The effect of socioeconomic status and social inclusion on the mental health of Chinese migrants: A comparison between interprovincial and intra-provincial migrants

Abstract

This article sought to explore the impacts of socioeconomic status and social inclusion on intra-provincial and interprovincial migrants’ mental health by constructing the Bayesian structural equation model. A total of 14,584 migrants aged 15–59 years living in eight cities of China were selected. It was found that the impacts of socioeconomic status and social inclusion on mental health were converse for these two groups. And the manifest variables coefficients of socioeconomic status and social inclusion were also converse. Therefore, governments should make some policies to further improve the mental health of migrants, including strengthening the community cohesion, social atmosphere, and governmental support.

Keywords

Bayesian structural equation model interprovincial migrants intra-provincial migrants mental health social inclusion

Introduction

Global population migration is increasing. Migration influences the mental health of individuals and populations (Zimmerman et al., 2011). However, ideas on how migration influences people’s mental health are controversial. There are large numbers of floating populations in China. With the rapid development of the economy and expansion of urbanization in the 1970s, a great number of rural labors migrated to cities. In 2015, the number of migrants reached 247 million, accounting for 18 percent of the total population in China. It means approximately one out of six Chinese is a migrant (National Health and Family Planning Commission, 2016).

These groups are at a disadvantage because of their unfavorable socioeconomic status, poorer quality of life (QOL), and lower social benefits than those of urban residents. In addition, they are more likely to be excluded by society and experience poor mental health (Chen et al., 2011; Wang et al., 2010; Wong et al., 2007). This article intends to explore the effect of socioeconomic status and social inclusion on the mental health of Chinese migrants and compare the differences between interprovincial and intra-provincial migrants.

Previous studies have been conducted on the mental health of Chinese migrants. It was suggested that these groups are disadvantaged. Wong and Leung (2008) found that migrants living in four major districts in Shanghai were diagnosed as mentally unhealthy (25 percent for male, 5 percent for female). They were more likely to be exposed to a harmful environment. Consequently, their physical and psychological health were reduced (Chen et al., 2013). Negative impact factors included employment difficulties and interpersonal tensions (Wong and He, 2008).

However, many small-scale studies in China supported the “healthy migrant effect,” under which migrants were selected on the bias of healthier ones (Tong and Piotrowski, 2012). Li et al. (2007) compared the mental health of migrants and permanent urban and rural dwellers in Hangzhou, finding the former reported better results. An investigation conducted in Beijing indicated the rural-to-urban migrants had better self-rated physical health, however, higher rates of mental distress than the urban residents (Chen, 2011). Dai et al. (2015) illustrated that the young migrants in the Sichuan Province were less likely to report higher psycho-QOL or 1-year suicidal behaviors. Instead, they had a lower risk of suffering from depression.

The above-mentioned studies have not reached a consensus on the influence of migration on migrants’ mental health. Many of them compare migrants to the urban residents who lived in the migration destination or the rural residents who lived in the same place where the migrants originally came from. Characteristics such as the migration range were not taken into account. China is a country with a large population and vast territory. Therefore, migrants could either migrate within the province or cross provinces. Although the proportion of interprovincial migrants is growing, currently intra-provincial migrants are still the mainstream (Li and Wang, 2016). By comparing interprovincial and intra-provincial migrants in Fujian province, it was found that "the size of interprovincial migrants in a region depended largely on the economic development and the employment opportunities this region can provide; the size of intra-provincial migrants not only closely related to the economic development, the commercial services, and professional employment opportunities this region offered but also depended on the level of social development, including medical conditions, QOL, and personal development conditions” (Tian et al., 2015). It has been found that there has significant difference between interprovincial and intra-provincial migrants (Poncet 2006). The reason is that as the spatial distance between the two places increased, the economic costs and psychological costs of migration would rise, the earnings would be relatively reduced (Harris and Todaro, 1970; Lee, 1966). Compared to intra-province migrants, inter-province migrants more needed to adapt to the new environment, including the dialect, local customs, urban lifestyle, and new social network. Therefore, it is necessary to consider the effect of migration characteristics. Moreover, the number of migrants has increased sharply in recent years. Rapid changes have taken place within China’s economic and social environment, so it is necessary to explore the mental health of migrants using the latest data. Moreover, socioeconomic status, utilization of medical and health services, and so on have been indicated as key factors of migrants’ mental health. Therefore, they should be included in the health measurement model (Liang and Guo, 2015; Zhou et al., 2015).

In China, the rural and urban areas have different social security systems. Migrants with rural resident identities, though living in urban areas, cannot join in the city’s welfare and public services. Therefore, they are unable to enjoy the same social services as urban residents including medical, occupational, and social security services. They are excluded from the urban system (Chen, 2011; Peng et al., 2010; Qiu et al., 2011; Wong et al., 2008; Yang et al., 2012). The experiences of discrimination, exclusion, decreased interpersonal trust, and perceived social inequity could have a negative impact on migrants’ psychological well-being (Li et al., 2006; Liang 2015; Lin et al., 2011). This suggests that researchers should take social psychological factors into consideration when exploring the mental health of Chinese migrants. As a result, this study aims to establish a relatively complete model of mental health for different types of migrants in China and to provide suggestions for policy improvement or service.

Methods

Data

The data used in this study were derived from the Migrants’ Social Health and Mental Health Survey by the Chinese Health Service Commission in 2014. This survey was carried out in eight migrant inflow cities (districts), including Chaoyang District in Beijing, Jiaxing City in Zhejiang Province, Xiamen City in Fujian Province, Qingdao City in Shandong Province, Zhengzhou City in Henan Province, Shenzhen City, Zhongshan City in Guangdong Province, and Chengdu City in Sichuan Province.¹ Respondents who were migrants had inflew for a minimum of a month, had non-local resident identity, and were aged 15–59 years. Each city (district) sampled 2000 respondents. A total of 14,584 valid samples were obtained after deleting the missing cases. There were 7951 interprovincial migrants and 6633 intra-provincial migrants, accounting for 54.52 and 45.48 percent, respectively.

Table 1 also indicated the sample distribution of eight sampled cities. The frequency of each city was even. When it comes to the migration characteristic, there were great differences. First, none of the respondents in Beijing were intra-provincials, indicating all of them came from outside of Beijing. The similar phenomenon occurred in Zhejiang and Guangdong provinces. Majority of them were interprovincial migrant. Since these three regions were the most economically developed coastal provinces in Eastern China, they could attract many people migrating to there. For other less developed provinces, like Henan and Sichuan, most of their migrants came from within the province.

Table 1.

Sample distribution (sample size: 14,584).

Distribution by region	Number of sub-group samples (%)	Number of interprovincial migrants (%)	Number of intra-provincial migrants (%)
Chaoyang District in Beijing	1847 (12.665%)	1847 (23.230%)	0 (0.000%)
Jiaxing city in Zhejiang	1856 (12.726%)	1719 (21.620%)	137 (2.065%)
Xiamen city in Fujian	1848 (12.671%)	838 (10.540%)	1010 (15.227%)
Qingdao city in Shandong	1789 (12.267%)	356 (4.477%)	1433 (21.604%)
Zhengzhou city in Henan	1893 (12.980%)	293 (3.685%)	1600 (24.122%)
Shenzhen city in Guangdong	1672 (11.465%)	1167 (14.677%)	505 (7.613%)
Zhongshan city in Guangdong	1779 (12.198%)	1459 (18.350%)	320 (4.824%)
Chengdu city in Sichuan	1900 (13.028%)	272 (3.421%)	1628 (24.544%)

Measurement

Mental health

In this study, we choose the items related to self-rated mental health in the questionnaire. (1) “In general, how is your health condition?”, coded good = 1, general = 2, and poor = 3. (2) “Life satisfaction,” coded satisfied = 1, general = 2, and dissatisfied = 3. (3) “How they could control the important things in their lives in recent months,” coded never = 1, sometimes = 2, and always = 3. (4) “If the respondents feel intense, desperate, upset, depressed, or senseless,” coded never = 1, sometimes = 2, and always = 3. (5) “What extent they could choose and control their own life,” coded completely = 1, general = 2, and not completely = 3.

Socioeconomic status

The majority of migrants come from rural areas, with low education and insufficient skills; therefore, they have to perform some unfavorable work, such as labor-demanding or unskilled jobs. These occupations suffer from social discrimination and isolation from urban residents, hence affecting their mental health condition. So, in this article, we include three basic variables: education, income, and occupation. Education is divided into four levels, college and above = 1, high school = 2, middle school = 3, and primary school = 4.

Income is divided into three levels according to the Incomes in China in 2014, >6700 yuan = 1, 2800–6700 = 2, and ≤2800 = 3. Occupation is re-categorized based on the EGP² program put forward by Erikson et al. (1983) and merged into four levels, the upper class (classes I and II in EGP classification), including charges in the central or provincial governments, enterprises, and institutions; professional class (classes IIIa and IIIb in EGP classification), including professional and technical personnel, civil servants, staff, and associated personnel; the petty bourgeoisie class (classes Iva, IVb, and IVc in EGP classification), including employers and self-employers and the worker; peasant class (classes V, VI, VIIa, and VIIb in EGP classification), including junior technicians, manual workers, workers, and agricultural laborers. Generally, migrants do not own the urban resident indentity; although they live and work in the city, they are not eligible to be included into the social service and welfare system of urban areas. Therefore, they could not enjoy the same treatment in educational, medical, social security, and pension as the urban residents. Besides, we also explore the impact of housing and medical insurances. For housing variable, coded self-own houses = 1 and others = 2; for medical insurances variable, coded yes = 1 and no = 2.

Social inclusion

In this study, three items were included. (1) “If they or their families get along well with the locals,” coded very pleasant = 1, general = 2, and not pleasant = 3. (2) “How would you rate your social status comparing with the relatives, friends, and colleagues in your hometown?”, coded good = 1, general = 2, and poor = 3. (3) “How would you rate your social status comparing with the people in the whole society?”, coded good = 1, general = 2, and poor = 3.

The descriptive charateristics of variables are shown in Table 2.

Table 2.

Descriptive statistics of two groups of migrants.

	N	Mean value	SD	95% confidence interval of mean value		Minimum	Maximum
				Lower limit	Upper limit
Education
Interprovincial	7951	2.576	0.859	2.557	2.595	1	4
Intra-provincial	6633	2.487	0.853	2.466	2.508	1	4
Total	14,584	2.535	0.857	2.522	2.549	1	4
Salary
Interprovincial	7951	2.156	0.579	2.143	2.169	1	3
Intra-provincial	6633	2.320	0.580	2.306	2.334	1	3
Total	14,584	2.231	0.585	2.221	2.240	1	3
Job
Interprovincial	7951	3.549	0.675	3.534	3.564	1	4
Intra-provincial	6633	3.470	0.654	3.455	3.486	1	4
Total	14,584	3.513	0.667	3.503	3.524	1	4
House
Interprovincial	7951	1.926	0.263	1.918	1.931	1	2
Intra-provincial	6633	1.885	0.319	1.877	1.892	1	2
Total	14,584	1.907	0.291	1.902	1.912	1	2
Insurance
Interprovincial	7951	1.171	0.377	1.163	1.180	1	2
Intra-provincial	6633	1.093	0.291	1.086	1.100	1	2
Total	14,584	1.136	0.342	1.13	1.141	1	2
Whether the family and oneself got along with the locals?
Interprovincial	7951	1.381	0.579	1.368	1.393	1	3
Intra-provincial	6633	1.251	0.493	1.239	1.263	1	3
Total	14,584	1.322	0.545	1.313	1.330	1	3
The status level in the community
Interprovincial	7951	1.916	0.755	1.899	1.933	1	3
Intra-provincial	6633	1.904	0.746	1.886	1.922	1	3
Total	14,584	1.910	0.751	1.898	1.923	1	3
The extent to be respected
Interprovincial	7951	2.139	0.746	2.122	2.155	1	3
Intra-provincial	6633	2.161	0.740	2.143	2.179	1	3
Total	14,584	2.149	0.744	2.137	2.161	1	3
Health
Interprovincial	7951	1.112	0.323	1.105	1.119	1	3
Intra-provincial	6633	1.111	0.325	1.104	1.119	1	3
Total	14,584	1.112	0.324	1.106	1.117	1	3
Life satisfaction
Interprovincial	7951	1.641	0.798	1.625	1.660	1	3
Intra-provincial	6633	1.612	0.783	1.593	1.634	1	3
Total	14,584	1.629	0.792	1.616	1.642	1	3
Whether can control important things in lives
Interprovincial	7951	1.520	0.850	1.502	1.539	1	3
Intra-provincial	6633	1.540	0.863	1.519	1.560	1	3
Total	14,584	1.529	0.856	1.515	1.543	1	3
Emotions in past 30 days
Interprovincial	7951	1.130	0.347	1.122	1.138	1	3
Intra-provincial	6633	1.124	0.335	1.116	1.132	1	3
Total	14,584	1.127	0.342	1.122	1.133	1	3
Whether can choose and master own life
Interprovincial	7951	1.537	0.645	1.523	1.552	1	3
Intra-provincial	6633	1.509	0.628	1.494	1.525	1	3
Total	14,584	1.525	0.637	1.514	1.535	1	3

SD: standard deviation.

Analysis model

In this study, the Bayesian structural equation model (SEM) was used to analyze the mental health of migrants. The Bayesian approach is a method of estimating the SEM developed in recent years, which focuses on the original observed values, rather than the sample covariance matrices of interest to other estimation methods. The sampling-based Bayesian approach relies less on the progressive theory so that even in the case of small samples, it is possible to obtain reliable results (Ansari et al., 2002; Ansari and Jedidi, 2000). Figure 1 shows the relationship of all variables in this study. There are 13 observed variables and 3 latent variables. The three latent variables in the model were individuals’ mental health, socioeconomic status, and social inclusion. They were indicated by the corresponding indicators, respectively.

Figure 1.

The Bayesian SEM of analysis.

Results

The Bayesian SEM was used for both the intra-provincial migrants and the interprovincial migrants. For each model, the initial distribution for each observable ordinal categorical variable was set before calculating the model. The Bayesian computations were then made. First, two chains were run in parallel, and each chain was burned-in for 1000 iterations. Afterward, 1000 more iterations were conducted as soon as the two chains converged. Thus, the results were calculated based on the final 2000 posterior samples. Moreover, thin = 10 (random number should be extracted for every 10 times of posterior sampling) was established to overcome self-correlation.

The trace (burn-in of 1000 iterations) shows that the distribution of points did not change as the chain progressed. A linear or quadratic trend was not observed. These findings indicate that the chain is sufficiently mixed and stationary. The first four trace plots are indicated in Figure 2(a) and (b). Others were omitted due to space constraints. For the interprovincial migrants and intra-provincial migrants, PSRF (potential scale reduction factor) was less than 1.2; therefore, all the chains achieved convergence.

Figure 2.

(a) The convergence trace plot of Model 1 (interprovincial migrants) and (b) The convergence trace plot of Model 2 (intra-provincial migrants).

Table 3 shows the path coefficients of the socioeconomic status, social inclusion, and mental health for two groups. Post.Mean, Post.SD, and PSRF under the table refers to the posterior mean, the posterior standard deviation, and the potential scale reduction factor for assessing chain convergence, respectively (Gelman and Rubin, 1992).

Table 3.

Estimation results of Bayesian SEM.

	Interprovincial migrants			Intra-provincial migrants
	Post.Mean	Post.SD	PSRF	Post.Mean	Post.SD	PSRF
Socioeconomic status ← Education	1.000			1.000
Socioeconomic status ← Salary	4.863	0.165	1.010	−3.340	0.116	1.037
Socioeconomic status ← Job	5.028	0.178	1.008	−3.476	0.120	1.035
Socioeconomic status ← House	1.371	0.079	1.007	−1.007	0.089	1.010
Socioeconomic status ← Insurance	0.747	0.092	1.001	−0.147	0.068	1.001
Social inclusion ← Whether the family and oneself got along with the locals	1.000			1.000
Social inclusion ← The status level in the community	4.863	0.165	1.010	−3.340	0.116	1.037
Social inclusion ← The extent to be respected	5.028	0.178	1.008	−3.476	0.120	1.035
Mental health ← Health	1.000			1.000
Mental health ← Life satisfaction	4.083	0.165	1.015	4.372	0.182	1.010
Mental health ← Whether can control important things in lives	2.292	0.133	1.007	2.633	0.154	1.004
Mental health ← Emotions in past 30 days	1.131	0.057	1.014	1.158	0.062	1.007
Mental health ← Whether can choose and master own life	4.209	0.165	1.021	4.009	0.166	1.012
Mental health ← Socioeconomic status	0.085	0.050	1.051	−0.069	0.047	1.002
Mental health ← Social inclusion	−0.585	0.031	1.005	0.402	0.022	1.035
Whether the family and oneself got along with the locals ← Education	0.001	0.009	1.003	0.001	0.007	1.000
Whether the family and oneself got along with the locals ← Salary	0.049	0.013	1.013	−0.021	0.012	1.001
Whether the family and oneself got along with the locals ← Job	0.065	0.011	1.007	0.006	0.010	1.004
The status level in the community ← Education	0.006	0.009	1.010	−0.017	0.010	1.000
The status level in the community ← Salary	−0.079	0.023	1.026	−0.055	0.022	1.005
The status level in the community ← Job	0.002	0.019	1.071	0.008	0.021	1.039
The extent to be respected ← Education	−0.009	0.010	1.010	−0.034	0.010	1.004
The extent to be respected ← Salary	−0.020	0.024	1.043	0.007	0.022	1.005
The extent to be respected ← Job	0.002	0.018	1.072	0.014	0.022	1.029

SEM: structural equation model; SD: standard deviation; PSRF: potential scale reduction factor.

In terms of the coefficient values, the values of the interprovincial migrants were different from those of the intra-provincial migrants. The specific features are described as follows:

For the interprovincial migrants, the latent coefficients were 0.085 for work and −0.585 for social inclusion. This means the relationship between work and mental health is positive, while social inclusion and mental health is negative. For the intra-provincial group, the latent coefficients were −0.069 for work and 0.402 for social integration. This means the relationship between work and mental health is negative, while social inclusion and mental health is positive.

For the interprovincial migrants, the manifest coefficients for work were 1.000 (education), 4.863 (salary), 5.028 (job), 1.371 (house), and 0.747 (insurance), respectively; for the intra-provincial group, the manifest coefficients for work were 1.000 (education), −3.340 (salary), −3.476 (job), −1.007 (house), and −0.147 (insurance), respectively.

For the interprovincial migrants, the manifest coefficients for social integration were 1.000 (whether the family and oneself got along with the locals), 4.863 (the status level in the community), and 5.028 (the extent to be respected), respectively; for the intra-provincial group, the manifest variables coefficients for work were 1.000 (whether the family and oneself got along with the locals), 4.863 (the status level in the community), and 5.028 (the extent to be respected, respectively.

Both of the coefficients for mental health were positive for the two groups, but the coefficients for the interprovincial migrants were less than the intra-provincial group except for the variable of whether can choose and master own life. For the interprovincial group, the coefficients were 1.000 (health), 4.083 (life satisfaction), 2.292 (whether can control important things in lives), 1.131 (emotions in past 30 days), and 4.209 (whether can choose and master own life); for the intra-provincial group, the coefficients were 1.000 (health), 4.372 (life satisfaction), 2.633 (whether can control important things in lives), 1.158 (emotions in past 30 days), and 4.009 (whether can choose and master own life).

Discussion

The health condition of migrants in China has received increased attention due to their disadvantaged social status. This study aimed to explore the effect of socioeconomic status and social inclusion on mental health by comparing the interprovincial and intra-provincial migrants. The main findings were as follows:

Descriptive statistic results indicate that interprovincial migrants were better at education, jobs, houses, and medical insurance; however, they earned a lower salary. They thought themselves or their family could get along better with the local people, however, felt less be respected. They were more satisfied with life and thought they were able to control their own lives.

For the interprovincial migrants, the latent coefficients were 0.085 for socioeconomic status and −0.585 for social inclusion. This means that the impact of socioeconomic status on mental health is positive and the impact of social inclusion on mental health is negative. For the intra-provincial migrants, the latent coefficients were −0.069 for socioeconomic status and 0.402 for social inclusion. This means that the impact of socioeconomic status on mental health is negative and the impact of social inclusion on mental health is positive.

Migrants’ original intention was to seek a steady occupation and earn a stable income in order to improve their QOL (Knight and Gunatilaka, 2010; Zhao, 1999; Zhu, 2007). Many of them migrated from the less developed central or western provinces to the rapidly growing coastal eastern provinces. The interprovincial migration has made great contributions to the regional development (Fan, 2005). However, interprovincial migrants live far from the administrative center of the city, and it is difficult to effectively integrate into the management of the city under the current system. In the aspects of social security, medical care, and public services, they experience greater difficulty accessing such services, although social security and other public services for those migrants within the province are not great. Besides, the hukou registration system was thought to hinder the migrants from enjoying the same social service and welfare as the local residents. Although the migrants lived in the cities, they did not have the local urban resident identities; therefore, they did not have the sale privilege to use the public services including healthcare, housing, education, work, and social welfare (Chan, 2010; Fan, 2002; Wong et al., 2007; Zhang, 2010). The social inequity may be the cause of poor mental health of migrants across provinces lower than those in-provinces.

The differences between interprovincial and intra-provincial migrants were consistent with the studies about international immigrants. Compared with internal migrants, the cross-national immigrants including refugees had more difficulties in adapting to the new social and cultural environment, which could worsen their health and increase pressure (Kuo, 1976; Nielsen and Krasnik, 2010). When it comes to the Chinese migrants, they might share the similar Confucian culture, but there were great regional differences in different provinces. The intra-provincial migrants often share the same linguistic environment and social culture with the local residents, thus they are more adaptable, and their psychological states are more stable, while interprovincial migrants tend to face language differences. It is easy to distinguish outlander and local workers through dialect or accent so that the external identity of interprovincial migrants is obvious, and thus, problems arise in the most basic linguistic adaptation.

Another important explanation about how migration affected health was social exclusion. Many migrants were excluded from the social network when they were new in a place. They migrated with the expectation to lead a better living. Therefore, they have goal-striving stress, but their actual achievement could not fulfill the original aspiration (Kuo, 1976). For the Chinese interprovincial migrants, their interpersonal networks and social relations have undergone greater changes compared with intra-provincial migrants, and the relationships in their hometowns become minimal. When the social interaction of interprovincial migrants increases, which can boost potential support or actual support, it will be easier to obtain stable work and improve adaption; correspondingly, the mental health of interprovincial migrants will develop in a positive direction. And the network construction would be easier and more common for the younger generation of migrant (Liu et al., 2012; Liang 2015). However, some of the social networks of those intra-provincial migrants still exist and play a role; they face fewer difficulties and uncertainties, so their social network demand is weaker than that of interprovincial migrants. Therefore, social inclusion can effectively regulate the mental health of interprovincial migrants.

It is important to improve the mental welfare of migrants by constructing social networks for migrants in the city and strengthening the support of the urban residents to them. For migrants, their job mobility, human capital, and the socioeconomic status have generally been hindered by the hukou registration system (Palmer et al., 2011; Zhang, 2010). Thus, their social rights should be emphasized and protected (Liang, et al., 2014; Morissens and Sainsbury, 2005). And it is also necessary to construct the social inclusion network of the migrants (Wang and Luo, 2007; Zhang and Lei, 2008). From the emotional attribution point of view, the public support and friendly attitude of the urban citizens play a positive role in promoting the psychological inclusion of the migrants and the value of identity. On one hand, it is necessary to strengthen the community interaction for good interpersonal relationships for the migrants and provide increased emotional support for them; on the other hand, it is necessary to improve the support of the government, society, and local residents, such as providing social protection (Boccagni, 2011; Liang and Cao, 2015), in order to make the migrants work and live well in the cities.

This study has a few limitations. The investigation only sampled eight regions in China, so its representativeness are limited. Considering the vast territory of China, a large-scale investigation should be conducted in the future. Furthermore, the impact factors of the migrants’ mental health model only contained two factors; however, there are many other physiological and social factors. Other migration characteristics, such as migration time, distance from hometown, and reason to migrate, are important factors. Future research may consider including them. Finally, Chinese migrants’ mental health is an important issue related to Chinese social development. Therefore, there should be continuous concern regarding the health and social status of this population group and efforts to improve their well-being and QOL.

Conclusion

China had the most populous migration in the world, but the migrants were in disadvantaged social status and health condition. Yet it is controversial how migration affected the migrants’ health. Using 14,584 samples in the Migrants’ Social Health and Mental Health Survey who lived in eight cities in China, as well as the Bayesian approach of SEM, this article aimed to construct a complete model of Chinese migrants’ health and compared the differences between interprovincial and intra-provincial migrants. Results indicated interprovincial migrants had better social status in terms of education, jobs, houses, and medical insurance, but they earned a lower salary. Although they could get along better with the local people, they felt less respected. SEM results found that socioeconomic status had positive effect on the mental health of interprovincial migrants, while social inclusion had a negative effect. The opposite relationships were found for intra-provincial migrants. That is, the socioeconomic status had negative effect, while social inclusion had a positive effect on the mental health of intra-provincial migrants. Besides, the effect sizes of interprovincial migrants were greater than that of intra-provincial migrants.

It is suggested that social and cultural differences between provinces contribute to the different experiences under migration. There were great social and cultural differences in different regions in China. So, the interprovincial migrants might have more difficulties in adapting the new environment than the intra-provincial migrants. What is more, they were more likely to be excluded by the local residents, hence damaging their social network. These negative experiences could have negative effect on the migrants’ mental health. Based on the research finding, this article also provided with some policy implication, including strengthening the community cohesion, social atmosphere, and governmental support. Migrants have made great contribution to the economic development and urbanization to China, their social status and mental health deserved our consistent concerns.

Footnotes

Acknowledgements

Y.Y. conceived of and designed the study, analyzed and interpreted the data, and drafted the manuscript. Y.L. designed the study, assisted in interpreting the data, and helped to draft the manuscript. All authors read and approved the final manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Fund for Excellent Young Scholar of 2016 (71622013), Social Security and Public Policy, the General Program of National Natural Science Foundation of China (71473117 and 71173099), the Philosophy and Social Sciences Outstanding Innovation Team of Jiangsu University (2015ZSTD005), the Major Program of Philosophy and Social Science Research in Jiangsu (2015ZDIXM003 and 2015ZDAXM007), and the Key Project of National Social Science Fund (16AZZ014).

Notes

References

Ansari

Jedidi

(2000) Bayesian factor analysis for multilevel binary observations. Psychometrika 65: 475–496.

Ansari

Jedidi

Dube

(2002) Heterogeneous factor analysis models: A Bayesian approach. Psychometrika 67: 49–77.

Boccagni

(2011) Migrants’ social protection as a transnational process: Public policies and emigrant initiative in the case of Ecuador. International Journal of Social Welfare 20: 318–325.

Chan

(2010) The household registration system and migrant labor in China: Notes on a debate. Population and Development Review 36: 357–364.

Chen

(2011) Internal migration and health: Re-examining the healthy migrant phenomenon in China. Social Science & Medicine 72: 1294–1301.

Chen

Landry

(2013) Migration, environmental hazards, and health outcomes in China. Social Science & Medicine 80: 85–95.

Chen

Stanton

Kaljee

, et al. (2011) Social stigma, social capital reconstruction, and rural migrants in urban China: A population health perspective. Human Organization 70: 22–32.

Dai

Zhong

Xiang

, et al. (2015) Internal migration, mental health, and suicidal behaviors in young rural Chinese. Social Psychiatry and Psychiatric Epidemiology 50: 621–631.

Erikson

Goldthorpe

Portocarero

(1983) Intergenerational class mobility and the convergence thesis: England, France and Sweden, 1979. The British Journal of Sociology 61(1):185–219.

10.

Fan

(2002) The elite, the natives, and the outsiders: Migration and labor market segmentation in urban China. Annals of the Association of American Geographers 92: 103–124.

11.

Fan

(2005) Interprovincial migration, population redistribution, and regional development in China: 1990 and 2000 census comparisons. The Professional Geographer 57: 295–311.

12.

Gelman

Rubin

(1992) Inference from iterative simulations using multiple sequences. Statistical Science 7: 457–472.

13.

Harris

Todaro

(1970) Migration, unemployment and development: A two-sector analysis. The American Economic Review 60(1): 126–142.

14.

Knight

Gunatilaka

(2010) Great expectations? The subjective well-being of rural–urban migrants in China. World Development 38: 113–124.

15.

Kuo

(1976) Theories of migration and mental health: An empirical testing on Chinese-Americans. Social Science & Medicine 10: 297–306.

16.

Lee

(1966) A theory of migration. Demography 3(1): 47–57.

17.

Wang

, et al. (2007) The mental health status of Chinese rural–urban migrant workers. Social Psychiatry and Psychiatric Epidemiology 42: 716–722.

18.

Wang

(2016) The distribution, relevance and influencing factors of interprovincial and intra-provincial migrants. Urban Problems 10: 51–58.

19.

Stanton

Fang

, et al. (2006) Social stigma and mental health among rural-to-urban migrants in China: A conceptual framework and future research needs. World Health & Population 8: 14–31.

20.

Liang

(2015) Correlations between health-related quality of life and interpersonal trust: Comparisons between two generations of Chinese rural-to-urban migrants. Social Indicators Research 123(3): 677–700.

21.

Liang

Cao

(2015) Employment assistance policies of Chinese government play positive roles! The impact of post-earthquake employment assistance policies on the health-related quality of life of Chinese earthquake populations. Social Indicators Research 120: 835–857.

22.

Liang

Guo

(2015) Utilization of health services and health-related quality of life research of rural-to-urban migrants in China: A cross-sectional analysis. Social Indicators Research 120: 277–295.

23.

Liang

Sun

(2014) The impact of migration on fertility under China’s underlying restrictions: A comparative study between permanent and temporary migrants. Social Indicators Research 116(1): 307–326.

24.

Lin

Wang

, et al. (2011) Discrimination, perceived social inequity, and mental health among rural-to-urban migrants in China. Community Mental Health Journal 47: 171–180.

25.

Liu

Breitung

(2012) The social networks of new-generation migrants in China’s urbanized villages: A case study of Guangzhou. Habitat International 36: 192–200.

26.

Morissens

ANN

Sainsbury

(2005) Migrants’ social rights, ethnicity and welfare regimes. Journal of Social Policy 34: 637–660.

27.

National Health and Family Planning Commission (2016) Report About Chinese Migrants in 2016. Beijing.

28.

Nielsen

Krasnik

(2010) Poorer self-perceived health among migrants and ethnic minorities versus the majority population in Europe: A systematic review. International Journal of Public Health 55: 357–371.

29.

Palmer

Perkins

(2011) Social capital and community participation among migrant workers in China. Journal of Community Psychology 39: 89–105.

30.

Peng

Chang

Zhou

, et al. (2010) Factors associated with health-seeking behavior among migrant workers in Beijing, China. BMC Health Services Research 10: 69.

31.

Poncet

. (2006) Provincial migration dynamics in China: Borders, costs and economic motivations. Regional Science and Urban Economics 36(3): 385–398.

32.

Qiu

Yang

Zhang

, et al. (2011) Rural-to-urban migration and its implication for new cooperative medical scheme coverage and utilization in China. BMC Public Health 11: 520.

33.

Tian

Zhu

Lin

, et al. (2015) Differences in the spatial distribution and its determinants between inter and intra-provincial floating population. Population Journal 37(6): 56–67.

34.

Tong

Piotrowski

(2012) Migration and health selectivity in the context of internal migration in China, 1997–2009. Population Research and Policy Review 31: 497–543.

35.

Wang

Stanton

, et al. (2010) The influence of social stigma and discriminatory experience on psychological distress and quality of life among rural-to-urban migrants in China. Social Science & Medicine 71: 84–92.

36.

Wang

Luo

(2007) A study on the current situation of peasant migrants’ social integration in Shanghai. Journal of East China University of Science and Technology (Social Science Edition) 3: 024.

37.

Wong

DFK

(2008) The resilience of migrant workers in Shanghai China: The roles of migration stress and meaning of migration. International Journal of Social Psychiatry 54: 131–143.

38.

Wong

DFK

Leung

(2008) The functions of social support in the mental health of male and female migrant workers in China. Health & Social Work 33: 275–285.

39.

Wong

DFK

Leung

, et al. (2008) Mental health of migrant workers in China: Prevalence and correlates. Social Psychiatry and Psychiatric Epidemiology 43: 483–489.

40.

Wong

DFK

Song

(2007) Rural migrant workers in urban China: Living a marginalised life. International Journal of Social Welfare 16: 32–40.

41.

Yang

, et al. (2012) Mental health status and related characteristics of Chinese male rural–urban migrant workers. Community Mental Health Journal 48: 342–351.

42.

Zhang

(2010) The Hukou system’s constraints on migrant workers’ job mobility in Chinese cities. China Economic Review 21: 51–64.

43.

Zhang

Lei

(2008) The urban new immigrants’ social inclusion: Internal structure, present situation and influential factors. Sociological Studies 5: 117–141.

44.

Zhao

(1999) Leaving the countryside: Rural-to-urban migration decisions in China. The American Economic Review 89: 281–286.

45.

Zhou

, et al. (2015) Socio-economic factors related with the subjective well-being of the rural elderly people living independently in China. International Journal for Equity in Health 14: 5.

46.

Zhu

(2007) China’s floating population and their settlement intention in the cities: Beyond the Hukou reform. Habitat International 31: 65–76.

47.

Zimmerman

Kiss

Hossain

(2011) Migration and health: A framework for 21st century policy-making. PLoS Medicine 8: e1001034.