Social Science Research 49 (2015) 179–190

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Explaining the subjective well-being of urban and rural Chinese: Income, personal concerns, and societal evaluations Chunping Han The Walter H. Shorenstein Asia-Pacific Research Center, Encina Hall E301, 616 Serra Street, Stanford, CA 94305, USA

a r t i c l e

i n f o

Article history: Received 26 April 2013 Revised 6 June 2014 Accepted 8 August 2014 Available online 18 August 2014 Keywords: Subjective well-being Income Personal concerns Societal evaluations Contextualization China

a b s t r a c t This study makes an integrated investigation of how subjective well-being is associated with income, personal concerns, and societal evaluations and how these social and psychological correlates of subjective well-being are contextualized within a country. Data used for the empirical analysis come from a nationally representative sample survey conducted in China in 2009. It is found that subjective well-being is independently linked to income, personal concerns, and societal evaluations. Comparisons of urban and rural Chinese further reveal that income, structural attributions of inequality, and evaluations of governance are related to subjective well-being among both groups. Nevertheless, different sets of other evaluative correlates of subjective well-being between urban and rural people stand out, which is conjectured to be related to the long-time institutional, economic, and social segmentation of the two groups. This study has contributed to both the subjective well-being theories and the understanding of the consequences of social inequality. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Subjective well-being has received an increasing scholarly interest in the past several decades (Easterlin, 1974, 2001; Diener, 1984; Kahneman et al., 2003; Eid and Larsen, 2008; Veenhoven, 2008). Subjective well-being reflects individuals’ satisfaction with their life, indicates ‘‘the quality of the social system in which they live’’ (Veenhoven, 2008, p. 11), and predicts many life outcomes such as longevity, health, income, and social skills (Danner et al., 2001; Diener and Biswas-Diener, 2002; Lyubomirsky et al., 2005). Recognizing its importance, the United Nations Development Programme has adopted subjective well-being as a crucial component of quality of life in its Human Development Report (UNDP, 2010). Social scientists have found that subjective well-being is associated with income (Diener et al., 1999; Helliwell and Wang, 2012; Stevenson and Wolfers, 2008), social psychological mechanisms including social comparisons and life goal orientations (Diener and Lucas, 2000; Kasser and Ryan, 1993, 1996; Michalos, 1985), and the macro context of living such as income inequality (Alesina et al., 2004; Oshio and Kobayashi, 2011), effectiveness of governance (Bjørnskov et al., 2007; Ott, 2010), and social capital (Helliwell and Putnam, 2004; Helliwell and Wang, 2011; Helliwell et al., 2013; Ram, 2010; Winkelmann, 2009). While being inspiring, the existing explanations of subjective well-being have a few limitations: First, the effects of income, personal concerns, and the macro context of living are usually examined in discrete studies rather than simultaneously. Second, the inquiry of the influence of the macro context such as income inequality and effectiveness of governance predominantly uses objective measures such as the Gini coefficient, government size, and quality of public services (Bjørnskov et al., 2007; Oshio and Kobayashi, 2011; Ott, 2010), but seldom tests the importance of subjective evaluations of the macro context (Kim and Kim, 2012). Third, research on the contextualization of the correlates of subjective well-being,

E-mail address: [email protected] http://dx.doi.org/10.1016/j.ssresearch.2014.08.006 0049-089X/Ó 2014 Elsevier Inc. All rights reserved.

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such as income and values, primarily focuses on cross-national differences (Diener and Biswas-Diener, 2002; Inglehart and Rabier, 1986) and is yet to be extended inside a society marked by systematic economic and social stratification. This study attempts to address those limitations. It examines how income, personal concerns, and evaluations of social circumstances are related to subjective well-being simultaneously and whether and how those correlates vary between systematically segmented social groups, using data from a nationally representative sample survey in China. China is a particularly suitable setting for addressing our research questions given the dramatic transformation it has experienced over the last three decades. It has witnessed impressive economic growth, drastic expansion of inequality, widespread internalization of consumerism, growing tension between the state and society, and prevailing of individualism and atomization over the collectivist-oriented social fabric. Those dynamic developments make China a pertinent case to examine how money and multi-faceted evaluations of individual and social circumstances bear on subjective well-being. Furthermore, there have been sweeping disparities in income, standards of living, social welfare, outlooks on life, and values between urban and rural Chinese, which are larger than what have been found in many developing countries and developed countries during industrialization (Knight, 2008; Whyte, 1995). The sharp urban–rural inequality makes China theoretically relevant to study whether and how the correlates of subjective well-being are contextualized across systematically divided groups within a country. Empirical research on subjective well-being in China using national data is emerging but scarce. Appleton and Song (2008) find that income and satisfaction with economic growth and low inflation contribute to overall life satisfaction among urban Chinese. Knight et al. (2009) discover that happiness is weakly affected by income but highly sensitive to the subjective relative position of one’s household’s income in the village, perceived importance of personal relationships, and the degree of personal materialism in rural China. Studies that directly compare the correlates of subjective well-being between urban and rural Chinese are even rarer. Among them, Knight and Gunatilaka (2010) reveal that happiness is significantly related to income and social comparisons in both urban and rural China and to perceived extent of fairness of income distribution in the city. Han (2012b) finds that livelihood satisfaction is significantly associated with social comparisons among both urban and rural residents and with material aspirations only among urbanites. Brockmann et al. (2009) discover that relative deprivation measured by financial dissatisfaction plays a robust role in life satisfaction among both groups and that political disaffection matters only among urban people. While these findings are informative, different measures of subjective well-being are used and varying sets of predictive variables are examined across studies. It is unclear how the patterns of the subjective well-being for urban and rural Chinese look like when the associations with income, personal concerns, and societal evaluations are simultaneously scrutinized in one study. In sum, this study makes a comprehensive investigation of how subjective well-being is related to income, personal concerns, and societal evaluations, factors that have been either discretely or rarely examined in previous research. It pays particular attention to the contextualization of those correlates of subjective well-being within a country through comparison of urban and rural Chinese. 2. Income, personal concerns, and subjective well-being Existent social science literature attributes subjective well-being to an array of social and psychological factors. Income has shown a positive association (Diener et al., 1999). Recent studies at the individual level further elaborate that the income effect varies by different measures of subjective well-being. Specifically, income is more closely and steadily correlated with cognitive evaluations of life, while its effect on the emotional report of feelings is often smaller and may flat at a threshold (Helliwell and Wang, 2012; Kahneman and Deaton, 2010). Findings at the aggregate level are mixed. Easterlin (1974) finds that increases in national income over time do not generate increases in national average happiness, while Stevenson and Wolfers (2008) discover robust links between economic growth and increase in overall subjective well-being with data from more countries. Other explanations than income have been provided for subjective well-being. Social comparison theorists argue that feelings about life partly depend on comparison of one’s own life with that of relevant reference groups (Diener and Lucas, 2000; Merton and Rossi, 1968), particularly people of similar social characteristics such as the level of educational attainment and within the immediate social milieu such as relatives, coworkers, and neighbors (Michalos, 1985; Walker and Pettigrew, 1984). If individuals believe that what they receive is more than what is possessed by their relevant reference groups, they are likely to feel happy (Crosby, 1982; Diener and Lucas, 2000; Stouffer et al., 1949). The goal orientation approach suggests that the nature of the life goals that are valued most affects subjective well-being. The contrast between materialistic and non-materialistic pursuits is particularly stressed. Materialistic goal orientation implies that the aspiration for wealth is elevated by the increase in income; therefore, the improvement in material conditions does not produce greater happiness automatically (Easterlin, 2001; Kahneman, 1999). In contrast, if priority is given to intrinsic, non-materialistic goals such as family life and social relations, subjective well-being may be enhanced (Kasser and Ryan, 1993, 1996; Ryan et al., 1999; Sheldon and Kasser, 1998). The ultimate influence of life goal orientations depends on how materialistic and intrinsic goals countervail each other. 3. Societal evaluations and subjective well-being Inspiring as they are, explanations for subjective well-being from the social comparison and goal orientation perspectives focus on personal concerns. However, people also attempt to make sense of the social environment in which they live.

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Subjective evaluations of the same economic, political, and social contexts of living may vary across individuals due to the availability of information, entrenched ways of thinking, and personal orientations. Those evaluations set a ground on which people assess legitimacy of the status quo, the structure of opportunities and constraints, and the predictability and security of future, all of which may influence subjective well-being. Societal concerns may be particularly salient in Chinese people’s subjective well-being since China has been undergoing immense transformations in the economic and social domains, value systems, and state-society relations for several decades. Therefore, in addition to investigating the effects of income and personal concerns, this study also explores the impact of societal concerns on subjective well-being, which has received less attention in the current literature. The societal concerns we study span economic, political, and social contours and specifically involve perceived sources of inequality, assessments of governance, and social trust. Research on the effect of income inequality, a central concern in any society, on subjective well-being usually uses objective measures of the extent of inequality such as the Gini coefficient (Oshio and Kobayashi, 2011). However, the sense of justice about inequality may weigh more than the extent of inequality in people’s reaction. Research has found that whether an outcome is considered just or not is decided more by the rules and the procedure leading to the outcome (procedural justice) than by the desirability of the outcome (distributive justice) (Tyler, 2000) and that perceived procedural justice enhances justification of the existing system and personal well-being (Jost and Hunyady, 2005; Jost et al., 2003; Lucas, 2009). Therefore, perceived sources of inequality, which bear on the sense of justice about the current distributional order, may influence subjective well-being. Scholars have distinguished between individualistic and structural attributions of inequality (Della Fave, 1980, 1986). Individualistic attributions emphasize the role of individual qualities such as talent and effort in causing wealth and poverty, while structural explanations highlight the importance of external, structural reasons beyond personal control such as the openness of opportunities, discrimination, and system deficiencies. This study further adds a dimension of ascribed attributions, i.e., attributing inequality to family economic and social background, which imply perceived impediment to intergenerational mobility (Blau and Duncan, 1967). Overall, individualistic attributions may promote subjective well-being through fostering the sense of justice about the current inequality, confidence in open opportunities of getting ahead, and the sense of personal control, while structural and ascribed attributions are likely to dampen subjective wellbeing through evoking the sense of injustice, perceived blocked opportunities of moving upward, and feelings of powerlessness. Existing literature asserts an important role of effectiveness of governance in subjective well-being (Ott, 2010). Nonetheless, objective measures such as government size and quality of public services prevail in this line of research (Bjørnskov et al., 2007; Ott, 2010). The impact of subjective evaluations of governance effectiveness has been called for but rarely empirically tested (Kim and Kim, 2012). This study probes two essential dimensions of perceived effectiveness of governance in relation to subjective well-being. The first dimension is institutional trust. Institutions and government policies define the structure of opportunities and constraints and guide the distribution of benefits and losses. Hence, trust in the incumbent government as a responsible, capable, and fair agent may enhance the confidence that one’s interest is appropriately represented and hence contributes to subjective well-being. Otherwise, institutional distrust may deepen the sense of uncertainty and insecurity and exacerbate unhappiness. Institutional trust bears particular relevance to subjective well-being in China, as the one-party regime has persisted and the state involvement in the economic and social spheres has been more extensive and intensive than in democratic systems (Walder, 1986, 2002; Wang, 2008; Whyte, 2009; Zhou et al., 1996; Zhou, 2000). Another crucial dimension of evaluations of governance is perceived effectiveness of social protection. Government provision of health insurance, education, and old age support is common in contemporary time despite variations in the coverage and benefit levels. These protective measures may help adjust the inequality generated in the market and provide instrumental and psychological buffers against shocks in life. Despite the confirmation of the role of quality of public service in subjective well-being (Ott, 2010), the impact of subjective evaluations of social welfare policies, which may vary across individuals, has received little attention with scarce exceptions (Wong et al., 2006). People’s uncertainty and anxiety about the future may be alleviated and their subjective well-being may be boosted if they consider the existing provision of social protection effective. Emerging research on the role of social capital in subjective well-being has identified positive influences of social capital (Helliwell and Putnam, 2004; Ram, 2010; Winkelmann, 2009). Social trust is regarded as the most crucial dimension of social capital (Uslander, 1999). It implies a belief that most people are of integrity, adhere to commonly accepted rules and norms, and behave responsibly and predictably. This cognitive social capital has been found to enhance subjective well-being (Bjørnskov, 2003, 2006; Helliwell and Wang, 2011; Helliwell et al., 2013). Conversely, a lack of social trust may aggravate the feelings of alienation, lack of control, and powerlessness (Ross, 2011) and ultimately impair subjective well-being.

4. Contextualization of the correlates of subjective well-being within a country Social correlates of subjective well-being have been found to vary across contexts. For example, income shows a stronger positive association with subjective well-being in poorer societies than in highly developed ones (Diener and Biswas-Diener, 2002). The relationship between values and life satisfaction also differs across societies with different political histories (Inglehart and Rabier, 1986). These findings of the contextualization of the correlates of subjective well-being are primarily derived from cross-national comparisons, and a hidden assumption is that the same set of mechanisms lead to different

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levels of subjective well-being across groups within a country. Nonetheless, the contextualization of the correlates of subjective well-being is not necessarily limited to the national level. There is stratification in any society. Different sets of mechanisms may underlie the subjective well-being of different groups within a society who live in different economic, social, and cultural circumstances. The long-time institutional, economic, and social segmentation of urban and rural Chinese makes China a particularly suitable case to identify whether and how the associations between subjective well-being and income, personal concerns, and societal evaluations vary across systematically segmented groups within a country. The urban–rural disparity has been larger in China than in most other developing countries and former socialist societies (Knight, 2008; Whyte, 1995), largely due to the enforcement of the household registration (hukou) system and associated policies favoring the city.1 Consequently, urban and rural Chinese have experienced enormous inequalities in income, standards of living, and access to social services such as health care, education, and old age support, which have constituted the greatest source of inequality in contemporary China (Khan and Riskin, 1998, 2005; Knight, 2008; Li and Sicular, 2014; Whyte, 2010b). The urban–rural cleavage is also distinct in the domains of social life, belief systems, and values (Sun, 2003; Whyte, 1995). For example, residents and social interactions in the city are highly heterogeneous, while social contacts are more homogeneous and closely knit in the rural area. Rural Chinese tend to internalize their identity as a different category of citizens from urbanites due to the institutional, social, and residential segmentation (Hu and Salazar, 2010) and are likely to regard those in close rural vicinity as meaningful reference frames (Knight and Gunatilaka, 2010). Despite the penetration of atomization in both the city and the countryside during marketization, many dimensions of public and social life such as the construction of roads and schools and mutual assistance in everyday life still largely rely on closely-knit social networks in rural China (Tsai, 2007), so the community well-being and solidarity are practically more important in rural China. In addition, the consumerist culture, individualism, and embracement of meritocracy are likely to be more prevalent in the industrialized, urban context, while certain traditional values such as celebration of familial inheritance of status still persist in rural China. The above-mentioned stark urban–rural discrepancies raise the question of whether and how the correlates of subjective wellbeing differ between urban and rural Chinese. To sum up, this study examines how subjective well-being is related to income, personal concerns, and societal evaluations in an integrated manner. It particularly focuses on whether and how those correlates of subjective well-being are contextualized within a country through comparison of urban and rural Chinese.

5. Data, measures, and methods 5.1. Data Data used in this study come from the China Inequality and Distributive Justice Project. This nationally representative sample survey project is the first of its kind to specifically study attitudes toward inequality and distributional issues in contemporary China and was conducted by a team of American and Chinese social scientists in 2004 and 2009. It asked respondents a broad range of questions about their socioeconomic and demographic background and evaluations of personal life and social conditions, which provide rich data relevant to our research questions. Stratified and spatial probability sampling procedures were combined to select a nationally representative sample. The whole country was first divided into seven strata according to geographic location and administrative jurisdiction. Then GPS-assisted spatial sampling method was used to select sampling units at multiple stages, with all the residential addresses in the final unit included in the sample. A respondent was randomly drawn from each household with the Kish method. Urban residents were oversampled so that there is a reasonably large urban sample for separate analysis. Face-to-face interviews were conducted to collect survey data. Further details of the implementation of the project and the discussion of the validity and reliability of data are presented in Whyte’s (2010a) book. This study uses data collected in 2009 as they include all the indicators that are necessary to address our research questions. The response rate of this wave of survey is about 69%. The nationally representative probability sample consists of 2866 respondents aged between 18 and 70 from 22 of 31 provincial units in China. This study centers on urban hukou holders as well as rural hukou holders living in the countryside. A few cases that did not answer the question about hukou status as well as rural-to-urban migrants are excluded. While rural-to-urban migrants warrant separate analysis due to their special dual status (holding rural hukou but working or living in the city), unique life experiences, and particular orientations, the size of this subsample in our data is relatively small and likely to lead to large standard errors. Furthermore, the comparison of urban and rural residents is sufficient to address the theoretical concerns in this study. Therefore, the following analyses focus on urbanites (N = 1028) and rural residents (N = 1611).2 1 Every Chinese citizen has been assigned the agriculture (rural) or non-agricultural (urban) hukou at birth according to the residential location since 1958. Hukou status has served as an overriding basis for the state to allocate resources in favor of the city to facilitate industrialization and maintain urban stability. Meanwhile, restrictions are imposed on rural citizens’ migration into the city, so that the majority of rural Chinese have been effectively confined to land. The hukou system has persisted despite some relaxation during the reform period. 2 In the following analysis, sampling weights that take account of oversampling of urban respondents as well as gender and age distributions of the Chinese population are used in calculation of descriptive statistics so as to represent the whole population. Unweighted data are used for regression analyses as unweighted estimates are unbiased, consistent, and more efficient than weighted estimates when sampling weights are only a function of independent variables (Winship and Radbill, 1994).

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5.2. Dependent variable Subjective well-being is measured by the mean of the responses to two questions. Respondents were asked how often they (1) felt happy and (2) enjoyed life during the previous week. Four-point-scale responses were provided for the respondent to choose from: often, sometimes, rarely, and never. Responses are reversely coded so that higher values mean more frequent experiences of happy feelings. An index of subjective well-being is created by taking the mean of the responses to the two questions. The Cronbach’s a for the index in urban and rural models are .910 and .829 respectively (Table 1), indicating good internal consistency. Global measures of subjective well-being have shown to be valid and reliable in numerous large-sample surveys (Oishi, 2010). While it is important to recognize the impact of the immediate context such as the transient mood on global reports of happy feelings, empirical research has shown that the influence of current moods is small in normal testing settings and that test–retest reliability is substantial (Eid and Diener, 2004; Schimmack and Oishi, 2005). Moreover, global reports of happiness are well consistent with data collected with other methods such as daily mood reports, external reports on respondents, and recollection of positive versus negative life events (Sandvik et al., 1993; Seidlitz et al., 1997). Therefore, summary measures reflect a relatively stable condition of subjective well-being. The weighted mean of subjective well-being is 3.141 in the urban model and 3.062 in the rural model (Table 1), indicating that the average frequency of feeling happy among the two groups is slightly higher than sometimes. Urban Chinese report a slightly higher level of subjective well-being than do rural Chinese, a pattern unlike what is found by Knight and Gunatilaka (2010). The difference is possibly due to the different measures of subjective well-being used in the two studies. In our survey, two questions were asked to identify how often the respondents ‘‘felt happy’’ and ‘‘enjoyed life’’ in the time frame of the previous week. In Knight and Gunatilaka’s (2010) data, the respondents were asked ‘‘generally speaking, how happy are you these days?’’ (very happy, happy, so-so, not happy, and not at all happy), which captures happiness in the less distinct period of ‘‘these days’’. Future research using the same indicator is needed to compare the level of subjective well-being between urban and rural Chinese across studies. 5.3. Independent variables Income is estimated by the logarithm of the midpoint of 18 household income categories, from which the respondents were asked to choose, and treated as a continuous variable. Social comparison is measured by four questions that asked the respondents to compare their current standard of living with the average level of their relatives, former classmates, coworkers, and neighbors. An index is constructed by calculating the mean of the five-point-scale responses (reverse-coded, 1 = much worse, 2 = somewhat worse, 3 = about the same, 4 = somewhat better, and 5 = much better) to the four questions.

Table 1 Descriptive statistics of dependent, independent, and control variables. Range

Urban

Rural

Minimum

Maximum

Mean (s.d.)

Cronbach’s a

Mean (s.d.)

Cronbach’s a

Dependent variable Subjective well-being

1

4

3.141 (.797)

.910

3.062 (.808)

.829

Independent variables Log household income Social comparison Relative value of materialistic pursuit Individualistic attributions of inequality Structural attributions of inequality Ascribed attributions of inequality Institutional trust Assessment of social protection Social trust No social trust Social trust hard to say

2.40 1 10 1 1 1 1 1 0 0 0

5.48 5 5.50 5 5 5 4 5 1 1 1

4.457 (.439) 2.989 (.551) .544 (2.793) 3.830 (.598) 3.306 (.635) 3.128 (.723) 2.956 (.589) 3.419 (.713) .280 (.449) .391 (.488) .329 (.470)

Control variables Years of education Female Age Age2/100 Married CCP member Household size Self-reported health

0 0 18 3.24 0 0 1 1

22 1 70 49 1 1 16 5

11.061 (3.670) .464 (.499) 39.78 (14.156) 17.825 (11.987) .729 (.444) .136 (.342) 3.70 (1.334) 3.887 (.874)

Note: I do not present the descriptive statistics for provincial dummies.

.872 .728 .836 .812 .848 .780

3.980 (.490) 2.859 (.576) .128 (2.015) 3.897 (.693) 3.145 (.073) 3.106 (.825) 3.021 (.570) 3.431 (.733) .401 (.490) .351 (.477) .248 (.432) 5.850 (3.922) .510 (.500) 43.63 (14.700) 21.194 (12.680) .856 (.351) .030 (.172) 4.79 (1.702) 3.632 (1.011)

.864 .768 .822 .834 .830 .700

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The relative value assigned to materialistic pursuit is measured at two stages. First, an index of intrinsic, non-materialistic pursuits is computed by taking the mean of the evaluations of the importance of (1) family happiness and (2) making contributions to the happiness of others and the entire society in life. Then the variable for the relative value of materialistic pursuit is created by deducting the value for the index of intrinsic, non-materialistic pursuits from the value for perceived importance of personal wealth as a life goal. Responses to all three individual items are on an eleven-point scale (0 = very unimportant, 10 = very important). Perceived sources of inequality in society are measured by three indicators. Individualistic attributions of inequality are assessed by the mean of the five-point-scale responses to the importance of four factors in contributing to inequality: lack of talent and ability causing poverty, lack of personal effort causing poverty, talent and ability leading to wealth, and hard work leading to wealth (reverse-coded, 1 = no importance at all, 5 = complete importance). Structural attributions of inequality are estimated by the mean of the five-point-scale evaluations of the importance of six factors in generating inequality: prejudice and discrimination, lack of equal opportunities, and deficiencies in current economic institutions for poverty; and having connections (guanxi), more opportunities to start with, and unfair factors in the economic system for wealth (reverse-coded, 1 = no importance at all, 5 = complete importance). Ascribed attributions of inequality are measured by the mean of the five-point-scale responses to the importance of four family-related factors in causing inequality: being born and brought up in a poor family and low levels of parental education for poverty, and being born and brought up in a wealthy family and high levels of parental education for wealth (reverse-coded, 1 = no importance at all, 5 = complete importance). There are two indicators of evaluations of governance. Institutional trust is estimated by four questions that asked the respondents to what extent they trusted the central government, the provincial government, the municipal or county government, and the rural village committee or the urban neighborhood committee (reverse-coded, 1 = do not trust at all, 4 = trust very much). An index is constructed by computing the mean of those four evaluation scores. Assessment of social protection is measured by three questions that asked the respondents to evaluate the government’s performance in health care, primary and secondary education, and old age support on a five-point scale (reverse-coded, 1 = very bad, 5 = very good). An index is created by taking the mean of the three responses. Social trust is treated as a categorical variable. The respondents were asked whether they believed that most people could be trusted or not in dealing with people. Three response categories were provided: (1) Most people can be trusted; (2) I need to be wary of most people; and (3) Hard to say and it depends. ‘‘Most people can be trusted’’ is used as a reference category in the regression analyses. As indicated by Cronbach’s a in Table 1, the reliability coefficients for above independent variables that are measured by a composite index range from .700 to .872, showing good internal consistency. 5.4. Control variables A host of socioeconomic and demographic status measures are dealt with as control variables. They include years of education, gender, age, marital status, the Chinese Communist Party (CCP) membership, household size, self-reported health (reverse-coded, 1 = very poor, 5 = excellent), and provincial dummies. Age-squared divided by 100 is also included to control for the curvilinear effect of age. The weighted descriptive statistics of the independent and control variables are listed in Table 1. Bivariate correlations between the dependent and independent variables are provided in Tables A1 and A2. 5.5. Analytic strategy Linear regressions using the full information maximum likelihood (FIML) method are run to predict subjective well-being. An advantage of the FIML estimation is that it uses all available information in the data when there is missing information and computes more unbiased estimates than does listwise deletion or imputation (Enders, 2001). Regressions of the index of subjective well-being are run for urban and rural samples respectively. In each set of regressions for urban and rural samples, income and control variables are analyzed first. Then respective evaluative variables are added in five intermediate models. Finally, a full model including all the independent and control variables is examined. Regressions are run with the Amos program, which employs the FIML method in its estimation.3 6. Findings and discussion Tables 2 and 3 report the results from the regressions for urban and rural samples respectively. As the province is not our major concern, we do not present regression estimates for provincial dummies in the interest of conserving space. We compare the findings across the two tables in the following discussion. Model 1 in the two tables examines the relationships between subjective well-being and income and the control variables. Household income is significantly and positively linked to subjective well-being among both urban and rural Chinese. Among the control variables, only self-rated health shows a significantly positive correlation in both models. Age indicates a 3

Alternative ordinal logistic regressions for subjective well-being were also run and yielded similar results.

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C. Han / Social Science Research 49 (2015) 179–190 Table 2 Regressions of subjective well-being: The urban sample (N = 1028). Model 1 Independent variables Log household income Social comparison Relative value of materialistic pursuit Individualistic attributions of inequality Structural attributions of inequality Ascribed attributions of inequality Institutional trust Assessment of social protection No social trust Social trust hard to say

.312***

Model 2 .252** .100*

Model 3

Model 4

Model 5

Model 6

Model 7

.317***

.297***

.329***

.316***

.123+ .060

.290*** .047 .033* .196*** .075+ .076+ .134** .095* .047 .016

.047*** .243*** .108* .077+ .201*** .120**

Control variables Years of education Female Age Age2/100 Married CCP member Household size Self-reported health

.008 .080 .018 .023+ .102 .017 .027 .176***

.007 .080 .016 .020 .099 .009 .027 .165***

.007 .079 .016 .021 .097 .029 .024 .171***

.007 .092+ .020 .025+ .105 .017 .023 .150***

.012 .076 .016 .020 .074 .031 .030 .134***

.008 .074 .017 .022 .101 .024 .027 .171***

.009 .084+ .015 .018 .081 .034 .023 .118***

Intercept R2

.703 .141

.686 .142

.650 .151

.651 .178

.336 .173

.724 .144

.155 .202

Note: The full information maximum likelihood method is used in the regressions. Entries are unstandardized regression coefficients. I do not present the estimates for provincial dummies. + p < .10. Two-tailed test. * p < .05. Two-tailed test. ** p < .01. Two-tailed test. *** p < .001. Two-tailed test.

Table 3 Regressions of subjective well-being: The rural sample (N = 1611).

Independent variables Log household income Social comparison Relative value of materialistic pursuit Individualistic attributions of inequality Structural attributions of inequality Ascribed attributions of inequality Institutional trust Assessment of social protection No social trust Social trust hard to say

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

.262***

.195*** .196***

.257***

.266***

.283***

.258***

.201*** .187***

.219*** .173*** .003 .003 .073* .052+ .113** .080** .168*** .147**

.008 .045 .014 .016 .216** .028 .020 .116***

.008 .044 .011 .012 .192* .033 .018 .089***

.008 .032 .096** .078* .130*** .094**

Control variables Years of education Female Age Age2/100 Married CCP member Household size Self-reported health

.009 .055 .015 .018 .223** .038 .021+ .120***

.008 .052 .012 .015 .216** .004 .021+ .104***

.009 .054 .015 .018 .226** .036 .021+ .120***

.010 .055 .013 .016 .208** .031 .019 .120***

.008 .053 .015 .018 .212** .009 .019 .104***

Intercept R2

.918* .148

.679 .165

.920* .148

.788* .155

.106 .166

1.046* .162

Note: The full information maximum likelihood method is used in the regressions. Entries are unstandardized regression coefficients. I do not present the estimates for provincial dummies. + p < .10. Two-tailed test. * p < .05. Two-tailed test. ** p < .01. Two-tailed test. *** p < .001. Two-tailed test.

.123 .195

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weakly curvilinear effect in the urban category with middle-aged urbanites being most unhappy. Married people report significantly greater happiness and those with a larger household feel weakly happier in the rural model. Gender, education, and CCP membership do not exhibit a significant association in either sample. Model 2 through Model 6 in Tables 2 and 3 present the results from the intermediate models, in which respective evaluative measures are added. Social comparison of one’s current standard of living with the average levels of relatives, former classmates, coworkers, and neighbors is positively associated with subjective well-being in both samples (Model 2 in Tables 2 and 3). As to the pattern for the life goal orientation, urbanites who place a greater emphasis on materialistic pursuit than non-materialistic concerns feel significantly unhappier (Model 3 in Table 2). The association with materialistic versus nonmaterialist pursuit is minor and insignificant among rural residents (Model 3 in Table 3). In terms of perceived sources of inequality, individualistic attributions of wealth and poverty are related to significantly greater happy affects, structural attributions demonstrate a significantly negative association, and the coefficient for ascribed attributions to family background is negative and weakly significant in the urban model (Model 4 in Table 2). In the rural model, structural explanations exhibit a significantly negative relationship with subjective well-being, ascribed attributions show a significantly positive correlation, and the positive link to individualistic attributions is statistically insignificant (Model 4 in Table 3). Regressions on the two measures of evaluations of governance (Model 5 in Tables 2 and 3) indicate a significantly positive association between institutional trust and subjective well-being in both urban and rural samples. The more trust in the government, the higher the level of subjective well-being. Assessments of the government’s performance in social protection also demonstrate a significantly positive association in both models, with those who think that the government has performed well in health care, education, and old age support feeling happier. As to the influence of social trust, the coefficients for distrust in most others and the feeling of uncertainty about the trustworthiness of others in dealing with people are negative in both samples (Model 6 in Tables 2 and 3). However, only the coefficient for distrust in most others is weakly significant in the urban category, while both relationships are significant in the rural model. Model 7 in Tables 2 and 3 is the full model and shows the results from the regressions that include all the independent and control variables. The patterns for the independent measures are similar to those in Model 1 through Model 6 in the two tables, although the magnitude of the coefficients for all independent variables declines and a few coefficients become weakly or not significant. More household income is still significantly associated with greater happiness in both urban and rural samples. This pattern of continuously significant despite diminishing income impact when other independent variables are added is similar to the finding in Knight and Gunatilaka’s (2010) examination of China. It illustrates the persistent salience of the financial circumstance in subjective well-being across context in current China. Self-evaluation in comparison with others in the immediate social milieu remains significantly and positively related to subjective well-being among rural residents, but becomes statistically insignificant in the urban model. The particularly salient role of comparison with relevant reference groups among rural residents probably reflects the difference in the degree of homogeneity and closeness of social contacts in urban and rural areas and rural residents’ inclination to weigh a narrower frame of comparison more heavily (Knight and Gunatilaka, 2010). Relative importance of materialistic pursuit continues to show a significantly negative association in the urban sample and remains statistically insignificant among rural Chinese, suggesting greater salience of materialism in the formation of feelings about life in the city. Similar urban–rural difference in the role of life goal orientation in livelihood satisfaction has also been found in prior research (Han, 2012b). Individualistic attributions of inequality remain significantly linked to happier affect, while both structural and ascribed attributions show weakly significant, negative associations among urban citizens. The weakly significant estimates for structural and ascribed attributions among urban people are still noteworthy as there is a moderate bivariate correlation between the two measures in the urban sample (r = .473, see Table A1), which has attenuated each other’s relationship with subjective well-being.4 In the rural model, structural attributions continue to show a significantly negative correlation, the positive link to ascribed attributions becomes weakly significant, and the coefficient for individualistic attributions remains statistically insignificant. The salient negative impact of structural explanations for inequality in both urban and rural groups points to the centrality of perceived injustice of the existing distributional order in subjective feelings about life across context. A robust association with individualistic explanations only among urban residents may mirror particular valuing of individualism and meritocracy in the more industrialized urban setting. The negative correlation between ascribed attributions and happiness in the urban sample indicates urban citizens’ discontent with the blockage of the channels for intergenerational mobility, while the positive impact of ascribed attributions in rural China may signal the continuous embrace of familial inheritance of status there. Institutional trust and the assessment of government performance in social protection remain significantly positive in the full urban and rural models, suggesting important bearings of government accountability and welfare provisions on both urban and rural people’s life. Both measures of social trust are insignificant in the full model for urbanites, while social 4 As a test, I have run two alternative full models excluding structural attributions and ascribed attributions of inequality respectively for the urban sample (results not shown here). The coefficient for either type of attributions becomes larger and statistically significant (p < .01) when the other measure is absent from the full model, indicating that structural attributions and ascribed attributions of inequality attenuate each other’s association with subjective well-being among urban people.

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distrust and the feeling of uncertainty about the trustworthiness of most others continue to exhibit a significantly negative association in the full rural model, probably an indication of greater importance of community well-being and solidarity in the countryside than in the city.

7. Conclusion This study makes a comprehensive exploration of how subjective well-being is related to income, personal concerns (social comparisons and life goal orientations), and societal evaluations (perceived sources of inequality, evaluation of governance, and social trust), factors that are either discretely or scarcely investigated in prior research, based on the analyses of data from a nationally representative sample survey in China. Particular attention is devoted to identification of robust correlates among urban and rural Chinese respectively so as to enhance our understanding of the contextualization of the correlates of subjective well-being within a society. Higher income is linked to significantly greater happiness among both urban and rural Chinese, even after other objective conditions and personal and societal concerns are held constant. This finding illustrates the prevalent salience of the material circumstances in Chinese people’s feeling about life although money is not the only determinant. Among subjective correlates, structural attributions of inequality consistently show a negative correlation in both urban and rural samples. This pattern echoes the findings about significant bearings of structural explanations for inequality on popular sentiments and policy attitudes in previous research (Han, 2012a; Han and Whyte, 2009; Whyte, 2010a) and lends credence to the centrality of the sense of injustice about the current system in the feelings about life across context. The robust coefficients for the two measures of evaluations of governance in both rural and urban models exhibit the importance of government accountability and effective social protection in people’s life throughout China, which is compatible with the pervasive role of the Chinese state in the country’s economic and social development. Different sets of other evaluative measures in relation to subjective well-being between urban and rural Chinese stand out, which to a large extent reflects the economic, social, and cultural discrepancies between urban and rural China. Perceived importance of materialistic pursuit versus non-material concerns in life and individualistic attributions of inequality demonstrate a significant association only in the urban sample, which plausibly suggests the particular dominance of materialistic aspirations and cherishing of individualism and meritocracy in the more industrialized city. Comparison with people in the immediate social milieu and social trust show a robust effect only among rural Chinese, which possibly reflects the relative homogeneity and closeness of social contacts as well as the importance of community solidarity in the rural setting. The opposite effects of ascribed explanations for inequality in the two samples are striking and suggest contrasting attitudes toward ascribed status among urban and rural Chinese. The dampening effect of ascribed attributions on happiness among urban people is consistent with their valuing of meritocracy and suggests their denial of ascribed status as a fair outcome, while the positive effect of this variable in the rural model may reflect persistent embracement of familiar inheritance of status among rural Chinese. It should be noted that there are some limitations in this study. Cross-sectional data reveal correlations but not the causal direction. For example, it is possible that people with higher levels of subjective well-being are likely to be less critical of the existing distributional order and more accepting of the status quo of social protection. In addition, subjective well-being and many key independent variables are self-reported measures. It is possible that the associations discovered in this study to some extent reflect the influence of some confounding factors, such as personal disposition or temperament, which are not controlled for. Findings in the previous research increase our confidence in the causality of some relationships we study. For example, justice beliefs have been found to be antecedent to personal well-being (Jost and Hunyady, 2005). That said, longitudinal research design and data measuring potential confounding factors are needed to systematically address causality in future. Despite those caveats, this study makes important contributions to the subjective well-being theories. It integrates the examination of the relationships between subjective well-being and income, personal concerns, and societal evaluations in a single study and reveals to what extent those factors are independently associated with happiness. This study also enhances our knowledge of the contextualization of the correlates of subjective well-being. Prior research along this line tends to focus on cross-national comparisons. This study demonstrates that entrenched division of social groups in a country, such as urban and rural Chinese, may also contextualize the correlates of subjective well-being across systematically segmented groups within the country. In so doing, it also extends the theories on the consequences of social inequality by illustrating how the contour of objective inequality may shape the formation of subjective feelings about life. This study also has several policy implications. For example, continuous delivery of economic growth, adherence to fair distributional rules, and enhancement of governance are crucial for cultivating Chinese people’s satisfaction with life, an importance source of regime legitimacy and social stability in China. Measures are also needed to minimize the negative impact of deteriorating social fabric, such as diminishing social trust, which may accompany the urbanization of rural China. At the general level, any government that cares about its people’s subjective well-being is recommended to go beyond material indicators, grasp the complexity of the influences of multiple objective and subjective factors, and note varying hosts of pertinent factors among different groups.

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Acknowledgments The author is indebted to the second wave of the China Inequality and Distributive Justice Project conducted in 2009. Martin King Whyte at Harvard University was the Principal Investigator for the project. The other investigators included Jieming Chen at Texas A&M University-Kingsville, Juan Chen at Hong Kong Polytechnic University, Chunping Han then a Ph.D. candidate at Harvard University, Pierre Landry then at Yale University, Albert Park at Oxford University, Mingming Shen at Peking University, Feng Wang at the University of California-Irvine, Jie Yan at Peking University, and Ming Yang at Peking University. Funding for the survey came from the Smith Richardson Foundation, Harvard China Fund, Yale University, Harvard Weatherhead Center for International Affairs, and Harvard Asia Center.

Appendix A See Tables A1 and A2.

Table A1 Bivariate correlations between dependent and independent variables: The urban sample (N = 1028). 1 1. 2. 3. 4.

Subjective well-being Log household income Social comparison Relative value of materialistic pursuit 5. Individualistic attributions of inequality 6. Structural attributions of inequality 7. Ascribed attributions of inequality 8. Institutional trust 9. Assessment of social protection 10. No social trust 11. Social trust hard to say + * ** ***

2

3

4

5

.194*** .166*** .127***

.317*** .089*

.048

.211***

.056

.122***

.057+

.081* .062+ .149*** .139*** .042 .053+

.063+ .019 .014 .009 .022 .092*

.052+ .035 .116*** .144*** .041 .077*

.028 .095** .121*** .139*** .142*** .009

6

.161*** .125*** .193*** .217*** .043 .122***

7

.473*** .164*** .033 .061+ .005

8

.102** .003 .068* .015

9

.318*** .092** .048

10

.009 .063*

.542***

p < .10. Two-tailed test. p < .05. Two-tailed test. p < .01. Two-tailed test. p < .001. Two-tailed test.

Table A2 Bivariate correlations between dependent and independent variables: The rural sample (N = 1611). 1 1. 2. 3. 4.

Subjective well-being Log household income Social comparison Relative value of materialistic pursuit 5. Individualistic attributions of inequality 6. Structural attributions of inequality 7. Ascribed attributions of inequality 8. Institutional trust 9. Assessment of social protection 10. No social trust 11. Social trust hard to say + * ** ***

p < .10. Two-tailed test. p < .05. Two-tailed test. p < .01. Two-tailed test. p < .001. Two-tailed test.

2

3

4

5

.200*** .221*** .044+

.287*** .067*

.073**

.042

.023

.010

.047+

.089*** .010 .093*** .104*** .081** .086***

.073* .064* .096*** .035 .006 .002

.053* .014 .011 .035 .034 .048+

.039 .031 .021 .027 .026 .001

6

.162*** .045+ .218*** .115*** .011 .003

7

.413*** .038 .037 .023 .001

8

.064* .125*** .047+ .070**

9

.327*** .036 .032

10

.006 .078**

.337***

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