Abstract
Using data from the nationally representative China Family Panel Studies (CFPS), we describe Chinese adults’ attitudes towards three specific aspects of social environments: local government performance, severity of major social issues, and social trust. We further explore how county level contextual factors and personal experiences relate to subjective social environments, while controlling for individual demographics. On average, Chinese adults in the CFPS endorsed moderately positive ratings for their local governments, but perceived high severities in various social issues, ranking economic inequality as the most severe. A moderate level of generalized trust (54%) was found, together with very high trust in parents and very low trust in Americans and strangers. Further analyses revealed that variations in subjective social environments at the prefectural level were relatively small compared with individual level variations. At the individual level, personal experiences such as perceived unfair treatment showed consistently negative effects on how people evaluated their social environments. At the contextual level, employment rates appeared more influential than other studied factors. Regional economic inequality, as indicated by prefectural Gini, was not associated with most studied outcomes.
Social environment is a complex construct that encompasses the physical, social, and cultural aspects of a society (Barnett & Casper, 2001). It has been shown to have a significant impact on individual behaviors and health outcomes (Wilkinson and Marmot 2003; van Wormer and Besthorn 2010). Subjective evaluation of social environments by the general public is relatively new in China, but it is gaining popularity, especially in the midst of booming online social media (He 2009; Shen 2009). Many local governments have adopted public evaluations to augment the dominant self-appraisal system (Wang 2007). The purpose of the current study is to describe how Chinese adults perceive three aspects of their social environments, based on a nationally representative survey, and to further explore how both contextual factors and personal experiences may relate to subjective social environments.
Background
The Importance of Contextual Factors
How people perceive their social environments is naturally affected by the objective features of those environments. Research has provided empirical support for the idea that contextual factors (e.g., regional economic conditions) affect social trust and public evaluation of government officials, among other things (Duch & Stevenson, 2006; Revelli, 2010; Uslaner & Brown, 2005).
Economic inequality is one specific contextual factor that has been studied in terms of its effect on subjective social environments, including social cohesion and trust (Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997; Tsai, Laczko, & Bjørnskov, 2011). In general, higher degrees of economic inequality are associated with lower levels of social trust. For example, a study using state level data in the U.S. found that general social trust was lower in states with higher levels of economic inequality and that economic inequality was the most important determinant of social trust (Uslaner & Brown, 2005). The authors further explained that trust builds upon a psychological foundation of optimism and control, and people are less likely to believe in a bright future in the presence of high economic inequality. In addition, a high degree of inequality often parallels underinvestment in human capital, causing people to feel frustrated and have health problems (Kaplan, Pamuk, Lynch, Cohen, & Balfour, 1996; Kawachi, et al., 1997).
Despite empirical evidence supporting a negative effect of economic inequality on subjective social environments, studies based on Chinese samples indicate that the Chinese have a surprisingly high tolerance for inequality (Wu, 2009). Furthermore, many Chinese consider economic growth a driving force for increasing inequality (Xie, Thornton, Wang, & Lai, 2012). There are two possible reasons for the high tolerance for inequality among the Chinese. First, Chinese adults perceive great opportunities for social mobility despite economic inequality; second, a core belief among Chinese is that talent, education and hard work are the key routes to economic success, and thus inequality is somewhat justified (Wu, 2009). Under these circumstances, the detrimental effect of economic inequality on the subjective social environment in China may be smaller than in western populations.
Unemployment is another contextual factor that has been shown to have a significant negative impact on subjective social environments. For example, Hansen (1999), after studying survey data from eight U.S. states from 1967 to 1997, concluded that state unemployment rates have a significant impact on governors’ job performance evaluations, even with controls for national economic trends and political factors. Other researchers also found that state unemployment had a larger impact on the popularity of governors than other factors, including state-level inflation and per capita income tax (Niemi, Bremer, & Heel, 1999).
Personal Experiences
In addition to environmental characteristics, individuals’ personal experiences may also significantly influence how they view their social environments (Lind, Kray, & Thompson, 1998). The negative effect of perceived unfair treatment has been extensively studied in organizational research (Cohen-Charash & Mueller, 2007; Rutte & Messick, 1995), and such an effect has also been observed with physical and mental health (Robbins, Ford, & Tetrick, 2012).
Research linking perceived unfairness to social attitudes is limited. A study in the U.S. in the 1980s reported that perceived fairness had a larger influence on endorsement of political leaders than did outcome-related concerns (Tyler, Rasinski, & McGraw, 1985). Empirical studies in China revealed that personal encounters with local government officials may have a huge impact on how ordinary people evaluate their local governments (Ning, 2010). Li and Chen (2008) interviewed 1,600 respondents living in rural areas from 26 provinces and found, while controlling for a number of other factors, that the efficacy of government in providing rural residents with solutions to problems had an appreciable impact on local government ratings.
Purpose
The purpose of the current study is two-fold. The first purpose is to provide simple descriptive profiles of how Chinese adults view three aspects of their social environments: local government performance, severity of major social issues, and social trust. The second purpose is to analyze how contextual and individual level factors are associated with evaluations of social environments. We are particularly interested in the contributions of perceived unfairness at the individual level and economic inequality and employment rate at the prefectural level.
Data and Methods
This study utilizes data from the nationally representative China Family Panel Studies (CFPS) (Xie, 2012). The CFPS is a longitudinal survey that follows all members of sampled families every two years and collects data on the socioeconomic, demographic, educational and health aspects at the community, family and individual levels (Xie, 2012). It was initiated in 2010, and was the first survey of its kind in China. A multi-stage probability sampling design was adopted, in which counties were the primary sampling units; communities were then sampled within counties, and families were selected within communities (Xie, Qiu, and Lv 2012). Its baseline survey collected information on 57,155 individuals from 14,960 families and 634 communities in China. In the current study, we utilized data on evaluations of social environments from the second wave of the CFPS, conducted in 2012.
Outcome Variables
Local Government Performance Rating
Respondents were asked “How would you rate the performance of the county/district government last year?” The following five options were provided: good achievement, some achievement, not much achievement, no achievement, and worse than before. We coded the data from 1 to 5, with higher values indicating more positive ratings.
Perceived Severity of Major Social Issues
CFPS 2012 listed eight major social issues and asked respondents to rate their severity on a 0-to-10 scale, with higher scores indicating higher levels of severity. The eight issues were corruption, environmental issues, economic inequality, employment, education, health care, housing, and social security.
Social Trust
Trust was measured with both a single item on generalized trust and a six-item scale on specific trust in six types of people. Generalized trust was measured by asking respondents, “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” In terms of specific trust, the CFPS asked respondents the rate the level of trust on a 0-to-10 scale in the following six types of people: parents, neighbors, Americans, strangers, cadres, and doctors.
County/District Level Factors
Gini coefficient
A Gini coefficient was computed for each sampled county or district based on the 2005 mini-census conducted by the National Statistics Bureau in China. The unit of computation was district if the individual lived in a metropolitan city with multiple administrative districts. Otherwise, the statistics were computed at the county level. The 2005 mini-census was conducted on 1% of the total Chinese population and included all individuals residing in the sampled communities on October 31, 2005, as well as those who had a resident permit in the sampled community but were not present on the night of October 31, 2005. The computation of the Gini coefficient restricted the analytical sample to individuals who were at least 16 years old, employed for at least 35 hours a week, and reported a positive monthly income.
Employment Rates, GDP per Capita
Both measures were computed at the county or district level based on published figures from the 2010 census. Employment rates were computed as percentage of people who were employed over the total population ages 16 and above. GDP per capita was based on statistics from the 2010 census.
Individual Level Factors
Perceived unfair treatment
CFPS 2012 asked respondents whether they had experienced any of the following: being treated unjustly due to the gap between rich and poor, being treated unjustly due to household registration, being treated unjustly due to gender, being treated unjustly by government officials, having conflict with government officials, unreasonable delays and stalling when going to government offices for business, or being overcharged when going to government offices for business. A composite score was formed by counting the total number of types of unfair treatment perceived by the respondents.
Working at government organizations
This is considered almost a privilege in China, as better social benefits are often provided for people working for government organizations. Research already shows that families with members working for government organizations had significantly higher income per capita than other families (Xie, Zhang, Xu, & Zhang, 2013). We considered an individual to be working for a government organization if the person listed any of the following categories as his/her work unit: government/party/people’s organization or military, state-owned or collectively owned public institution or research institute, stated-owned or state-controlled enterprise.
Migrant status
We considered an individual a migrant if his/her place of self-reported urban resident permit status (hukou) was outside the sampled county or district he or she was residing in.
Other demographic variables
We further controlled for a number of key demographic variables, including age in years, gender (male = 1, female = 0), years of education, individual annual income in tertiles, and hukou (urban = 1, rural = 0). Age, gender, education, and hukou status were largely self-reported. When self-reported data was missing, however, we pulled additional information from family surveys.
Analytical Method
To study the effects of contextual and individual factors on subjective social environments and conform to the multi-stage sampling scheme used in the CFPS, we conducted a two level analysis, with individuals nested within counties or districts. This type of analysis is also convenient for examining the possible contributions of contextual factors at the county or district level. For each outcome, we calculated the intra-class correlation (ICC) in an unconditional model without any covariates to evaluate the variation of the outcome at the county/district level. The ICC is a measure of how individuals were clustered within certain groups. ICC ranges from zero to one, with higher values indicating a stronger clustering effect. In the current case, it would reflect how individuals were similar to one another within counties/districts.
A two-step modeling approach was adopted. Our first step was to evaluate the observed effect of each predictor by including one predictor at a time in the model. Our second step was to simultaneously control for all predictors and evaluate their adjusted effects. All three contextual level factors were categorized into tertile scores for easier interpretation.
Results
Sample Characteristics
A total of 21,585 respondents aged 16 and above from 105 counties/districts participated in the long form of the adult questionnaires (denoted as the full sample hereafter). Descriptive statistics from Table 1 show that the counties/districts were heterogeneous in terms of general levels of economic development and inequality. The full sample had slightly more females than males and their average age was 44.85 (SD = 16.63). Average years of education were 7.13 (SD = 4.81), and 27.7% claimed to have urban hukou. Respondents reported an average of .47 type of unfair treatment. About 7% of the respondents worked in government organizations, and 4.4% were migrants living outside the registered counties of their hukou.
Table 1.
Descriptive statistics of contextual and individual variables
| Variables | Mean/Percent | sd | min | max |
|---|---|---|---|---|
| County/District level (n=105) | ||||
| Gini Coefficient | 0.38 | 0.04 | 0.28 | 0.50 |
| Employment Rate | 0.67 | 0.11 | 0.43 | 0.90 |
| GDP Per Capita (in RMB) | 39577 | 47391 | 4224 | 242966 |
| Individual Level (n=21,585) | ||||
| Male | 49.2% | - | - | - |
| Age | 44.85 | 16.63 | 16 | 99 |
| Years of Education | 7.13 | 4.81 | 0 | 22 |
| Yearly Income (in RMB) | 10356 | 22769 | 0 | 1203800 |
| Urban Hukou | 27.7% | - | - | - |
| Number of Types of perceived unfair treatment | 0.47 | 1.05 | 0 | 7 |
| Government Jobs | 7.3% | - | - | - |
| Migrant | 4.4% | - | - | - |
Local Government Performance Rating
When respondents were asked to evaluate their local governments, 93% of the full sample provided a valid rating. Among those who responded, over half endorsed positive ratings of the local government, 50.2% of them rating their local governments as having accomplished “some achievement” and another 7.7% rating their local governments as having made “good achievement.” Negative ratings of “no achievement” and “worse than before” were endorsed by 12.1% and 2.8% of the respondents. The remaining 27.2% rated their governments as having accomplished “not much achievement.”
In an unconditional model, we evaluated the ICC to be .08 at the county/district level, indicating that about 8% of the total variation was attributed to variation at the county or district level. This is relatively small considering that the respondents were asked to evaluate their government at the corresponding level.
Table 2 displays both observed and adjusted effects of contextual and individual factors on ratings of local governments. In the bivariate models, none of the studied contextual variables was related to government rating. At the individual level, most of the studied factors showed significant effects, except for years of education and income levels. In the adjusted model, perceived unfair treatment was negatively correlated with local government rating, with one additional type of unfair treatment associated with a .12-point reduction. Those working for government organizations tended to rate the performance of their local government higher, as did migrants. On average, those working for government organizations rated local government performance .08 point higher than matched peers, and migrants .11 point higher. In addition, we also noted higher ratings from respondents who were older and had more years of education.
Table 2.
Multilevel regression of contextual and individual factors on local government performance rating
| Covariates | Observed | Adjusted |
|---|---|---|
| County/District level | ||
| Gini | −.04 | −.03 |
| Employment Rate | .03 | .06 |
| GDP Per Capita | .01 | .01 |
| Individual level | ||
| Perceived Unfair Treatment | −.12*** | −.12*** |
| Government Jobs | .09** | .08** |
| Migrant | .05* | .11** |
| Male | .02* | .02 |
| Age in years | .003*** | .005*** |
| Years of Education | .001 | .01*** |
| Income tertile | .004 | −.002 |
| Urban Hukou | .06*** | .01 |
Note. Final analytical sample size = 19,143. Observed estimates were from bivariate models where only one predictor was included at a time; adjusted estimated were from models where all predictors were added simultaneously.
Perceived Severity of Social Issues
Between 91% and 94% of the full sample provided valid ratings on severity of eight major social issues. On average, they rated all issues in the middle to upper ranges of severity on a 0- to-10 scale. Table 3 shows that economic inequality received the highest average rating of 6.76 (SD = 2.59), followed by corruption (6.01, SD = 3.02). The least severe issue as perceived by the respondents was social security (5.29, SD = 2.76). ICCs at the county/district level ranged from .04 to .08 across the eight outcomes, indicating a relatively low level of clustering effect within counties or districts for the eight outcomes.
Table 3.
Average severity rating of eight major social issues and prefectural level ICCs
| Social Issues | mean | sd | ICC |
|---|---|---|---|
| Corruption | 6.01 | 3.02 | 0.07 |
| Environmental Issues | 5.70 | 2.75 | 0.06 |
| Economic Inequality | 6.76 | 2.59 | 0.05 |
| Employment | 5.89 | 2.61 | 0.05 |
| Education | 5.35 | 2.80 | 0.04 |
| Health Care | 5.52 | 2.79 | 0.06 |
| Housing | 5.49 | 2.90 | 0.08 |
| Social Security | 5.29 | 2.76 | 0.06 |
Note. Sample sizes ranged from 19,731 (corruption) to 20,315 (health care). Observed estimates were from bivariate models where only one predictor was included at a time; adjusted estimated were from models where all predictors were added simultaneously.
Table 4 presents the observed and adjusted effects of all studied covariates. In the observed effects models, almost all individual level factors were significant correlates of the eight outcomes. At the contextual level, employment rate was the most predictive, followed by GDP per capita. Higher levels of employment were consistently associated with lower severity ratings in all eight outcomes.
Table 4.
Multilevel regression of prefectural and individual level factors on perceived severity of major social issues
| Covariates | Corruption | Environmental issues | inequality | employment | education | health care | housing | social security |
|---|---|---|---|---|---|---|---|---|
| County/District level | Observed effects | |||||||
| Gini (tertiles) | .01 | −.02 | −.07 | 0.01 | .05 | .01 | .07 | −.01 |
| Employ rate (tertiles) | −.32** | −.17* | −.32*** | −.31*** | −.18* | −.34*** | −.29** | −.20* |
| GDP per capita (tertiles) | .22 | .19* | .24** | .06 | −0.03 | .20* | .11 | .18 |
| Individual level | ||||||||
| #Perceived Unfairness | .45*** | .21*** | .35*** | .22*** | .20*** | .24*** | .25*** | .27*** |
| Government Job | .72*** | .78*** | .52*** | .44*** | .44*** | .44*** | .60*** | .41*** |
| Migrant | .45*** | .59*** | .47*** | .20 | .59*** | .56*** | .93*** | .66*** |
| Male | .35*** | .10** | .25*** | −.12*** | −.28*** | −.18*** | −.18*** | −.22*** |
| Age | −.02*** | −.04*** | −.02*** | −.03*** | −.03*** | −.02*** | −.03*** | −.03*** |
| Education | .09*** | .13*** | .08*** | .08*** | .08*** | .06*** | .09*** | .07*** |
| Income (tertiles) | .28*** | .26*** | .25*** | .10*** | .08*** | .11*** | .15*** | .06* |
| Urban Hukou | .67*** | .62*** | .52*** | .73*** | .42*** | .48*** | .63*** | .30*** |
| County/District level | Adjusted effects | |||||||
| Gini (tertiles) | .02 | −.01 | −.06 | .03 | .06 | .02 | .09 | .01 |
| Employ rate (tertiles) | −.15 | .01 | −.18* | −.20** | −.12 | −.22** | −.17 | −.10 |
| GDP per capita (tertiles) | .06 | .09 | .07 | −.12 | −.07 | .03 | −.04 | .12 |
| Individual level | ||||||||
| #Perceived Unfairness | .44*** | .20*** | .34*** | .22*** | .20*** | .24*** | .25*** | .27*** |
| Govern’t Job | .24** | .24** | .08 | .08 | .18* | .19* | .26** | .22** |
| Migrant | .13 | .18 | .20* | −.06 | .30** | .34*** | .62*** | .38** |
| Male | .20*** | −.01 | .09* | −.17*** | −.32*** | −.24*** | −.23*** | −.26*** |
| Age | −.02*** | −.03*** | −.01*** | −.02*** | −.02*** | −.02*** | −.03*** | −.02*** |
| Education | .03*** | .06*** | .05*** | .03*** | .03*** | .02*** | .02*** | .02*** |
| Income (tertiles) | .07** | .03 | .08*** | −.07** | −.05* | −.01 | −.03 | −.07** |
| Urban Hukou | .50*** | .41*** | .30*** | .68*** | .37*** | .40*** | .58*** | .27*** |
Note. Sample sizes ranged from 18797 (for corruption) to 19358 (for health care). Observed estimates were from bivariate models where only one predictor was included at a time; adjusted estimated were from models where all predictors were added simultaneously.
When all other factors were fully adjusted, the effects of regional employment rates were largely attenuated but remained significant on ratings of economic inequality, employment, and health care. At the individual level, most of the significant effects persisted in the fully adjusted models. Perceived unfairness was predictive of higher severity ratings on all eight social issues, with the largest impact on corruption. Interestingly, those working for government organizations perceived higher severities than their matched peers in six of the eight social issues. Migrants also rated five of the eight issues to be more severe than local residents did, the largest effect being on housing. In addition, respondents who were younger, more educated, and with urban hukou tended to assign higher severity ratings. Higher income groups perceived greater severity in corruption and economic inequality, but lower severity in employment, education, and social security.
Social Trust
A high response rate was obtained for the generalized trust question, with more than 96% of the respondents providing valid responses. About 54% endorsed the option that “most people can be trusted.” Table 5 reports the observed and adjusted effects of the studied factors on generalized trust. In the observed effects model, lower employment rates, higher per capita GDP and higher urban level were associated with higher generalized trust. However, all the effects became non-significant in the adjusted model. At the individual level, all the studied factors were significant in the bivariate model. In the adjusted model, perceived unfair treatment remained significantly associated with lower levels of trust, with an additional type of unfair treatment associated with an odds ratio of .87 in positive generalized trust. Respondents with government jobs were more likely to have generalized trust, with an odds ratio of 1.17, and so were migrants, with an odds ratio of 1.12. Higher probabilities of generalized trust were found among those who were male, older, more educated, and had urban hukou.
Table 5.
Odds ratios for Generalized trust
| Covariates | Observed | Adjusted |
|---|---|---|
| County/District level | ||
| Gini (tertiles) | .97 | 1.01 |
| Employ rate (tertiles) | .90* | 1.03 |
| GDP per capita (tertiles) | 1.17** | 1.04 |
| Individual level | ||
| #Perceived Unfair Treatment | .88*** | .87*** |
| Working at Government Organizations | 1.54*** | 1.17* |
| Migrant | 1.21** | 1.12* |
| Male | 1.16*** | 1.07* |
| Age in years | .99*** | 1.01*** |
| Years of Education | 1.07*** | 1.07*** |
| Income (tertiles) | 1.10*** | .98 |
| Urban Hukou | 1.51*** | 1.17*** |
Note. Observed estimates were from bivariate models where only one predictor was included at a time; adjusted estimated were from models where all predictors were added simultaneously.
Table 6 presents the average level of trust in parents, neighbors, Americans, strangers, cadres, and doctors. Not surprisingly, parents were ranked as the most trusted group on the list, with an average level of trust at 9.07 on the 0-to-10 scale. Doctors and neighbors ranked second (6.61) and third (6.38) among the six types, followed by cadres (4.90). Americans received low level of trust among Chinese adults, with an average level of 2.46, only ahead of complete strangers (2.18). ICCs at the county/district level ranged from .04 to .10.
Table 6.
Average rating of trusts towards six types of people and prefectural level ICCs
| Types of Trust | mean | SD | ICC |
|---|---|---|---|
| Parents | 9.07 | 1.69 | 0.07 |
| Neighbors | 6.38 | 2.20 | 0.05 |
| Americans | 2.46 | 2.47 | 0.10 |
| Strangers | 2.18 | 2.15 | 0.06 |
| Cadres | 4.90 | 2.46 | 0.04 |
| Doctors | 6.61 | 2.25 | 0.05 |
Note. Sample sizes ranged from 19,977 (for Americans) to 20,788 (for neighbors). Observed estimates were from bivariate models where only one predictor was included at a time; adjusted estimated were from models where all predictors were added simultaneously.
It is interesting to note from Table 7 that in the bivariate models, reverse directions of associations were found across trust levels towards different people. For example, a higher Gini was associated with lower trust in parents, but higher trust in Americans. Similarly, higher employment rates were associated with lower trust in parents, but higher trust in cadres and doctors. Higher GDP per capita and urban levels were associated with higher trust in parents, but lower trust in cadres and doctors. In the fully adjusted models, employment rates were positively associated with trust in all groups except parents, a higher employment rate being associated with lower trust in parents. The Gini and GDP per capita were much less predictive than employment rates. The Gini was only positively associated with trust in Americans, and GDP per capita was negatively associated with trust in cadres.
Table 7.
Multilevel regression of prefectural and individual level factors on specific trust
| Covariates | Parents | Neighbors | American | Strangers | Cadres | Doctors |
|---|---|---|---|---|---|---|
| County/District level | Observed effects | |||||
| Gini (tertiles) | −.11* | −.08 | .21* | .11 | .08 | .09 |
| Employ rate (tertiles) | −.20** | .08 | .11 | .09 | .32*** | .26*** |
| GDP per capita (tertiles) | .17*** | .06 | .20* | .06 | −.27*** | −.22** |
| Individual level | ||||||
| #Perceived Unfair Treatment | −.02 | −0.15*** | −.05** | −.04** | −.42*** | −.17*** |
| Working at Government Organizations | .27*** | .29*** | .34*** | .41*** | .01 | −.08 |
| Migrant | .15* | −.12 | .19* | .14 | −.29** | −.11 |
| Male | .08** | .27*** | −.01 | .37*** | −.02 | −.05 |
| Age in years | −.01*** | .01*** | −.01*** | −.01*** | .02*** | −.001 |
| Years of Education | .06*** | .02*** | .07*** | .04*** | −.04*** | .003 |
| Income (tertiles) | .13*** | .08*** | .36*** | .10*** | −.15*** | −.04* |
| Urban Hukou | .20*** | .07 | .22*** | .24*** | −.33*** | −.27*** |
| County/District level | Adjusted effects | |||||
| Gini (tertiles) | −.08 | −.07 | .26** | .12 | .08 | .08 |
| Employ rate (tertiles) | −.13* | .14* | .28* | .18** | .20* | .17** |
| GDP per capita (tertiles) | .06 | .06 | .06 | .08 | −.18** | −.12 |
| Individual level | ||||||
| #Perceived Unfair Treatment | −.03* | −.16*** | −.05** | −.06*** | −.42*** | −.17*** |
| Working at Government Organizations | .02 | .17** | .06 | .22** | .27*** | −.01 |
| Migrant | −.02 | −.09 | .07 | .11 | −.03 | −.08 |
| Male | .02 | .22*** | −.05 | .32*** | .05 | −.05 |
| Age in years | −.01*** | .01*** | −.01*** | .001 | .02*** | .002 |
| Years of Education | .03*** | .04*** | .05*** | .03*** | .01 | .02*** |
| Income (tertiles) | .03* | .02 | −.02 | −.01 | −.08*** | −.02 |
| Urban Hukou | .06 | −.11* | .24*** | .11* | −.35*** | −.30*** |
Note. Sample sizes ranged from 19,045 (for Americans) to 19,815 (for neighbors). Observed estimates were from bivariate models where only one predictor was included at a time; adjusted estimated were from models where all predictors were added simultaneously.
At the individual level, most of the effects were significant in the bivariate models. In the fully adjusted model, unfair treatments remained negatively associated with trust in all, and the largest impact was on trust in cadres. In contrast, those working for government organizations showed more trust in cadres than their matched peers, and they also showed more trust in neighbors and strangers than other respondents. In addition, we noted a generally positive effect of higher education, while the effects of other individual factors were mixed.
Conclusions
Based on survey data from the nationally representative China Family Panel Studies, we found that Chinese adults on average rated the performance of their local governments as moderately positive, but they also perceived high severities in major social issues, with economic inequality deemed the most severe social issue. Slightly more than half of the Chinese adults believed that most people can be trusted. A clear divide was present in terms of whom the Chinese tended to trust, characterized by a very high level of trust in parents and very low level of trust in Americans and strangers. We further found that at the county/district level, employment rates were more influential on subjective social environments than the other studied contextual factors such as economic inequality or GDP per capita. At the individual level, perceived unfairness showed consistent negative effects on all studied outcomes. Working for government organizations was associated with higher ratings of local government performance and higher social trust, but also higher perceived severities in many social issues.
The overall non-significant effect of economic inequality, as indicated by county/district Gini coefficient, was unexpected. One possibile reason for the weak association was that economic inequality was not as salient a feature as employment rate to the general public, and ordinary people may not be aware of the level of inequality (Xie, et al., 2012). This was somewhat supported in our analyses of perceived severities of social issues. More specifically, we observed that Gini was not associated with perceived severity of economic inequality, but higher employment rates were indeed related to lower severity ratings on employment issues. Xie et al. (2012) also reported that ordinary people were aware of the general levels of economic development of other countries, but not their levels of inequality. Another possibility was that Chinese in general were highly tolerant of inequality and were optimistic about their futures through social mobility despite inequality (Wu 2009). Using data from the CFPS, the authors (Wu & Xie, 2013) reported a relatively high level of confidence in the future among Chinese adults. Based on our own calculation, in answering the question, “How confident are you about your future?” in CFPS 2012, over 85% of Chinese adults endorsed a rating 3 or above on the 1-to-5 scale.
We studied three types of variables that were related to personal experiences: perceived unfair treatments, working for government organizations, and migrant status. As expected, perceived unfair treatments were consistently and negatively associated with all ratings of social environments. It was interesting to further note the differentiating effects across different outcomes. More specifically, in terms of severities of social issues, perceived unfair treatments had the largest negative effect on corruption, followed by inequality. Likewise, in terms of trust, perceived unfairness had a much larger detrimental effect on trust in cadres than on trust in other groups. This was to some extent related to the measurement of perceived unfairness in the CFPS, as it mostly targeted unfairness by the authorities. On the other hand, it also confirmed that even if the unfair treatment was inflicted by individuals, people tended to interpret it as actions on the part of authorities and translated it into dissatisfaction and distrust towards authorities.
Empirical studies have found having family members working for government organizations to be a significant predictor of income gaps among Chinese families (Xie, et al., 2013). However, those working for government organizations may not be aware of this, as they perceived higher severities in many social issues than their matched peers except in economic inequality and employment. As expected, they also gave higher ratings to local government performances and showed higher levels of trust in cadres.
Due to the operationalization of migrants in the current analysis, we captured both migrants doing professional jobs and those doing labor intensive jobs. Migrants gave higher ratings to local governments, perceived greater severities in social issues, and also showed higher levels of generalized trust. By comparing the different sizes of effects associated with migrant status in Table 4, we were able to ascertain that migrant status had the largest impact on perceived severity of the housing problem, followed by health care, social security, and education. This perhaps has something to do with the varying availabilities of different types of resources for migrants as compared to local residents.
Our study had a number of limitations. First, the measurement of subjective social environments, especially the local government performance rating, was relatively crude and may contain bias. The CFPS tried to minimize bias by asking that no government officials be present during the individual surveys. However, we still expected some degree of bias due to social desirability. In addition, as the government rating was asked at either the county, city or district level, it is highly likely that different respondents interpreted the question at different levels based on personal experiences. Such variability in respondents’ interpretation of the question may potentially lower the correlation between contextual level variables and the outcomes. Second, with cross-sectional data, our analyses mostly supported associations but not causal claims. We have tried to minimize confounding by including a number of key covariates at both the individual and the contextual levels and estimating the model in a multilevel framework.
In sum, based on nationally representative data from the CFPS, we reported Chinese adults’ mixed attitudes towards their social environments. They gave generally positive ratings to local governments, but also perceived middle to high levels of severity on all major social issues. Social trust was characterized by high trust in parents but low trust in Americans and strangers. At the contextual level, employment rates showed consistently positive effects on attitudes towards major social issues and social trust, but economic inequality had virtually no effect on subjective social attitudes. At the individual level, perceived unfair treatments were negatively associated with most outcomes.
Contributor Information
Qiong Wu, Institute for Social Science Survey, Peking University, Beijing 100871, China.
Yu Xie, Department of Sociology, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104.
References
- Barnett E, Casper M. A definition of "Social Environment". American Journal of Public Health. 2001;91(3):465. doi: 10.2105/ajph.91.3.465a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cohen-Charash Y, Mueller JS. Does perceived unfairness exacerbate or mitigate interpersonal counterproductive work behaviors related to envy? J Appl Psychol. 2007;92(3):666–680. doi: 10.1037/0021-9010.92.3.666. [DOI] [PubMed] [Google Scholar]
- Duch RM, Stevenson R. Assessing the magnitude of the economic vote over time and across nations. Electoral Studies. 2006;25(3):528–547. doi: http://dx.doi.org/10.1016/j.electstud.2005.06.016. [Google Scholar]
- Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL. Inequality in income and mortality in the United States: analysis of mortality and potential pathways. Bmj. 1996;312(7037):999–1003. doi: 10.1136/bmj.312.7037.999. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kawachi I, Kennedy BP, Lochner K, Prothrow-Stith D. Social capital, income inequality, and mortality. Am J Public Health. 1997;87(9):1491–1498. doi: 10.2105/ajph.87.9.1491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li L, Chen J. Rural residents' evaluation of local governments and the assoicated factors. Journal of Tianjin Administration Institute. 2008;10(2):47–51. (in Chinese) [Google Scholar]
- Lind EA, Kray L, Thompson L. The Social Construction of Injustice: Fairness Judgments in Response to Own and Others' Unfair Treatment by Authorities. Organ Behav Hum Decis Process. 1998;75(1):1–22. doi: 10.1006/obhd.1998.2785. [DOI] [PubMed] [Google Scholar]
- Niemi RG, Bremer J, Heel M. Determinants of State Economic Perceptions. Political Behavior. 1999;21(2):175–193. [Google Scholar]
- Ning K. Trust in local governments and the assoicated factors. Intelligence. 2010:260–261. (in Chinese) [Google Scholar]
- Revelli F. Spend more, get more? An inquiry into English local government performance. Oxford Economic Papers. 2010;62(1):185–207. [Google Scholar]
- Robbins JM, Ford MT, Tetrick LE. Perceived unfairness and employee health: A meta-analytic integration. Journal of Applied Psychology. 2012;97(2):235–272. doi: 10.1037/a0025408. [DOI] [PubMed] [Google Scholar]
- Rutte C, Messick D. An integrated model of perceived unfairness in organizations. Social Justice Research. 1995;8(3):239–261. [Google Scholar]
- Tsai M-C, Laczko L, Bjørnskov C. Social Diversity, Institutions and Trust: A Cross-National Analysis. Social Indicators Research. 2011;101(3):305–322. [Google Scholar]
- Tyler TR, Rasinski KA, McGraw KM. The Influence of Perceived Injustice on the Endorsement of Political Leaders1. Journal of Applied Social Psychology. 1985;15(8):700–725. [Google Scholar]
- Uslaner EM, Brown M. Inequality, Trust, and Civic Engagement. American Politics Research. 2005;33(6):868–894. [Google Scholar]
- Wu X. Income Inequality and Distributive Justice: A Comparative Analysis of Mainland China and Hong Kong. The China Quarterly. 2009;200:1033–1052. [10.1017/S0305741009990610]. [Google Scholar]
- Wu, Xie Y. China Family Panel Studies: 2013. Beijing Peking University Press; 2013. Attitudes; pp. 344–367. (in Chinese) [Google Scholar]
- Xie Y. China Family Panel Studies: user's manual for the baseline survey. Beijing: Peking University; 2012. (in Chinese) [Google Scholar]
- Xie Y, Thornton A, Wang G, Lai Q. Societal projection: Beliefs concerning the relationship between development and inequality in China. Social Science Research. 2012;41:1069–1084. doi: 10.1016/j.ssresearch.2012.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xie Y, Zhang X, Xu Q, Zhang C. China Family Panel Studies: 2013. Beijing: Peking University Press; 2013. Income distribution; pp. 27–53. (in Chinese) [Google Scholar]