Understanding the Link Between Discrimination and Juvenile Delinquency Among Chinese Migrant Children

Abstract

Prior research has identified discrimination as a cause of delinquency among migrant children. Few studies, however, have examined how discrimination is related to delinquency. The current research aims at bringing more understanding to this issue. Based on the general strain theory, this study posits that discrimination facilitates delinquent behavior because it reduces social support, generates negative emotions, and erodes social bonds. To test these hypotheses, this study collected survey data from a probability sample of 1,300 migrant children who attended secondary schools in one of the largest cities in China. Structural equation modeling analysis was conducted to test the direct and indirect effects of discrimination on delinquency. The results show that perceived discrimination reported by the students was positively related to delinquency through all three mediating mechanisms. This study suggests that strengthening social support may provide an effective strategy to reduce delinquency among migrant children in a short and intermediate term while ending discrimination represents a more long-term solution.

Keywords

Chinese migrant children migration discrimination delinquency general strain theory

Research on crime committed by migrant population can be traced back to the last century (Hart, 1896). Existing literature mostly indicates that migrant population have a higher rate of criminal involvement than nonmigrant population. This pattern has been found in the United States (Mears, 2002), Europe (Simon & Sikich, 2007), Western Asia (Walsh, Fogel-Grinvald, & Shneider, 2015), and other regions and areas (Larsen, 2010; Schloenhardt, 2001). Unlike children of immigrants who sometimes demonstrated better developmental outcomes than children with native-born parents in receiving countries, children of internal migrants who leave home to work in another part of their country, typically urban areas with more employment opportunities, are at higher risk for delinquent involvement and related problems than their peers who grew up in the local communities (Cheung, 2013; Tong & Piotrowski, 2012). Two major structural forces have been identified as explanations of the underlying mechanisms: First, migrants face more challenges in life than nonmigrants. In addition to the common adversities shared by both groups, migrants are more likely to experience discrimination and suffer from blocked opportunities and related stresses (Hällsten, Szulkin, & Sarnecki, 2013). Second, migrants are more likely to live in socially and ethnically segregated and disorganized communities, which provide “greater exposure to risk factors than protective factors” (Peterson & Krivo, 2005, p. 345).

Compared with their parents, the second generation of migrants is generally confronted with more social and emotional stressors (Birman & Poff, 2011). Discriminations on the basis of language, ethnicity, and family background are major disadvantages facing the younger generation (Deng, Kim, Vaughan, & Li, 2009; Walsh et al., 2015). Moreover, migrant children experience stronger emotional stress, mainly because they do not receive the help they need, especially from their parents (Wong, Chang, & He, 2009; Yu, Stewart, Liu, & Lam, 2014). Recent studies also reveal other negative consequences of discrimination such as weakened social bond (Portes & Rumbaut, 2001). All these disadvantages might directly or indirectly lead to a variety of delinquent behaviors, including violence (Caldwell, Kohn-Wood, Schmeelk-Cone, Chavous, & Zimmerman, 2004), substance abuse (Gibbons, Gerrard, Cleveland, Wills, & Brody, 2004), and status offenses (Ewert, 2009).

With more than 20 million of school-aged children migrating from rural to urban areas (Wong et al., 2009), China has experienced huge social and economic impact caused by migrant children. Studies have found that migrant children are disproportionately represented in the criminal justice system such as the juvenile correctional facilities. Ying and Dun (2007), for example, found that more than 50% of the juvenile offenders in several major cities in China are children of migrants from other provinces. Similar statistics are found in other studies (Chen & Zhong, 2012; C. Liu, 2013). Researchers have presented divergent views on the reliability of the statistics. Most of them hold that the statistics reflect the fact that migrant workers and their children have higher risk for crime and delinquency, perpetuated by social exclusion and other disadvantages experienced in their lives (Z. Wang, 2002; Zhong, Xu, & Piquero, 2017). Others maintain that the numbers, especially those from the official sources, are artificially inflated because of widespread policies and practices specifically targeting rural-to-urban migrants, resulting in higher likelihood of arrest and incarceration among the migrant population (Trevaskes, 2010; J. Xu, 2013).

Although some studies have found links among discrimination, negative emotions, lack of social bond, and delinquent behavior among migrant children, the mechanisms underlying these relationships are not very well understood (Chen & Zhong, 2012; C. Liu, 2013). Specifically, the question about whether the effect of discrimination on delinquency is mediated by other factors, to the best of our knowledge, has not been systematically investigated in Asian societies. The current study aims to fill this gap. The primary objective of the study is to assess how discrimination interplays with related factors such as negative emotions, social bond, and social support to influence delinquent involvement among Chinese migrant children.

Literature Review

Discrimination and Delinquency

The positive relationship between discrimination and delinquent behavior has been demonstrated worldwide. Smokowski and Bacallao (2006) found that perceived discrimination is positively associated with aggressive behavior among Latino youth. Similarly, through the analysis of survey data collected from African American youth, Caldwell et al. (2004) and Unnever, Cullen, Mathers, McClure, and Allison (2009) found that discrimination acts as one of the strongest predictors of violent behavior. In addition, scholars have also observed a connection between discrimination and delinquency in the former Soviet Union and Ethiopia (Walsh et al., 2015) and among Chinese American adolescents (Deng et al., 2009).

Collectively, these studies demonstrated that the general strain theory (GST) may provide a useful framework for the understanding of the link between discrimination and delinquency. As defined by Agnew (1992), strains are relationships in which the individual is not treated the way he or she would like to be treated by others. Strains may lead to delinquency and crime when they are seen as unjust and high in magnitude, when they are associated with low social control, and when they create some pressure or incentive to engage in criminal coping (Agnew, 2001). According to the GST, strains may lead individuals to commit crime for several reasons. The most important one is the effect of strains on negative emotions. Reduced level of social control is another factor through which strains may promote delinquent behavior. Agnew (2001) specifically identifies discrimination as one of the strains more likely to cause crime.

To our best knowledge, there has been little empirical research investigating the relationship between discrimination and delinquency among Chinese migrant youth. However, seen from the perspective of the GST, loosely connected information provided by prior research may be brought together to reveal a possible link between discrimination and delinquency. X. Liu and Zhao (2016) attributed perceived discrimination by Chinese migrant youth to the registration (Hukou) system, which limits migrant children’s opportunities for education, medical care, and other social services in the cities they live. Children affected by these policies and practices often feel unfairly targeted and frustrated. In terms of severity, a number of researchers have reported that discrimination experienced by Chinese migrant youth is high in both quality and quantity (Chen & Zhong, 2013; Yuan, Fang, Liu, & Lin, 2012). Moreover, previous studies also point out that adolescents from migrant families in China spend more time outside home and receive little parental monitoring (Fisher, Wallace, & Fenton, 2000; X. Liu & Shen, 2010), which could be signs of low informal social control. In sum, discrimination experienced by Chinese migrant children meets most of the criteria of the strains described by the GST as risk factors increasing the propensity to commit crime. We thereby hypothesize the following:

Hypothesis 1 (H1): Chinese migrant youth who experienced more discrimination will have a higher level of delinquent involvement than those who experienced less discrimination.

Discrimination, Negative Emotions, and Social Bond

Researchers have found a positive relationship between discrimination and negative emotions among migrant youth in China and several other countries (X. Liu & Shen, 2009; Mays & Cochran, 2001; Stevens & Vollebergh, 2008). Chinese scholars have conducted several studies examining the relationship between perceived discrimination and psychological well-being of Chinese migrant youth. In a survey of more than 1,300 migrant children in China, X. Liu and Shen (2009, 2010) found that perceived discrimination negatively affects self-esteem. Through the analysis of data collected from 1,164 students in both regular public schools and schools for migrant children, Lin, Fang, Liu, and Lan (2009) found that migrant children suffer a higher level of depression, anxiety, and interpersonal strain, and perceived discrimination is one of factors significantly related to those negative emotions. In the United States, research that focuses on discrimination and psychological well-being among minorities has also found similar results (Araújo & Borrell, 2006; Mays & Cochran, 2001). A recent systematic review of 20 studies conducted in multiple countries, including Europe, the United States, Canada, Australia, and Israel, found that negative psychological symptoms are prevalent among migrant youth (Stevens & Vollebergh, 2008). Stevens and Vollebergh (2008) offered three explanations of why migrant children have severe negative emotions. First, the process of migration itself causes negative emotions. For example, children who migrate to cities face difficulties maintaining physical contact with close friends. They also have to adapt to a new environment which may be different from their places of origin in terms of lifestyle, moral value, and language. Second, migrant children often experience high-level stress as a result of constrained opportunity for developing economic, educational, and social capital. Finally, the specific cultural background of migrants, such as familial roles, communication patterns, and affective styles, may contribute to negative emotions.

The mediating mechanism of negative emotions is fundamental to GST’s explanation of the relationship between discrimination and delinquency. Although strains may lead to criminal coping, the process is not straightforward. Agnew (2013) identified four steps in the process of linking strains and criminal coping: experiencing an objective strain, subjective evaluation of the strain, emotional reaction to the strain, and coping with the strain. He suggested that negative emotions resulted from subjective evaluation of strains provide the major impetus for criminal coping. In consistency with this argument, several studies have shown that negative emotions reduce an individual’s ability to engage in legal coping and increase their tendency to select criminal coping (Seery, Holman, & Silver, 2010; Slocum, Simpson, & Smith, 2005).

Another consequence of discrimination for migrant children might be weakened social bond. Criminological research has provided strong validation of the four elements of social bond proposed by Hirschi (2002), including attachment, commitment, belief, and involvement, as causes of juvenile delinquency. Evidence indicates that discrimination might threaten nearly all of the bond elements. Chen et al. (2011) conducted in-depth interviews of 40 migrant workers and 38 urban residents living in Beijing, China. They found that both migrant workers and their children had been stigmatized in the media and by permanent urban residents. Migrant people are often labeled as “Mang Liu,” which means blind and mindless nomads. Negative images such as thief, robber, and cheater are dubbed onto those migrants. The labeling may facilitate the feeling of being segregated, lonely, and unattached, which may in turn reduce school commitment, academic competence, and peer acceptance (Chen, Wang, & Wang, 2009; X. Liu & Shen, 2009; X. Liu & Zhao, 2016).

To sum up, prior research demonstrates that discrimination may lead to an increase in negative emotions and a decrease in social bond, both of which facilitate delinquent involvement (Hirschi, 2002; Krohn & Massey, 1980; Yuan et al., 2012). Based on this evidence, we expect that negative emotions and social bond mediate the relationship between discrimination and delinquent behavior. It is therefore hypothesized that

Hypothesis 2a (H2a): Discrimination is positively related to negative emotions and negatively related to social bond among Chinese migrant children.

Hypothesis 2b (H2b): Negative emotions and social bond mediate the relationship between discrimination and delinquency among Chinese migrant children.

Discrimination, Social Support, and Delinquency

Youth development is influenced by a plethora of social, psychological, and physical factors. As one of the coping strategies adopted by migrant youth when being discriminated against, deviant behavior is constrained by other contextual and social processes. Specifically, social support, defined as the state of a person being “loved, valued, cared for and belongs to a network of mutual emotional support” (Robbers, 2004, pp. 547-548), has been identified as one of the protective factors against delinquency (Cullen, 1994; Jiang, Li, & Feng, 2011).

Agnew (2013) defined social support as an individual or background characteristic that influences the subjective evaluation of objective strains and the emotional reactions to strains. He stresses that social support may play an important role facilitating benign coping strategies (Agnew, 2013). For example, it could enhance an individual’s ability to regulate emotional reactions to strains. In this regard, social support could counterbalance the adverse effect of stains by reducing negative emotions and strengthening social bond. Indeed, several recent studies have shown that social support reduces the negative effects of strains among youth. Similarly, Colvin, Cullen, and Ven (2002) proposed in their theory of differential social support and coercion that social support reduces the impact of strain by providing resources to help individuals cope with adversity through socially acceptable means. Furthermore, they contend that social support creates the context that facilitates the formation of strong social bond. Through these mechanisms, social support alleviates the negative influences of discrimination and reduces delinquent involvement.

The meditating effect of social support has undergone a number of empirical tests. In their study of African American college students, Prelow, Mosher, and Bowman (2006) found that social support partially mediates the effect of discrimination on depression. Samaniego and Gonzales (1999) found that social support significantly mediates the relationship between discrimination and delinquent behavior in Mexican American adolescents. Among Chinese migrant youth, research has observed the mediating effect of social support on the relationship between discrimination and social identity (Fan, Fang, Liu, Lin, & Yuan, 2012), interpersonal relationship (Jiang et al., 2011), and mental illness (B. Wang, Li, Stanton, & Fang, 2010). Studies guided by the GST usually treated social support as an exogenous variable independent of strains. We believe that the process might be somewhat different in the circumstances where discrimination operates as the strain. As indicated by several studies reviewed earlier in this article, discrimination comes in many different forms, some of which is policy-based. Migrant families who face discrimination will find it difficult to receive the support they need, especially from communities and government agencies, which have been carrying out many discriminatory policies and practices against the rural-to-urban migrant population in the last several decades (Zhong et al., 2017). Therefore, discrimination may at least partially reduce social support among migrant children. Based on these considerations, we hypothesize the following:

Hypothesis 3a (H3a): Discrimination is negatively related to social support among Chinese migrant children.

Hypothesis 3b (H3b): Social support mediates the relationship between discrimination and negative emotions among Chinese migrant children.

Hypothesis 3c (H3c): Social support mediates the relationship between discrimination and social bond among Chinese migrant children.

The theoretical relationships proposed in the hypotheses are illustrated in Figure 1.

Figure 1.

Conceptual model.

Method

Data

To test these hypotheses, we collected data from a multi-stage, stratified probability sample of 3,407 students who attended secondary schools designated for migrant children in one of the largest cities in China. To ensure sample representativeness, we first selected districts in the city, then schools for migrant children within the districts, and finally students from the schools using a stratified, proportion to size sampling procedure. We received the list of the schools from the municipal Department of Education. It was apparent that many local children, especially those with lower socioeconomic status, also attended these schools designated for migrant children although they were not from migrant families,

To collect information about migration, we asked a series of questions about place of origin and adaptation to life in the city. Students were instructed to skip this set of questions if they considered themselves as a “local resident” of the city. Respondents included in this study were 1,300 students who answered the questions on migration. The remainder of the sample consisted of students who were enrolled in the schools but chosen not to answer the questions on migration.

As such, the current study applies self-identification as a way to identify migrant students. This method is different from those used in other studies that rely on information concerning the household registration system (Hukou) to determine the migrant status of the student (e.g., H. Xu & Xie, 2015). The main reason for this approach is that secondary school students do not always know their family’s Hukou status. We included a question in the survey about family’s Hukou status. However, many students answered “do not know/unclear” on this question. Following the common practices employed in censuses and population-based surveys (Mather, Rivers, & Jacobsen, 2005; Smith, Marsden, Hout, & Kim, 2013), we used the respondent’s self-nomination to identify demographic characteristics of the adolescents who attended the schools, including their migrant status.

Using a structured, self-administrated questionnaire, we collected data on demographic characteristics, discrimination, social support, negative emotions, social bond, and delinquent behaviors. Trained researchers administered the survey in the classrooms. To prevent potential interference, we asked the teacher of the class to step out of the classroom when conducting the survey. The overall response rate was about 95%.

Measures

Key variables included in the current study, including discrimination, social support, negative emotions, social bond, and delinquent behaviors, are all measured using well-tested instruments developed by previous researchers. To increase the reliability of the measurements, multiple indicators are used to measure the underlying concepts to the extent possible.

Discrimination

Discrimination is measured by Perceived Discrimination Among Migrant Children (PDAMC) developed by X. Liu and Shen (2009). PDAMC consists of 20 Likert-type scale questions divided into four subscales. Physical Discrimination is the first subscale and is measured by eight questions including “local students ridicule me for the way I dress up” and “beaten by local students.” Avoidance is the second subscale (five items) measuring perceived alienation and avoidance from the local children. Questions in this category include “local students don’t want to talk to me and avoid me” and “local students don’t like to play with me.” Policy Discrimination is the third subscale measured by three questions including “I have to pay a lot of money to go to public school,” “I must go back to my hometown to take the college entrance examination,” and “public schools don’t accept me.” The final subscale measures General Discrimination perceived by migrant children. Questions in this subscale include “compared with local students, I felt I am unfairly treated” and “being looked down upon for being a migrant student.”

PDAMC has been adopted in more than 17 studies as a measure of discrimination and has been shown consistently as having good validity and reliability (X. Liu & Shen, 2010; Shen & Chen, 2014). Similarly, we find a Cronbach’s α value of .96 in the current research, which indicates a high level of reliability. Our confirmatory factor analysis (CFA) also indicates that the 20 questions load onto four factors that are conceptually consistent with the four subscales.¹ As suggested by Shen and Chen (2014), we take the mean of each of the four subscales and use them as indictors of a latent variable measuring discrimination in the structural equation model (SEM) analysis.

Social support

Social support is measured by Index of Sojourner Social Support (ISSS), another widely used instrument developed by Ong and Ward (2005). Consisting of 18 Likert-type scale questions, ISSS measures two types of social support: social emotional support and instrumental support, each of which has nine questions. Questions measuring social emotional support include “have someone comfort you when you feel homesick” and “have someone listen and talk to you when you feel lonely or depressed.” Questions for instrumental support include “have someone provide necessary information to help you adjust to your new surroundings” and “have someone tell you what you can and cannot do in the city.” In our sample, the overall Cronbach’s α of the 18 items equals to .96 while the average inter-item covariance is .90. Furthermore, the 18 questions load onto two different factors as suggested by Ong and Ward (2005). We computed mean scores of the items measuring social emotional support and instrumental support and used them as indicators of the latent concept of social support.

Negative emotions

In the current study, we applied J. Wang, Li, and He’s (1997) Middle-School Students Mental Health Inventory (MMHI) to measure negative emotions. MMHI has been widely used to measure negative emotions of secondary students in China and has shown to have strong validity and reliability (J. Wang et al., 1997; J. Wang, Yan, & Li, 1998). In its entirety, MMHI consists of 60 questions with 10 subscales for 10 different types of negative emotions. Due to time constraint, we only included 30 items from MMHI that measure five common psychological disorder symptoms related to youth behavioral problems, including six items for Depression (DEP), six items for Anxiety (ANX), six items for Interpersonal Sensitivity and Strain (ISS), six items for Hostility (HOS), and six items for Paranoid Ideation (PAI). The MMHI subscales show high internal consistency in our sample (α equals to .84, .88, .78, .86, and .84 for DEP, ANX, ISS, HOS, and PAI, respectively). As suggested by J. Wang et al. (1998), we take the mean of each of the five scales to generate five scores as measures of negative emotions. In SEM analysis, we treat these five items as indicators of a single latent variable of negative emotions.

Social bond

Among the four elements of social bond proposed by Hirschi (2002), attachment and commitment are the ones mostly widely tested in empirical research and most consistently found to be related to delinquency (Krohn & Massey, 1980; Li, 2004). We focus on these two elements in the current study. Attachment is measured by the average of 10 five-item Likert-type scale questions asking the respondent if “your dad/mom comforts you,” “you share your feeling with dad/mom,” “you feel close to dad/mom,” “you want to be like your dad/mom,” “you want to be with your dad/mom,” and “your dad/mom understands what you worry about” (α = .90). Commitment is measured by asking the respondent if “you like go to school,” “getting good grades is important for you,” “you care about how teachers think of you,” “you do your best to finish homework” and “you think study is fun.” Cronbach’s α of the five questions equals to .70. We constructed a measurement model of social bond using these questions as indicators in the SEM analysis.

Delinquent behavior

Delinquent behavior is measured by the sum of several dichotomous variables about the respondents’ involvement in delinquent activities, including “vandalism,” “physical or verbal threat,” “burglary,” “using drug,” “selling drug,” “stealing something worth more than 500 RMB,” “stealing something worth less than 500 RMB,” “robbery,” “bringing knife to school,” “running away from home,” “having sex with someone against their will,” “involving in a gang fight,” “beating somebody up,” “snatching property from others,” “fighting,” and “hurting somebody with weapon.” The range of delinquent behavior is from 0 to 16.

Analytical Framework

Two types of statistical analyses were conducted: descriptive analysis and inferential analysis. Descriptive analysis was used to describe characteristics of the sample while inferential analysis focused on testing the hypotheses. In the latter part, correlational analysis was first conducted to examine the bivariate relationships between perceived discrimination, social support, negative emotions, social bond, and delinquency. SEM analysis was then conducted to test the direct and indirect relationships between discrimination and delinquency.

Several technical issues need to be taken into consideration before conducting these analyses. First, the distribution of measure of delinquency is highly skewed, which might violate the multi-normal assumptions of SEM (West, Finch, & Curran, 1995). To reduce the possible bias, we applied bootstrapping techniques to calculate the standard errors of related parameters in SEM. Bootstrapping is a resampling method to calculate the empirical standard error from the data themselves. Nevitt and Hancock (2001) conducted comprehensive simulation test and found that traditional maximum likelihood (ML) yields inflated model test statistics when the normal assumption is violated. They suggest that when sample size is larger than 200, the bootstrapping method could prevent biases and produce more reliable standard error than ML. We applied bootstrapping with 1,000 replicates. As suggested by Nevitt and Hancock (2001), increasing the number of replicates might not yield better results when the sample size is sufficiently large. Second, the result might be biased due to item nonresponse. The software STATA 14.1 contains functions to deal with missing data in SEM. Taking advantage of this feature, we used the maximum likelihood with missing value (mlmv) estimator in STATA to estimate the parameters (Acock, 2013). The final issue might be the unequal sampling probability for each individual. Due to the different sizes of the classes included in our survey sample, the structure of the sampling procedure, and some other potential issues (e.g., some replacement effect), the probability of selection for all respondents might vary to some degree. To address this issue, we calculated ad hoc sampling weight based on the selection probability (Lee & Forthofer, 2005) and incorporated it into the analysis.

Results

Basic descriptive statistics of age, gender, discrimination, negative emotions, social bond, social support, and delinquency for migrant children are listed in Table 1. As we can see, the average age of the migrant children is 14.11 and the proportion of females is 46%. In term of perceived discrimination, we find the average scores of physical, avoidance, and general discrimination are similar but policy discrimination is much higher than other three types of discrimination, indicating that migrant children perceived more policy discrimination than other kinds of discrimination. With regard to negative emotions, J. Wang et al. (1997) suggested that a score larger than 2 may indicate borderline mental illness in the corresponding items. As shown in Table 1, only the average score of hostility is less than the threshold, suggesting the migrant children suffer from depression, anxiety, interpersonal sensitivity and strain, and paranoid ideation. The mean scores of social emotional support and instrumental support indicate migrant children received above-average social support in both areas. Finally, the delinquent behavior score indicates that migrant children on average committed 0.61 of the 16 delinquent acts asked in the questionnaire.

Table 1.

Descriptive Analysis.

Variables	n	Weight	M	SD	Minimum	Maximum
Demographic characteristics
Female	1,280	1,437.74	0.46	0.50	0	1
Age	1,300	1,459.07	14.11	1.56	11	24
Discrimination
Physical	1,200	1,352.19	1.50	0.77	1	5
Avoidance	1,236	1,391.22	1.46	0.81	1	5
Policy	1,288	1,445.60	2.13	0.91	1	5
General	1,172	1,321.30	1.53	0.83	1	5
Negative emotions
Depression	1,275	1,435.22	2.11	0.85	1	5
Anxiety	1,274	1,434.00	2.24	0.93	1	5
Interpersonal sensitivity	1,275	1,435.22	2.10	0.80	1	5
Hostility	1,274	1,434.00	1.97	0.86	1	5
Paranoid ideation	1,274	1,434.99	2.10	0.81	1	5
Social bond
Attachment	1,300	1,459.07	3.10	0.83	1	5
Commitment	1,273	1,434.88	3.31	0.80	1	5
Social support
Emotional support	1,149	1,300.44	2.84	1.00	1	5
Instrumental support	1,133	1,283.11	2.91	1.03	1	5
Delinquency
Delinquency	1,300	1,459.07	0.61	1.46	0	16

Note. All statistics in this table are weighted based on the selection probability and survey design.

As for social bond, migrant children in the sample showed above-average level of attachment and commitment. On a 5-point scale, the mean scores of attachment and commitment are 3.10 and 3.31, respectively.

SEM analysis is conducted to test all the research hypotheses.² As we can see from Figure 2 (details of SEM are provided in the note below Figure 2), the overall model goodness of fit is good with χ² = 346.19, df = 88, p < .001, root mean square error of approximation (RMSEA) = 0.05, comparative fit index (CFI) = 0.98, and Tucker–Lewis index (TLI) = 0.97. In the measurement part, we found that factor loadings of all the latent variables, including discrimination, negative emotions, social support, and social bond, are larger than 0.5. In the structural part, social support is found to be negatively related to negative emotions (β = −0.07, p < .05) and positively related to social bond (β = .77, p < .001), while discrimination is found to be positively related to negative emotions (β = .20, p < .001) and negatively related to social bond (β = −0.13, p < .001). Furthermore, the relationship between discrimination and social support is negative (β= −0.23, p < .001). As for the direct relationship between the explanatory variables and delinquency, social bond is found to be negatively correlated (β= −0.30, p < .001) and negative emotions are found to be positively correlated with delinquency (β = 0.12, p < .001). However, the direct link between discrimination and delinquency fails to reach the p < .05 level. These results support H2a, H2b, and H3a. H1, H3b, and H3c involve indirect and total effects and need to be investigated further.

Figure 2.

SEM model.

Direct, indirect, and total effects of discrimination, social support, negative emotions, and social bond on delinquency and other endogenous variables are provided in Table 2. The top panel in the rows shows the results when delinquency is a dependent variable while the bottom two panels list the results when negative emotions and social bond are dependent variables.

Table 2.

Direct, Indirect and Total Effects of Negative Emotions, Social Bond, Social Support, and Discrimination.

	Discrimination	Social support	Negative emotions	Social bond
Delinquency
Direct	0.06	0.15	0.12***	−0.30***
Indirect	0.08***	−0.24***	—	—
Total	0.14***	−0.09***	0.12***	−0.30***
Negative emotions
Direct	0.20***	−0.07*	—	—
Indirect	0.02*	—	—	—
Total	0.22***	−0.07*	—	—
Social bond
Direct	−0.13***	0.77***	—	—
Indirect	−0.18***	—	—	—
Total	−0.31***	0.77***	—	—

Note. All coefficients are standardized.

p < .05. **p < .01. ***p < .001.

Delinquency as a Response Variable

Negative emotions are positively associated with delinquency (β1 = 0.12, p < .001), whereas social bond is negatively associated with delinquency (β2 = −0.30, p < .001). The relationship between social support and delinquency is also negative but is exclusively indirect (β = −0.24, p < .001) through the mediating mechanisms of negative emotions and social bond. The total relationship between discrimination and delinquency is significant and positive (β = 0.14, p < .001) but is fully mediated by negative emotions and social bond (β = 0.08, p < .001).

Negative Emotions and Social Bond as Response Variables

Social support is found to partially mediate the relationships between two pairs of variables, that is, discrimination and negative emotions, and discrimination and social bond. Specifically, social support seems to buffer the aggravating impact of discrimination on negative emotions and mitigate the attenuating effect of discrimination on social bond. These results lend support for H3b and H3c. As for H1, the findings here provide further evidence that the relationship between discrimination and delinquency is largely indirect. In addition to negative emotions and social bond, social support appears to play an important role effectuating the indirect links.

Discussion

This study focuses on the relationship between discrimination and delinquency among children of migrant workers in China. By most accounts, a large proportion of crimes that occurred in Chinese cities were committed by migrant children (Chen & Zhong, 2012; X. Liu, 2013; Ying & Dun, 2007). To prevent criminal involvement among this segment of the population, it is important to understand the mechanisms contributing to their delinquent behavior. As shown in the literature review, one of the key factors related to crime might be discrimination experienced by migrant children. Our analysis of data collected from a large sample of migrant children in one of the largest cities in China supports this view. It shows that discrimination is positively and significantly associated with delinquency.

The current study, moreover, takes a step further than simply demonstrating a significant association between discrimination and delinquency. We conducted some additional analyses to investigate how discrimination is related to delinquency among migrant children. The results of these analyses suggest that discrimination leads to negative emotions and weakened social bond, which in turn increases delinquent involvement. The mediating effect of negative emotions on the relationship between discrimination and delinquency has been confirmed in other studies (see Agnew, 2007, 2013, for comprehensive review). The mediating role of social bond in this context, however, has seldom been tested empirically despite Agnew’s (2013) assertion that strain may increase crime “by reducing social control” (p.654). This study provides evidence that discrimination can indeed lead to delinquency through weakening adolescent attachment and commitment to conventional social institutions. The mediating effect of social bond is probably caused by the mistrust and stigma associated with discrimination. Our findings suggest that, as victims of the discriminatory practices, the migrant students experience difficulties in gaining social acceptance and making friends. They are also more likely to be bullied by their peers and unfairly treated by teachers at school. As parents struggle with making ends meet in an unfamiliar city, they may not be able to provide the help that the adolescents need. All of these might expose the adolescents to more interpersonal conflicts and difficult relationships as well as uncertainty about social norms and expectations, which might increase their use of the unconventional means to cope with the strain.

Prior research has identified social support as a protective factor facilitating the formation of social bond and suppressing the development of negative emotions. To understand the role of social support in the process that involves discrimination, negative emotions, social bond, and delinquency, we examined the potential buffering effect of social support in our analyses. Indeed, in consistency with previous research, we found that children with strong social support were less likely to have negative emotions and weak social bond even when they experienced the same level of discrimination as children with weak social support. Social support appears to be able to significantly alleviate the adverse effect of discrimination on negative emotions, social bond, and eventually compliance with the law (e.g., refraining from delinquent behavior). The finding on the interrelationship among social support, social bond, and delinquency is consistent with the theory of differential social support and coercion that social support prevents crime by increasing social bond (Colvin et al., 2002).

Taken together, these results provide an important elaboration of the GST in the context of migration and delinquency. Although the GST contends that discrimination might serve as a source of strain that leads to delinquency, it does not specify how discrimination may foster delinquent behavior. For instance, it is not clear whether discrimination operates as a proximal or distal facilitator of delinquency and what else must happen for discrimination to exert the influence. Our study shows that discrimination may not directly cause delinquency. Rather, its effect may be primarily indirect. Discrimination fosters delinquency through reducing social support, aggravating negative emotions and weakening social bond.

These findings have important policy implications. Discrimination is often structural and culturally based and difficult to eradicate. Ending discrimination will remove one of the root causes of delinquency among migrant children but it may take considerable effort to achieve. For example, to abolish the form of policy discrimination that the students felt most strongly about, the requirement that children of rural-to-urban migrants return to their hometown to take the national college entrance examination, will likely trigger widespread discontent among the local residents in the city who may see their children’s chances of going to good colleges being diminished. However difficult it may be, the government needs to take on the challenge of removing the restrictions that make it hard for migrant children to attend colleges in the cities they live if it is serious about promoting optimal development of these children. The existence of the special schools for migrant children may also have unintended consequences. On one hand, these schools provide a place for migrant children to continue to receive education. On the other hand, these substandard schools deprive migrant children, who are often at higher risk for problem behaviors, of the opportunities of attending regular schools that are safer and better in quality of instruction. Because of it, many migrant students in our sample felt that they were mistreated and segregated in schools. The government should try to break down the barriers and improve the quality of the schools for migrant children. In addition to these structural changes, local governments and communities should develop effective strategies to increase family and social support that can reduce negative emotions experienced by migrant students and build up their social bond. These strategies may include assisting migrant families with pressing economic needs, facilitating community involvement of migrant students and their parents, training teachers to be more sensitive to the needs of migrant students, and promoting compassion and mutual respect among secondary school students regardless of residential status. The combined measures of curtailing discrimination and increasing social support will create a better system to reduce delinquent involvement and promote adolescent development among the migrant population.

Although the current study made a series of improvement in conceptual and methodological approaches over similar studies conducted in the past, it is not without limitation. First, the overreliance on theories developed in the West might have undermined our ability to identify culturally specific mechanisms underlying the relationship between discrimination and delinquency among Chinese migrant population. Second, the research design is cross-sectional. As a result, the relationships among the key concepts observed in this study are correlational in nature. Third, the sample employed in this study may not be representative of all migrant children in China. Depending on their geographical locations, Chinese cities in different regions may draw migrant workers with different background. Furthermore, cities do not all have the same policies and support for migrant children and their families. As such, the patterns observed in this study may not necessarily hold true in other cities. For these reasons, the findings from this study should be taken with caution. More studies in different cities are needed to validate the observed patterns of the current study. Furthermore, future studies should aim toward developing more culturally relevant theories to explain the unique roles of discrimination in facilitating delinquent behavior among migrant children. Methodologically, future research should prioritize collecting longitudinal data to test the causal relationship between discrimination and juvenile delinquency.

Footnotes

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) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Spencer D. Li is a professor and associate dean in the Faculty of Social Sciences at University of Macau. His research interests include juvenile delinquency, corrections, substance abuse, child development, and sociology of religion. He has served as principal investigator on a number of publicly and privately funded projects related to juvenile delinquency, substance abuse, and corrections, including grants from the U.S. National Institutes of Health, U.S. Administration for Children and Families, and Social Welfare Bureau of Macao Special Administrative Region Government. His publications have appeared in several major academic journals, including Criminology, Journal of Research in Crime and Delinquency, Justice Quarterly, Evaluation Review, and Journal of Early Adolescence.

Yiwei Xia is a PhD candidate in the Department of Sociology, Faculty of Social Sciences at University of Macau. His research interests include quantitative methodology, juvenile delinquency, and substance abuse. He is a winner of First Prize Paper Award at the 2015 National Conference on Substance Abuse Prevention and Treatment held in Chengdu, China. His publications have appeared in Chinese Journal of Drug Dependence.

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