Subjective Wellbeing of Chinese People: A MultifacetedView

Yanjie Bian • Lei Zhang • Jianke Yang • Xiaoxian Guo •

Ming Lei

Accepted: 9 April 2014� Springer Science+Business Media Dordrecht 2014

Abstract This paper reports the most recent subjective wellbeing (SWB) assessments by

the respondents of the China Survey of Social Change. Of the total 10,927 respondents,

44.2 % are ‘‘always happy’’ and others vary from ‘‘sometimes happy’’ to ‘‘not happy at

all’’. To explain variation in SWB, the authors offer a multifaceted view taking into

account the roles that personal health, demographic attributes, socioeconomic statuses, and

the networks and relationships of social integration play in SWB. It is found that SWB

assessments are higher for women and older persons than for men and younger persons,

respectively, and they increase with improved physical and mental health, more educa-

tional and financial resources, greater social participation, wider social networks, and

greater trust in others and institutions. Economic development, ethnic cultures, and reli-

gious beliefs are important factors of SWB assessments.

Keywords Subjective wellbeing � Happiness � China

1 Introduction

Subjective wellbeing (SWB) is an issue of increasing attention in China today. After more

than three decades of economic growth, most Chinese individuals and families now live in

An earlier draft was presented at an IESSR workshop (June 15, 2013), and at an international conference‘‘Western China: Lessons Learned and Lessons Borrowed’’, University of Saskatchewan, Saskatoon, Canada(August 29–30, 2013).

Y. Bian � L. Zhang � J. Yang � X. Guo � M. LeiIESSR, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China

Y. Bian (&) � L. ZhangDepartment of Sociology, University of Minnesota, 267 19th Ave South, Minneapolis,MN 55455, USAe-mail: yjbian@mail.xjtu.edu.cn

123

Soc Indic ResDOI 10.1007/s11205-014-0626-6

a much improved lifestyle (Davis and Wang 2008). But are Chinese people happy? Are

they satisfied with quality of life? Recent studies show mixed results. While the level of

happiness has increased overtime (Liu et al. 2012) and is affected more by individual

factors than by collective values (Steele and Lynch 2013), more income does not always

improve subjective assessments of personal, family, and social lives (Wong et al. 2006;

Xing 2011). On the whole, a substantial proportion of Chinese people are still unhappy

about or unsatisfied with the current status-quo of life.

In this paper we report the newest findings on SWB assessments of individuals in

western China, a 12-province region that is less studied, less developed, but yet a spotlight

of China’s continuous economic growth in the decades to come. To explain variation in

SWB, we develop a multifaceted view to guide our analysis of a 2010 China Survey of

Social Change (CSSC) in which data on SWB assessments and relevant predictors were

collected. Our view is that SWB is a function of people’s physical and mental health, their

demographic attributes, their socioeconomic statuses, and, most importantly, the extent to

which they are socially integrated into society at large. We will begin with an elaboration

of this view before proposing research hypotheses and presenting the results of our data

analysis.

2 Theoretical Image and Research Hypotheses

2.1 Theoretical Image

The research of human wellbeing is concerned with the extent to which individuals live

fulfilling lives. While objective conditions of wellbeing are about the capabilities that

individuals and families have in order to maintain and improve quality of life (Sen 1973,

1999), subjective assessments of wellbeing, or SWB, are cognitive (e.g. life satisfaction)

and affective (e.g. happiness) evaluations of general and specific aspects of people’s lives

(Stiglitz et al. 2009; Blore et al. 2011). SWB has attracted interdisciplinary attentions

(Diener 2000) in psychology (Cummins and Nistico 2002), economics (Easterlin 1995,

2005; Bruni and Porta 2005), and social indicators research (Cummins et al. 2003; Ve-

enhoven, 2008), and it has been a focus of the internationally influential World Value

Surveys and the Global Attitude Survey (Diener et al. 2010). A similar effort has been

given to analyze SWB assessments in China, both domestically and internationally (Wong

et al. 2006; Xing 2011; Easterlin et al. 2012; Liu et al. 2012; Tsai et al. 2012; Steele and

Lynch 2013). These studies inform us of the values of SWB for measuring people’s quality

of life in both developed and developing countries. This paper aims at conducting an

analysis of the most recent SWB assessments by Chinese individuals residing in the less

studied western region.

In analyzing contemporary French society, Bourdieu (1986) envisions a social space in

which individuals are placed on the intersections of two dimensions, horizontal and ver-

tical, and these individuals are thought to vary from one another in social positions and

socioeconomic resources. Consequently, the individuals produce different behavioral and

attitudinal outcomes. Building upon Bourdieu’s notion of social space, as inspired by SWB

studies in multiple perspectives (Diener 2000; Landau and Litwin 2001; Qiu and Li 2012),

for China we develop an image of a four-layer social space in which a person formulates

and adjusts his/her SWB. The first layer is a narrowly defined human-personal space, in

which an individual’s SWB is maintained by his/her physical and mental health. The

second layer is a horizontally expanding demographic-personal space, in which an

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individual’s group-identity attributes alter his/her SWB. The next layer is a hierarchically

stratified socioeconomic-personal space, in which the socioeconomic statuses that a person

has obtained from the family and work increase his/her SWB. And the fourth layer, which

is horizontally and hierarchically contextualized, is a socially-integrated-personal space, in

which an individual’s connections to society at large enhance his/her SWB.

2.2 Hypotheses in Non-Relational Perspective

The human-personal space is a space confined to the individual as a presumably isolated

person. In this space, since one’s survival is the ultimate goal, an individual’s physical and

mental health determines his/her SWB. Biological factors, adequate diet, and body exer-

cises are highly relevant to one’s physical health, and psychological factors, such as short-

term and long-term moods, condition one’s mental health (George and Landerman 1984;

Diener et al. 1999). Studies in the West show that better physical and mental health

conditions maintain SWB at a certain level (Lucas and Fujita 2000).

In the demographic-personal space, the assumption about an isolated individual

becomes invalid. Instead, every individual is considered as a member of one or multiple

social groups, and one’s group-identity attributes affect his/her SWB. We term this space

‘‘demographic-personal,’’ not ‘‘social-personal’’, because social relationships among

individuals are not yet considered as relevant: it is the group-identity attributes or

demographic categories, rather than social relationships and social networks, which

characterize individuals and distinguish them from one another. In existing studies, such

demographic variables as gender, age, religious belief, and race/ethnicity are found to be

influential factors of people’s SWB. In an exemplary study, Wilson (1967) concludes that

in the United States well-educated young people with good personal health and strong

religious beliefs enjoy a higher level of happiness. While other studies have shown that the

generalization of Wilson’s conclusion is open for questions, it is an opportunity to

empirically examine the extent to which Wilson’s variables are the factors of Chinese

people’s SWB.

While the demographic-personal space extends horizontally from the human-personal

space, our image about a socioeconomic-personal space is one that extends into a hierarchy

in which one’s SWB is affected by one’s socioeconomic statuses, such as education,

employment, and income. It is widely observed that the more educated, employed, and

better paid have higher SWB scores in China (Wong et al. 2006; Xing 2011; Steele and

Lynch 2013) and around the world (Diener 2000). It appears that at the individual level,

people’s SWB is sensitive to the level of income when it is sufficient to meet basic

surviving needs, and beyond that level SWB is no longer correlated with socioeconomic

resources such as income (Diener and Biswas-Diener 2002; Clark et al. 2008). It will be

interesting to see if we can replicate these findings for western China, a developing region

in which there is a large variation in both the level of income and the satisfaction of basic

surviving needs among families and individuals across different provinces.

We summarize the above discussions into the following three research hypotheses:

Hypothesis 1 People’s physical and mental health positively contributes to their SWB

assessments.

Hypothesis 2 People’s demographic attributes, such as gender, age, religion, and eth-

nicity, significantly alter their SWB assessments.

Subjective Wellbeing of Chinese People

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Hypothesis 3 People’s socioeconomic status resources, such as education, employment,

and income, significantly increase their SWB assessments.

While these hypotheses have been tested in previous studies, we retest them in the

unique western region of China. First, the western region is highly stratified, within which

there is large inter-province variation in development and income. Therefore, besides

testing the hypotheses at the individual-level, we will conduct aggregate analyses of

development, income, and SWB (which we will discuss shortly). Second, the western

region is also ethnically and religiously diverse, with about a 30 % representation of ethnic

minority peoples and of religious believers. In China, the Han people take up more than

90 % of its population and are mostly nonbelievers. Thus, neither the national surveys of

China (Liu et al. 2012; Tsai et al. 2012) nor the China portion of the World Value Surveys

(Steele and Lynch 2013) have a sufficient number of cases to permit for an analysis of

ethnic or religious differences in SWB. Our study will be the first of doing so.

2.3 Hypotheses in Relational Perspective

Our fourth image about a socially-integrated-personal space will make this study another

unique contribution to the research of SWB. Under this image, horizontally and hierar-

chically contextualized individuals are connected to and socially integrated into society at

large through the networks of ongoing social relationships. We have learned a great deal of

the importance of social relationships and social networks from classic (Cooley 1902;

Simmel 1950) and contemporary (Granovetter 1973, 1985; Lin 1982, 2001; Coleman 1988;

Burt 1992; Portes 1998) scholars, although we have not seen studies of SWB, happiness, or

satisfaction be specifically guided by a relational approach. Here, our emphasis is not so

much with the information and opportunities people access through the networks of

ongoing social relationships for goal attainments. Rather, our interest is in the very fun-

damental function of such networks and relationships of integrating individuals into society

at large. Socially integrated individuals are expected to be happier than isolated ones

simply because human beings are social creatures. As social creatures, people feel fulfilling

their lives when they are recognized, accepted, valued, and honored by the significant

others surrounding them. China has a strong relational culture, in which social connections

are fundamental ways of social organization and social life in both traditional and con-

temporary eras (Bian 1997, 2010; Bian and Ikada 2014; Gold et al. 2002). We expect that

variables of social integration are important factors of people’s SWB in the following

ways:

Hypothesis 4 People who are more socially integrated have greater SWB assessments

than those who are less socially integrated. More specifically,

Hypothesis 4.1 Married persons are more socially integrated through marital relations

and therefore have higher SWB assessments than do the unmarried, divorced, separated, or

widowed persons.

Hypothesis 4.2 Actively participating in public activities makes people more socially

integrated through community-based networks and therefore increases their SWB assess-

ments than those who have less or no community engagement.

Hypothesis 4.3 People who are connected to a larger number of others through wider

social networks are expected to have higher SWB assessments than those who are con-

nected to a smaller number of others through the narrowly confined kinship networks.

Y. Bian et al.

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Hypothesis 4.4 People who have greater trust in generalized others and/or social insti-

tutions are expected to have higher SWB assessments than those who lack generalized trust

or institutional trust.

2.4 Hypothesis About SWB Variation by Economic Development

Our fifth and last hypothesis is concerned with how aggregate level of development matters

for people’s SWB assessments of a given locality. While earlier studies show that residents

in richer countries are happier than those in poorer countries (Larson 1978; Diener and

Diener 1995), most recent studies present contradictory findings and conclusions about

whether or not economic growth generate higher average happiness in developed countries

(Inglehart 1990; Stevenson and Wolfers 2008; Easterlin et al. 2005). The World Value

Survey and Global Attitude Survey, which have been conducted in a great number of

countries of varying levels of development, show that people in richest countries aren’t

necessarily happiest, and the top two happiest nations are developing countries, Porto Rico

and Mexico (Diener 2000). The research attention has been given to a hotly debated and

widely tested ‘‘Easterlin Paradox’’: increasing real incomes in developed countries will

lead to no noticeable increase in average happiness. Theoretical and empirical works have

contributed to a rich literature (Easterlin 1995, 2005; Diener and Diener 1995; Clark et al.

2008). With time series data, Easterlin himself has recently brought a supporting test to

China (Easterlin et al. 2012).

Our study of China’s western region is based on a cross-section survey, and we have no

time series data to bring a direct test of the Easterlin Paradox. But the western region has

been fast developing since 1999, and the region not only has a relatively low income but

also a large within-region income variation across provinces. Therefore, our data will allow

us to analyze the province-level relationship between development and SWB measures,

and at the same time to examine happiness ‘‘returns’’ to income across provinces. To guide

our analysis, we state our fifth hypothesis as follows:

Hypothesis 5 The higher the development of a province, the higher the average happi-

ness of its people, and the lower the individual income effect on individual happiness.

3 Data

Our data come from the 2010 CSSC, which was conducted, under the leadership of the first

author of this paper, in the western region of China. The region contains 12 province-level

administrative units (thereafter ‘‘provinces’’), spans on a vast geographic area of 6.81

million Km2 (71 % of China), and is home for 28 % of China’s population and 80 % of

Chinese ethnic minority peoples. Except for Inner Mongolia, all other provinces rank

below the national average of per capita GDP, and the region’s urbanization, at 43 %, is

significantly lower than the national average, at 61 % (National Statistical Bureau of China

2012). Yet, the western region has been under tremendous development since 1999, mostly

ignited by a central government-led program of ‘‘large-scale development of western

China’’. With relatively cheaper labor and richer natural resources, the region is the

spotlight for China’s economic growth now and in the decades to come.

The CSSC is a within-province probability sample. First, each province had 60 resi-

dential committee level units (PSU) selected through probability-proportional-to-size

sampling (PPS). Within each chosen PSU, a mapping method was used to obtain a

Subjective Wellbeing of Chinese People

123

sampling frame of all households in which 20 households were sampled using simple

random sampling. Finally, in each selected household, one adult aged 18 or above was

randomly selected by using the ‘‘Kish Grid’’ to serve as our respondent. The final valid

sample size was 10,935 respondents.

4 Measures

4.1 Subjective Wellbeing (SWB)

Our SWB is a single-item measure from the question ‘‘did you feel happy last week?’’ As

shown in Table 1, the respondents were provided with four response categories, and their

wordings and percentage distributions are: ‘‘always happy’’ (44.15 %), ‘‘sometimes

happy’’ (35.55 %), ‘‘seldom happy’’ (12.59 %), and ‘‘not happy at all’’ (7.71 %). The

average happiness is 3.16. The question and the responses categories were adopted from

previous surveys of China for the purpose of comparison. With a focus on the ‘‘always

happy’’ category, the 44.15 % from the CSSC is more than three percent-points lower than

the national average of 47.90 % obtained from a CCTV survey of 2012-2013.1 Since SWB

is an orderable discrete variable, we will use ordered logistic regression models to explain

SWB variation from the following four sets of independent variables.

4.2 Physical and Mental Health

Physical health is measured by a self-evaluated health status and the regular engagement in

physical exercises in the past 6 months, both being dichotomous variables. Of the total

respondents, 63.14 % assessed that they were ‘‘in good health’’, and 41.09 % did physical

exercises regularly. Mental health is measured by short-term mood and long-term feeling

of being a loser. Short-term mood is a dichotomous variable generated from the question

‘‘in the past week did you have a bad mood?’’ (always, sometimes, seldom, not at all). We

classify the first two outcomes as ‘‘in bad mood’’ (47.33 %) and the last two ‘‘in good

mood’’ (52.67 %). The other variable is generated by the question ‘‘do you think you are a

loser in your personal life?’’ (not at all, seldom, sometimes, always). We classify the first

two outcomes as having no feeling of being a lower (81.52 %) and last two as having a

feeling of being a loser (18.48 %).

4.3 Demographic Attributes

These include age, gender, ethnicity, and religious belief. All of these variables are found

to affect SWB in the studies of western countries (Horley and Lavery 1995). Age ranges

from 18 to 96, with a mean of 45.10 and a standard deviation of 14.79. The gender

composition is fairly even, with 50.66 % males and 49.36 % females. Ethnicity is a simple

dichotomy, contrasting the major ethnic Han (72.57 %) with ethnic minorities (27.43 %).

Religion is also a simple dichotomy of whether the respondent has a religious belief of any

kind (29.04 %) or not (70.96 %). In the Chinese context, folk religions are considered as a

religious category, along with officially recognized religions.

1 http://finance.people.com.cn/n/2013/0307/c153179-20705370.html.

Y. Bian et al.

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4.4 Socioeconomic Status

One measure is years of schooling, with a mean of 7.75 (a level of unfinished middle

school) and a large standard deviation of 4.66; the range is huge, from 0 (no formal

schooling) to 19 years (graduate degree). Another measure is annual household income of

2009, with a mean of 28,999 RMB and an expressively large standard deviation of 60,833

RMB. If one divides the standard deviation by the mean, the resulting coefficient of

variation is 2.098, which signals a huge income inequality in China’s western region.

The debate about the Easterlin Paradox points to the relative income (as compared to

others or to one’s own past), rather than current income, that affects one’s SWB (Clark

Table 1 Descriptive statistics for variables

Variables N Mean/% SD Min Max

Subjective wellbeing (SWB) 10,927

4 = Always happy 44.15 %

3 = Sometimes happy 35.55 %

2 = Seldom happy 12.59 %

1 = Not at all happy 7.71 %

Average 3.16 .92 1 4

Physical health (Good = 1) 10,936 63.14 % 0 1

Regular exercises (Yes = 1) 10,905 41.09 % 0 1

In good mood last week (Yes = 1) 10,928 52.67 % 0 1

Feel like a loser (No = 1) 10,915 81.52 % 0 1

Age 10,930 45.10 14.79 18 99

Sex (Male = 1) 10,942 50.66 % 0 1

Ethnic minority (Yes = 1) 10,932 27.43 % 0 1

Believer of a religion (Yes = 1) 10,932 29.04 % 0 1

Years of schooling 10,935 7.75 4.66 0 19

Annual household income (RMB) 10,779 28,999.44 60,833.08 0 3,000,000

Family’s decade status change 10,916

Decreased 8.06 %

Unchanged 57.84 %

Increased 34.10 %

Employment status 10,918

Employed in non-agricultural job 32.97 %

Employed in agricultural job 35.35 %

Unemployed 8.93 %

Retired 9.98 %

Homemaker 12.77 %

Marital status (currently married = 1) 10,936 82.28 % 0 1

Social participation 10,928 3.42 .97 1 5

Number of New Year visitors 10,511 34.02 37.96 1 300

Kin proportion of New Year visitors 10,507 .63 .24 0 1

Trust in general others (yes = 1) 10,933 47.72 % 0 1

Trust in institutions (yes = 1) 10,931 64.19 % 0 1

Subjective Wellbeing of Chinese People

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et al. 2008). For our study of the low-income western region of China, we believe that both

household income and household relative socioeconomic status are determinants of SWB.

Thus, our third measure is about the family’s socioeconomic status change over the past

decade (2000–2010). In this decade, China’s western region experienced tremendous

socioeconomic change, and living standards improved significantly. Accordingly, the

survey respondent was asked to assess whether his/her family’s socioeconomic status over

the past decade has decreased (7.9 %), unchanged (57.4 %), or increased (34.6 %). We

intend to use this variable to examine the extent to which a perceived family status change

will contribute to one’s SWB assessment. The resulting frequencies make it explicit that in

the least developed western region, the great majority of Chinese people did not feel that

their relative socioeconomic status had changed from 2000 to 2010. It is also clear,

however, that those who felt that they their relative socioeconomic status had improved are

four times more than those who felt that their living conditions were relatively getting

worse.

Our last measure of this section is employment status. In our initial analysis (available

upon request), we have tried to include a few measures about working conditions of the

respondents. Besides employment status, we also included job type (managerial, profes-

sional, clerical, and manual), work sector (public vs. private), and migration (migrant vs.

permanent residents). But none of these are significant predictors of SWB. We therefore

exclude these variables from our analysis and focus only on employment status. This

variable has five categories: employed in agricultural job (35.35 %), employed in non-

agricultural job (32.97 %), unemployed (8.93 %), retired (9.98 %), and homemaker

(including voluntarily unemployed, 12.77 %).

4.5 Social Integration

We develop four variables to measure social integration, and each is a distinctive way

through which an individual is socially integrated into Chinese society. The first is marital

status. Marital relationship is a fundamental way in which a person is accepted by the

intimate other (Lee et al. 1991; Gottman 1996). This is a dichotomy, with currently married

(82.28 %) being considered higher in social integration than unmarried, separated,

divorced, or widowed (17.72 %). The second is social participation in activities organized

by the communities to which one belongs. This is measured by a 5-point scale of self-

evaluated participation in local public affairs (5 = very active, 4 = active, 3 = so–so,

2 = not active, 1 = no participation). This scale results in a mean of 3.42, a fair but not

high level of social participation. The third is social networks. Here our interest is in the

extent to which an individual is socially integrated into Chinese society through close-knit

kinship networks (‘‘narrowly integrated’’) or larger networks full of nonkin contacts

(‘‘widely integrated’’) (Helliwell 2006). For this interest, we have two indicators: the

number of Chinese New Year visitors and the proportion of one’s relatives out of total

visitors [see Bian (2001) for measurement details]. Our respondents have an average 34.02

New Year visitors, with a standard deviation of 37.96. Among these visitors, 63 % are

relatives, with a fairly large standard deviation of 24 %. The fourth and final set of

variables are generalized trust (trust in others) and institutional trust (trust in such insti-

tutions as courts, hospitals, local governments, etc.). It is reasonable to assume that the

higher degrees of generalized trust and institutional trust imply a higher degree of social

integration (Pollner 1989). Of the total sample, 47.72 % of respondents trust in generalized

others and 64.19 % trust in social institutions.

Y. Bian et al.

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5 Analytic Strategies and Statistical Results

5.1 Analytic Strategies

We will present three sets of statistical results obtained to test our hypotheses. The first set

of tests is obtained under the assumption that western China is a unified world. Under this

assumption, we conduct ordered logistic regression analyses in which respondents from all

the 12 provinces are treated equally, with provincial dummy variables included as statis-

tical controls to minimize aggregate effects of developmental, ethnic, and religious vari-

ations among the provinces. This means that these models are used to test our first four

research hypotheses strictly at the individual levels. The second set of tests is obtained

under the assumption that the western region is not a unified world, but one that has been

disproportionately developing among the 12 provinces. Here, we want to examine the

extent to which macro-level variations of economic development may impact upon both

the general levels of SWB and the effects of micro-level variables on people’s SWB,

testing Hypothesis 5. The third and final set of tests is obtained under the assumption that

the western region is an ethnically and religiously diversified, not integrated, world, in

which ethnic and religious group variations alter the directions or magnitudes of the effects

of individual-level predictors on people’s SWB. This set of tests will allow us to more

clearly establish an understanding of how people’s SWB assessments are affected by their

ethnic and religious backgrounds.

5.2 Testing Hypotheses at the Individual Levels

Table 2 presents ordered logistic regression estimates of predictors of SWB. Model 1,

under the human-personal space assumption, shows that people who are in good physical

health (coefficient = .442), do regular exercises (.350), are in good mood (.558), and have

no feeling as a loser (.965) tend to have higher SWB assessments than their counterparts.

These findings hold true even after other variables are included (in Model 2), thus lending

strong support for Hypothesis 1.

Model 2 is estimated under the assumptions of demographically diverse, socioeco-

nomically stratified, and socially integrated personal space (to save space, we present this

‘‘final’’ model and skip those models separately estimated under each of the three

assumptions). The model shows that age (-.005) and gender (-.107) significantly affect

people’s SWB assessments, but ethnicity and religion do not. More specifically, older

people and females tend be happier than younger people and males, respectively, and these

effects hold true even after other variables are entered into the model, providing partial

support for Hypothesis 2. The finding that ethnicity and religion do not produce effects at

the individual levels is to be revisited later.

As can be seen from Model 2, education has a significant positive effect (.017) on SWB,

so does household annual income (.040). The decade change in family’s relative socio-

economic status presents interesting results: as compared to a decreased family status, an

increased family status significantly elevates SWB (.247), and an unchanged status has a

positive, though statistically insignificant, effect on SWB. As compared to agricultural

employment, nonagricultural employment significantly increases one’s SWB (.124), so do

retirement (.172) and homemaker (.195), but unemployment, which mostly in the urban

areas, does not deviate one’s SWB from that of agricultural employment (.094, statistically

insignificant). Note that the higher levels of happiness for retirees and homemakers are

likely to imply the positive effect of their, respectively, pre-retirement or spousal

Subjective Wellbeing of Chinese People

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nonagricultural jobs on SWB. Clearly, holding other variables constant, employment status

makes a significant difference in people’s SWB, with unemployed and agricultural job

holders equally the lowest in SWB. Current and past paid work in the urban areas increases

happiness. These results hold true even after other variables are entered into the model,

lending strong support for Hypothesis 3.

Our final set of predictors in Model 2 includes five variables about social integration.

First, marital relations make the currently married happier than those who are unmarried,

separated, divorced, or widowed (.388), supporting Hypothesis 4.1. Second, a larger net-

work size has a positive impact on SWB (.001), supporting Hypothesis 4.2.2 Third,

2 An alternative measure is the proportion of kinship ties in one’s social networks. But this is highlycorrelated with network size and therefore cannot be included in the model simultaneously. A separateanalysis using this variable (available upon request) shows that a higher proportion of kinship ties in one’ssocial networks has a negative impact on SWB (-.172, not shown), which implies that people who arewidely integrated into society through non-kin contacts are happier than their counterparts who are narrowlyconfined to their kinship networks. This point becomes more explicit in the three coefficients of socialparticipation, generalized trust, and institutional trust.

Table 2 Coefficients of ordered logistic regression model of SWB

(1) (2)

Physical health (good = 1) .442*** .442***

Regular exercises (yes = 1) .350*** .267***

In good mood last week (yes = 1) .558*** .523***

Feel like a loser (no = 1) .965*** .869***

Age in years .005**

Sex (male = 1) -.107**

Ethnic minority (yes = 1) -.045

Believer of a religion (yes = 1) .098

Years of schooling .017**

Ln (annual household income) .040**

Unchanged family status .114

Increased family status .247**

Employment status (agricultural job = Omitted)

Employed in nonagricultural job .124*

Unemployed .094

Retired .296***

Homemaker .319***

Marital status (currently married = 1) .388***

Number of New Year visitors .001*

Social participation .081***

Trust in general others (yes = 1) .173***

Trust in institutions (yes = 1) .271***

BIC 24,469.691 22,646.178

N 10,869 10,175

For simplicity, the coefficients of provincial dummies, standard errors, and cut points of ordered logisticregressions are not presented

* p \ . 05, ** p \ .01, *** p \ .001

Y. Bian et al.

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participation in social activities organized at the community levels significantly improves

SWB (.081), which is in support of Hypothesis 4.3. Fourth, generalized trust in others,

including strangers, significantly increases one’s SWB (.173). And Fifth, institutional trust

also increases one’s SWB significantly (.271). The last two results lend consistently strong

support for Hypothesis 4.4.

5.3 SWB Variation by Economic Development

A standing controversy in the studies of SWB is the extent to which income effect on SWB

at the individual levels is conditional upon the level of economic development across

localities (Easterlin 1995, 2005). A particular concern is whether or not the income effect

decreases or changes its direction beyond a certain high level of economic development

(Stevenson and Wolfers 2008). Although the western region of China is far from an

advanced world, economic development for the past three decades has taken a differential

pace across the 12 provinces. In the Chinese context, a province is a relatively independent

budgetary unit for administrative, economic planning, and taxation purposes, and level of

income and living standards are consequently unequal among the provinces. Thus, we

obtained aggregate statistics and reassessed income effect on SWB in each province.

Results are presented in Table 3.

As shown in Table 3, the 12 provinces vary tremendously in economic development, as

measured by per capita GDP (second column). In reference to the national average (30,015

RMB), except for Inner Mongolia (47,347 RMB), per capital GDP in all other provinces is

significantly lower, with Guizhou at the lowest (13,119 RMB). The range of 34,228 RMB

for per capita GDP indicates a large within-western-region inequality in economic

development. Are people generally happier in more developed provinces? The aggregate

correlations endorse a positive answer. We obtained the percentage of ‘‘always happy’’

(third column) and the average SWB score (fourth column) for each province, and these

two measures are highly correlated with per capital GDP, .730 and .755, respectively (both

correlation coefficients are significant at the .01 level). Although 12 units of analysis form

a very small sample, the high magnitudes of the aggregate correlations do raise our

confidence that SWB assessments are not uncorrelated with economic development. To the

contrary, the tentative conclusion is that provincial development matters for quality of life

as subjectively evaluated by the people living in the provinces. Hypothesis 5 is partially

supported.

Does provincial development make a difference in SWB returns to income? In other

words, does household annual income make a differential impact on SWB assessments

across provinces of differential development? The answer is not a straightforward one. We

conducted ordered logistic regressions within each province and present income coeffi-

cients in the fifth column of Table 3. We make three notes here. In nine of the 12 prov-

inces, income coefficient is positive and statistically significant. This means that in those

provinces the higher the income, the happier people are. Second, in the remaining three

provinces, Inner Mongolia, Xinjiang, and Tibet, income coefficient is statistically insig-

nificant, implying that household annual income does not necessarily increase or decrease

people’s happiness. Third, the directions and magnitudes of income coefficients do not

linearly correlate with provincial per capita GDP. Based on a county-level analysis using

hierarchical linear models (not shown), we found no effects of county-level per capita GDP

(macro-level) on income coefficients (micro-level), which are consistent with our cross-

province analyses presented here in Table 3. How macro-level development alters micro-

level processes of SWB assessments in the Chinese context remains a research question.

Subjective Wellbeing of Chinese People

123

5.4 SWB Variation by Ethnicity and Religion

Inner Mongolia, Xinjiang, and Tibet are the three western provinces in which ethnic

minority peoples form strong within-province majorities of residents. Other provinces

where ethnic minority peoples have high representations include Guangxi, Qinghai,

Yunnan, and Gansu. In these provinces, there are expectedly strong ethnic-cultural heri-

tages and influences, which, by and large, are reflected in the distinctive religious beliefs

and ethnic values of their peoples. For example, Tibetan Buddhists, like other strong

Christian believers in western countries (Pollner 1989), have a distinctive notion of hap-

piness, which is highly correlated with spiritual comfort and relief in the name of Buddha

rather than with external capacities to pursue a prosperous material life (Bian 2013). In

fact, the officially recognized 55 ethnic minority peoples vary significantly in terms of

historical pedigree, cultural heritages, lifestyles, and religions. If, logically, ethnic values

and religious beliefs are determinative, then, we should separate these ethnic peoples from

other western Chinese populations when analyzing their SWB assessments. This means

that we must give way our earlier assumption of a unified western China to the assumption

of an ethnically and religiously diversified Chinese West. Under this new assumption, the

simplified, yet incorrect, majority-minority analytic strategy, which guided us to use the

Han-nonHan dichotomy and the believer-nonbeliever dichotomy in Table 2, should be

replaced by a strategy that recognizes greater ethnic and religious diversity. While we do

not intend, nor are able, to uncover all ethnic and religious differences, we do want to make

an effort to reconstruct our ethnicity and religion variables for the purpose of re-estimating

Table 3 Level of SWB and Income Effects on SWB by Province

Province Per capita GDP(RMB)a

% alwayshappy

AverageSWB

Coefficientsof Ln (income)

N BIC

In. Mongolia 47,347 59.92 3.45 .028 989 1,958.872

Chongqing 27,596 49.83 3.29 .297*** 842 1,853.502

Shaanxi 27,134 45.83 3.18 .147*** 1,151 2,719.804

Ningxia 26,860 44.86 3.21 .238*** 953 2,185.889

Xinjiang 25,034 57.84 3.41 -.037 609 1,265.709

Qinghai 24,115 31.51 2.97 .104** 1,014 2,539.080

Sichuan 21,182 45.38 3.25 .144*** 911 2,057.924

Guangxi 20,219 42.44 3.13 .184*** 991 2,396.357

Tibet 17,319 41.84 3.10 -.081 619 1,541.892

Yunnan 15,752 39.93 3.10 .282*** 905 2,187.468

Gansu 16,113 38.59 3.00 .197*** 879 2,246.635

Guizhou 13,119 35.60 2.90 .424*** 897 2,323.900

Total Sample 30,015b .730c .755d .133*** 10,760 25,464.841

For simplicity, other independent variables (as shown in Table 2), standard errors, and cut points of orderedlogistic regressions are not presenteda Data source China Data Online ‘‘http://chinadataonline.org/’’b National average of per capita GDPc Correlation coefficient between per capital GDP and % always felt happyd Correlation coefficient between per capita GDP and average SWB

Y. Bian et al.

123

ethnic and religious effects on people’s SWB assessments. This effort has resulted in the

statistical analyses of Tables 4 and 5.

Table 4 presents percentage distributions of religion by ethnicity, which takes into

account the areas in which people of different religions and ethnicities are concentrated.

The Han ethnicity takes 72.6 % of the total sample. A great majority (85.2 %) of the Han

Table 4 Percentage distribution of religion by ethnicity (row %)

Ethnicity Religious distribution

Islam Christian Taoism Buddhism Folkreligions

Non-believers

% ofsample

Han .1 2.2 .9 9.7 2.0 85.2 72.6 %

Mixed North 1.8 3.2 .0 35.6 .0 59.4 2.0 %

Islamic Northwest 93.7 .2 .0 2.4 .4 3.3 7.6 %

Buddhist Southwest .1 .1 1.2 71.1 1.0 26.5 12.2 %

Mixed Southwest .2 .3 1.0 10.4 8.7 79.5 5.6 %

% of Sample 7.2 % 1.7 % .8 % 17.2 % 2.1 % 71.0 % 100 %

Main ethnic groups in the four areas

Mixed North: Mongolian, Manchu, Tu, Daur

Islamic Northwest: Uygur, Kazakh, Kirgiz, Dongxiang, Hui

Buddhist Southwest: Tibetan, Yi/Lolo, Tujia, Qiang, Baipho, Haqniq, Ladhulsi, Nakhi

Mixed southwest: Zhuang, Bouyei, Hmong, Yao/Dao, Dong/Gaeml, Dai, Sui, etc

Table 5 Level of SWB, ethnic and religious effects on SWB

Variables and groups % always happy Average SWB Ethnic/religious coefficients

By ethnicity

Mixed North 50.23 3.247 -.099

Islamic Northwest 52.48 3.267 .191*

Buddhist Southwest 37.14 3.018 -.047

Mixed Southwest 39.31 3.072 -.130

Han 44.73 3.180

BIC 22,674.483

N 10,923 10,923 10,179

By religion

Islam 52.84 3.282 .240**

Christian 44.92 3.198 -.040

Taoism 47.73 3.216 .463*

Buddhism 39.46 3.081 .023

Folk religions 42.54 3.101 .084

No religious beliefs 44.37 3.168

BIC 22,690.453

N 10,927 10,927 10,186

For simplicity, the coefficients of employment status dummies, provincial dummies, standard errors, and cutpoints of ordered logistic regressions are not presented

* p \ .05, ** p \ .01, *** p \ .001

Subjective Wellbeing of Chinese People

123

people is nonbelievers, close to one tenth (9.7 %) are Buddhists, and the rest 5 % are

believers in other religions. The mixed North area is only 2 % of the total sample, and it

has a simple majority for nonbelievers (59.4 %), more than one third for Buddhists

(35.6 %), and a good proportion (5 %) for Muslins and Christians. The Islamic Northwest

has a 7.6 % share of the sample. The area is predominated by Muslins (93.7 %), with a

small presentation of nonbelievers (3.3 %), Buddhists (2.4 %), folk religion believers

(.4 %), and Christians (.2 %). The Buddhist Southwest has a 12.2 % of the sample. The

area is concentrated by Tibetan Buddhists (71.1 %), with more than one fourth nonbe-

lievers (26.6 %) and other believers combined (2.3 %). The mixed Southwest has a 5.6 %

share of the sample. While nonbelievers are the great majority (71 %), different religions

each have a fairly good representation in the area. Although ethnicity and religion cannot

substitute for one another, there appears to a high correlation between these two variables,

making it impossible to simultaneously estimate their effects on SWB in the same equa-

tions. Accordingly, we examine SWB variations by ethnicity and religion separately, and

the results are presented in Table 5.

In the top half of Table 5, compared to the Han, ethnic minorities living in the four

different areas show different levels of SWB assessments, measured by the percentage of

‘‘always happy’’ (second column) and the average SWB scores (third column). The Han

people are somewhere in the middle in both SWB measures. In the ordered logistic

regression analyses (fourth column), however, only the ethnic peoples in the Islamic

Northwest (notably Xinjiang) significantly differ in SWB assessments than the Han. The

significant and positive coefficient of .186 indicates that, when individual characteristics as

measured in Table 2 are held constant, those living in the Islamic Northwest have sig-

nificantly higher SWB assessments than do the Han people living throughout the western

region. We find in separate analysis (not shown) that Muslims in the Islamic Northwest

region have a significantly lower income variation among them than the Muslims and non-

Muslims in other areas of the western region. In a relative income interpretation (Stevenson

and Wolfers 2008), this narrower income gap would make the Northwestern Muslims

happier. In-depth observations also show that when ethnic people live in their own life-

styles, rather than under the strong influences of the Han people, they tend to be happier.

This tentative hypothesis needs a rigorous further testing because ethnic peoples living in

other administratively autonomous areas of the western region do not necessarily differ in

their SWB assessments from the Han people.

In the lower half of Table 5, we observe a stable pattern that Islamic and Taoist

believers have significantly higher SWB assessments than do the non-believers. On the

aggregate level, these two religious groups show higher percentages of ‘‘always happy’’

(second column) and average SWB scores (third column) than other groups. In the ordered

logistic regressions (fourth column), compared to nonbelievers, Islamic (a coefficient of

.236) and Taoist (.462) believers are significantly happier. These differences are obtained

after individual characteristics are being controlled for. However, believers in other reli-

gions do not differ from the nonbelievers in SWB assessment.

6 Conclusion and Discussion

The 2010 CSSC has given us a valuable opportunity to analyze the most recent SWB

assessments by Chinese individuals residing in the less studied but fast-growing western

region. The survey respondents (N = 10,927) were asked the question ‘‘did you feel happy

last week?’’ Slightly more than 44 % felt ‘‘always happy’’, nearly 36 % ‘‘sometimes

Y. Bian et al.

123

happy’’, about 13 % ‘‘seldom happy’’, and close to 8 % ‘‘not happy at all’’. While the

overall distribution is comparable to that of national surveys, the happiest category is 3 %

points lower than in the country.3 But still, the first two categories add to an impressive

80 % happy western Chinese, and the four categories together result in an average SWB

score of 3.13. These numbers are favorably compared to those of similar measures

obtained from many countries around the world (Tsai et al. 2012), as reported in the World

Values Surveys and the OECD Reports.4 We are, however, less concerned about how to

place western China in the map of the country or of the world. Instead, we are more

interested in the micro- and macro-level variables that help explain the observed variation

in SWB assessments among western Chinese people.

At the individual levels, our multifaceted view has guided us to look at different sets of

predictors of SWB assessments. The analysis leads to the following conclusions. First, a

physically active and mentally healthy person is happier than a person whose physical and

mental health is in less desirable conditions. Second, older persons and women tend to be

happier than younger persons and men, respectively, and believers of Islam and Taoism are

happier than Christians, Buddhists, folk religion believers, and nonbelievers. Third, people

who have higher or improving socioeconomic statues, in terms of education, income,

family status, and employment, are happier than their counterparts. And fourth, people who

are more socially integrated into Chinese society are happier than those who are less

socially integrated. At the aggregate levels, people’s SWB assessments tend to be higher in

richer or more developed provinces, but the level of provincial development does not

linearly impact upon the micro-level effects of income on happiness.

In his classic study of SWB, Wilson (1967) found that a happy American emerged as ‘‘a

young, healthy, well-educated, well-paid, extroverted, optimistic, worry-free, religious,

married person with high self-esteem, high job morale, and modest aspirations, of either

sex and of a wide range of intelligence’’ (cited from Diener 2000). In striking similarities

and dissimilarities, a happy western Chinese is older, healthier, physically more active, in

good mood, confident, more educated, employed currently or previously in a nonagri-

cultural job, better-paid, with improving family status, currently married, more engaged in

community activities, connected to others through larger and wider social networks,

trusting others and institutions, and more likely to be a woman than a man. While many

components of this characterization about a happy Chinese person in the western region are

consistent with those obtained from previous and recent Chinese studies (Liu et al. 2012;

Xing 2011; Steele and Lynch 2013), two particular findings deserve further attention.

First, in the Chinese context, ethnicity and religion are highly overlapping variables.

Theoretically, we use ethnicity to mean the unique cultural traditions and heritages that are

preserved and valued across generations of a given nationality or a living group, and use

religion to mean the set of sacred beliefs which bind people spiritually together under a

god. In reality, however, the Han people, who take up 90 % of Chinese population, are

dominated by nonbelievers. It is among some, but not all, of the 55 officially recognized

ethnic minority peoples that religion becomes a major social phenomenon. Statistically,

this prevents any consideration of simultaneously estimating the effects of ethnicity and

religion on people’s SWB assessments in the same equations. Because 80 % of China’s

ethnic minority peoples reside in the western region, our study of this region is unique in

itself. However, we did not replicate the finding from many studies of Western countries

that a religious person is happier than a nonbeliever. Rather, we found that a believer of

3 Source: http://finance.people.com.cn/n/2013/0307/c153179-20705370.html.4 Sources: http://www.worldvaluessurvey.org; tttp://www.oecd.org/statistics.

Subjective Wellbeing of Chinese People

123

Islam (a historically foreign religion to China) or Taoism (a native religion) is happier than

a believer of any other religion or a nonbeliever. To be sure, when Islamism and Buddhism

are practical in Xinjiang and Tibet, respectively, people’s SWB is no longer associated

with their income or improved socioeconomic statuses. What are the underlying logic and

mechanisms whereby ethnic cultures and religious beliefs affect Chinese people’s happi-

ness? This is a research task to carry out.

Second, one of the newer findings from this study, which are not obtained from other

studies of China, is that more socially integrated people are happier. We measure social

integration in four different dimensions: marital relations, engagement in community

activities, social networks, and trust in others and institutions. Each contributes to personal

happiness significantly, and all together substantially increase explained variance in SWB

(as can be seen in the largely reduced BIG coefficient in Table 2). While marital status has

been included in previous studies of China, all other variables improve our empirical

knowledge about SWB in the Chinese context. A happy western Chinese person is actively

engaged in community activities, has a wider social network going beyond his/her kinship

boundary, and have a good deal of trust in generalized others and social institutions. While

there is an observed tendency that China is moving toward individualism as a result of

economic transformation away from collective farming and state ownership of the means

of production (Steele and Lynch 2013), our study shows an excellent sign that happy

people are socially integrated into Chinese society.

Confined to the dataset, our study has a number of limitations. Our SWB measure is a

simple-item variable, our data is a cross-sectional sample, and our statistical models do not

do enough to explore the possible problem of endogeneity (e.g., bad health is a cause of

lower income, less social engagement, and poorer social networking) and the contextual

effects on the ways in which individual-level variables affect people’s SWB. Future

researchers should follow Diener’s (2000) suggestion for elaborating more accurate mea-

sures, justifying causal directions, modeling interactions between personal and environ-

mental factors, and conducting longitudinal studies. For example, without panel data the

causal effects of our four sets of predictors on SWB cannot be said to be free of spuriousness.

Within our dataset, our further effort should be given to estimate more rigorous models, such

as propensity matching, in order to solve any possible problems of endogeneity.

Acknowledgments The research of this paper was funded by a ‘‘985’’ grant through the Institute forEmpirical Social Science Research (IESSR) at Xi’an Jiaotong University, by a key project grant fromChina’s Social Science Foundation (11AZD022), and by a centrally-important project from China’s SocialScience Foundation (13&ZD177). The authors are grateful to the participants in the China Survey of SocialChange analyzed in this paper, and to Jieming Chen, Harley Dickinson, Yaming Hao, Peter Lee, Ming-Chang Tsai, and a unanimous reviewer for their helpful comments on the earlier drafts.

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