Health Implications of Socioeconomic Characteristics,Subjective Social Status, and Perceptions of Inequality:An Empirical Study of China

Chunping Han

Accepted: 12 November 2013� Springer Science+Business Media Dordrecht 2013

Abstract This study explores how health is associated with socioeconomic status, sub-

jective social status, and perceptions of inequality simultaneously. Two health outcomes

(self-reported health and psychological distress) are examined, and the subtlety of their

relationships with each of the three dimensions of inequality is probed. Data used come

from a nationally representative sample survey conducted in China. Several findings

emerge from empirical analyses: (1) Overall, the three dimensions of social inequality are

associated with self-reported health and psychological distress net of each other and other

control variables; (2) among focal socioeconomic characteristics, income and Chinese

Communist Party membership are significantly associated with both health outcomes,

education exhibits a robust effect on self-reported health, and rural and migrant statuses are

linked to less distress; (3) subjective social status in comparisons with both socially

proximal and broad referents is associated with both health outcomes, and the association

with downward comparisons is more salient than with upward comparisons; and (4) per-

ceived degree and perceived sources of inequality in society show varying relationships

with the two health outcomes. These findings add to our understanding of the multidi-

mensionality and complexity of social inequality in relation to health.

Keywords Self-rated health � Psychological distress � Socioeconomic

characteristics � Subjective social status � Perceptions of inequality � China

1 Introduction

The implication of social inequality for health has been one of the central themes in health

research. The positive relationship between socioeconomic status and health has been

C. Han (&)The Walter H. Shorenstein Asia-Pacific Research Center, Stanford University, Stanford, CA 94305,USAe-mail: hancping@gmail.com

123

Soc Indic ResDOI 10.1007/s11205-013-0514-5

firmly established (Link and Phelan 1995; Mirowsky and Ross 2003; Phelan et al. 2010).

Subjective social status has consistently shown to be linked to health across population and

context (Adler et al. 2000; Ostrove et al. 2000; Singh-Manoux et al. 2003; Wolff et al.

2010). Studies of the impact of income inequality have also found the depressing impact of

unequal distribution of income at the aggregate level on health (Wilkinson and Pickett

2006).

While the current literature on the health implications of social inequality is

informing, a couple of issues remain open to research. First, the inquiry of the health

impact of income inequality has focused on objective income distribution. However,

different social groups may interpret the same objective inequality differently. Sub-

jective understanding of inequality in society may affect health through eliciting

positive or negative emotions and shaping health behaviors. Yet, direct test of the

relationship between perceptions of inequality and health is lacking except for a very

recent attempt (Oshio and Urakawa 2013). Second, the influences of socioeconomic

characteristics, subjective social status, and income inequality are often scrutinized

separately in discrete research. It is yet to be identified whether these various dimen-

sions of social inequality are significantly linked to health when being investigated

together in a single study.

This study attempts to fill the above-mentioned gaps. It simultaneously examines how

health is associated with socioeconomic characteristics, subjective social status, and

perceptions of inequality based on the analysis of data from a nationally representative

sample survey in China. China has experienced dramatically expanding inequality for

over three decades since its market reforms. Its Gini coefficient has surpassed .4, a

threshold of alert for excessive inequality by the international standard, since the mid-

1990s and exceeded that in the United States and many other developing countries

(Knight 2008; Whyte 2010b). In the meantime, the state has substantially withdrawn

from social services such as health care, education, and pension. Such dynamics of

inequality make China a theoretically pertinent setting to investigate the health impli-

cation of objective and subjective inequality. Moreover, the national data collected in

China this study employs include an array of questions about health conditions, socio-

economic and demographic background information, evaluations of personal circum-

stances, and views on inequality in society, which provide abundant relevant data to

address our research questions.

This study builds upon and extends prior literature on social inequality and health in

its integrated examination of health implications of objective and subjective inequality.

First, it investigates not only income and education, two conventional socioeconomic

status attributes that have been widely examined in health research, but also unique

status markers in China including the urban versus rural origin and Chinese Communist

Party (CCP) membership. Second, it addresses health associations with subjective social

status in comparisons with both proximal and broad referents, while comparison with

one type of referents dominates previous studies. Third, the nascent research on the

health association with subjective income inequality focuses on perceived degree of

inequality (Oshio and Urakawa 2013). This study scrutinizes multiple facets of per-

ceptions of inequality, including not only perceived degree of inequality but also per-

ceived sources of wealth and poverty in society. These inquiries will add to our

understanding of the multidimensionality and complexity of social inequality in relation

to health.

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2 Literature Review and Research Aims

2.1 Literature on Health Implications of Socioeconomic Characteristics, Subjective

Social Status, and Income Inequality

There has been voluminous research on the relationship between social inequality and

health. Individual socioeconomic characteristics have shown to be crucial predictors of

health. Those who are wealthy and highly educated are less likely to suffer diseases and

mortality as they tend to enjoy advantaged access to health-enhancing resources such as

health insurance, proximity to quality health facilities, knowledge of diseases and healthy

lifestyles, information about new medicine and technologies, social support, self-esteem,

and the sense of control over their life (Crockerham 2010; Elo 2009; Link et al. 2008;

Mirowsky and Ross 2003; Mirowsky et al. 2000; Ross and Wu 1995; Winkleby et al.

1992). This linkage persists across a wide spectrum of health outcomes, context, and time,

so that socioeconomic conditions have been considered as the fundamental causes of

health, mortality, and morbidity (Link and Phelan 1995; Phelan et al. 2010).

Studies of the relationship between subjective social status and health have emerged in

recent years, and empirical analyses have yielded consistently positive correlations

between perceived self position in the social hierarchy and health (Adler et al. 2000;

Ostrove et al. 2000; Singh-Manoux et al. 2003; Wolff et al. 2010). Some scholars argue

that the sense of relative deprivation makes subjective social status matter for health, with

status anxiety, negative affect, and unhealthy coping behaviors occurring to those who feel

faring worse than others (Wilkinson 1999). Others propose that subjective social status

may manifest one’s sociocultural conditions more comprehensively than directly mea-

surable objective traits (Singh-Manoux et al. 2003).

Despite the agreement on the positive relationship between subjective social status and

health, there has been a lack of consensus on what reference frame should be analyzed.

Relative deprivation theorists hold that people are inclined to compare themselves with

others with similar social characteristics (e.g., education, gender, and race) and in the close

milieu of social interaction (e.g., relatives, colleagues, and neighbors) (Stouffer et al. 1949;

Crosby 1982; Merton and Rossi 1968). For them an emphasis on socially proximal ref-

erents is relevant and meaningful. Other scholars argue for the importance of the com-

parison with general others in the whole society rather than with near equals (Wilkinson

and Pickett 2006).

Empirical studies have widely used a broad referent when testing the relationship

between subjective social status and health. For example, Singh-Manoux et al. (2003) use a

10-rung ladder to measure British civil servants’ perceived self position in society and

reveal a strong association with angina, diabetes, respiratory illness, self-reported general

health, and psychological distress. The relationships for self-reported health and mental

health persist when socioeconomic indicators are controlled for. Similar patterns have been

found in other studies of both physical and mental health outcomes among various pop-

ulations and in various societies (Adler et al. 2000; Ostrove et al. 2000).

Simultaneous analyses of the heath implication of comparisons with both socially

proximal and broad referents are limited and have produced mixed results. Goodman et al.

(2001, 2003) find that adolescents’ comparison with peers within their school rather than

with general others in society is a particularly strong predictor of various health outcomes.

Using data from a national survey of American adults, Wolff et al. (2010) find that

comparison with the broadest referent (others in American society) illustrates a stronger

association and yields a more parsimonious model than comparisons with others of the

Health Implications of Objective and Subjective Inequality

123

same race/ethnicity, neighbors, and parents when they were their age. These mixed find-

ings may be due to different measures of referents and different populations under study.

Research on how income inequality at the aggregate level affects health started in the

1990s and has prospered since then (Wilkinson 1996, 1997, 1999; Wilkinson and Pickett

2006). Studies along this line often use objective indicators such as the Gini coefficient and

the top–bottom income ratio to measure income inequality. Many of them have identified a

negative association between unequal income distribution at the national or subnational

(state, regional and metropolitan) level and health (Kaplan et al. 1996; Kawachi et al. 1997;

Kennedy et al. 1996; Oshio and Kobayashi 2009; Wilkinson 1996, 1997, 1999; Wilkinson

and Pickett 2006), while some other studies find no significant relationship (Beckfield

2004; Lynch et al. 2004; McLeod et al. 2004).

Inspiring as the present literature on the health implication of social inequality is, two

major issues are yet to be addressed. First, previous studies of the impact of income

inequality focus on actual inequality and often measure it with such objective indicators as

the Gini coefficient or the top–bottom income ratio. However, cognitions of social reality

cannot be assumed to be uniform across individuals. People may interpret the same

objective inequality landscape differently due to the availability of information, entrenched

ways of thinking, and personal orientations. Subjective understanding of inequality in

society may promote or harm health through evoking positive or negative affect and

shaping health-enhancing or -impairing behaviors. Research on the health implication of

subjective interpretation of inequality is still lacking. A very recent exception is Oshio and

Urakawa’s (2013) finding of a negative association between perceived income inequality

and self-reported health in Japan. This inquiry is yet to be made in other settings, and the

bearing of more aspects of perceptions of inequality is yet to be elaborated.

Second, the associations between various dimensions of social inequality and health are

often highlighted separately in the existing literature. It is unknown what patterns will

emerge if they are examined simultaneously in a single study. Specifically, are various

aspects of social inequality significantly associated with health net of each other and other

control variables? An integrated exploration of various facets of inequality is required to

enhance our understanding of the multidimensionality of social inequality in association

with health.

2.2 Contributions of This Study: Integration and Extension

This study intends to fill aforementioned gaps in the current literature. It simultaneously

investigates the relationships between health and socioeconomic characteristics, subjective

social status, and perceptions of inequality using data from a nationally representative

sample survey in China. Furthermore, it extends prior literature as follows in its exploration

of the relationship between each specific dimension of inequality and health.

As for the health implication of socioeconomic characteristics, this study probes not

only income and education, two objective status measures that have been most widely

analyzed in previous research, but also characteristics that capture added aspects of

socioeconomic status due to the unique stratification scenario in China. One additional

status attribute is the urban versus rural origin. China has witnessed a larger urban–rural

disparity than have most other developing countries and former socialist societies (Knight

2008; Whyte 1995), primarily because of the long-time enforcement of the household

registration (hukou) system and affiliated policies. First instituted in 1958, hukou status has

served as an overriding basis for the state to allocate resources in favor of the city to

facilitate industrialization and maintain urban stability. At the same time, rural hukou

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holders’ migration into the city was restricted for a long time. The hukou system has been

basically intact despite some relaxation during market reforms. Although rural people are

now allowed to work and live in the city, they are still prone to discrimination in work,

social benefits, and everyday life due to their rural hukou status. As a result, there have

been across-the-board urban-rural discrepancies in income, standard of living, and social

services, which have constituted the most important source of inequality in contemporary

China (Khan and Riskin 1998, 2005; Knight 2008; Li and Luo 2010; Sicular et al. 2010;

Whyte 2010b). The urban–rural disparity has also been distinct in the lifestyle, culture,

belief systems, and values (Sun 2003; Whyte 1995). This study investigates whether the

stark urban–rural inequality is manifested in health.

Political capital represents another important status attribute in China. During the

socialist period, the state granted privileges to CCP members and cadres in exchange for

their political loyalty (Walder 1986; Whyte and Parish 1984). Political capital has con-

tinued to generate privileges in access to state-sponsored resources and benefits during

market transition (Walder et al. 2000). Moreover, people with political capital are also

likely to enjoy a wide social networks and a prestigious self-image, factors that have shown

to bear on health (Thoits 2011). This study examines whether the benefit accrued to

political capital has spilled over to the domain of health.

This study also attempts to grapple with the subtlety of the health association with

subjective social status. As reviewed in the previous section, simultaneous investigation of

comparisons with proximal and broad referents is still rare and has engendered incon-

clusive findings. This study addresses both levels of comparisons. Attention to proximal

referents is consistent with the contextual nature of social comparison. The interaction

between the observer and people with similar social characteristics and in close social

milieu is usually frequent and intensive. Moreover, the observer is likely to share with

people in social proximity similar level of education, occupational prestige, and context of

living, factors usually used as benchmarks for social comparison. Meanwhile, the observer

may use a broad comparison frame. The boundary of contemporary social environment has

broadened thanks to rising geographic mobility, advancement of technology, and flour-

ishing of new media (Stiles et al. 2000). These developments may enhance observers’

awareness of the conditions of others far beyond their close social milieu and expand their

horizon of comparison. Self-evaluation in social comparisons at both levels may affect

health through the impact on self-esteem, the sense of self-worth, and adoption of health-

bearing coping behaviors.

Finally, this study endeavors to grasp multiple facets of perceptions of inequality in

dealing with their associations with health. It has been found that various inequality beliefs

do not necessarily cohere with each other tightly. For example, transitional China and post-

socialist countries in East Europe have similar views on the degree of income inequality

but substantially divergent beliefs about the routes to wealth and poverty (Whyte and Han

2008; Whyte 2010a). Hence, we distinguish between perceived degree and perceived

sources of inequality. Whether the extent of inequality in society is excessive or not may be

the most salient, direct experience of inequality through everyday observations and

quantitative indexes publicized by media. Due to its strong visibility, perceived degree of

inequality may immediately prompt health-bearing emotional reactions such as anxiety and

anger.

Nevertheless, people may explain the same objective inequality differently. Research

finds that rules and procedures leading to the outcomes may affect the justification of the

existing system and hence influence health through stimulation of stress and adaptive health

behaviors (Jost et al. 2003; Lucas et al. 2008). Therefore, this study also scrutinizes how

Health Implications of Objective and Subjective Inequality

123

health is associated with perceived sources of inequality, that is, assessment of what leads to

wealth and poverty in society. Scholars have been differentiating between individualistic and

structural attributions of inequality, the former explaining inequality by individual factors

such as talent and effort while the latter ascribing inequality to external, structural reasons

beyond personal control such as the availability of equal opportunities, discrimination, and

deficiencies in the existing institution (Della Fave 1980, 1986). Overall, individualistic

explanations for inequality may foster the beliefs that the wealthy and the poor deserve their

outcomes and that the current distributional order is just. On the contrary, structural attri-

butions may lead people to question the deservedness of the rich and the poor and the fairness

of the current economic order. The resulting senses of justice and injustice about current

inequalities may influence perceived opportunities and constraints of getting ahead, the sense

of personal control, positive and negative affect, and healthy and unhealthy coping behaviors,

factors that have consequences for health (Ross 2011).

2.3 Research Aims

In summary, this study aims to explore the multidimensionality and complexity of social

inequality in relation to health. It examines how health is associated with socioeconomic

characteristics, subjective social status, and perceptions of inequality simultaneously and

disaggregates distinct aspects of objective and subjective inequality in doing so. Specifically, it

examines not only income and education but also other status attributes unique in China,

scrutinizes subjective social status in comparisons with both proximal and broad referents, and

probes perceived degree and perceived sources of inequality in its elaboration of the rela-

tionships between various aspects of inequality and health.

3 Data and Methods

3.1 Data

Data used in this study come from the China Inequality and Distributive Justice Project

(CIDJP). CIDJP is a nationally representative sample survey conducted by a team of

American and Chinese social scientists in 2004 and 2009. It is the first of its kind that

systematically examines popular attitudes toward inequality and distributional issues in

contemporary China. This survey included a wide range of questions about health, social

and demographic background information, perceptions of personal life, and views on

inequality and hence provided rich relevant data to address our research questions.

Stratified and spatial probability sampling procedures were combined to select a nationally

representative sample. The whole country was first divided into seven strata (Northeast,

North, East, Central, South, Northwest, and Southwest) according to the geographic

location and administrative jurisdiction. Then GPS-assisted spatial sampling method was

used to select primary, secondary, and tertiary units, with all the residential addresses in the

final unit included in the sample. A respondent was randomly selected from each house-

hold with the Kish method. Further details of the implementation of the project and the

discussion of the validity and reliability of data are elaborated in Whyte’s (2010a) book.

This study uses data from the second wave of CIDJP collected in 2009.1 The response rate

1 See Acknowledgements for the list of the American and Chinese social scientists participating in thiswave of survey.

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of this wave is 69 %. The national probability sample consists of 2,866 respondents aged

from 18 to 70. To ensure there is a sufficiently large urban subsample, urban people were

oversampled. Appropriate weights taking account of this oversampling of urban respon-

dents, the probability of a respondent’s being drawn into the sample, the response rate, and

gender and age distributions of the whole Chinese population are used in the following

analysis where necessary, so that the results are representative of the Chinese adult

population.

3.2 Dependent Variables

Two health outcomes are dealt with as dependent variables: self-reported health and

psychological distress. Self-reported health is estimated by respondents’ self rating of their

general health as excellent, good, fair, somewhat poor, or very poor. Responses are recoded

so that higher scores represent better perceived health. Being a summary self evaluation as

it is, self-reported health has proved an effective predictor of such health outcome as

mortality (Idler and Benyamini 1997) and been widely used as a valid indicator of current

health status in the health literature.

Psychological distress is measured by six items extracted from the original 20 items in

the Center for Epidemiological Studies Psychological Distress Scale (CES-D) (Radloff

1977). CES-D has shown to be valid measurement of distress in China (Lai 1995; Lin and

Lai 1995). In the CIDJP survey, respondents were asked whether they never, rarely,

sometimes, or often (1) were bothered by small things, (2) did not feel like eating, (3) had

trouble keeping their mind on what they were doing, (4) thought their life had been a

failure, (5) had restless sleep, and (6) felt lonely during the past one week. These six

questions cover both negative moods, including depression and anxiety, and bodily

symptoms such as losses of appetite and sleep. A summary psychological distress scale is

created by adding all the four-point-scale responses to the six questions. Cronbach’s alpha

for the scale is .816, illustrating a high level of reliability for the scale.

3.3 Independent Variables

Three sets of independent variables measure socioeconomic characteristics, subjective

social status, and perceptions of inequality.

Among socioeconomic characteristics, income is measured by the logarithm of the

midpoint of 18 household income categories. Education is estimated by the years of

completed education. Urban/rural/migrant status is coded according to the household

(hukou) status and de facto residence. Urban hukou holders are grouped as urban citizens.

Rural hukou holders who were staying in the countryside during the interview are included

in the category of rural residents. Rural hukou holders who were living in the city are

defined as rural-to-urban migrants (abbreviated as migrants hereafter). Migrants are dealt

with as a separate group due to their special circumstance. While they work and live in the

city, their rural hukou status subjects them to discrimination in work, social benefits, and

everyday life. This dual condition distinguishes migrants from urban citizens and rural

residents. The urban category is used as the reference group in the following regression

analyses. Political capital is measured by CCP membership and treated as a dichotomous

variable with CCP members coded as 1 and 0 otherwise.

Subjective social status is estimated by two indicators. Comparison with socially

proximal referents is estimated by four questions that asked respondents to compare their

current standard of living with the average levels of their relatives, former classmates,

Health Implications of Objective and Subjective Inequality

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coworkers, and neighbors. A composite index is computed by taking the mean of the five-

point-scale responses (reverse-coded, 1 = much worse, 5 = much better) to these four

questions (Cronbach’s alpha = .865). Scores lower than 3, 3, and scores higher than 3 are

recoded into the categories of low, middle, and high perceived self status in comparison

with proximal referents respectively. Comparison with the broad referent is estimated by

self-reported social position in the whole China on a ten-point scale from 1 (lowest) to 10

(highest). Scores of 1–4 are recoded as low perceived social status, 5 and 6 as middle

perceived social status, and 7–10 as high perceived social status.,2,3

As to perceptions of inequality, perceived degree of inequality is estimated by a

question that asked respondents whether they thought the current income gap in China was

too large, somewhat large, about right, somewhat small, and too small. Answers to this

question are heavily skewed toward ‘‘too large’’ and ‘‘somewhat large’’, while only .6 % of

the respondents chose ‘‘too small’’, 1.9 % chose ‘‘somewhat small’’, and 12.6 % chose

‘‘about right’’. Due to this skewed distribution, a dichotomous variable is created with ‘‘too

large’’ and ‘‘somewhat large’’ responses coded as 1 and the other responses as 0.

There are two indicators of perceived sources of inequality. Individualistic attributions

are measured by the mean of the five-point-scale evaluations of the importance of four

factors in contributing to inequality: lack of talent and ability causing poverty, lack of

personal effort causing poverty, talent and ability leading to wealth, and hard work leading

to wealth (reverse-coded, 1 = no importance at all, 5 = complete importance, Cronbach’s

alpha = .753). Structural attributions are estimated by the mean of the five-point-scale

responses to perceived importance of six factors in causing inequality: prejudice and

discrimination, lack of equal opportunities, and deficiencies in current economic institu-

tions for poverty; and having connections (guanxi), more opportunities to start with, and

unfair factors in the economic system for wealth (reverse-coded, 1 = no importance at all,

5 = complete importance, Cronbach’s alpha = .810).4

3.4 Control Variables

A set of social and demographic attributes are treated as control variables. They include

gender as a dichotomous variable (female = 1 and male = 0), age as a continuous vari-

able, marital status as a dichotomous variable (married = 1 and 0 otherwise), unemployed

status as a dichotomous variable (unemployed = 1 and 0 otherwise), region of residence as

a dummy variable (eastern, central, and western; residence in eastern provinces as the

reference group in regression analyses), household size as a continuous variable, and

number of children in the household as a continuous variable. Age-squared was initially

2 As to be shown in the latter section, the weighted proportions of the three categories of perceived socialstatus coded this way are .11, .52, and .37 respectively. Alternative coding of scores of 1–3, 4–7, and 8–10into low, middle, and high perceived social status respectively was also attempted. The resulting proportionsof the three categories are .03, .78, and .20 respectively with an overwhelming majority concentrating in themiddle group and an excessively small proportion of people reporting high status. Regressions using the twodifferent coding methods for this variable also show a better fit for the models using the coding methodreported in the text.3 Alternative models without recoding the two types of subjective social status indicators into categoricalvariables were also run and have produced consistent results.4 The composite indexes for proximal comparison, individualistic attributions of inequality, and structuralattributions of inequality computed with the method in this paper have been used in various studiesinvolving perceptions of inequality and shown to be valid and reliable in a variety of societies includingChina (Han 2012a; Mason and Kluegel 2000; Whyte 2010a; Whyte and Han 2008). The high values ofCronbach’s alpha for the three composite indexes in this study further indicate their high level of reliability.

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included in regression analyses to control for possible quadratic effect of age and then

dropped since its coefficient is not significant in any model.

3.5 Analytic Strategy

Linear regressions using the full information maximum likelihood (FIML) method are run

to predict self-reported health and psychological distress respectively. An advantage of the

FIML estimation is that it uses all available information in the data when there is missing

information and computes more unbiased estimates than does listwise deletion or impu-

tation (Enders 2001). Analyses are done in the Amos program, which employs the FIML

method in its estimation.5

In each set of analyses of self-reported health and psychological distress, a short model

with only key socioeconomic status measures and the control variables is examined first.

Then measures of subjective social status and perceptions of inequality are inserted in two

intermediate models respectively. Finally, a full model including all independent variables

and control variables is analyzed.

4 Results

4.1 Descriptive Statistics

Table 1 lists the weighted descriptive statistics of all the variables. The mean self-reported

health is 3.72 in 5, indicating that perceived general health of Chinese adults is close to

good. The mean of the psychological distress scale is 6.03, showing that overall the

respondents rarely feel distressed.

Among independent variables, the mean logarithm of household income is 4.13. The

respondents have completed 7.44 years of education on average. The proportions of urban

hukou holders, rural residents, and rural-to-urban migrants are .28, .67, and .05 respec-

tively. CCP members account for six percent of the respondents.

The means for those who report their current standard of living is high than, about equal

to, and lower than the average levels of their relatives, former classmates, coworkers, and

neighbors are .22, .48, and .30 respectively. The means for those who place themselves in

the high, middle, and low rungs of the social hierarchy in China are .11, .52, and .37

respectively. The descriptive statistics for these two subjective social status variables share

two similar patterns: First, more Chinese people are likely to locate themselves on the

middle than on high or low rungs of the social ladder. Second, the proportion of people

who perceive a low social status is larger than the proportion for high rating. Perceived

degree of inequality has a mean of .85, indicating that an overwhelming majority of the

respondents regard the income gap in China today as excessive. The means for individ-

ualistic and structural attributions of inequality are 3.87 and 3.20 respectively. They

demonstrate a belief that both categories of factors are important for causing wealth and

poverty in China while individual factors play an even larger role than do external,

structural reasons.

The Pearson’s bivariate correlations between all dependent and independent variables

are listed in the Appendix (Table 4). The coefficients for the bivariate correlations between

5 Alternative models using the observations without missing information and ordinal logistic models forself-reported health were also run and have yielded similar results.

Health Implications of Objective and Subjective Inequality

123

all independent variables range from -.469 to .458, which helps remove our concern about

multicollinearity in regression analyses.

4.2 Results from the Regressions of Self-reported Health

Table 2 presents the findings from the regressions of self-reported health. According to the

results in the short Model 1, more income, higher levels of educational attainment, being a

rural resident, and CCP membership are significantly associated with better self-rated

heath. The coefficient for migrant is positive but not statistically significant. As to control

variables, women, older people, residents in central and western regions, and those with

more children in the household report significantly poorer health. The coefficient for

Table 1 Weighted descriptivestatistics of dependent, indepen-dent, and control variables

Minimum Maximum Mean SD

Health measures

Self-reported health 1 5 3.72 .98

Psychological distress 0 18 6.03 3.72

Socioeconomic characteristics

Log household income 2.40 5.48 4.13 .52

Education 0 22 7.44 4.49

Urban 0 1 .28 .45

Rural 0 1 .67 .47

Migrant 0 1 .05 .21

CCP member 0 1 .06 .24

Subjective social status

Proximal comparison-high 0 1 .22 .41

Proximal comparison-middle

0 1 .48 .50

Proximal comparison-low 0 1 .30 .46

Broad comparison-high 0 1 .11 .31

Broad comparison-middle 0 1 .52 .50

Broad comparison-low 0 1 .37 .48

Perceptions of inequality

Degree of inequality 0 1 .85 .36

Individualistic attributions 1 5 3.87 .67

Structural attributions 1 5 3.20 .69

Control variables

Female 0 1 .49 .50

Age 18 70 42.05 14.63

Married 0 1 .81 .39

Unemployed 0 1 .02 .14

Eastern region 0 1 .51 .50

Central region 0 1 .39 .49

Western region 0 1 .10 .30

Household size 1 19 4.44 1.67

Number of children 0 10 1.77 1.14

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married status is positive but statistically insignificant, and the coefficient for household

size is insignificant too.

Two sets of subjective measures are added to the short model in Model 2 and Model 3

respectively. Model 2 investigates subjective social status. The coefficient for those who

rank their status lower than that of others in their immediate social milieu is significantly

negative, an indication of a tendency for these people to rate poorer health. The coefficient

for those who rank their status higher than that of their proximal referents is tiny and

statistically insignificant. The coefficients for both high and low perceived own locations in

broad comparison are significant. Specifically, high subjective social status in the whole

China is linked to positive self-reported general health, while low perceived social status is

related to low self rating of health. In addition, the magnitude of the coefficient for

downward comparison is larger than that for upward comparison.

Model 3 explores the associations between various perceptions of inequality and self-

reported health. The significantly negative coefficient for perceived degree of inequality

shows a link of perceived excessive income gap in China to poorer self-rated health. As to

two types of attributions of the current inequality, individualistic attributions show a sig-

nificantly positive correlation: The more inclined individuals are to explain wealth and

poverty in society by personal talent and effort, the significantly better health they report. The

association with structural attributions of inequality is negative but statistically insignificant.

Model 4 includes all independent and control variables. The patterns for focal socio-

economic characteristics, subjective social status, and perceptions of inequality are largely

consistent with those in separate analyses in Model 1 through Model 3. The coefficients for

household income, educational attainment, being a CCP member, high subjective social

status in comparison with broad referents, and individualistic attributions of inequality

remain positive and statistically significant. Low subjective social status in both proximal

and broad comparisons and perceived degree of the current inequality continue to show a

significantly negative association. The persistence of these patterns represents a sign of the

robustness of the relationships between the factors involved and self-rated health. The

coefficient for the rural status remains positive but becomes insignificant in this full model,

indicating mediation of the effect of the rural status on self-reported health by the other

objective status attributes and subjective measures of inequality under study. The coeffi-

cient for migrant is significantly positive and similar in size to that in Model 2, the only

previous model in which it is significant. The coefficients for high subjective social status in

proximal comparisons and structural attributions of inequality remain insignificant in

Model 4.6

4.3 Results from the Regressions of Psychological Distress

The results from the regressions of psychological distress are reported in Table 3.7 The

short model (Model 1) shows that having higher income, being a rural resident, being a

6 As suggested by the anonymous reviewers, the interaction between gender and each inequality dimensionas well as the interaction between unemployed status and each inequality measure are also investigated.Only the interaction between female and household income is significantly negative, indicating that self-reported health is more likely to be positively related to income among males than among females. None ofthe other interaction coefficients is statistically significant.7 The values of R2 in the four regressions for psychological distress range from .070 to .102. Larger valuesof R2 are desirable. Nevertheless, the four regressions are intended to explore whether and to what extent thesocioeconomic characteristics, subjective social status, and perceptions of inequality under study are relatedto psychological distress. In this sense, the four models are sufficient.

Health Implications of Objective and Subjective Inequality

123

Table 2 Results of the regression analyses of self-reported health (N = 2,866)

Model 1

b (se)

Model 2

b (se)

Model 3

b (se)

Model 4

b (se)

Socioeconomic characteristics

Log household income .269***

(.046)

.166***

(.047)

.263***

(.046)

.168***

(.047)

Education .018**

(.005)

.014**

(.005)

.017**

(.005)

.014**

(.005)

Rural .139**

(.047)

.095*

(.046)

.108*

(.047)

.075

(.046)

Migrant .105

(.073)

.156*

(.072)

.115

(.073)

.161*

(.072)

CCP member .221**

(.070)

.173*

(.069)

.222**

(.070)

.177*

(.069)

Subjective social status

Proximal comparison-high -.018

(.046)

-.017

(.046)

Proximal comparison-low -.256***

(.041)

-.248***

(.041)

Broad comparison-high .131*

(.054)

.132*

(.054)

Broad comparison-low -.247***

(.040)

-.235***

(.040)

Perceptions of inequality

Degree of inequality -.136*

(.054)

-.107*

(.053)

Individualistic attributions .130***

(.027)

.107***

(.026)

Structural attributions -.027

(.027)

.003

(.026)

Control variables

Female -.118**

(.036)

-.122***

(.035)

-.122***

(.036)

-.124***

(.035)

Age -.016***

(.002)

-.016***

(.002)

-.016***

(.002)

-.016***

(.002)

Married .011

(.052)

.003

(.051)

.014

(.052)

.005

(.051)

Unemployed -.030

(.126)

.021

(.123)

-.042

(.125)

.009

(.123)

Central region -.184***

(.038)

-.156***

(.037)

-.182***

(.038)

-.156***

(.037)

Western region -.245***

(.064)

-.175**

(.063)

-.237***

(.063)

-.173**

(.063)

Household size .004

(.012)

.005

(.012)

.004

(.012)

.005

(.012)

Number of children -.051**

(.019)

-.053**

(.018)

-.050**

(.019)

-.052**

(.018)

Intercept 3.273***

(.215)

3.897***

(.221)

3.032***

(.251)

3.576***

(.254)

R2 .146 .182 .155 .188

The full information maximum likelihood method is used in the regression analyses

*** p \ .001; ** p \ .01; * p \ .05. Two-tailed test

C. Han

123

migrant, and CCP membership are significantly associated with less psychological distress.

The coefficient for education is negative but not statistically significant. As for control

variables, females, older people, residents in both central and western provinces, and those

with more children in the household report significantly higher frequencies of psycho-

logical distress. Married status is significantly associated with less distress. The coefficient

for household size is statistically insignificant.

Intermediate Model 2 and Model 3 present findings from the analyses in which two

categories of subjective inequality are added respectively. Model 2 indicates that low

subjective self status in comparison with others in close social proximity is significantly

linked to high levels of psychological distress, while the coefficient for high subjective self

status in proximal comparison is not significant. Low subjective social status in comparison

with others in Chinese society at large shows a significantly negative relationship with

psychological distress, with those who rank their status low reporting larger frequencies of

distress, while high subjective social status indicates a weakly positive association with the

level of distress. Again the size of the association for downward broad comparison is larger

than that for upward comparison.

The associations between perceptions of inequality and psychological distress are

examined in Model 3. The coefficient for perceived excessive inequality is positive but not

statistically significant. Both individualistic and structural attributions of inequality show a

significant relationship, with those who embrace individualistic explanations reporting

lower frequencies of distress symptoms and those who endorse structural attributions

expressing stronger distressful feelings.

In Model 4 all focal socioeconomic traits, subjective social status, and perceptions of

inequality as well as control variables are analyzed together. The patterns for all

independent variables displayed in Model 1 to Model 3 recur in this full model. Spe-

cifically, household income, the status as a rural resident, being a migrant, CCP

membership, and approval of individualistic attributions of inequality remain signifi-

cantly and negatively related to the level of psychological distress. Low subjective self

status in both proximal and broad comparisons and the tendency to explain inequality

by external, structural reasons continue to be significantly linked to severe distress. The

coefficient for high perceived self status in broad comparison with others in society

stays weakly negative. The overall persistent patterns in this full model demonstrate the

robustness of the associations between these factors and mental health. The coefficients

for high subjective self status in proximal comparison and perceived degree of

inequality remain statistically insignificant.8

5 Discussion and Conclusion

This study investigates how health is related to socioeconomic status, subjective social

status, and perceptions of inequality simultaneously, an inquiry that has not been made in

8 As suggested by the anonymous reviewers, the interaction between gender and each inequality dimensionas well as the interaction between unemployed status and each inequality measure are also examined. Onlythe interaction between female and downward broad comparison and the interaction between female andindividualistic attributions of inequality are significantly positive, showing that low perceived self status insociety and explanations of inequality by individual factors are more likely to be related to great psycho-logical distress among females than among males. None of the other interaction coefficients is statisticallysignificant.

Health Implications of Objective and Subjective Inequality

123

Table 3 Results of the regression analyses of psychological distress (N = 2,866)

Model 1

b (se)

Model 2

b (se)

Model 3

b (se)

Model 4

b (se)

Socioeconomic characteristics

Log household income -.729***

(.176)

-.421*

(.179)

-.705***

(.176)

-.420*

(.179)

Education -.021

(.021)

-.009

(.020)

-.020

(.021)

-.008

(.020)

Rural -1.109***

(.177)

-.965***

(.176)

-1.009***

(.178)

-.900***

(.176)

Migrant -.731**

(.280)

-.904**

(.277)

-.764**

(.279)

-.917***

(.276)

CCP member -.724**

(.268)

-.568*

(.265)

-.713**

(.267)

-.569*

(.264)

Subjective social status

Proximal comparison-high .081

(.176)

.080

(.176)

Proximal comparison-low .829***

(.157)

.808***

(.157)

Broad comparison-high -.393?

(.206)

-.370?

(.206)

Broad comparison-low .822***

(.153)

.762***

(.154)

Perceptions of inequality

Degree of inequality .249

(.205)

.165

(.203)

Individualistic attributions -.297**

(.101)

-.221*

(.100)

Structural attributions .356***

(.102)

.255*

(.102)

Control variables

Female .590***

(.138)

.602***

(.135)

.598***

(.137)

.607***

(.135)

Age .013*

(.006)

.014*

(.006)

.014*

(.006)

.014*

(.006)

Married -.896***

(.198)

-.866***

(.195)

-.895***

(.198)

-.865***

(.195)

Unemployed .779

(.480)

.611

(.472)

.804?

(.478)

.635

(.472)

Central region 1.067***

(.145)

.977***

(.143)

1.061***

(.145)

.978***

(.143)

Western region .949***

(.243)

.712**

(.240)

.909***

(.242)

.695**

(.240)

Household size -.070

(.047)

-.073

(.046)

-.071

(.047)

-.073

(.046)

Number of children .216**

(.071)

.222**

(.070)

.217**

(.071)

.223**

(.070)

Intercept 9.123***

(.820)

7.184***

(.846)

8.714***

(.961)

7.031***

(.977)

R2 .070 .099 .077 .102

The full information maximum likelihood method is used in the regression analyses

*** p \ .001; ** p \ .01; * p \ .05; ? p \ .10. Two-tailed test

C. Han

123

such an integrated manner in previous research. The empirical analyses reveal that overall,

the three dimensions of social inequality are associated with self-reported health and

psychological distress net of each other and other control variables.

Among the socioeconomic characteristics under study, income and CCP membership

are significantly associated with both health outcomes, indicating a robust bearing of the

economic and political capital on health in contemporary China. Education significantly

matters for self-reported health net of other variables, which implies distinct impact of

some resources uniquely embedded in education but not captured by other factors

associated with education, possibly access to information and effective use of knowl-

edge about new drugs and technology. Education is insignificant in multiple regressions

of psychological distress. Nonetheless, the bivariate correlation between the two vari-

ables is significant (see Table 4 in the Appendix). It is plausible that the effect of

education on distress is attenuated by other variables that are correlated with education.

The positive coefficients for rural and migrant statuses for self-reported health are not

always significant, depending on what variables are controlled for. Notwithstanding, the

less distressed inclination among the two groups is salient even in the full model. This

positive mental health state inconsistent with the objective underprivileged circum-

stances of rural residents and migrants echoes the findings about positive attitudes and

feelings among the two groups in other studies (Han 2012b; Han and Whyte 2009;

Whyte 2010a; Whyte and Sun 2010). It suggests the possibility that the life experi-

ences, values, and outlooks on life uniquely associated with rural and migrant mem-

bership may prevail over objective disadvantages in their influence on mental health.

Taken together, these findings call for attention to both universal and society-specific

status attributes to tap the richness of the relationship between socioeconomic charac-

teristics and health.

Subjective social status in comparisons with both socially proximal and broad referents

is significantly associated with the two health outcomes. The association with subjective

status in broad comparison is even more salient than that in proximal comparison. Fur-

thermore, the association with downward comparisons with both types of referents is more

robust than with upward comparisons, a pattern consistent with the rare previous research

that differentiates upward and downward comparisons (Dunn et al. 2006; Wolff et al.

2010). These findings provide new evidence for the inconclusive inquiry about the link of

subjective social status to health and point to the multilayer nature and complexity of social

comparisons in relation to health.

The health associations with different perceptions of inequality in society vary.

Perceived degree of current inequality is only significantly related to self-reported

health, individualistic attributions of inequality show a significant correlation with both

health outcomes, and structural attributions of inequality are only robustly linked to

psychological distress. Further research is needed to grapple with the subtlety and

underlying mechanisms of how societal concerns about inequality affect different health

outcomes.

This study has some limitations. First, as data used are cross-sectional, the direction

of revealed associations between many independent variables and health outcomes

cannot be readily established. Second, both health outcomes and measures of subjective

social status and perceptions of inequality are self-reported, and it may be argued that

significant relationships between these variables are possibly due to unidentified con-

founding factors. While these two concerns are reasonable, findings in prior research

somewhat increase our confidence in the causality of key independent variables and

health measures under study. For example, justice beliefs have been found to be

Health Implications of Objective and Subjective Inequality

123

antecedent to personal well-being (Jost and Hunyady 2005). It has also been discovered

that personality traits do not cancel out the relationship between perceptions of the

social environment and health (Oshio and Urakawa 2012, 2013). That said, longitudinal

data and direct controls of personal predispositions are needed to test causality in

future. The third limitation is that data used come from a single country. While the

complex inequality contour makes China a theoretically relevant case for our research

questions, similar inquiry is yet to be replicated in other societies so as to identify

whether similar or different patterns will be found in different economic, social, and

cultural settings.

Despite those caveats, this study has made theoretical contributions to health

research. It is among the first of its kind that examines how various health outcomes are

associated with socioeconomic status, subjective social status, and perceptions of

inequality simultaneously in a single study. Its findings call for our attention to the

multidimensionality of social inequality in relation to health. The revealed complexity of

the relationship between each set of inequality measures and the two health outcomes

further points to the importance of grasping the multi-faceted nature of each dimension

of inequality and its subtle associations with different health outcomes. In so doing, this

study has enriched the theory about the relationship between social inequality and

health.

This study also carries practical implications. Over the past several decades, inequality

has enlarged in many countries, and numerous measures have been implemented in

response to altered inequality landscape. Findings in this study suggest that decision

makers take account of not only people’s objective circumstances but also their subjective

responses to inequality such as subjective social status and perceived extent and sources of

inequality in their decision making, since these multidimensional indicators of inequality

bear important implications for people’s health.

Acknowledgments The author is indebted to the second wave of the China Inequality and DistributiveJustice Project conducted in 2009. Martin K. Whyte at Harvard University was the Principal Investigatorfor the project. The other investigators included Jieming Chen at Texas A&M University-Kingsville, JuanChen at Hong Kong Polytechnic University, Chunping Han then a Ph.D. candidate at Harvard University,Pierre Landry then at Yale University, Albert Park at Oxford University, Mingming Shen at PekingUniversity, Feng Wang at the University of California-Irvine, Jie Yan at Peking University, and MingYang at Peking University. Funding for the survey came from the Smith Richardson Foundation, HarvardChina Fund, Yale University, Harvard Weatherhead Center for International Affairs, and Harvard AsiaCenter. The author would also like to thank the Social Indicators Research editor and anonymousreviewers for their helpful comments on an earlier version of this article.

Appendix

See Table 4.

C. Han

123

Ta

ble

4B

ivar

iate

corr

elat

ions

bet

wee

ndep

enden

tan

din

dep

enden

tvar

iable

s(N

=2

,866

)

12

34

56

78

910

11

12

13

1.

Sel

f-re

port

edhea

lth

2.

Psy

cholo

gic

aldis

tres

s-

.363***

3.

Log

house

hold

inco

me

.238***

-.1

13***

4.

Educa

tion

.256***

-.0

71***

.458***

5.

Rura

l-

.100***

-.0

53**

-.3

68***

-.4

69***

6.

Mig

rant

.062***

-.0

30

.063**

-.0

04

-.3

27***

7.

CC

Pm

ember

.069***

-.0

54**

.137***

.213***

-.1

61***

-.0

56**

8.

Pro

xim

alco

mpar

ison-h

igh

.107***

-.0

67***

.228***

.151***

-.1

19***

.068***

.101***

9.

Pro

xim

alco

mpar

ison-l

ow

-.1

94***

.166***

-.2

03***

-.0

92***

.011

.014

-.0

69***

-.3

53***

10.

Bro

adco

mpar

ison-h

igh

.114***

-.0

83***

.154***

.153***

-.1

07***

-.0

48*

.114***

.141***

-.1

06***

11.

Bro

adco

mpar

ison-l

ow

-.2

12***

.172***

-.2

22***

-.1

66***

.005

.107***

-.0

66***

-.1

43***

.278***

-.2

81***

12.

Deg

ree

of

ineq

ual

ity

-.0

32

?.0

30

.022

.092***

-.0

87***

.028

.015

-.0

53**

.036

?-

.038

?.0

51*

13.

Indiv

idual

isti

cat

trib

uti

ons

.068***

-.0

47*

-.0

33

-.0

23

.103***

-.0

66***

-.0

21

-.0

26

-.0

51**

-.0

17

-.0

63***

.029

14.

Str

uct

ura

lat

trib

uti

ons

-.0

14

.072***

-.0

36

?.0

03

-.0

66***

.044*

-.0

17

-.0

30

.049*

.079***

.127***

.123***

.154***

***

p\

.001;

**

p\

.01;

*p\

.05;

?p\

.10.

Tw

o-t

aile

dte

st

Health Implications of Objective and Subjective Inequality

123

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