at home (Li & Li, 2007). Consequently, these parents may also have fewer opportunities to communicate with and supervise their children (Guo et al., 2005). With respect to children’s personal values, our results suggest that migrant children value peer recognition more than rural children do. This difference is perhaps related to migrant children’s low self-esteem or feelings of vulnerability (Devine, 2013; Lee, 2011). On the other hand, it might instead indicate that migrant children are simply more socially conscious and interconnected. The impact of migration on children’s personal values and social development therefore merits further exploration. Additionally, we find that girls on average value their parents’ praise, peer recognition, and school grades significantly more than boys do. Perhaps this is owed to gender-specific aspects of social and emotional development, or to the disparate expectations and treatment that girls experience. In our view, this gender disparity also warrants further research. Conclusion Our findings suggest disparities in school enrollment and years of education by child migration and residency status. Family migration negatively impacts children’s access to stable and continuous education. Additionally, among children older than 15, migrant and rural children have significantly lower odds of school enrollment than urban children, which indicates their higher likelihood of dropping out after middle school. Our finding that urban children are more likely to be overweight than rural children suggests the importance of promoting healthy diets and encouraging physical exercise in urban areas. At the same time, the positive and negative effects of increased farm and domestic work on leftbehind children’s physical health require further study. The significantly higher presence of smoking among migrant children likely reflects the anxiety, frustration, and stress they experience while adjusting to new environments and challenges. Lastly, we find that parental supervision varies significantly among urban, rural, migrant, and left-behind children. Specifically, urban children receive the highest level of parental supervision, followed by rural, 66

migrant, and left-behind children. Urban children most strongly value their parents’ praise, followed by rural, migrant, and left-behind children. These similar trends, in parents’ level of supervision and the level of value children place on parents’ praise, indicate that the more supervision or attention children receive from their parents, the more they care about parents’ attitudes toward them. This demonstrates the importance of parenting style and parent–child interaction on the wellbeing of migrant and left-behind children. Admittedly, there are some limitations to this study. Among the sample of 2,600 children, 552 were dropped because of missing values for key variables. Our robust test showed no significant differences between children with complete information and those with missing values in dependent variables, but future studies should certainly seek to ensure high response rates and representative sampling. Moreover, children under age 10 did not complete their own questionnaires; instead, their questionnaires were completed by their parents or other guardians. This may have given rise to inaccurate information, particularly from parents or guardians who spent limited time with their children. On the other hand, these parents may have been influenced by social desirability and overestimated their level of supervision. Finally, left-behind and migrant children, respectively, comprised only 8 and 12 percent of the sample. Both of these figures are significantly lower than national averages, and this disparity compromises the generalizability of our findings. Migrant and left-behind child populations comprise a significant proportion of China’s young generation. In turn, these children’s unique challenges and circumstances have increasingly elicited social concern and public debate. This study illustrates the importance of further studying and addressing the issues these children face. The findings also suggest that policy makers should address barriers facing migrant families and support the healthy development of migrant and leftbehind children. Current welfare programs have not sufficiently changed the underlying rural–urban dichotomization in China, and migrant workers are marginalized by a number of institutional barriers (Smart & Smart, 2001). Because of the urban–rural stratification and government control of population migration, geographically fixed unequal resource distribution and unequal access to benefits impede migrant families’ participation and utilization of welfare programs and services (Xu et al., 2011). The Household Registration System must be reformed before a more balanced economic structure and universal social welfare system can be established. Second, it is important to recognize that migration may significantly impact the well-being of children. Welfare benefits for migrant workers will not only improve their quality of life, but also the well-being of their children.



For instance, raising the minimum wage and limiting maximum working hours for migrant workers would enable migrant parents to spend more time with their children. Welfare policies should focus on providing coverage for not only migrant workers, but also their children, spouses, and parents – such as childcare, community elderly care services, education subsidies, and health insurance. Educational background has been shown to be a significant contributing factor to the income inequality between migrant and urban laborers (Li & Li, 2007). In one study, migrant children whose parents had higher levels of education and higher incomes were more likely to receive education in urban areas (Xu, Zhu, & Li, 2013). Thus, an important means to reduce inequality and support migrant children and their parents is to offer more opportunities for educational advancement and professional training. Investing meaningfully in human capital for migrant workers, and for migrant and left-behind children, can help to slow and eventually stop the cycle of intergenerational poverty. Migrant and left-behind children should be granted equal access to public education and other educational benefits, and schools for migrant and left-behind children should receive much greater support from both government and private entities. The well-being of migrant and left-behind children is not only an issue facing China; similar dynamics and challenges exist for internal migrant and transnational immigrant populations in other nations. Xu et al. (2011) suggested that China’s neglect of its migrant population parallels the US policy stance on welfare for undocumented immigrants. The Chinese and US governments have played a limited and passive role in improving migrant populations’ well-being (Xu et al., 2011). Given that migration generates multidimensional impacts on the well-being of migrant laborers and their children, more universal welfare benefits and specific programs that target migrant populations are of great importance and an urgent need for not only the migrants themselves but also the migrants’ families and the society. Similar to corporatist-conservative welfare regimes (such as that in Germany), the Chinese welfare system offers benefits primarily on the basis of job type and occupation (Esping-Andersen, 1990). In China, for instance, government employees receive higher workrelated benefits than employees in the private sector. Jonathan London (2009) compared social welfare in China with that in Vietnam, as both represent market-Leninist welfare regimes. Within this type of welfare regime, markets develop through Leninist institutions and welfare provisions are subordinate to developmentalist economic policies – which intensify inequality among migrant, rural, and urban laborers, and among migrant, left-behind, rural, and urban chil-

dren. These cross-national comparisons indicate that studies of China’s migrant workers and their children will provide implications for examining the effects of migration on a wide range of individuals, families, and societies. Additionally, such studies may help to identify the impacts of migration domestically and internationally, and explore approaches to migrationrelated issues. Acknowledgment The authors would like to thank the Huamin Charity Foundation for its generous support of this study. References de Brauw, A. & Mu, R. (2011). Migration and the overweight and underweight status of children in rural China. Food Policy, 36(1), 88–100. Cai, F. & Wang, M. (2010). Growth and structural changes in employment in transition China. Journal of Comparative Economics, 38(1), 71–81. Chan, K. W. & Zhang, L. (1999). The Hukou system and ruralurban migration in China: Processes and changes. China Quarterly, 160, 818–855. Chang, H., Dong, X., & MacPhail, F. (2011). Labor migration and time use patterns of the left-behind children and elderly in rural china. World Development, 39(12), 2199–2210. Chen, X., Wang, L., & Wang, Z. (2009). Shyness-sensitivity and social, school, and psychological adjustment in rural migrant and urban children in China. Child Development, 80(5), 1499–1513. Chen, Y. & Feng, S. (2012). Access to public schools and the education of migrant children in China. The Institute for the Study of Labor (IZA) Discussion Paper Series, No. 6853, 194–201. Cole, T. J., Flegal, K. M., Nicholls, D., & Jackson, A. A. (2007). Body mass index cut-offs to define thinness in children and adolescents: International survey. British Medical Journal, 335(7612), 194. Devine, D. (2013). ‘Value’ing children differently? Migrant children in education. Children & Society, 27(4), 282–294. Dong, J. (2010). Neo-Liberalism and the evolvement of China’s education policies on migrant children’s schooling. Journal of Critical Education Policy Studies, 8(1), 137–161. Duan, C. & Liang, H. (2005). Guanyu liudong ertong wenti de diaocha yanjiu [A study on the compulsory education for temporary migrant children]. Renkou yu Jingji [Population & Economics], 148(1), 11–17. Esping-Andersen, G. (1990). The three worlds of welfare capitalism. Princeton, NJ: Princeton University Press. Guo, L., Yao, Y., & Yang, B. (2005). Adaptability of migrant children to the city: A case study at a migrant school in Beijing. Youth Studies, 3, 22–31. Guo, X., Mroz, T., Popkin, B., & Zhai, F. (2000). Structural change in the impact of income on food consumption in China: 1989–1993. Economic Development and Cultural Change, 48(4), 737–760. Jia, Z. & Tian, W. (2010). Loneliness of left-behind children: A cross-sectional survey in a sample of rural china. Child: Care, Health & Development, 36(6), 812–817. Lee, M. H. (2011). Migration and children’s welfare in China: The schooling and health of children left behind. The Journal of Developing Areas, 44(2), 165–182. Li, P. & Li, W. (2007). Economic status and social attitudes of migrant workers in China. China and World Economy, 15(4), 1–16.


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