The Impact of Income Inequality on IndividualHappiness: Evidence from China
Peng Wang • Jay Pan • Zhehui Luo
Accepted: 7 May 2014� Springer Science+Business Media Dordrecht 2014
Abstract Using survey data from China, this paper tests the association between indi-
vidual self-reported happiness and income inequality. The hypothesized relationship
between income inequality and individual happiness is an inverted-U shaped association
based on the tunnel effect theory proposed by Hirschman and Rothschild (Q J Econ
87(4):544–566, 1973). Using the Chinese General Social Survey data, we empirically
investigate the relationship between income inequality and individual happiness and we
find evidence confirming the tunnel effect hypothesis. Specifically, individual happiness
increases with inequality when county-level inequality measured by the Gini coefficient is
less than 0.405, and decreases with inequality for larger values of the Gini coefficient,
where approximately 60 % of the counties in the study have a Gini coefficient larger than
0.405. The inverted U-shaped association exists in both urban and rural China; however,
the turning points for urban and rural areas are 0.323 and 0.459, respectively. Between the
rich and poor groups, the inverted-U shape relationship exists only in the poor subsample.
Keywords Income inequality � Happiness � Subjective wellbeing � China
Everything we do we do to ensure that the people live a happier life with more
dignity and to make our society fairer and more harmonious.
P. WangSchool of Economics, Southwest University for Nationalities, Chengdu, Chinae-mail: [email protected]
J. Pan (&)West China School of Public Health, Sichuan University, Chengdu, Chinae-mail: [email protected]
J. PanWest China Research Center for Rural Health Development, Sichuan University, Chengdu, China
Z. LuoDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
123
Soc Indic ResDOI 10.1007/s11205-014-0651-5
- Delivered by Premier of the State Council of China (Wen Jiabao) at the Third
Session of the Eleventh National People’s Congress on March 5, 2010.
1 Introduction
The recent rapid economic growth of the Chinese economy is accompanied by a marked
increase in income inequality. The Gini coefficient increased from 0.293 in 1978, to 0.372
in 2000, and to 0.474 in 2012 (Kanbur and Zhang 2005; National Bureau of Statistics of
China 2013). Based on available data from the World Bank (2013); out of 154 countries,
only 53 countries have higher Gini coefficients than China.
The rising income inequality of China may produce serious social and economic
problems. Income inequality might slow economic growth (Aghion et al. 1999; Benjamin
et al. 2006), cause frequent social conflicts (Alesina and Perotti 1996), lead to higher levels
of violent crime (Hsieh and Pugh 1993), reduce social trust (Jordahl 2007; Rothstein and
Uslaner 2005), and harm the health of the citizens (Asafu-Adjaye 2004; Li and Zhu 2006).
While inequality may affect society and its economic development in many ways, this
paper focuses on a more integral part of the socio-economic effects of inequality, the
impact of income inequality on individual happiness.
Happiness, or subjective wellbeing, is a person’s cognitive and affective evaluation of
his or her own life (Diener et al. 2009). Happiness is an important comprehensive index of
the quality of individual and social life. Job security, social status and power, pecuniary
rewards, and even good health are desirable not for the goods per se but for the satisfaction
and happiness afforded through these things (Frey and Stutzer 2002).
Although there is a large body of research on income ineqaulity and happiness, few
researchers have investigated the relationship in developing countries, especially China.
The limited existing research on this relationship has focused exclusively on the linear
effect of income inequality on happiness. In contrast, the tunnel effect hypothesis, pro-
posed by Hirschman and Rothschild (1973), states that happiness rises with income
inequality when inequality is low; however, happiness decreases once income inequality
continues to increase after reaching a certain threshold. In this study, we test the nonlinear
relationship between income ineqaulity and happiness in China and estimate the threshold
where the relationship between happiness and income inequality changes direction, if the
tunnel effect hypothesis is confirmed.
The structure of the paper is as follows: Sect. 2 provides a brief review of the literature
on the relationship between happiness and income inequality, Sect. 3 describes the data and
presents our methodology, and Sect. 4 summarizes the results of empirical analyses,
conclusions, and policy implications.
2 Literature Review
This paper studies the impact of income inequality on individual happiness by focusing on
the economic literature that utilizes large-scale survey data regarding happiness.
Empirical evidence on the sign and significance of the relationship between income
inequality and happiness is widely disputed amongst economic literature. Some studies
have found a negative correlation between income inequality and happiness. The first
contribution, Morawetz et al. (1977), compares the self-rated happiness of the citizens of
P. Wang et al.
123
two neighboring communities in Israel that have different levels of income inequality, with
the conclusion that higher levels of income inequality lead to lower average self-rated
happiness. Using aggregated data of eight nations over a 25 year period, Hagerty (2000)
shows that a decrease of the skewness, or the inequality, of the income distribution for a
country increases its average national happiness. Alesina et al. (2004) discover the negative
effects on happiness due to income inequality in Europe and the United States. Graham and
Felton (2006) use survey data from 18 countries in Latin America and ascertain that
income inequality, as an indicator of persistent unfairness, has a negative effect on hap-
piness. Schwarze and Harpfer (2007), using the German Socio-Economic Panel Study data
find that German people are negatively affected by income inequality. Using a compiled
dataset from the European and the World Values surveys from 1981 to 2004, Verme (2011)
finds that income inequality has a negative and significant effect on life satisfaction. Their
result is robust to changes of explanatory variables and estimation choices, persisting
across different income groups and across different types of countries. Oishi et al. (2011)
find that Americans, on average, are happier in years with less national income inequality
than in years with greater inequality. Oshio and Kobayashi (2011) also find that individuals
who reside in areas of high income inequality in Japan tend to self-report less happiness.
In contrast, some studies have indicated a positive association between income
inequality and happiness. Tomes (1986) uses survey data from Canada and finds that self-
reported happiness is lower when the share of income going to the poorest 40 % of a
community is larger, holding personal characteristics constant. Using the British House-
hold Panel Survey data, Clark and Delta (2003) similarly discover that a positive corre-
lation exists between happiness and inequality measured by either the Gini coefficient or
the 90th/10th income percentile ratio for the employed population. Ohtake and Tomioka
(2004) find that both the Gini coefficient and perception of rising income inequality have a
weak but positive correlation to happiness in Japan.
Other researchers have found no relationship between happiness and income inequality.
Helliwell (2003) presents no evidence that income inequality is correlated with subjective
well-being. Using panel data from the Russian Longitudinal Monitoring Survey, Senik
(2004) finds that neither national level inequality nor regional inequality has a significant
effect on reported life satisfaction. Berg and Veenhoven (2010) use a dataset involved 119
nations during the period 2000–2006, and find little relation between income inequality and
the average happiness of its citizens.
Only a few studies on income inequality and happiness have been conducted in China,
with mixed conclusions. Smyth and Qian (2008) find persons who perceive income dis-
tribution as unequal report lower levels of happiness in urban China, with differing results
between high and low income individuals. Knight et al. (2009) study the determinants of
happiness among Chinese rural residents and finds that an increase in the Gini coefficient at
the county level increases the happiness of peasants, attributing the results to a ‘‘demon-
stration effect’’ of income inequality. Jiang et al. (2012) find that people feel unhappy with
between-group income inequality;1 however, city-level Gini coefficients in China are
positively correlated with happiness.
Empirical studies regarding income inequality and happiness reach conflicting con-
clusions. Different results are supported by different theories that can lead to the prediction
of either a negative or a positive impact of income inequality on happiness.
1 They measure ‘‘between-group inequality’’ by the income gap between migrants without local hukou andurban residents.
The Impact of Income Inequality on Individual Happiness
123
First, there is an assumption that aversion to inequality is an intrinsic social preference
(Bolton and Ockenfels 2000; Fehr and Schmidt 1999; Kahneman and Krueger 2006).
Recently, Tricomi et al. (2010) use functional magnetic resonance imaging to directly test
for the existence of inequality-averse social preferences in the human brain. The results
show that the brain’s reward circuitry is sensitive to both inequalities of advantageous and
disadvantageous natures. The inequality-averse social preference indicates that higher
income inequality is associated with lower level of happiness.
Second, Runciman (1966) proposes a social justice theory based on the notion of an
individual’s sense of deprivation. Relative deprivation is the feeling of being deprived of
an individual’s perceived entitlement (Walker and Smith 2002). It refers to the discon-
tentment people feel when they compare their positions to others and realize that they have
less (Bayertz 1999). Yitzhaki (1979) applies this concept to income and proposes a
measure of relative deprivation as the sum of the distances of a person’s income from all
higher incomes in a given income distribution, and this measure is shown to be equivalent
to the absolute Gini coefficient. The Runciman and Yitzhaki framework states that
increasing income inequality increases relative deprivation and thus decreases happiness.
On the other hand, Hirschman and Rothschild (1973) argue that people may appreciate
inequality if it signals social mobility, a phenomenon also called the ‘‘tunnel effect’’. They
use an analogy to explain: I am driving through a two-lane tunnel with both lanes running
the same direction; however, I run into a traffic jam with cars stopped in both lanes for as
far as the eye can see. I am in the left lane and feel dejected; however, after a few minutes
the cars in the right lane begins to move. Naturally, my spirits are lifted considerably, for I
know that the traffic jam has been broken and the left lane will begin to move shortly Even
though I sit still, I feel better than before because I am expecting to begin moving soon.
Suppose that after a while, the right lane continues moving while the left lane is still
stopped, in which case I, along with all the other left lane drivers suspect foul play and
many of the left lane drivers will become discontent and take action, such as illegally
crossing the double line, in order to correct the injustice that they feel has been placed upon
them.
Analogously, people who observe that others around themselves move upwards in the
income ladder would increase their expectations about their own income in the future.
Therefore, inequality makes them happier because inequality improves a person’s
expectations about their own future. Hence, people accept and tolerate income inequality
within reasonable limits for some time, but after the limits are reached, reactions turn
negative. This theory attempts to explain the mechanisms regarding how income
inequality affects individual happiness. In reality, the findings from empirical studies are
often the results of compound effects originating from different mechanisms. Some
theories during a certain period of time or in a certain country are more prominent than
other theories.
The happiness effect of inequality for countries transitioning to a market economy such
as China may be different from relatively stable and prosperous countries, such as the
United States. Sanfey and Teksoz (2007) find that income inequality has a positive effect
on life satisfaction in transitioning countries, whereas the impact is negative in stable,
prosperous countries. Grosfeld and Senik (2010) study the changing attitudes toward
inequality during the transition to a market economy in Poland. By using repeated cross-
sections of the population, they find a negative association between inequality and hap-
piness for the second half of the transition period (1997–2005); however, they find a
positive association in the early years of transition (1992–1996).
P. Wang et al.
123
In recent years China is in its most important period of reform, a transition to a market-
oriented economy. Meanwhile, along with the break of the traditional mode of egalitarian
income distribution, China sets up a new distribution mode that allows some people and
regions to get rich first, when and where conditions permit. Persons and regions with faster
economic development can help promote the progress of persons and regions with slower
development, or can set an example to motivate the lagging ones to work harder. Thus,
income inequality improves production enthusiasm, promotes economic development, and
enhances the overall happiness of people. In other words, the ‘‘positive tunnel effect’’ of
proper income inequality on happiness may exist in China. On the other hand, the ‘‘neg-
ative tunnel effect’’ could also exist in China. Due to historically relatively equal distri-
butions of income and wealth, the Chinese people are unlikely to tolerate an increase in
income inequality, which can bring many social problems that could hinder economic
development, interfere with people’s lives, and eventually lead to the reduction of an
individual’s level of happiness.
Based on previous literature, we propose the core hypothesis of our study: income
inequality increases happiness when inequality is low and decreases happiness when
inequality crosses a certain threshold; specifically, the relationship between happiness and
income inequality may follow an inverted-U shape and conform to the ‘‘tunnel effect’’
theory.
3 Data and Estimation
3.1 Data
The data used in this study are from the China General Social Survey (CGSS), jointly
conducted by the Department of Sociology at Renmin University of China and the Survey
Research Centre of Hong Kong University of Science and Technology in September and
October, 2006, with the goal of understanding the quality of life for Chinese citizens. The
survey utilizes a four-phase (county, town, village and household) stratified sampling
design for the collection of data for a nationally representative sample of Chinese citizens.
The samples of the first three phases were identified under the sampling frame of China’s
Fifth National Population Census and families were randomly selected within the selected
villages or neighborhood committees (Hu et al. 2011). Overall, 10,151 households from
969 villages (neighborhood committees), 236 towns (streets), 125 counties (districts), and
28 provinces (municipalities)2 were selected for a face-to-face interview. The CGSS
collects a wide range of socio-demographic indicators, such as gender, marriage, age,
education, employment, income, and household size in addition to information on hap-
piness. The sample is restricted to respondents aged between 18 and 70 that were not
missing information on key demographic variables such as age, income, and gender. After
cleaning the data for the data analysis, a final sample size of 8,208 observations was
chosen. Table 1 (Column 2) summarizes the definitions and their descriptive statistics for
the key variables in our final sample.
2 In total, China has 31 provinces, municipalities, and autonomous regions in mainland China. Qinghai,Ningxia, and Tibet were not covered in this survey, because the population of the three regions onlyaccounts for a very small proportion of the whole nation, missing them should not affect the nationalrepresentativeness of the survey.
The Impact of Income Inequality on Individual Happiness
123
3.1.1 Happiness Indicator
The outcome variable in our study is the happiness score of the household respondent,
obtained from a multiple-choice question: ‘‘Taken all together, how happy are you at
present?’’ The five possible responses to the question are ‘‘not happy at all’’, ‘‘not happy’’,
‘‘so–so’’, ‘‘happy’’, and ‘‘very happy’’. Happiness is the most widely used indicator in the
related literature (Ferrer-i-Carbonell and Frijters 2004; Knight and Gunatilaka 2010;
Knight et al. 2009; Tomioka and Ohtake 2004). Veenhoven (1996) reviews findings on
validity and reliability, concluding that single item measures on subjective wellbeing are
akin to muli-item inventories, providing sufficiently validity and reliability as a happiness
measurement. For our analysis, we construct an ordinal variable ‘‘Happiness’’ with a value
ranging from 1 to 5, corresponding to ‘‘not happy at all’’, ‘‘not happy’’, ‘‘so–so’’, ‘‘happy’’,
and ‘‘very happy’’, respectively.
The column (1) of Table 2 reports the sample summary statistics concerning happiness
status. Among all individuals, more than 45 % reported being happy or very happy while
less than 8 % of respondents perceive their situation as not happy or not happy at all.
Examining the data by gender and rural/urban status in Table 2, we find that men are
slightly less happy than women, with 7.92 % of men but only 7.36 % of women self-
reporting a lack of happiness. By contrast, the rural/urban disparity in happiness seems to
be larger with 46.62 % of urban residents self-reporting themselves to be ‘‘very happy’’ or
‘‘happy’’, compared with 44.22 % for rural residents.
3.1.2 Income Inequality Measures
The survey includes several inequality measures as shown in Table 1. The Gini coefficient
is the main inequality measure we will use. The county is the basic unit of local gov-
ernment and administration in China (Knight et al. 2009); therefore, the Gini coefficient is
based on county-level individual income. Since China has 125 counties, or districts, that
are included in our sample, there are 125 distinct income inequality indexes. Table 1 shows
that the Gini coefficient within the counties ranges from 0.194 to 0.662, with an average of
0.428. The Gini coefficient also shows that there is larger income inequality in rural
counties than in urban counties in China.
Several other inequality measures are also created for data analysis and comparison,
including the Theil index and the income share of the richest 50 %. For consistency with
the Gini, we group individual income within the same county to generate these inequality
indicators. Unlike the Gini and income share of the richest 50 %, which ranges between 0
and 1, the Theil index is continuous. For concise presentation of our results, we use the
Gini for main analysis and other measures for the robustness test.
3.1.3 Other Explanatory Variables
Subjective happiness is determined by many factors. Following the literature (for example,
Argyle 1999; Frey and Stutzer 2002; Alesina et al. 2004; Dolan et al. 2008; Smyth and
Qian 2008; Knight et al. 2009; Jiang et al. 2012), a set of variables, including individual
and household characteristics, was added in the regression model to control for potential
confounding factors. We utilize the log of annual individual income and relative income to
control for the influence of household income on happiness. Since the expectation of future
income is an important determinant of current happiness (Luo 2006; Knight et al. 2009;
Knight and Gunatilaka 2010), dummy variables indicating anticipated income levels for
P. Wang et al.
123
Table 1 Sample summary statistics
Variables Definitions Mean SD Min Max
Happiness status
Happiness 1 = ‘‘not happy at all’’; 2 = ‘‘not happy’’;
3 = ‘‘so–so’’; 4 = ‘‘happy’’; 5 = ‘‘very
happy’’
3.430 0.740 1 5
Income inequality
Gini coefficient Gini coefficient of income within the county 0.428 0.082 0.194 0.662
Gini coefficient in
urban
Gini coefficient of income within the county in
urban subsample
0.378 0.072 0.194 0.687
Gini coefficient in
rural
Gini coefficient of income within the county in
rural subsample
0.445 0.082 0.246 0.667
Theil index Theil index of income within the county 0.348 0.155 0.064 1.123
Inc50 % Income share of the richest 50 % within the
county
0.792 0.053 0.629 0.932
Individual
characteristics
Age Age of the respondent in years 43.205 12.895 18 70
Gender A dummy variable where 1 = female; 0 = male 0.505 0.500 0 1
Residence A dummy variable where 1 = urban; 0 = rural 0.564 0.496 0 1
Hukou A dummy variable where 1 = non-agricultural;
0 = agricultural
0.495 0.500 0 1
Ethnicity A dummy variable where 1 = minority;
0 = Han
0.066 0.248 0 1
Political status A dummy variable where 1 = member of
communist party of China; 0 otherwise
0.098 0.297 0 1
Marital status:
single
A dummy variable where 1 = single; 0
otherwise
0.100 0.300 0 1
Marital status:
married
A dummy variable where 1 = married; 0
otherwise
0.841 0.366 0 1
Marital status:
other
A dummy variable where 1 = other marital
status; 0 single or married
0.060 0.237 0 1
Human capital
Health A dummy variable where 1 = rather or very
good; 0 otherwise
0.785 0.411 0 1
Education Years of education received from school 8.086 4.198 0 22
Social capital
Relationship A dummy variable where 1 = rather or very
good; 0 otherwise
0.918 0.274 0 1
Work status
Paid work A dummy variable where 1 = paid work; 0
otherwise
0.726 0.446 0 1
Never work and
looking for a job
A dummy variable where 1 = never work and
looking for a job; 0 otherwise
0.004 0.059 0 1
Unemployed and
looking for a job
A dummy variable where 1 = Unemployed and
looking for a job; 0 otherwise
0.021 0.143 0 1
Student or
military
A dummy variable where 1 = student or
military; 0 otherwise
0.007 0.085 0 1
Retired A dummy variable where 1 = retired; 0
otherwise
0.132 0.339 0 1
The Impact of Income Inequality on Individual Happiness
123
the next 3 years are added to the model. Considering people with membership to the
Communist Party in China would have higher social recognition and status, such as greater
opportunity for promotion in their careers (Li et al. 2008); a dummy variable was included
in the model indicating whether an individual is a Party member. The other socioeconomic
control variables include age, gender, ethnicity, marital status, residence, hukou (household
registration status), health, education, work status, and household size.
Table 1 continued
Variables Definitions Mean SD Min Max
Housework A dummy variable where 1 = housework; 0
otherwise
0.055 0.229 0 1
Others status A dummy variable where 1 = others status; 0
otherwise
0.055 0.227 0 1
Household
characteristics
Household size Population of household 2.334 0.953 1 9
Socioeconomic
status
Income Individual income in year 2005 10,167 15,965 20 670,000
Relative income
in horizontal
A dummy variable where 1 = above the average
income of county (district); 0 otherwise
0.353 0.478 0 1
Relative income
in vertical/up
Compared with 3 years ago, what did your
income change? Up = 1; 0 otherwise
0.451 0.498 0 1
Relative income
in vertical/no
Compared with 3 years ago, what did your
income change? No change = 1; 0 otherwise
0.426 0.495 0 1
Relative income
in vertical/down
Compared with 3 years ago, what did your
income change? Down = 1; 0 otherwise
0.123 0.329 0 1
Income
anticipated/
better
What’s your revenue anticipation in next
3 years? Better = 1; 0 otherwise
0.566 0.496 0 1
Income
anticipated/no
What’s your revenue anticipation in next three
years? No change = 1; 0 otherwise
0.382 0.486 0 1
Income
anticipated/
worse
What’s your revenue anticipation in next
3 years? Worse = 1; 0 otherwise
0.052 0.222 0 1
The sample size is 8,208 for all variables, the urban size is 4,633, and the rural size is 3,575
Table 2 Distribution of individual happiness by gender and residence
Happiness Total Male Female Urban Rural
Not happy at all (%) 0.99 1.08 0.89 0.93 1.06
Not happy (%) 6.65 6.84 6.47 5.61 8.00
So–so (%) 46.22 46.06 46.38 45.85 46.71
Happy (%) 40.63 40.85 40.42 41.90 38.99
Very happy (%) 5.51 5.17 5.84 5.72 5.23
N 8,208 4,064 4,144 4,633 3,575
P. Wang et al.
123
The bottom panel of Table 1 shows that the average age for individuals in our sample is
43.2 years old. Also, women account for 50.5 % of the analyzed sample. The proportion of
urban respondents (56.4 %) is higher than rural respondents; however, the hukou status
shows that more than half of the sampled individuals in urban counties (50.5 %) have
agriculture hukou, reflecting the fact that some urban residents are rural-to-urban migrants.
Minorities account for 6.6 % of the individuals and approximately 10 % of the individuals
are members of the Communist Party of China. The majority of the sample is married
(84.1 %), which is consistent with the sample’s age range. As for the health and social
capital statuses, 78.5 and 91.8 % individuals self-report good health and good social
relationships, respectively. The average number of years of formal education is approxi-
mately 8 years. Most the individuals have a paying job (72.6 %) or are retired (13.2 %);
while very few are unemployed and looking for job (2.1 %). The average individual
income in the study sample is 10,167 Yuan, while 35.3 % of the individuals had incomes
above the average income within their county, implying a right-skewed distribution of
income within counties. Mirroring the rapid economic growth in China, 45.1 % individuals
reported that their income increased within the last 3 years and 56.6 % respondents believe
their income will increase in next 3 years.
3.2 Estimation Method
Following previous literature, a regression model has been chosen to estimate the impact of
income inequality on individual happiness:3
Happinessij ¼ aþ b1Ineqj þ b2Ineq2j þ Xijcþ lij ð1Þ
where i and j are subscripts for individual and county, respectively. Happinessij denotes the
respondent’s self-reported happiness. Ineqj stands for the county-level income inequality
while Xij is the vector of other individual, household, and county variables. We also include
the squared term of inequality to capture the potential non-linear, U-shaped effect. We
hypothsize that happiness deteriorates with income inequality (b1 [ 0), but also hypoth-
esize that the relation might not be linear (b2 = 0).
Empirically, taking into account the characteristics of the happiness score, the ordered
responses model should be employed to estimate formula (1) (Wooldridge2009). To satisfy
different assumptions on the distribution of residual lij, the ordered probit model or
ordered logit model could meet those assumptions (Jones 2000). However, as Ferrer-i-
Carbonell and Frijters (2004) pointed out, while the ordinal dependent variable is the
psychological assessment indicator, the estimated result from ordered probit model is
similar to the one from ordinary least squares model (OLS). Since OLS coefficients rep-
resent the marginal effects directly (Jiang et al. 2012; Knight and Gunatilaka 2010; Knight
et al. 2009), we will choose OLS to explore the effect of income inequality on individual
happiness. For the robustness test for the model specifications, the ordered probit model
estimates the income inequality impact, resulting in the same pattern as OLS.
3 In order to check whether the functional form of income inequality is reasonable, i.e. why we choosequadratic of income inequality, not the cubic or the biquadrate, we carry out the following tests. LR-testresult for linear and quadratic model: LR Chi2(1) = 22.62, Prob [ Chi2 = 0.0000; LR-test result for cubicand quadratic model: LR Chi2(1) = 1.87, Prob [ Chi2 = 0.1712; LR-test result for biquadrate and qua-dratic model: LR chi2(1) = 3.92, Prob [ Chi2 = 0.1408; Ramsey RESET test using powers of the fittedvalues of happiness for the quadratic model, Ho: model has no omitted variables, F(3, 8,173) = 1.09,Prob [ F=0.3534.
The Impact of Income Inequality on Individual Happiness
123
4 Results
4.1 The Impact of Income Inequality on Happiness
Lowess curves (Cleveland 1979) are used to examine the relationship between happiness
and income inequality for total sample, the rural/urban subgroup, and the rich/poor sub-
group. Although the curves are unadjusted for control variables and few observations are
present at very high and low values of the Gini, Fig. 1 illustrates important features on the
relationship between individual happiness and income inequality. Among the total sample
(Fig. 1a), happiness directly increases with the Gini until the Gini reaches a value between
0.42 and 0.44. Above a Gini between 0.42 and 0.44, the happiness decreases as inequality
increases. Compared to rural counties (Fig. 1b), the urban curve reaches the maximum at a
lower Gini coefficient, somewhere between 0.38 and 0.39. A respondent is classified as
rich if their income is above the average income in their county; otherwise, the respondent
is classified as poor. Although rich individuals are found to be happier on average than
poor individuals, both curves reach a maximum with a Gini in between 0.42 and 0.44, after
which happiness gradually declines (Fig. 1c).
In short, all Lowess curves in Fig. 1 show that the happiness first increases and then
decreases at different rates after reaching their respective maximums with the increase of
the Gini coefficient, thus we describe the curves as inverted-U shaped relationships
between happiness and income inequality, coinciding with our original hypothesis of a
nonlinear relationship between happiness and income inequality.
The OLS regression method is then implemented to control for other variables that
affect individual happiness in order to better understand the partial effect of income
inequality on happiness. Table 3 presents the estimation results.4 With further support for
our hypothesis in Fig. 1, the regression results also exhibit the inverted-U shaped (a
quadratic relationship) between happiness and the Gini.
For comparison with existing literature, the Gini squared term is not included in Column
(1). The coefficient of the Gini is negative and significant at the 10 % significance level,
with all other potential confounding variables controlled for. When we add the squared
term in Column (2), the coefficients of the Gini and Gini squared are both significant at the
1 % significance level. The positive coefficient of the Gini and negative coefficient of Gini
squared indicate an inverted-U shaped relationship between the Gini and happiness.
Specifically, happiness increases with inequality when the Gini coefficient is less than
0.4055 and decreases with income inequality for larger Gini coefficient. In the study
sample 59.2 % of the counties have Gini values higher than 0.405, which corresponds to
60 % of study subjects living in high income inequality counties.
Rather than relying on a single measure of income inequality such as the Gini coeffi-
cient, we also chose two other indicators to test; the Theil index and the income share of
the richest 50 %, for a robustness test, and columns (3) and (4) in Table 3 report their
respective estimations. The results present further consistent evidence for an inverted-U
association between happiness and income inequality. The peak of happiness is at 0.291 for
the Theil index, where 60 % of the sample counties have higher inequality than this turning
4 Addressing the potential heteroskedasticity of the error term, we report the robust standard errors for allthe regression. In fact, we have also estimated the standard errors and find the results are similar.5 The formula: turning point for Gini = -[effect of Gini/(2�effect of Gini2)], that is, the Gini at the peak ofthe inverted U-shaped curve of Gini and happiness.
P. Wang et al.
123
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Gini Coefficient
(a)
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Gini Coefficient
Urban Rural
(b)
33.
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pine
ss
.25 .35 .45 .55 .65
Gini Coefficient
Rich Poor
(c)
Fig. 1 Lowess curves. (a) Total sample. (b) Urban and rural subsample. (c) Rich and poor subsample
The Impact of Income Inequality on Individual Happiness
123
Table 3 Effects of income inequality on individual happiness
Variables Dependent variable: individual happiness
(1) (2) (3) (4)
Income inequality
Gini -0.204*(0.106)
3.132***(0.699)
Gini squared -3.866***(0.813)
Theil 0.309*(0.161)
Theil squared -0.531***(0.161)
Inc50 % 13.052***(3.225)
Inc50 % squared -8.330*** (2.060)
Individual characteristics
Residence (urban = 1) -0.056**(0.027)
-0.057**(0.027)
-0.055**(0.027)
-0.057**(0.027)
Hukou (non-agricultural hukou = 1) 0.065**(0.026)
0.064**(0.025)
0.063**(0.025)
0.069***(0.025)
Gender (female = 1) 0.089***(0.016)
0.086***(0.016)
0.089***(0.016)
0.086***(0.016)
Ethnicity (minority = 1) -0.087***(0.032)
-0.093***(0.032)
-0.092***(0.032)
-0.092***(0.032)
Age -0.029***(0.005)
-0.029***(0.005)
-0.029***(0.005)
-0.028***(0.005)
Age square/100 0.035***(0.005)
0.035***(0.005)
0.035***(0.005)
0.035***(0.005)
Political status (member of party = 1) 0.065***(0.025)
0.063**(0.025)
0.064**(0.025)
0.061**(0.025)
Marital status: single 0.087*(0.047)
0.089*(0.047)
0.088*(0.047)
0.088*(0.047)
Marital status: married 0.334***(0.035)
0.335***(0.035)
0.334***(0.035)
0.335***(0.035)
Human capital
Health 0.281***(0.019)
0.279***(0.019)
0.279***(0.019)
0.280***(0.019)
Education 0.012***(0.002)
0.011***(0.002)
0.011***(0.002)
0.011***(0.002)
Social capital
Relationship 0.284***(0.027)
0.280***(0.027)
0.281***(0.027)
0.280***(0.027)
Work status
Paid work 0.116**(0.051)
0.116**(0.051)
0.118**(0.051)
0.117**(0.051)
Never work and looking for a job 0.257(0.171)
0.276(0.170)
0.269(0.170)
0.275(0.170)
Student or military 0.484***(0.112)
0.481***(0.112)
0.485***(0.112)
0.488***(0.111)
P. Wang et al.
123
point. The peak happiness occurs at 0.783 of the income share of the richest 50 %, and
57.6 % of the sample counties have higher inequality than this value.
The results from the paper show that the tunnel effect theory holds for modern-day
China. The appropriate income inequality, within personal tolerances, is beneficial to
Table 3 continued
Variables Dependent variable: individual happiness
(1) (2) (3) (4)
Retired 0.137**(0.057)
0.140**(0.057)
0.138**(0.057)
0.144**(0.057)
Housework 0.229***(0.062)
0.227***(0.062)
0.227***(0.062)
0.233***(0.061)
Others status 0.059(0.062)
0.056(0.062)
0.055(0.062)
0.060(0.062)
Household characteristics
Household size 0.025***(0.008)
0.024***(0.008)
0.023***(0.008)
0.025***(0.008)
Socioeconomic status
Log of income 0.056***(0.012)
0.050***(0.012)
0.055***(0.012)
0.050***(0.012)
Relative income in horizontal 0.098***(0.021)
0.106***(0.021)
0.098***(0.021)
0.107***(0.021)
Relative income in vertical/up 0.114***(0.017)
0.114***(0.017)
0.113***(0.017)
0.116***(0.017)
Relative income in vertical/down -0.062**(0.027)
-0.062**(0.027)
-0.060**(0.027)
-0.062**(0.027)
Income anticipated/better 0.219***(0.016)
0.219***(0.016)
0.220***(0.016)
0.218***(0.016)
Income anticipated/worse -0.235***(0.039)
-0.232***(0.039)
-0.227***(0.039)
-0.235***(0.039)
Level of living place
Provincial capital city 0.028(0.029)
-0.012(0.030)
0.007(0.029)
-0.012(0.030)
Counties (districts) in east 0.143***(0.031)
0.098***(0.032)
0.115***(0.032)
0.098***(0.032)
Counties (districts) in middle 0.039(0.030)
-0.004(0.031)
0.024(0.030)
-0.009(0.031)
Counties (districts) in west 0.109***(0.034)
0.066*(0.035)
0.077**(0.035)
0.064*(0.034)
Constant 2.204***(0.164)
1.603***(0.205)
2.117***(0.161)
-2.886**(1.251)
N 8,208 8,208 8,208 8,208
R-squared 0.183 0.185 0.186 0.184
Robust standard errors are shown in parentheses
The reference group for marital status is ‘‘other’’, for work status is ‘‘unemployed and looking for a job’’, forrelative income in vertical and income anticipated are ‘‘no change’’, for level of living place is municipalitycity
*, **, *** Significance at the 10, 5, and 1 % levels, respectively
The Impact of Income Inequality on Individual Happiness
123
individual happiness; however, excessive income inequality weakens individual happiness.
Our explanation for the results is that appropriate levels of income inequality can enhance
income mobility, since income mobility has an egalitarian nature. In an era of rapid
economic development with a certain degree of income inequality, strong motivation to
obtain more wealth is highly stimulated by society as a whole, especially for the middle
and lower class income earners. High expectations accompanied by enhanced confidence
for the future lead to greater individual life satisfaction when faced with income inequality
within their personal tolerances. Within personal tolerances, people believe that income
inequality provides the opportunity for individuals to decrease poverty and that an indi-
vidual can also achieve wealth growth through their own efforts, rather than relying on
their social relationships, the background of their family, and other factors. With a certain
degree of income inequality, people expect that their future opportunities will place them at
a higher level of income distribution. So in this case, income inequality makes a positive
impact to an individual’s subjective happiness, with the positive tunnel effect playing a
major role. However, when the income inequality passes the individual tolerance level of
people for inequality, middle and lower class income earners lose confidence for the future,
and aversion to inequality and relative deprivation would become more prominent, ulti-
mately leading to a negative effect on happiness. Simultaneously, facing higher income
inequality, upper class income earners may worry about the loss of their fortune, resulting
in their decreased individual happiness.
Compared with other empirical results in previous studies on happiness in China
(Knight and Gunatilaka 2010; Knight et al. 2009), our results largely confirm known
correlations with happiness. Compared with rural residents, people with non-agricultural
hukou are happier since household registration is a social status symbol to some extent in
China. Individuals with non-agricultural hukou would enjoy more generous social security,
such as pension insurance (Wang 2006). Females, the Han ethnic group, and communists
also have higher happiness scores. Age has a U-shaped effect on happiness, with a turning
point at 40.7 years of age. Compared with other marital status; including separation,
divorce, and widowed, married persons enjoy family life and have higher happiness scores.
Also, health and education have a positive, significant effect on happiness. People with
better social relationships are happier. Compared with the unemployed, individuals with
paid employment tend have higher self-reported happiness. An interesting finding with
regards to work status is that students, military members, retired persons, and individuals
occupied with full-time housework all have a higher degree of overall happiness than
unemployed individuals. Since family members are able to provide more support for each
other in bigger households, the bigger the size of household, the happier individuals in the
household are. In short, all analyzed socioeconomic status variables have a positive sig-
nificant impact on individual happiness score.
The proposed hypothesis is supported by the results of our empirical analyses and is
robust to other common measurements of inequality. The complete tunnel effect theory is
applicable in China, stating that increasing income inequality improves happiness within a
certain range of inequality, but the effects of higher income inequality become detrimental
to happiness.
4.2 Robustness Checks
According to Oshio and Kobayashi (2011), there is a danger for over-controlling due to the
introduction of too many predictors at the individual level. There is easiness in reporting
findings or failing to identify important associations because there is difficulty in assessing
P. Wang et al.
123
the dynamics between individual-level variables and county-level income inequality. In
addition, there is no well-established theory regarding which control variables matter and
should be included in a model.
Hence, we examine the sensitivity of our results to changes in control variables.
Concretely, we control for two key attributes: individual characteristics including resi-
dence, hukou, gender, ethnicity, age, political status, and marital status in addition to
socioeconomic status characteristics which includes the logarithm of income, relative
income in horizontal, relative income in vertical and anticipated income. We also select
additional variables with other attributes: household characteristics including household
size, human capital referring to both education and health, social capital through rela-
tionships, and work status. Table 4 shows how Gini and Gini squared are affected by the
choice of individual and household controls. The 1 % significantly positive and negative
coefficients of Gini and Gini squared are found in all models. Further, we find that the
turning point varies from 0.400 to 0.413, which is relatively narrow. Our findings dem-
onstrate that the inverted-U shaped relationship between income inequality and happiness
is robust.
Moreover, for the robustness test of the model specifications, the ordered probit and
logit models are also employed to estimate the impact of income inequality. The results,
although not shown in this paper, demonstrate that OLS and second order model have
similar results.
4.3 Subsample Analysis
Considering the tremendous differences in many aspects between urban and rural living
conditions, we further examine the effects of income inequality in rural and urban areas
separately. In addition, the relationship between happiness and income inequality among
the rich and the poor is also examined separately. The subsample results are shown in
Table 5.
As presented in Column (1) and (2) in Table 5, all the coefficients of inequality and
inequality squared variables are significant at the 5 % significance level. The inverted-U
shape relationship between happiness and income inequality occurs for both urban and
rural residents; however, the tolerance of urban residents for income inequality is lower
than rural residents; as shown by the urban internal turning point at 0.323, while the turning
point for rural counties is 0.459. Column (3) and (4) in Table 5 present the results for poor
and rich group individuals, respectively. Income inequality still has an inverted-U shaped
effect on the happiness of the poor; however, the same relationship does not exist for the
rich. In the early stages of China’s economic reform, opening up is ‘‘to allow some regions
and some people to get rich first, and the rich will help underdeveloped regions and the
poor, eventually achieving common prosperity’’. After decades of development, urban and
coastal areas are relatively rich while income inequality widens. Compared to urban res-
idents, rural residents have a lower level of education, limited literacy, higher enthusiasm
for the call of government, and a stronger sense in obedience to law and morality. Rural
residents believe that someone would help them get rich and therefore, they are more
tolerant of income inequality while urban residents tend to view the income inequality as
unfair and are less tolerant of income inequality.
Other interesting findings are shown in Table 5. The impacts of hukou on happiness are
different in urban and rural areas; in urban areas, hukou have a statistically significant
positive coefficient, an indication that urban residents with non-agricultural hukou are
happier than individuals with agricultural hukou. However, there is no significant
The Impact of Income Inequality on Individual Happiness
123
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P. Wang et al.
123
Table 5 Income inequality effects on individual happiness for subsamples
Variables Dependent variable: individual happiness
Urbansubsample
Ruralsubsample
The richsubsample
The poorsubsample
(1) (2) (3) (4)
Income inequality
Gini 1.779**(0.811)
4.283***(1.043)
1.436(1.208)
3.937***(0.858)
Gini squared -2.756***(1.028)
-4.670***(1.156)
-2.001(1.413)
-4.727***(0.995)
Individual characteristics
Residence (urban = 1) -0.007(0.044)
-0.102***(0.035)
Hukou (non-agriculturalhukou = 1)
0.110***(0.029)
-0.050(0.056)
0.030(0.040)
0.093***(0.033)
Gender (female = 1) 0.089***(0.021)
0.094***(0.026)
0.105***(0.028)
0.077***(0.020)
Ethnicity (minority = 1) 0.018(0.049)
-0.137***(0.041)
-0.121**(0.054)
-0.079**(0.039)
Age -0.038***(0.006)
-0.015**(0.007)
-0.026***(0.009)
-0.031***(0.006)
Age square/100 0.041***(0.007)
0.024***(0.008)
0.032***(0.010)
0.038***(0.006)
Political status (member ofparty = 1)
0.094***(0.030)
0.020(0.044)
0.100***(0.035)
0.035(0.036)
Marital status: single 0.085(0.056)
0.056(0.083)
0.118(0.078)
0.097*(0.059)
Marital status: married 0.373***(0.043)
0.262***(0.062)
0.381***(0.063)
0.324***(0.043)
Human capital
Health 0.272***(0.025)
0.307***(0.031)
0.284***(0.035)
0.271***(0.023)
Education 0.010***(0.003)
0.012***(0.004)
0.009**(0.004)
0.012***(0.003)
Social capital
Relationship 0.257***(0.036)
0.299***(0.041)
0.342***(0.049)
0.255***(0.033)
Work status
Paid work 0.153***(0.056)
-0.056(0.129)
0.108(0.090)
0.114*(0.061)
Never work and looking for a job 0.188(0.189)
0.362(0.364)
-0.156(0.163)
0.299(0.183)
Student or military 0.436***(0.119)
0.374(0.298)
0.116(0.199)
0.501***(0.124)
Retired 0.226***(0.064)
-0.090(0.179)
0.064(0.107)
0.182***(0.068)
Housework 0.250***(0.072)
0.073(0.139)
0.212*(0.125)
0.239***(0.071)
Others status 0.015(0.070)
0.058(0.142)
0.025(0.133)
0.067(0.071)
The Impact of Income Inequality on Individual Happiness
123
relationship between hokou and happiness in rural areas. The minorities living rural areas
tend to have lower happiness than the Han, but differences among the ethnicities of urban
residents are not significant. In addition, the work status and political status are significant
in urban areas, but are not significant in rural areas. The coefficient on residence variable is
negative and significant only in the poor subsample. This suggests that living in an urban or
rural setting doesn’t matter for the rich, but it matters for the poor since rural poor persons
are happier than urban poor persons. The coefficient on hukou is positive and insignificant
Table 5 continued
Variables Dependent variable: individual happiness
Urbansubsample
Ruralsubsample
The richsubsample
The poorsubsample
(1) (2) (3) (4)
Household characteristics
Household size 0.027**(0.011)
0.026**(0.012)
0.006(0.014)
0.035***(0.010)
Socioeconomic status
Log of income 0.046***(0.017)
0.080***(0.017)
0.061**(0.025)
0.050***(0.014)
Relative income in horizontal 0.108***(0.027)
0.064*(0.035)
– –
Relative income in vertical/up 0.086***(0.022)
0.137***(0.026)
0.104***(0.028)
0.118***(0.021)
Relative income in vertical/down -0.080**(0.034)
-0.032(0.043)
-0.108**(0.053)
-0.044(0.031)
Income anticipated/better 0.195***(0.021)
0.246***(0.025)
0.189***(0.028)
0.235***(0.020)
Income anticipated/worse -0.236***(0.051)
-0.230***(0.062)
-0.056(0.072)
-0.283***(0.046)
Level of living place
Provincial capital city 0.010(0.031)
0.011(0.052)
-0.013(0.038)
Counties (districts) in east 0.098***(0.034)
0.050(0.031)
0.100*(0.055)
0.114***(0.041)
Counties (districts) in middle 0.029(0.034)
-0.065**(0.029)
-0.003(0.053)
0.014(0.040)
Counties (districts) in west 0.095**(0.041)
– 0.119**(0.059)
0.056(0.044)
Constant 2.161***(0.254)
0.880***(0.334)
1.878***(0.370)
1.483***(0.253)
N 4,633 3,575 2,895 5,313
R-squared 0.202 0.180 0.136 0.180
Robust standard errors are shown in parentheses
The reference group for marital status is ‘‘other’’, for work status is ‘‘unemployed and looking for a job’’, forrelative income in vertical and income anticipated are ‘‘no change’’, for level of living place is municipalitycity
*, **, *** Significance at the 10, 5, and 1 % levels, respectively
P. Wang et al.
123
for the rich while it is significant for the poor. The poor with non-agricultural hukou have a
higher happiness level than the poor with agricultural hukou.
4.4 Cross-Level Effects
When aggregated data are used along with individual data, a hierarchical structure of
multilevel data exists and macroeconomic inequality indicators may affect individual
variables; commonly referred to as the cross-level effect. Income inequality may affect
happiness through individual characteristics since different people have different attitudes
and perceptions of income inequality. Next, we examine whether a cross-level effect of
income inequality exists by including a series of interactions between individual charac-
teristics and income inequality in regression models.
In Table 6 column (1), the coefficient of the interaction term between inequality and
residence has a negative sign and is significant at 5 % level, an indication that the urban
Table 6 Heterogeneity analysis of income inequality effects on individual happiness
Variables Dependent variable: individual happiness
(1) (2) (3) (4) (5)
Gini 3.962***(0.768)
4.078***(0.766)
3.028***(0.701)
3.129***(0.698)
4.295***(0.764)
Gini squared -4.540***(0.848)
-4.680***(0.854)
-3.854***(0.813)
-3.833***(0.811)
-4.524***(0.825)
Gini*residence -0.480**(0.219)
Gini*hukou -0.539**(0.212)
Gini*gender 0.190(0.178)
Gini*political status -0.289(0.302)
Gini*education -0.078***(0.023)
Residence (urban = 1) 0.158(0.101)
-0.056**(0.027)
-0.058**(0.027)
-0.057**(0.027)
-0.056**(0.027)
Hukou (non-agriculturalhukou = 1)
0.059**(0.026)
0.295***(0.093)
0.064**(0.025)
0.065**(0.025)
0.063**(0.025)
Gender (female = 1) 0.088***(0.016)
0.088***(0.016)
0.005(0.077)
0.086***(0.016)
0.086***(0.016)
Political status (member ofparty = 1)
0.063**(0.025)
0.064**(0.025)
0.063**(0.025)
0.186(0.130)
0.064**(0.025)
Education 0.011***(0.002)
0.012***(0.002)
0.011***(0.002)
0.011***(0.002)
0.045***(0.010)
Other control variables Yes Yes Yes Yes Yes
Observations 8,208 8,208 8,208 8,208 8,208
R-squared 0.186 0.186 0.186 0.186 0.187
Robust standard errors are shown in parentheses
Other control variables include the same with Table 3
*, **, *** Significance at the 10, 5, and 1 % levels, respectively
The Impact of Income Inequality on Individual Happiness
123
residents dislike income inequality more than rural residents. This demonstrates that urban
residents have less happiness on average because of the acute reaction to income inequality
for urban residents, i.e. the low tolerance of income inequality. In Column (2), the inter-
action term of household hukou and inequality is added, and the result is similar with
Column (1) indicating that people with non-agricultural identity dislike income inequality
more than people with agricultural hukou. We separately specify additional interaction
terms for income inequality with gender and political status in Column (3) and (4),
respectively, but neither of the interactions demonstrates statistical significance at 10 %, an
indication of no difference in the perception of income inequality between women and
men, or between party members and non-party members. When the interaction between
income inequality and education, there is a statistically negative cross-level effect, which
means that individuals with higher levels of education dislike income inequality more than
less educated individuals, likely explained by data that shows that people with higher
education typically earn a higher income.
5 Conclusion
This paper focuses on the effect of income inequality on individual happiness. Prior
theoretical and empirical work has supported positive, negative, or insignificant associa-
tions. With China in a critical period of reform and economic transformation, we
hypothesize that the relationship between income inequality and happiness in China
exhibits an inverted-U shape based on the ‘‘tunnel effect’’ theory, a theory that states that
happiness increases with income inequality when inequality is low; however happiness
subsequently decreases when inequality crosses a certain threshold. The Chinese General
Social Survey data is employed to test the inverted-U shape hypothesis.
Our empirical results support the inverted-U shaped association between income
inequality and happiness, confirming the ‘‘tunnel effect’’ theory exists in China. Overall,
individual happiness increases with income inequality when the county-level Gini coef-
ficient is less than 0.405 and decreases with inequality for larger county-level Gini coef-
ficients. About 59 % of the counties, or districts, have a Gini coefficient higher than 0.405.
The robustness of the findings is checked, with special attention to other measures for
income inequality and the specifications of the control variables. All estimates of the
effects of income inequality on happiness in China are consistent across different income
inequality measures and model specifications.
Rural/urban and poor/rich subsamples are also analyzed to explore the heterogeneous
effect of income inequality in different groups. The ‘‘inverted-U’’ associations still exist in
urban and rural China with the urban residents’ tolerance for income inequality at a lower
level than rural residents since the turning point for urban residents is 0.323, while it is
0.459 for rural residents.
Furthermore, considering income inequality may affect happiness through individual
characteristics, five interaction terms are added to regressions to study the cross-level
effects of income inequality. We find that people living in urban counties, people with non-
agricultural hukou, and people with higher education are less happy than their respective
counterparts at the same level of income inequality.
Currently, the Party Central Committee and the People’s Government of China pay
unprecedented attention to the wellbeing of its citizens, fully committed to ‘‘building a
socialist harmonious society’’ and our empirical results are informative for Chinese policy
makers. Income inequality does not necessarily harm social welfare; however, lower levels
P. Wang et al.
123
of income inequality are associated with a higher happiness level. On the one hand, income
inequality stimulates an individual‘s desire to improve their living decisions, but on the
other hand, high levels of income inequality might hurt morale and raise social unrest.
Keeping income inequality within reasonable bounds, rather than completely eliminating
income inequality, is a more reasonable policy response.
The study has two limitations. First, the measurement of individual happiness uses a
single item and is based on the survey results of a subjective assessment. Although hap-
piness is a complex and multi-dimensional concept, one overall score provides a simple
and easy interpretation for analyses. If one truly wishes to elucidate aspects of happiness
affected by income inequality, measurements with better psychometric properties would
benefit analysis. Second, we do not investigate the relationship between happiness and
income inequality over time due to a current lack of time series data. Further research is
needed to address this issue in the future.
Acknowledgments This study was funded by the Applied Economics Research Funds of SouthwestUniversity for Nationalities (2011XWD-S0202), the National Natural Science Foundation of China(71303165), Sichuan University (skqx201401), the China Postdoctoral Science Foundation (2013M540706)and China Medical Board (13-167). We are grateful to the seminar and conference participants at South-western University of Finance and Economics for their insightful comments, and to colleagues at the PKUCenter of Health Economics Research, Gordon G. Liu, Gergely Horvath and the anonymous referees. Dataanalyzed in this paper were collected by the research project ‘‘China General Social Survey (CGSS)’’sponsored by the China Social Science Foundation. This research project was carried out by Department ofSociology, Renmin University of China and Social Science Division, Hong Kong Science and TechnologyUniversity, and directed by Dr. Li Lulu and Dr. Bian Yanjie. The authors appreciate the assistance inproviding data by the institutes and individuals aforementioned. The authors are responsible for allremaining errors.
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