Global COE Hi-Stat Discussion Paper Series 020

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Happiness in the Dual Society of Urban China:
Hukou Identity, Horizontal Inequality andHeterogeneous Reference

Hi-Stat Discussion Paper

Shiqing Jiang
Ming Lu
Hiroshi Sato

January 2009

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

Happiness in the dual society of urban China:
Hukou identity, horizontal inequality and heterogeneous reference ∗Shiqing JIANG, Fudan University
Ming LU, Fudan University
andHiroshi SATO, Hitotsubashi University
Abstract
This paper studies the impact of income inequality on the subjective well-being of different
social groups in urban China. We classify urban social groups according to their hukou status:
rural migrants, “born” urban residents, and “acquired” urban residents who once changed
their hukou identity from rural to urban. We focus on how the horizontal inequality—income
disparity between migrants and urban residents—affects individual happiness. The main
results are as follows. First, migrants suffer from unhappiness when the horizontal inequality
increases, but urban residents show a much smaller aversion to the horizontal inequality.
Second, migrants will not be happier if their relative incomes within their migrant groupincrease, while urban residents do become happier when their incomes increase within theirgroup’s income distribution. Third, “acquired” urban residents have traits of both migrants
and “born” urban residents. They have an aversion to the horizontal inequality like migrants,
and they also favor higher relative income among urban residents. Fourth, “born” urban
residents have lower happiness scores when they are old. People who are Communist Party
members strongly dislike the horizontal inequality. Our findings suggest that migrants,“acquired” urban residents, elderly people and Party members from “born” urban residents are
the potential proponents of social integration policies in urban China.
Keywords: Horizontal inequality, Happiness, Hukou identity, Migration, Social integration
JEL classification: I31, O15, R23∗

Shiqing Jiang: Department of Economics, School of Economics, Fudan University, Shanghai 200433, China. E-mail:shiqing.jiang@gmail.com. Ming Lu: Department of Economics, and Employment and Social Security Research Center,
School of Economics, Fudan University, Shanghai 200433, China. E-mail: lm@fudan.edu.cn. Hiroshi Sato: Graduate Schoolof Economics, Hitotsubashi University, Tokyo 186-8601, Japan. E-mail: satohrs@econ.hit-u.ac.jp. We thank Zhao Chen, Jin
Feng, Shuang Zhang and seminar participants at Fudan University for their useful comments. We gratefully acknowledge
financial support for the CHIP 2002 survey provided mainly by the Ford Foundation, the Swedish International Development
Cooperation Agency (SIDA) and partly by the Japan Society for the Promotion of Science (JSPS) and Hitotsubashi
University. Financial support from the National Social Science Project (07BJL051), the “985” project of Fudan University,
the Shanghai Leading Academic Discipline Project (B101), JSPS Grants-in-Aid for Scientific Research (No. 18203018), theHeiwa Nakajima Foundation, and the Global COE Program “Research Unit for Statistical and Empirical Analysis in Social
Sciences”, Hitotsubashi University, and the Fukino Project, Hitotsubashi University is also gratefully acknowledged.

11. Introduction
With a foundation in the vast rural-to-urban labor migration, the urban–rural divides havebeen revealed in Chinese cities as social segmentation and inequality between the urban
residents who have local urban hukou status and rural migrants who live and work in urban
areas without urban hukou status. 1 Although the rural migrants contribute significantly to thecity construction and are also acknowledged as the key factor in the booming of
manufacturing industries, the hukou registration policy discriminates against migrants as a
“floating population” and denies them equal access to social welfare programs compared withthe local urban residents. The two connected but also segmented groups are forming “a dual
society” in Chinese cities: migrants earn higher incomes than their rural counterparts, but
under the urban–rural segmentation policy, their incomes are much lower than the local
residents’. 2 Such hukou-identity-induced between-group income inequality is termed“horizontal inequality”, which is generally defined as an income disparity between different
social groups in the same geographical area and is considered a more influential element thanoverall inequality in generating social conflict (Stewart, 2001). Does the horizontal inequality
affect the happiness of urban residents and their neighboring migrants simultaneously? Whichgroup of urban residents suffers most from the horizontal inequality with migrants? These are
two core questions we try to answer in this paper. To answer such questions is of crucial
importance because they guide welfare analysis of who suffers from income inequality, these1
Regarding the fundamental role of the hukou system in the socioeconomic segmentations in China, see Liu (2005), Wang
(2004), and Wu and Treiman (2007).
2
According to our sample data, the ratio of yearly household income per capita between the urban residents and rural
migrants varies from 1.29 to 3.34 in 27 sample cities. It may be argued whether the observed income gap truly reflects
discriminational urban–rural policies or just the ability differences between urban residents and migrants. If migrants are selfselected from the lower tail of the ability distribution, the observed income gap may overestimate the effects of urban–ruralsegmentation policies. However, Jiang et al. (2008) and Chen et al. (2008) found that migrants are positively selected along
the line of education level in rural China. Therefore it is more plausible that the observed income gap may evenunderestimate the effects of discriminational policies. We neglect the selection problem and do not discuss this issue in thispaper.

2

people being the potential proponents of institutional change for social integration in urban
China.
We identify three specific social groups in urban areas of China according to their hukou
status. The first group is rural migrants without urban hukou status. The second group is the“born” urban residents who were granted urban hukou status at birth. The third group is“acquired” urban residents who had a chance to change their hukou status from rural to urban
at a certain point in their life (nongzhuanfei). As Deng and Gustafsson (2006) pointed out, the“acquired” urban residents can be regarded as “permanent migrants” who have distinctive
socioeconomic characteristics.
The rest of this paper is organized as follows. Section 2 discusses happiness studies,
especially the empirical evidence from China and studies on happiness and inequality. Section
3 describes the data. In Section 4, we investigate how horizontal inequality affects people
with different hukou identities and characteristics using regression analysis, and also discuss
the policy implications of our findings. The final part concludes.
2. Literature review
Subjective well-being, or happiness, once a hot topic for psychologists and sociologists, isgaining more attention from economists. Frey and Stutzer (2002) provided an excellent
survey of subjective happiness research. Thanks to data availability, the number of studies
examining happiness determination functions in China is growing. Here, we only consider
happiness determination in China. Luo (2006) explored the effects of unemployment on
happiness and found that families with unemployed members do have lower happiness scores.
Knight et al. (2007) mainly examined the role of absolute and relative income on thehappiness determination of rural China residents, and the results are in accordance withintuition: higher household income per capita results in a higher happiness level; higher3

relative income status within the village also results in higher happiness. They also found thatthe effects of absolute/relative income vary with family economic conditions. Knight andGunatilaka (2007, 2008) studied the happiness determination of rural migrants and used
Easterling’s adaptation theory to explain that the reason for migrants having the lowest
happiness scores is because they have changed their income reference group to urban
residents, so their higher aspirations make them unhappier.
All these studies increase our understanding of what determines happiness in China, yetnone of them has also considered the effects of hukou identity and urban–rural segmentationon the happiness of migrants and urban residents. Knight and Gunatilaka (2008) examined thehappiness determination of migrants, but they did not control for the hukou identity to see its
effect on happiness. In fact, rural–urban segmentation in China is a political and economic
institutional arrangement. People are labeled as rural or urban hukou when they are born, andeven when rural migrants work in the cities, their hukou identities do not change. The city
governments accumulate physical capital and enhance the welfare of only urban residents
through rural–urban segmentation and urban-biased economic policy, thus leading to a huge
income gap between rural and urban areas (Chen and Lu, 2008). Such identity-induced
income inequality was also called “horizontal inequality” 3 by Stewart (2001). When there
only exists limited mobility at the group boundaries, horizontal inequality will cause social
conflicts and will thwart economic development (Stewart, 2001). We match the urban
residents’ data and rural migrants’ data from the same city, and measure the horizontal
inequality as the income ratio between urban residents and migrants to see whether hukouinduced horizontal inequality affects equally the happiness of urban residents and migrants;

3

In his seminal paper, Stewart (2001) proposed the concept of “horizontal inequality” and defined it as “existence of severe
inequalities between culturally defined groups, …, horizontal inequalities are multidimensional—with political, economic
and social elements.”

4thus, we are the first to examine the distributional effect of urban–rural segmentation on thewelfare of urban residents and migrants simultaneously.
The effect of inequality on happiness is also a relatively new area. Alesina et al. (2004)
found that the state-level Gini coefficient in the USA and the country-level Gini in the EU are
negatively correlated with individual happiness. However, the effect of the Gini coefficient is
unequally distributed among the rich and poor: poor people in the EU suffer from a larger
Gini, but this is not the case for the poor in the USA The explanation for different attitudes
toward inequality between Europe and the USA is that they have different subjective
perceptions on social mobility. However, the negative effect of the Gini on happiness is notsupported in China. Astonishingly, Knight et al. (2007) found a significant positive effect of
the Gini on happiness in rural China. They explain it as a demonstration effect: with rapid
income growth, the increasing Gini will make rural residents have greater expectations ofbeing more highly ranked in the income distribution in the future, so they are happier to see alarger Gini coefficient. 4
In this paper, we also consider the effect of inequality on happiness and set up thefollowing hypothesis: In a dual economy like China people living in a city may not besensitive to the Gini coefficient at the city level. Rather, segmented social groups in a city are
only concerned with the income status within their own group and with the income gap
between different social groups.
To examine this hypothesis, we decompose income inequality into two dimensions. The
first dimension is the within-group inequality. Specifically, we focus on relative income
within the same hukou identity group. The second dimension is the horizontal (betweengroup) inequality. We pay attention to the income ratio between different hukou identity
groups: the urban residents and the migrants. One advantage of such decomposition is that we
4Interestingly, using the same dataset but only urban household and migrant subsamples, we find the same significant
positive Gini effect when the squared term is not included. This will be discussed thoroughly below.

5can consider which kind of inequality people may dislike, thus gaining a better understandingof the channels through which inequality affects happiness.
3. Data description
The data used in our research are from the urban household survey and migrants survey
from the 2002 Chinese Household Income Project Survey (CHIP2002) collected by the
Chinese Academy of Social Science. The urban survey was conducted in 62 cities, but only
27 cities have the parallel migrant household survey. Thus we only use the 27 matched cities
in our subsample. The data include a series of individual and household characteristics andinformation on income. More importantly, there are attitude questions on “happiness” for theheads of households or a main member of the household. 5
The dependent variable is the subjective happiness score of the household respondent. The
same question was asked of one of the adults in each sampled household: “Generally speaking,how happy do you feel?” The six possible answers were: very happy, happy, so-so, not happy,
not happy at all, and don’t know. This is the key variable in our analysis. We drop theobservations with the answer “don’t know” and evaluate the other five answers as 4, 3, 2, 1, 0
respectively. We mainly use ordinary least squared (OLS) regression in our analysis. The
reasons are twofold: first, Ferrer-i-Carbonell and Frijters (2004) found that in a happiness
function, the significance and sign of coefficients are robust to either OLS or ordered
probit/logit. OLS regression is more intuitive and interpretable for the readers. The second
reason is that we control the interaction terms in our regression, and it is hard to interpret themarginal effects of the interacted variables when using ordered probit/logit. Knight et al.
(2007) and Knight and Gunatilaka (2007, 2008) also used OLS to explore happinessdetermination in China. We also estimate an ordered probit as a robustness check of the OLS

5For details of the sampling framework and sampling method of the CHIP 2002 survey, see Gustafsson et al. (2008).

6

results, however we stick with OLS because the interaction term in the ordered probit
regression is not intuitive.
The independent variables are structured as follows. First, we classify individual’s hukou
identity using a dummy variable that takes on a value of one if she/he has urban hukou statusand zero if she/he has rural hukou status. Second, based on the assumption that urban
residents and migrants have different reference groups, we introduce a measure of relative
income for the relevant hukou identity group calculated as household income per capita
divided by mean income of the same group in a city. Third, as the measure of horizontal
inequality, we utilize the income gap of urban residents and migrants calculated as the ratio
between the mean income of each hukou identity group. This variable is regarded as amonetary measure of the socioeconomic gap generated by the hukou status combined withother discriminatory urban–rural segmentation policies. We also add an interaction term
between horizontal inequality and hukou identity to examine the effect of income inequality
on each group. Fourth, following the previous studies that found a statistically significant
effect of the expectation for future income on current happiness (Luo 2006, Knight andGunatilaka 2008), we introduce a dummy variable that indicates respondent’s expectation forincome change over the next five years: “big increase”, “small increase”, “unchanged”, or“decrease” (“unchanged” as the base group). Fifth, we employ the log of annual householdincome per capita in order to control the influence of the absolute level of household income,
Other controlled variables include sex, age, age squared, years of schooling completed,health condition, marital status, political identity, employment status, household living square
meters per capita and city-level Gini coefficient.
Appendix Tables 1 and 2 provide variable definitions and statistical descriptions. Appendix
Table 1 presents the descriptions of horizontal inequality and city-level Gini for the 27 cities.
Figure 1 shows a significant positive correlation between horizontal inequality and city-level
7

Gini. The R-square for a simple regression of horizontal inequality on city-level Gini is 21%.
In order to see more closely the role of horizontal inequality on general inequality, we apply
the entropy index (with parameters 0, 1, 2) to decompose inequality into between-group(identity-induced inequality) and within-group inequality. From Appendix Table 2, we cansee that the hukou-identity-induced inequality can explain 12.83–17.84% of total inequality,
and the Theil index decomposition shows the amount of between-group inequality is 17.31%.
Therefore, the horizontal inequality is an important source of inequality. Appendix Table 3
gives the characteristics descriptions of migrants and urban residents. The last column is thep-value for the ANOVA test of equal means. From the table, we can see that the migrants andurban residents are two distinct groups of people: urban residents have higher happinessscores, and they have higher education levels and household incomes than the migrants;
however, migrants are overrepresented by the male gender and are also younger, healthier,and more optimistic about future income change.
4. Regression results
4.1. Hukou, horizontal inequality, relative income and happinessWe first examine how horizontal inequality and relative income impact on the happiness ofurban residents and migrants respectively. We establish the following happiness functions:

Happinessij = Hukouij + HI j + RI ij + Hukouij × HI j + Hukouij × RI ij + X ij β + ε ij .

HIj is horizontal inequality, and RIij represents relative income in the peer group.
Subscripts i and j denote the individual and city respectively. We also add interaction terms
between Hukouij and HIj, and Hukouij and RIij to examine whether urban residents andmigrants have different attitudes toward inequality. Regression results are in Table 1.
Table 1 about here8We report four regression results in Table 1. In column 1, we do not control for theinteraction terms between horizontal inequality, relative income and hukou, whereas column 2
includes the two interaction terms. Columns 3 and 4 are robustness checks of the results incolumns 1 and 2 respectively. What we are concerned with the most is the hukou, relative
income, horizontal inequality and the interaction terms between them. Let us first see theeffect of hukou identity. When the two interaction terms are not controlled for, the hukou
identity is negative but insignificant, but it becomes significantly negative when the twointeraction terms are included. Although the statistical descriptions of the CHIP2002 survey
data suggest that urban residents have higher happiness scores than migrants (Knight andGunatilaka, 2007), we find that the effect of hukou on happiness should also consider
horizontal inequality and relative income. For an urban resident whose income equals the city
mean, the marginal effect of hukou on happiness equals –0.2694 + 0.0864 + 0.1140 ×
horizontal inequality. Therefore for a city with a horizontal inequality greater than 1.6052, themarginal effect of hukou is positive, and vice versa. Because the horizontal inequality in ourdata ranges from 1.29 to 3.34, it is safe to say urban residents have greater happiness thanmigrants in most of the cities in our sample. The horizontal inequality created by rural–urban
segmentation policy significantly reduces the happiness of migrants. For the urban residents,
this effect is still negative, but the marginal effect is much smaller (–0.1035 + 0.0864 = –0.0171). It implies that the income inequality affects migrants more than urban residents. It is
plausible that when the rural migrants enter the cities, they change their perception of theliving quality of urban residents and change their income reference group from that of theirremaining rural counterparts to that of urban residents. Therefore, they suffer from
unhappiness when the income gap increases. 6 However, urban residents are concerned more

6

Another alternative explanation also leads to the above findings. Suppose there are no discrimination policies but only
ability differences: migrants are moving into different cities, the migrants with higher education (proxy for higher ability) are
more attracted to the wealthier cities to earn higher income; thus, the narrow horizontal income in such cities can be9about their own income status within the urban residents group but not too much about theincome inequality with migrants. The changing reference group hypothesis is also supportedby the coefficients on relative income: urban residents are happier if their relative income
status improves, but migrants are not happy when they improve their status within the
migrants group.
Rather surprisingly, the city-level Gini coefficient shows a significant positive sign.
Interestingly, Knight et al. (2007) also found a significant positive county-level Gini for
happiness when they explored the happiness determination of rural Chinese residents. We
have a similar explanation to Knight et al.: in an era of rapidly increasing incomes, people
may optimistically expect their future opportunities to be at the higher end of the income
distribution (demonstration effect). However, we give little attention to the Gini because it has
two problems: first, the population ratio between migrants and urban residents in our sample
is not the true one in the real world, so the calculated city-level Gini may over- or
underestimate the real Gini. Second, in our cross-sectional data, we cannot include the city
dummies, so the Gini coefficient may contain the city characteristics.
All the other coefficients in our paper are consistent with the findings in previous studies(Luo, 2006; Knight et al., 2007; Knight and Gunatilaka, 2007, 2008). Compared with females,
males have lower happiness scores, possibly because they shoulder more responsibility insociety and face more stress. Age has a U-shaped nonlinear effect on happiness, with aturning point at age 38.1 or 38.8 in columns 1 and 2 respectively. Marital status influenceshappiness, in that compared with unmarried people, married people can enjoy a family life
and thus they have higher happiness scores, but divorce significantly reduces happiness.
Educational attainment has an insignificant effect on happiness; it is possible that people with

explained by the higher-ability migrants. Higher ability positively correlates with happiness by intuition. In order to reject
this explanation, we do the following procedure and find that: (1) migrants’ education levels do differ among cities, (2) theireducation can lead to higher income, (3) the higher the city mean income, the higher migrants’ education level, (4) however,in such cities, the horizontal inequality is larger, not smaller as the explanation predicts. Therefore, we reject this explanation.

10

higher education are more stressed in society and have greater aspirations. The same findings
are in Luo (2006), where he used education level dummy variables, and all levels of education
had an insignificant effect on happiness. Unemployment significantly reduces happiness. The
log yearly household per capita income has a significant positive role on happiness even if therelative income is controlled for. The semielasticity of income is about 0.22 and is close to theestimation of Knight and Gunatilaka (2008). Political identity, measured as whether theindividual is a Communist Party member, significantly increases happiness. Previous studies
have confirmed that Party identity will bring higher income (Appleton et al., 2005; Li et al.,2008); however, even when we control for the log household income, Party identity still leads
to greater happiness because of nonmonetary benefits. The coefficients on self-reported healthalso accord with intuition: people who report themselves as in good health are happier thanthose reporting health as just so-so, and self-reported bad health significantly reduces
happiness. People who expect they will have a big or small increase in income after five years
are much happier, and big expectations lead to higher happiness scores than small
expectations. Pessimistic expectations on future income lead to lower happiness.
It is interesting to compare the magnitude of coefficients of horizontal inequality withother variables. Based on column 2 in Table 1, when the income ratio between urban residents
and migrants is reduced by 1, its effect on happiness equals increasing household per capita
income by 47.5%, or increasing living space by 25.2 square meters.

4.2. How does “acquired” urban hukou affect income inequality, relative income andhappiness?
Although the hukou policy will survive for a long time, it is still possible to change rural

hukou to urban hukou through attending college, becoming an official, joining the army,
losing land to the government, buying a house in a city or even buying a hukou, etc. How

11does a changed hukou status affect attitudes toward horizontal inequality? Will the once-rural

hukou owners assimilate to the “born” urban hukou residents if the duration of their urban
stay is long enough? These two questions need to be explored. If the once-rural hukou
residents are averse to horizontal inequality, and at the same time, their attitudes are notassimilated, they will be potential voters for social integration policy in the future. According
to the questionnaire, we further differentiate urban hukou into two new categories, with“born” urban hukou as 1 for those who have never changed their hukou status, and “acquired”
urban hukou as 1 for people who used to be rural residents but acquired urban hukou statuslater in life. The basement group is still the rural migrants. The interaction terms between“born”, “acquired” urban hukou, horizontal inequality, and relative income (four interaction
terms) are used to see whether these two groups of people have similar attitudes to inequality
compared with migrants. We also interact the interaction terms of “acquired” urban hukou andhorizontal inequality with years since getting hukou and years since getting hukou squared tocheck whether “acquired” urban hukou residents assimilate to the “born” urban residents. The
regression results are in Table 2.
Table 2 about here
We discuss the three cases of hukou status. First, consider the results in Table 2, column 1.
Like the results in Table 1, migrants show aversion to horizontal inequality (with a marginal
effect of –0.1022), and they do not care about their income status within the migrants group.
The “born” urban residents also feel unhappy about increasing horizontal inequality, but themarginal effect is quite small (only –0.0035 = –0.1022 + 0.0987). This implies that even witha tiny startup cost, the “born” urban residents may give up social integration policy. Again,
they feel happier because of the improvement in their relative income status within the urban
residents group. The most striking results here are those for the “acquired” urban hukou
12residents: they have the same attitudes toward horizontal inequality as migrants—that is, they
say they suffer from unhappiness when the income gap increases—but they also show greater
happiness when their position in the relative income distribution improves. 7 To some extent,
they have the traits of both migrants and “born” urban residents. Because the urban public
policy in China is only determined by the urban residents (there are no “voting” rights for thepeople who do not secure a local urban hukou), the “acquired” urban hukou residents are
actually the “spokespersons” for migrants. However, the sample statistics in our data show
they are still in the minority among the urban residents (only 25.78%) and not influential
enough to abolish rural–urban segmentation policy.
In columns 2 and 3, we put the interaction terms between “years since getting hukou”,“years since getting hukou squared” and “acquired urban hukou × horizontal inequality” into
regression functions. What we want to check is whether with a longer urban stay, “acquired”
urban hukou residents assimilate to the “born” urban residents. If so, the two new variables
will at least be significant. However, despite including a linear term in column 2 or adding asquared term in column 3, they are both insignificant. Therefore, it is safe to say that pastrural life experiences have a persistent effect on perceptions of horizontal inequality.

4.3. Who will be the potential proponents of social integration policy among “born” urban
hukou residents?
Our paper has already presented interesting findings. However, a more important andrealistic issue is how to reduce the income gap between rural and urban China, which reached3.33 to 1 by the end of year 2007, the highest in the 30 years of reform since the opening up
of China. What is worrying, with more and more migrants in Chinese cities, is that thetraditional rural–urban divide has gradually become a divide between migrants and urban
However, the coefficient on “acquired” urban hukou × relative income is only significant at the 10% confidence level, andits magnitude is smaller than the coefficient on “born” urban hukou × relative income. That is, the “acquired” urban hukou
residents do not aspire to higher income as much as the “born” urban residents.
7

13

residents. Meng and Bai (2007) found that during 2000–2004, wage increases for migrants
stagnated in Guangdong Province. Both Yan (2007) and Zhang and Meng (2007) found thatthe income gap between migrants and urban residents is still widening because of different
returns to human capital. Although we have concluded that urban hukou residents(particularly the “born” urban residents) are less affected by the horizontal inequality, we stillneed to further elaborate the attitude of “born” urban residents toward horizontal inequality.
This is because they are the most influential group on local policy. Specifically, we use aseries of interaction terms between individual characteristics and horizontal inequality. Table
3 reports the results.
Table 3 about here
In column 1 of Table 3, we add an interaction term between age and horizontal inequality.
This term has a significant negative sign, and we know that people who are older than 47.9
(0.3305/0.0069 = 47.9) turn from favoring horizontal inequality to disliking it. However, it isunclear whether this age effect truly captures the attitudes of elderly people toward horizontal
inequality or whether something else does. The turning point is 48 years, and it is people who
were born before 1954 (2002 – 48 = 1954) that are in the age group eligible to be sent to rural
China in the intellectual youth movement. We are concerned that if this intellectual youth isnot controlled for, the age effect only proxies life experiences in rural China. In column 2, we
use a dummy variable for intellectual youth and its interaction with horizontal inequality.
From the results, we can see that when intellectual youth experience is controlled for, the age
effect remain almost the same, so it is safe to say that when growing older, people tend to
dislike inequality. Another interesting finding is that intellectual youth experience brings
more happiness if horizontal inequality equals zero, perhaps because bitter past life

14

experience in rural China creates lower aspirations. At the same time, intellectual youth
dislike horizontal inequality compared with other “born” urban residents.
In column 3, we interact Communist Party membership with horizontal inequality. We findthat the Party members strongly dislike the horizontal inequality (with a coefficient of –0.1476). It is not a surprising result because Party members are drawn from the elite ofChinese society, and they may have a strong taste for social justice and a much deeper
understanding of the harm of horizontal inequality.
In column 4, we add an interaction term between log household income per capita andhorizontal inequality. Column 5 introduces an interaction term between years of schoolingcompleted and horizontal inequality. The two interaction terms are both insignificant.
Wealthier groups and well-educated groups seem to like the inequality.
The above regression produces the following conclusions: with urbanization in an aging
society, social integration between migrants and urban residents will be favored by “acquired”
urban hukou residents, the elderly and Communist Party members.
5. Conclusion
In this paper, we focused on how income inequality, specifically the horizontal inequality
between urban residents and migrants, affects happiness. Our main empirical results are as
follows. (1) Overall, the rural migrants will be unhappier when the income gap between themand urban residents increases, whereas urban residents show a much smaller dislike of thehorizontal inequality. (2) Migrants will not be happier if their relative incomes within the
migrants group increase, while urban residents will become happier when their relative
incomes among their peers increase. Our explanation for this finding is that, along with therise in relative income, migrants tend to change their income reference group from theirmigrant peers to the urban residents, while urban residents still compare income within their15

own group. It is noteworthy that the hukou identity determines not only the individual’s
income position in the urban society but also the pattern of formation of subjective well-being.
(3) The “acquired” urban residents have a somewhat dual identity. On the one hand, they
dislike horizontal inequality like migrants, but on the other hand, their income referencegroup is their urban resident peers, and they feel happier when they occupy a higher relative
income position within the urban residents group. (4) For the “born” urban residents, they
have lower happiness scores when they are aging. People who are Communist Party members
strongly dislike horizontal inequality.
Our empirical results contain strong policy implications. Accompanied by rapid
urbanization and lagging social integration policy in China, cities in China do not eliminate
the hukou institution; in fact, they divide urban residents and migrants. The major form of the“rural–urban divide” has been a transformation from a traditional “dual economy” between
urban and rural sectors to a “dual society” between urban residents and migrants in urban
areas. How to integrate the two groups is of crucial importance for sustainable economic andsocial development in China. The potential proponents of social integration policy will bethose who are the most sensitive to income inequality induced by hukou identity: migrants
who have no voice in local public policy yet; “acquired” urban residents who somewhat
preserve rural characteristics; elderly people and Communist Party members among “born”
urban residents. The characteristics of potential proponents imply that the urbanization
through gradual easing of hukou policy and the aging of society are also beneficial for social
integration in urban China.

16

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19

3.5
Horizontal Inequality
2
2.5
3
1.5
.27

.29

.31

.33
.35
City Gini Coefficients

Horizontal Inequality

.37

.39

.41

Fitted Line

Figure 1 Relationship between horizontal inequality and city Gini coefficients20

Table 1
Happiness functions of urban residents and migrants

Dependent variable: columns 1 and 2 are cardinal happiness scores; columns 3 and 4 areordinal happiness level.

Urban hukou identity
Horizontal inequality

(1)

(2)

OLS

OLS

–0.0223(0.0392)–0.0482**(0.0240)

1.0571***(0.3349)–0.2694***(0.0956)–0.1035***(0.0403)
0.0864*(0.0485)–0.0330(0.0329)0.1140***(0.0324)0.9402***(0.3388)–0.0505**(0.0224)–0.0213***(0.0063)0.0003***(0.0001)
0.1034*(0.0572)–0.3355***(0.1157)–0.1587(0.1073)
0.0034(0.0039)–0.1823**(0.0765)0.2448***(0.0360)
0.0756***(0.0273)
0.0045***(0.0013)0.3399***(0.0615)
0.1134***(0.0237)–0.3616***(0.0345)0.2202***(0.0256)–0.1462***(0.0551)–0.0494**(0.0224)–0.0218***(0.0063)0.0003***(0.0001)0.1000*(0.0569)–0.3274***(0.1165)–0.1602(0.1076)0.0017(0.0040)–0.1713**(0.0766)0.2177***(0.0372)
0.0676**(0.0274)
0.0041***(0.0013)0.3449***(0.0615)
0.1129***(0.0236)–0.3609***(0.0345)0.2172***(0.0255)–0.1447***(0.0551)

Urban hukou identity × horizontal
inequality
Relative income–0.0063(0.0292)

Urban hukou identity × relative
incomeCity-level Gini

(3)
Orderedprobit–0.0040(0.0505)–0.0677**(0.0333)

1.4235***(0.4567)(4)
Orderedprobit–0.3643***(0.1322)–0.1497***(0.0531)0.1289**(0.0640)–0.0317(0.0378)0.1609***(0.0449)
1.2599***(0.4598)–0.0683**(0.0310)–0.0314***(0.0090)0.0004***(0.0001)
0.1642**(0.0821)–0.4136***(0.1458)–0.1781(0.1343)0.0045(0.0055)–0.2079**(0.0841)0.3200***(0.0353)
0.1073***(0.0390)0.0065***(0.0019)0.5142***(0.0819)0.1575***(0.0335)–0.4701***(0.0440)0.3166***(0.0361)–0.1812***(0.0697)–0.0672**(0.0310)–0.0322***(0.0090)0.0004***(0.0001)
0.1581*(0.0825)–0.4049***(0.1459)–0.1825(0.1346)
0.0022(0.0055)–0.1929**(0.0843)
0.2816***(0.0372)
0.0962**(0.0391)0.0058***(0.0019)0.5225***(0.0820)0.1570***(0.0335)–0.4700***(0.0440)
0.3132***(0.0361)–0.1792***(0.0697)

0.0047(0.0341)

Personal Characteristics
Male
Age
Age squared
Marital status: Married
Divorced
WidowedYears of schooling completed
UnemployedLog household income per capita
Communist Party member
Square meters of housing per
capitaExpect big income increase
Expect small income increase
Expect income decrease
Health:

Good
Bad

21

Constant
Number of observations
F-test (or Log likelihood)
R-squared (or Pseudo R-squared)

0.1159(0.3365)0.5485(0.3834)

5674
43.63
0.14375674
41.19
0.14615674–6516.08
0.06155674–6507.94
0.0627

Note: *, **, ***: Coefficient different from zero at 10, 5, 1 percent significance levels respectively.
Robust standard errors are in parentheses.

22

Table 2
Happiness functions of “born” and “acquired” urban residents and migrants

Dependent variable: cardinal happiness scores; Regression method: OLS“Born” urban hukou identity“Acquired” urban hukou identity
Horizontal inequality
Relative income“Born” urban hukou identity ×
horizontal inequality“Born” urban hukou identity ×
relative income“Acquired” urban hukou identity ×
horizontal inequality“Acquired” urban hukou identity ×
relative income
City-level Gini

(1)
–0.3131***(0.1011)–0.1219(0.1357)–0.1022**(0.0404)–0.0338(0.0332)
0.0987*(0.0510)0.1202***(0.0351)
0.0416(0.0636)
0.0911*(0.0484)
0.9315***(0.3394)

Years since getting urban hukou ×“acquired” urban hukou identity ×
horizontal inequality
Years since getting urban hukou
squared × “acquired” urban hukou
identity × horizontal inequality
Personal Characteristics
Male
Age
Age squared
Marital status: Married
Divorced
WidowedYears of schooling completed
UnemployedLog household income per capita
Communist Party member
Square meters of housing per capita(2)–0.3082***(0.1012)–0.1077(0.1364)–0.1029**(0.0404)–0.0339(0.0331)0.0983*(0.0510)0.1223***(0.0353)–0.0049(0.0707)0.0852*(0.0480)
0.9362***(0.3392)(3)–0.3082***(0.1012)–0.1077(0.1365)–0.1029**(0.0404)–0.0339(0.0331)0.0983*(0.0510)0.1223***(0.0353)–0.0036(0.0903)0.0853*(0.0481)0.9365***(0.3396)0.0017(0.0011)0.0016(0.0049)0.0000(0.0001)–0.0514**(0.0225)–0.0207***(0.0064)0.0003***(0.0001)
0.0951*(0.0569)–0.3340***(0.1165)–0.1580(0.1079)0.0019(0.0040)–0.1677**(0.0765)
0.2198***(0.0377)
0.0658**(0.0275)
0.0039***(0.0013)23–0.0522**(0.0225)–0.0198***(0.0064)0.0003***(0.0001)
0.0957*(0.0570)–0.3386***(0.1168)–0.1554(0.1086)0.0017(0.0040)–0.1682**(0.0765)
0.2191***(0.0377)
0.0651**(0.0275)
0.0040***(0.0013)–0.0521**(0.0225)–0.0198***(0.0066)
0.0003***(0.0001)
0.0956*(0.0571)–0.3387***(0.1168)–0.1554(0.1085)0.0017(0.0040)–0.1683**(0.0765)
0.2191***(0.0377)
0.0652**(0.0276)0.0040***(0.0013)

Expect big income increase
Expect small income increase
Expect income decrease
Health:Good
BadConstant
Number of observations
F-testR-squared

0.3440***(0.0616)
0.1131***(0.0236)–0.3607***(0.0345)0.2141***(0.0256)–0.1462***(0.0551)0.5181(0.3881)5674
36.4300
0.14670.3451***(0.0615)
0.1142***(0.0236)–0.3598***(0.0345)0.2146***(0.0256)–0.1454***(0.0550)0.5124(0.3874)5671
35.0800
0.14710.3451***(0.0615)
0.1142***(0.0237)–0.3598***(0.0345)0.2146***(0.0256)–0.1454***(0.0550)
0.5116(0.3889)5671
33.7900
0.1471

Note: *, **, ***: Coefficient different from zero at 10, 5, 1 percent significance levels respectively.
Robust standard errors are in parentheses.

24

Table 3
Happiness functions of only “born” urban residents

Dependent variable: cardinal happiness scores; Regression method: OLS
Horizontal inequality
Age × horizontal
inequality
Party member × horizontal
inequality
Log household income percapita × horizontal
inequality
Years of schoolingcompleted × horizontal
inequality

(1)
(2)
(3)
(4)
0.3305**0.3259**0.05740.2466(0.1329)(0.1323)(0.0425)(0.4931)–0.0069** –0.0063**(0.0027)(0.0027)–0.1476**(0.0604)–0.0262(0.0552)

0.0041(0.0086)

0.0738(0.0613)0.4400(0.5268)

0.2591*(0.1341)–0.1244*(0.0678)0.0672(0.0620)0.4937(0.5304)

0.0698(0.0621)0.4922(0.5292)

0.0726(0.0632)
0.4555(0.5274)

0.0747(0.0614)0.4596(0.5267)–
0.0852***(0.0317)–0.0114(0.0122)0.0003***(0.0001)
0.1899*(0.1033)–0.2921*(0.1682)–0.0104(0.1473)0.0005(0.0056)–0.2182**(0.0880)0.2269***(0.0704)
0.0846**(0.0331)0.0044**(0.0020)0.3251***(0.1217)–0.0847***(0.0317)–0.0161(0.0130)0.0003***(0.0001)0.2004*(0.1038)–0.2860*(0.1683)
0.0020(0.1471)0.0007(0.0056)–0.2150**(0.0878)
0.2330***(0.0717)0.0839**(0.0332)0.0045**(0.0020)0.3200***(0.1219)–0.0851***(0.0317)–0.0253**(0.0104)
0.0003***(0.0001)0.1780*(0.1042)–0.3079*(0.1691)–0.0148(0.1482)0.0004(0.0056)–0.2220**(0.0878)0.2325***(0.0715)0.3688***(0.1201)
0.0043**(0.0020)0.3342***(0.1229)–0.0876***(0.0317)–0.0259**(0.0104)
0.0003***(0.0001)
0.1759*(0.1043)–0.3071*(0.1692)–0.0196(0.1483)0.0007(0.0056)–0.2209**(0.0881)0.2788*(0.1492)
0.0864***(0.0332)0.0043**(0.0020)0.3274***(0.1228)–0.0882***(0.0317)–0.0259**(0.0104)
0.0003***(0.0001)0.1772*(0.1045)–0.3053*(0.1691)–0.0187(0.1485)–0.0073(0.0177)–0.2211**(0.0880)0.2265***(0.0704)
0.0861***(0.0331)0.0043**(0.0020)0.3286***(0.1228)

Intellectual youth
Intellectual youth ×
horizontal inequality
Relative income
City-level Gini

(5)–0.0326(0.1029)

Personal Characteristics
Male
Age
Age squared
Marital status: Married
Divorced
WidowedYears of schoolingcompleted
UnemployedLog household income percapitaCommunist Party member
Square meters of housing
per capita
Expect big income
increase

25

Expect small income
increase
Expect income decrease
Health:Good
BadConstant
Number of observations
F-testR-squared

0.1348*** 0.1333*** 0.1333*** 0.1351*** 0.1350***(0.0326)(0.0325)(0.0325)(0.0326)(0.0326)–––––
0.3758*** 0.3740*** 0.3773*** 0.3777*** 0.3771***(0.0436)(0.0436)(0.0435)(0.0435)(0.0436)0.2236*** 0.2219*** 0.2220*** 0.2219*** 0.2224***(0.0312)(0.0312)(0.0312)(0.0312)(0.0312)–0.1280*–0.1246*–0.1274*–0.1287*–0.1287*(0.0662)(0.0663)(0.0662)(0.0663)(0.0663)–0.2767–0.26720.2179–0.0897
0.4571(0.7279)(0.7452)(0.6939)(1.3427)(0.7106)
2929
2929
2929
2929
2929
29.42
27.07
29.12
29.02
29.06
0.17200.17290.17200.17050.1705

Note: *, **, ***: Coefficient different from zero at 10, 5, 1 percent significance levels respectively.
Robust standard errors are in parentheses.

26Appendix Table 1
City-level variable definitions and descriptions
Variable
Horizontal
inequality
City-level
Gini

Definitions
Income ratio between
urban residents andmigrants in a city
Including urban
residents and migrants

Obs.

Mean

s. d.

Min

Max

27

1.92520.48981.29093.34

270.34610.03530.27940.4043

Appendix Table 2
Decomposition of entropy index based on the hukou group
Index

GE(0)
GE(1)Theil Index
GE(2)

Total
inequality

Within-groupinequality

Between-group inequality(Horizontal inequality)

0.25280.23600.2077
0.19520.04510.0407

Between-groupinequality/ total
inequality
17.84%
17.31%

0.29390.25620.037712.83%

27

Appendix Table 3
Personal characteristics variable definitions and descriptions
Variable

Definitions
Cardinal
happiness scores
Dummy variable,
male = 1

Happiness
Male
Age
Marital status: Married
Divorced
WidowedDummy variable,
married = 1
Dummy variable,
divorced = 1
Dummy variable,
widowed = 1

Years of education
Unemployed
Household income percapitaCommunist Party
memberDummy variable,
unemployed = 1
Per capita
household yearly
incomeDummy variable,
Communist Party
member = 1

House square meters per
capitaExpect big income
increase
Expect small income
increase

Expect income decrease
Health:Good
Bad

Dummy variable,
expect big income
increase = 1
Dummy variable,
expect small
income increase =1
Dummy variable,
expect income
decrease = 1
Dummy variable,
good Health = 1
Dummy variable,
good Bad = 1

Full sample
5674
Mean
s. d.

Urban residents
3729
Mean
s. d.

Migrants
1945
Mean
s. d.

2.44550.84412.48570.85942.36860.80850.00000.48130.49970.41380.49260.61080.48770.0000

43.04

11.73

47.14

10.88

35.16

8.98

0.00000.92330.26610.93430.24780.90230.29700.00000.01430.11860.01640.12690.01030.10090.06710.01960.13850.02680.16160.00570.07500.0000

10.02

3.33

11.08

3.08

8.00

2.80

0.00000.03140.17430.04290.20270.00930.09580.00007398.935676.078865.295615.814587.584637.560.00000.23420.4236

0.33740.47290.03650.18760.0000

14.15

9.59

17.26

8.34

8.21

9.02

0.00000.03680.18840.01960.1386

0.06990.25510.00000.47780.49960.43970.49640.55060.49760.00000.16460.37090.20060.40050.0956

0.29420.00000.69720.45950.58840.49220.90590.29200.00000.05150.22100.06780.25150.02010.14020.000028

ANOVA test
p value