Global COE Hi-Stat Discussion Paper Series 221

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

Hukou and Consumption Heterogeneity:
Migrants' Expenditure Is Depressed by
Institutional Constraints in Urban China

Hi-Stat Discussion Paper

Binkai Chen
Ming Lu
Ninghua Zhong

February 2012

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

Hukou and consumption heterogeneity:
Migrants’ expenditure is depressed by institutional constraints in urban China

Binkai Chen
School of Economics, Central University of Finance and Economics

Ming Lu
School of Economics, Fudan University and Zhejiang University

Ninghua Zhong *Department of Finance, Hong Kong University of Science and Technology

Abstract
This paper provides a new explanation for China’s extremely low consumption-to-GDP
ratio, highlighting the constraints of the “household registration system” (Hukou) on
China’s household consumption. Our baseline results show that the consumption of
migrants without an urban Hukou is 30.7% lower than that of urban residents. Moreover,consumption heterogeneity cannot be explained by migration effects, culture, social norms,
habits or some other forms of household heterogeneity. Further studies on the compositionof household consumption have shown that the gaps are largest in areas such as educationand culture, durable goods and health. As both the number and income level of migrants
are rising, the constraining effects of Hukou on household consumption will continue toincrease.
Keywords: Consumption; heterogeneity; Hukou system; migrants; urban residents
JEL code: R23, E21*

Binkai Chen: Email: chenbinkai@gmail.com; Telephone: 86-10-61776059; Cell: 86-13811709064;
Ming Lu: Email: lm@fudan.edu.cn; Ninghua Zhong: Email: ninghua.zhong@gmail.com. We
gratefully acknowledge the support of Shi Li, who provided the CHIP 2002 data. The helpfulcomments of the seminar participants at the International Food Policy Research Institute, Peking
University, Fudan University, Sun Yat-sen University, Southwestern University of Finance andEconomics and Shandong University are gratefully acknowledged. The authors are thankful for thefinancial support of the Young Scholar Program of the National Natural Science Foundation ofChina (No. 71003112), the Major Project of the National Social Science Foundation of China (No.
09 & ZD020), the Key Project of the National Natural Science Foundation (No. 71133004) and thethird phase of the “211 Project” at the Central University of Finance and Economics. This study
was also supported by the Fudan Lab of China Development Studies and the Shanghai Leading
Academic Discipline Project (B101).
0I. Introduction
China’s low consumption-to-GDP ratio, corresponding to a high savings ratio, is regarded
as an underlying cause of the U.S. housing price bubble and the global financial crisis(Greenspan, 2009). In recent years, this issue has attracted attention across the world (e.g.,
Modigliani and Cao, 2004; Chamon and Prasad, 2010; Wei and Zhang, 2011). It is worth
noting that China’s consumption-to-GDP ratio is not only lower than that of developed
countries, but that it is also lower than economies at a similar stage of development, like
Brazil and India, and those with a similar culture, like Japan and Korea (see Section 2 fordetails). Therefore, there must be some specific factors constraining Chinese household
consumption.
This paper provides a new explanation for China’s low household consumption.
Compared with the existing literature, we highlight the importance of one particularinstitution in transitional China, the “household registration system” (Hukou). This system
is a state institution that retains tight control over labor mobility across regions, andespecially migration from rural to urban areas. It also restricts access to state-sponsoredbenefits for the majority of China’s rural population, ranging from small benefits like being
able to buy a city bus pass to much more important matters such as urban services andpublic welfare, including enrolling children in public schools (Chan and Buckingh, 2008).
An individual’s Hukou status is inherited at birth and can be treated as almost exogenous(Afridi, Li, and Ren, 2009). As Hukou determines many important aspects of life, if not thefate of China’s people, the Hukou book which records the location and attributes ofhouseholds has been dubbed “China’s No. 1 document” (Chan, 2009).
0Hukou creates two different societies (Naughton, 2007). Within each city, there are
two segmented groups of people, shaped by the system: urban residents who have local
Hukou, 1 and migrants who do not. Without a local Hukou, migrants are discriminatedagainst within the labor market (Zhu, 2004; Wang et al., 2009; Friedman and Lee, 2010);
they are excluded from many urban jobs (Chan and Buckingh, 2008) and face many formal
and informal obstacles to securing jobs (Li, 2003). Moreover, they also have limited accessto social insurance and other forms of welfare (Zhu, 2003). Such heterogeneity has asignificant impact on their consumption behavior. Our study finds that migrants’ level ofconsumption was lower than that of urban residents by about 30.7%. We find evidence
suggesting that migrants save more for precautionary purposes due to higher income risks
and the lack of social security coverage. Further studies on the composition of household
consumption have shown that the gaps in consumption are largest in the areas of educationand culture, durable goods and health. With careful analysis, we find this consumption
heterogeneity to be explained mainly by the Hukou system, and not by other factors such as
migration effects, life cycle characteristics, culture or habits.
The implications of our study are twofold: first, China’s cross-region migrants (whoare mainly rural to urban migrants), who now amount to around 221 million people2(almost one-sixth of China’s total population) have depressed consumption levels, and if
the Hukou constraint was loosened or removed, aggregate household consumption in China

1

Please refer to the section entitled “The Hukou Dual Classification” in Chan and Buckingham(2008) for a detailed introduction to local Hukou.
2

Data source: State Statistical Bureau of China: The First Report on the Main Statistics of the

Sixth Population Census, http://www.stats.gov.cn/tjfx/jdfx/t20110428_402722253.htm
1would receive a significant boost. Our estimation is that, the removal of Hukou system
would lead to a rise in aggregate consumption of 222 billion yuan, which is equivalent to4.2% of household consumption and 1.8% of GDP. Second, since the Hukou system mainly
constrains the consumption of non-necessities, its negative effects on domestic demand will
increase as people become richer and the number of migrants increases. In the post-crisis
era, boosting China’s household consumption is critical for the economic rebalancing ofboth China and the world as a whole. This study shows, to this end, there is an urgent needto reform the Hukou system.
The structure of this paper is as follows: Section 2 presents some facts and existing
explanations of China’s savings ratio from the literature, and especially studies concerning
the household savings ratio; Section 3 introduces the econometric model and the data; andSection 4 presents baseline empirical results and provides evidence that migrants facehigher income risks that may induce a stronger precautionary savings motivation. Section 5contains robustness checks that rule out other possible channels that may confound theeffects of Hukou on migrants’ consumption; Section 6 discusses how the effects of Hukou
on consumption have changed over time; and Section 7 concludes.

II. China’s consumption and savings: Facts and literature review

Ever since China began its “Reform and Opening” process, its economy has been
growing at an average annual rate of nearly 10%. However, economic imbalances, both
external and internal, are becoming increasingly severe. In particular, China’s low
consumption level is widely believed to be the fundamental source of the imbalance that is2

threatening the sustainability of its long-term economic growth. Table I compares theconsumption-to-GDP ratio of China with that of several other major economies.
[Table I about here]
First, in 2009, China's consumption-to-GDP ratio was much lower than that of bothWestern developed nations (such as the U.S., Britain and Germany) and Asian developed
countries like Japan and Korea. Second, by comparing “final consumption” with “household
final consumption”, we can conclude that China’s government consumption is not
significantly lower than that of the others; instead, its low level of final consumption isdriven mainly by household consumption. In fact, China’s current household consumption
ratio is even lower than the lowest household consumption ratios experienced by Japan and
Korea in recent history. 3 Third, if we compare China with countries at a similar stage ofdevelopment, such as India and Brazil, China’s household consumption ratio is still lower bya large margin. Considering China’s low level of expenditure in public areas such as
education, health and pensions, the relative level of China’s household consumption ratio incomparison with those of other countries is even lower (Aziz and Cui, 2007). After
controlling for the level of economic development, the economic growth rate, demographic
features, the government’s fiscal policy, the development of a financial structure,urbanization, etc., China’s household consumption ratio is still lower than the expected levelby more than 10% (Kraay, 2000; Kuijs, 2005).

3

As shown by World Development Indicators, the lowest point of Japan’s household consumption

ratio was 48.4% in 1970, and for Korea, it was 49.3% in 1998. Both are much higher than China’s
2008 household consumption ratio of 35%.
3

China’s household consumption ratio is not only lower than that of other major countries
in the world; it has also been declining in recent years. Figure I presents China’s
consumption-to-GDP ratio, household consumption ratio and household savings ratio during
the period 1992-2008. It shows that, since 2000, China’s consumption ratio and household
consumption ratio have been continually declining. The consumption ratio declined from
62.3% in 2000 to 48.4% in 2008, and the household consumption ratio fell from 46.4% to35.1%. Correspondingly, the household savings ratio increased by a large margin during thesame period, from 27.5% in 2000 to 39.4% in 2008. If we use a time trend line to fit thehousehold savings ratio after 1992, the slope is 0.61. This means that, on average, thesavings ratio increased by 0.61 percentage points each year. If we only consider the savings
ratio after 2000, the annual increase in the savings ratio is 1.48 percentage points. We canalso learn from Figure I that the government consumption ratio (government
consumption/GDP, the gap between the overall consumption ratio and the household
consumption ratio) was almost a constant after 1992. This indicates that China’s declining
consumption ratio is driven mainly by the decline in China’s household consumption.
[Figure I about here]
With regard to China’s household consumption and savings, there are many explanations
which have been put forward in the existing literature. The first is based on life cycle theory.
The life cycles (Ando and Modigliani, 1963) are widely found to be an important
determinant of household consumption behavior. Modigliani and Cao (2004) argue that therising share of labor force in China’s population that has driven up the savings ratio.
However, Chamon and Prasad (2010) find this explanation to be inconsistent with the profile4

of consumption and savings at the household level in China, as older people have been foundto save more than middle-aged people. They also found that savings ratios increased acrossall demographic groups during 1995-2005. Furthermore, Kraay (2000) found that this theory
cannot explain the declining consumption ratio in aggregate-level data. The second
explanation is based on liquidity constraints (e.g., Kujis, 2005; Aziz and Cui, 2007). These
researchers argue that the underdevelopment of China’s financial market has forced
households and companies to save more, and has led to a lower consumption ratio.
Nevertheless, the efficiency of China’s financial markets is improving as time goes by, while
the household consumption ratio is still declining. This suggests that the level of financial
market development is, at most, a minor factor as regards China’s household consumption.
The third explanation is based on precautionary savings theory (e.g., Meng, 2003; Blanchard
and Giavazzi, 2005; Giles and Yoo, 2007; Chamon and Prasad, 2010), which argues that
China’s pension, healthcare, education and housing system reforms have increased theuncertainty of household income and expenditure, and increased household saving
correspondingly. Our view is that precautionary saving is an important perspective for
explaining China’s low level of household consumption; however, recent social safety net
reforms and the increasingly wide coverage of pensions and healthcare has not led to asignificant rise in China’s household consumption. This calls for further exploration of how
the precautionary saving mechanism works with China’s institutional background as well ason the effective policies targeting it. Finally, Wei and Zhang (2011) put forward aninteresting explanation for China’s rising household savings ratio. They argue that, as China
experiences a rising sex ratio imbalance, the increased competition in the marriage market
5has induced Chinese people, especially parents with a son, to postpone consumption in favorof wealth accumulation in order to increase the competitiveness of their son.
In contrast to the existing explanations, we connect consumers’ heterogeneity with oneof transitional China’s institutional features, the Hukou system. Consumption heterogeneity
has not been studied in depth in the empirical literature, but we think it is essential in orderto understand the features of China’s aggregate consumption and to generate effective
policies. With regard to the Hukou system, it is now common for studies of China toconsider it as the main variable which defines exogenous constraints on fundamental
individual behavior in social and economic studies (e.g., Chan and Buckingham, 2008;
Afridi, Li, and Ren, 2009). For example, Whalley and Zhang (2007) point out that Hukou
prevents better allocation of the economic resources in China and hinders Chinese
development. Liu (2005) and Whyte (2006) argue that the Hukou system is a major
contributing factor to rural-urban inequality.
During the process of rapid urbanization, an increasing number of rural laborers seekjobs in cities, but most of them cannot get an urban Hukou. According to the sixth
population census in 2010, the total number of migrants in China was 221 million, whichamounts to one-sixth of China’s total population. Without a Hukou, their consumption is
expected to be lower than that of urban residents for three reasons: (1) migrants are notcovered to the same extent by the social safety net and their jobs are less secure, meaningthat they have a stronger precautionary saving motivation; (2) migrant workers are
discriminated against in the labor market (Zhu, 2004; Wang et al., 2009; Friedman and Lee,2010), and so their experience may not be fully compensated, which could affect their6expectations of a long-term permanent income; and (3) migrants have greater mobility, andtherefore consume fewer durable goods. In reality, in China, only Hukou migration is
officially regarded as migration. Anything else is commonly called “population movement”
or “floating population”, implying a low degree of expected permanence (Chan andBuckingham, 2008). In the following, we quantitatively compare the consumption of
migrants with that of urban residents.

III. Data and model specification

The data used in this study come from the Chinese Household Income Project Survey(CHIPS, 2002). This survey was conducted by the income distribution research group of
the Chinese Academy of Social Sciences. It covered 22 provinces, 6835 urban households
and 2000 migrant households, encompassing 20,632 urban residents and 5327 migrants.
After removing observations for which major variables, such as household consumption,age, region, etc., are missing, we obtained 6784 observations for urban households and1966 for migrant households.
Our central empirical question is: if the major variables which affect the consumption ofurban residents and migrants are properly controlled, do migrants have lower consumption
levels than urban residents? In order to test this hypothesis, we employed a standard
econometric specification similar to that of Charles et al. (2009):
ln C =
α + β * migrant + γ *ln Y + η X + ε

7（1）where lnC is the natural logarithm of per capita consumption. 4 In this survey, consumption
includes eight sub-categories: food; clothing; household equipment; medicine and health;communication; education and culture; housing; and other. However, housing expenditure(mainly rent) is neither reported nor estimated for households who own a house, 5 and thedefinition of “other expenditure” is different for urban residents and migrants. In order toreduce the measurement error, we defined consumption as the sum of expenditure on six
sub-categories, excluding housing and other. Migrant is a dummy variable, taking a value of
1 for migrants (without Hukou) and 0 for urban residents (with Hukou). β is the coefficientof greatest interest for the purpose of this study. A significantly negative β means that
migrants’ consumption is lower than that of urban residents.
lnY is the natural logarithm of per capita income, which is a major control variable inhousehold consumption regressions. X denotes other controlling variables, and ε is the error
term. Based on the existing literature on the consumption function (e.g., Deaton, 1992;
Carroll, 1994; Attanasio and Weber, 1995), we controlled some other variables. They
included the characteristics of the head of the household, such as years of education, healthstatus, occupation and ownership and industry of his/her company. We also controlled for per
capita wealth. Furthermore, we included a set of provincial dummy variables in order to4

We use consumption rather than saving rate as our dependent variable in order to facilitate theanalysis of the mechanism through which Hukou affects household consumption. We will employ
saving ratio as a dependent variable in a test in the robustness checks section.
5

In China, the housing expenditure of urban residents should be much higher than that of migrants,

as the living conditions of urban residents are much better.
8

capture the location fixed effects.
[Table II about here]
Table II presents the statistical summary as regards the major variables for migrants andurban residents. It shows that, on average, urban residents have a higher level of income,total wealth and consumption, whereas migrant households have a smaller family size inurban areas. 6 The average per capita consumption of urban residents was about 1.5 times
that of migrants. The heads of migrant households are younger and have a lower level of
education. Based on these comparisons, in our cross-section estimation of consumption, we
need to carefully check whether the observed consumption-Hukou relationship originatesfrom other differences between urban residents and migrants.

IV. Empirical results

4.1 Baseline regression results
This section examines whether or not there are significant differences between the
consumption behavior of migrants and urban residents. First, column 1 of Table III reports
the baseline results. It shows that migrants' consumption is 30.7% lower than that of urbanhouseholds when other major household characteristics are controlled.
[Table III about here]
This result indicates that if migrants have the same consumption behavior as urban

6

Some members of migrant households do still live in rural areas. The average family size of the

migrant households was larger than that of the urban households when family members who still
lived in rural areas were included.
9

residents, the aggregate household consumption ratio can be raised by a large margin.
Using the baseline estimation of the difference in consumption (0.307), we can evaluate theaggregate impact of Hukou on the household consumption ratio. In our sample, themigrants' average per capita consumption was 4279 yuan. If the Hukou system wasremoved and migrants were treated like urban residents, their per capita consumption
would rise by 30.7%, that is, 1314 yuan. According to Sheng (2008), in 2002, migrant laboraccounted for around 21% of the total rural labor force, which amounted to 782 million
people, meaning that the total number of migrants in 2002 was around 169 million. If the
Hukou system was removed, aggregate consumption would rise by 222 billion yuan (169
million*1314) 7, which amounts to 4.2% of household consumption and 1.8% of GDP.
Noticeably, this consumption “loss” will continue for as long as Hokou is in effect.
In columns 2 to 5, we tried some other dependent variables in order to check therobustness of our major result. First, migrants may send part of their income back to rural
areas. The remittance, which is saved for the migrant’s family in the urban area, is at least
partly consumed by their family in the rural area. For this reason, in column 2, we assume
that the rural family has the same consumption-to-income ratio as their family members inurban areas. Therefore, we multiplied the remittance by the consumption ratio, and thenadded this adjusted remittance to the consumption of migrant households in urban areas. 8
7

As a comparison, China’s net export in 2002 was 309.4 billion.

8

The method of adjusting the migrants' consumption is given by the following formula: household

consumption_adjusted = household consumption + remittance*(household consumption/(householdincome-remittance)). The assumption of this adjustment is that the consumption ratio overremittances is the same as the consumption ratio over other income.
10

After this adjustment, the gap in consumption was reduced but still significant at 24.5%. In
column 3, we made the even stronger assumption that all remittances are used asconsumption. Therefore, we added remittances to household consumption. With thisadjustment, the consumption ratio of migrants was still significantly lower than that ofurban residents by 17.7%. As at least a portion of the remittance will become savings, theestimates in column 3 can be regarded as the lower bound of the Hukou effects.
In column 4, we use per capita consumption as a dependent variable instead of ln (percapita consumption). It shows that migrants' per capita consumption is 1274 yuan less thanthat of urban residents. Column 5 uses the savings ratio as the dependent variable.
Similarly to Chamon and Prasad (2010), we define the savings ratio as 1 - (household
consumption/household income). The estimated savings ratio of migrants is 13% higher
than that of urban residents. In summary, we find the consumption heterogeneity between
urban residents and migrants to be robust to changes in the dependent variables.
4.2 Hukou-consumption relationship: channel analysis
In the latter part of Section 2, we propose several channels from Hukou constraints tolower levels of consumption, mainly through migrants’ stronger motivation to save as aprecaution. However, these hypotheses cannot be fully tested. This is partly due to thelimits of the dataset and partly due to the lack of a standard approach with which toprecisely measure precautionary saving. Nevertheless, in the section below, we provide
some indirect evidence for these hypotheses.
First, when restricted by the Hukou system, migrants are less likely to be covered by11

insurance programs. In our sample, only 85 heads of household out of 1968 are covered bya pension system. In addition, only 52 are covered by a medical insurance program, andonly 21 are covered by an unemployment insurance program. In contrast, for the urban
residents, 4614 heads of household from among 6784 are covered by a medical insurance
program. The medical insurance program coverage ratio of the urban residents (68%) ismuch higher than that of the migrants (2.6%). 9[Table IV about here]
Second, Chamon and Prasad (2010) argue that the rising labor income risk is very
important for understanding the increasing saving rate in China. The survey contains
information on whether or not a person has previously changed his/her job. In our sample,
38.8% of migrants have changed jobs, while only 5.2% of urban residents have such an
experience. A recent survey in three Chinese cities, Beijing, WuXi and Zhuhai, shows thatthe institutional discrimination which is inherent in the Hukou system reduces the numberof jobs available to migrants and increases their job search costs and the cost of losing jobs.
Actually, migrants often take jobs which are unacceptable to local residents, but even inthese cases, the effects of institutional discrimination still increase costs of migrants to loseor change jobs (Zhang, 2010). These facts imply that migrants have a much higher labor
income risk than urban residents. Table IV further presents the difference in contract
structure between urban residents and migrants, showing that 77.52% of urban residents
have long-term or fixed contracts with their employers, whereas only 5.24% of migrants

9

The coverage ratio for pension systems and unemployment insurance programs was not available

in the urban survey.
12

have similar kinds of contracts, which also implies a higher labor income risk for
migrants. 10 Table IV also presents the wage structure of migrants and urban residents. It
shows that, on average, more than 70% of the income of urban residents comes from awage or salary, which is usually more stable than other sources of income. For the migrants,
however, only 43% of income is obtained in this form.
With regard to insurance and labor contracts, Friedman and Lee (2010) documented
some official estimates, which we cite here as further evidence:
According to a 40-city survey conducted by the Labour [sic] and Social Security
Ministry in 2004, among the 120 million strong migrant labour [sic] force from thecountryside, a paltry 12.5 per cent has signed a labour [sic] contract, while only 15 percent participate in social security scheme, and 10 per cent has medical insurance (StateCouncil Research Office Team 2006: 13). [Friedman and Lee, 2010, page 510] 11
In conclusion, the summary statistics outlined above indicate that migrants face highlevels of labor income uncertainty, which induces them to save more and consume less dueto the motivation of precautionary saving.

10

Li (2010) documents that in 2004, 79% of migrant workers had not signed a labor contract.

11

Following these words, Friedman and Lee (2010) also document the serious wage arrears

problem for migrants, which would also increase the precautionary saving motivation of migrants.
“Less than half (48 per cent) of the migrant workforce get paid regularly, while 52 per cent reported
regular or occasional wage non-payment (State Council Research Office Team 2006: 116).
Sixty-eight per cent of migrant workers work without any weekly day of rest, 54 per cent of
migrant workers have never been paid overtime wages as required by law and 76 per cent do notreceive the legal holiday overtime wages.” [Friedman and Lee, 2010, page 510]
13

V. Robustness checks

The baseline model shows that migrants’ consumption is much lower than that of urban
residents when other important factors as regards consumption are properly controlled. We
need to be very cautious before we conclude that consumption heterogeneity is due to theinstitutional constraints of the Hukou system, rather than other unobservable differences
between urban residents and migrants that may affect consumption. In this section, we
conduct tests to rule out these possibilities.
5.1 Consumption heterogeneity across different regions
It may be argued that the difference between migrants and urban residents is due tomigration effects, rather than Hukou restrictions. In other words, migrants may consumeless simply because they have just migrated to cities, and would not consume more even if
they were given urban Hukou. For this reason, we examined the effect of Hukou on
consumption heterogeneity across different regions with varying levels of Hukou
restrictions. If our results are driven mainly by migration effects, consumption
heterogeneities between urban residents and migrants should be fairly consistent acrossdifferent regions. Otherwise, if Hukou constraints constitute the dominant effect,consumption heterogeneity should be greater in areas with stronger Hukou restrictions.
Accordingly, we divided our sample into five groups: Beijing (the capital of China); capitalcities of coastal provinces; capital cities of non-coastal provinces; non-capital cities ofcoastal provinces; and non-capital cities of non-coastal provinces. In China, it is more14

difficult for migrant workers to achieve urban Hukou in bigger or higher-level cities,12
especially in coastal areas. 13 Table V presents the results of these sub-samples.
[Table V about here]
Column 1 presents the results for Beijing, where Hukou is restricted most rigorously. The
consumption gap between migrants and urban residents is 71.7%, which is much higher
than the baseline estimate. Columns 2 to 5 present the results for other regions: for thecapital cites of coastal provinces, the gap is 46.8%; for capital cities of non-coastal
provinces, it is 36.5%; for non-capital cities of coastal provinces, it is 28.4%; and for
non-capital cities of non-coastal provinces, it is 23.1%. These results are consistent with
our hypothesis that Hukou restriction, rather than migration, is the dominant reason for our
major finding.
5.2 Household heterogeneity or Hukou constraints?
It may also be argued that our baseline results are driven by some household-level
differences between migrants and urban residents. Therefore, in this section, we check
whether our results are robust by considering household heterogeneity.
First, the family sizes of urban residents and migrants are different. As shown in Table

12

For details, please refer to Appendix 1: “Principles of China’s Control of Internal Migration” by

Wang (2004). We quote here two paragraphs specifying “strict control” and “appropriate control”
respectively: “Hukou relocation from the rural to urban areas; or from other cities to Beijing,
Shanghai and Tianjin metropolises must be controlled as restrictively as possible” ;“Hukou
relocation from township to city; from small city to large city; from ordinary village to outskirts ofcity/township, … should be controlled appropriately.”[Wang, 2004, page 130]
13

According to the estimations of Li (2010), the Eastern region received over 70% of all rural

migration workers in 2008.
15

II, urban residents have larger families. In order to address this concern, column 1 of Table
VI uses the sub-sample of families with three household members or fewer. The coefficientof the migrant dummy is highly significant at 32.3%. Second, the age structure of urban
residents and migrants is also different. Migrants are younger; over 90% of the heads of
migrant households are under 50 years old. As regards this issue, column 2 uses a
sub-sample of families with heads of household who are 50 years old or younger. Thisshows that, in this subsample, the consumption ratio of migrants is 29.9% lower than thatof urban residents.
[Table VI about here]
Columns 3 to 5 consider some other household characteristics which are emphasized in
the literature. First, Chamon and Prasad (2010) studied the effects of house ownership on
household consumption and saving behavior. They argue that house ownership could be
important for understanding the high saving rate in China. In order to take this into account,we conducted a test in column 3 using the sub-sample of households who do not own ahouse. 14 The consumption gap between urban households and migrants is 28.9%. Second,
Yang and Chen (2009) and Chamon and Prasad (2010) highlight the importance of
expected expenditure on children’s education in determining household consumption. They
found that expectations regarding future expenditure on education increase current saving.
Column 4 therefore includes the number of children as an additional explanatory variable.15

14

As most of the migrants in our sample do not own a house, comparing the households who own

a house is problematic.
15

A child is defined as a person who is 18 years old or under.
16

The results in column 4 show that the number of children has a positive effect onhousehold consumption that includes expenditure on education. However, the addition ofthis measure does not affect the coefficient of the migrant dummy in our baseline model.
Third, Wei and Zhang (2011) argue that the sex ratio is important for determining
household saving. Column 5 addresses this possibility by including the number of boys as
an additional explanatory variable. We find that the estimated consumption gap between
migrants and urban households was no different from the previous estimations.
In summary, Table VI suggests that household heterogeneity does not account for the
majority of consumption heterogeneity between migrants and urban households.
5.3 Institutional constraints or culture?
Finally, the baseline consumption heterogeneity between urban households and migrants
who were born in rural areas could be due to some unobservable factors, such as culture,social norms or habits, rather than Hukou identity. Existing studies have shown that culture
and habits can affect household consumption and saving behavior (e.g., Carroll, et al., 1994,
1999). When comparing migrants with local residents, it is possible that migrants may havea lower consumption ratio simply because their preferences are different from those ofurban households. In order to address this concern, Table VII checks the robustness of ourbaseline results by controlling for measures relating to culture and social norms.
[Table VII about here]
Culture and social norms are not directly observable. In the CHIPS questionnaire, urbanhouseholds are asked whether they were born in an urban area or in a rural area but later17

obtained an urban Hukou. This information provides a good opportunity to separate theeffects of culture and Hukou. If culture or social norms are important, we should expectthat people born in rural areas (including migrants and urban households born in rural areas)
would have similar consumption patterns.
Column 1 of Table VII compares the consumption of migrants with that of urban
residents who were born in rural areas but later obtained an urban Hukou. In the dataset,there are 1775 urban residents who were born in rural areas. The results in column 1 showthat they have much higher consumption levels than migrants, with a gap of 25.9%. This
indicates that the majority of the gap in consumption is not explained by culture or habits.
Next, one may argue that people who were born in rural areas but finally achieved anurban Hukou are different in terms of certain unobservable characteristics from those who
did not get a Hukou. For this reason, we needed to explore how people born in rural areasachieved their urban Hukou. In China, most rural people obtain an urban Hukou throughachieving a degree in an institute of higher education, purchasing a house, working as acivil servant, joining the army or their land being expropriated by the government. 16 If
well-educated people are more likely to get an urban Hukou, education could be anunderlying force driving the difference in consumption behavior. For this reason, in column
2 of Table VII, 17 we used the sub-sample of urban residents with nine years of education or

16

For a detailed introduction, please refer to the section by Chan and Bucfkingham (2008) about“The System of Approving Hukou Migration and the Nongzhuanfei Reforms.”
17

We used a threshold of nine years of education because China requires everyone to attend schoolfor at least nine years, which implies that people with less than or equal to nine years of education18

less, which means that they received no more than compulsory education. The results show
that, for urban residents who were born in rural areas and did not receive a higher education,
their level of consumption was still much higher than that of migrants, and the gap was
24.5%. In column 3 of Table VII, we used the sub-sample of people who obtained their
Hukou through joining the army or because their land was expropriated by the government.
These are more exogenous events, and the people involved are less likely to besystematically different from other migrants in terms of ability, talent or preference. The
results in column 3 show that the level of consumption of these people is 22.1% higher thanthat of migrants.
Finally, one may still worry that people who move from rural areas to urban areas will beaffected by the urban culture or social norms, and that their consumption behavior will besimilar to that of urban residents. In order to address this concern, we controlled for “thenumber of years since the household migrated to an urban area” in column 4 for migrants. 18
If such changes to habit are important for consumption, the coefficient should besignificantly positive – migrants staying in urban areas for a longer period are more likely
to be changed by the urban culture. However, we find the coefficient to be insignificant,which cast doubt on the explanation based on culture and habit.
In brief, these results confirm our basic hypothesis that people with an urban Hukou

are less likely to be systematically different in terms of their ability, talent or preference, regardlessof whether or not they obtained urban Hukou.
18

We used the data for migrant households because the variable “number of years since thehousehold migrated to an urban area” is not available in the urban household survey.
19

have higher levels of consumption than migrants, and that the major part of the gap cannot
be explained by habit, preference, ability or other unobservable characteristics. Thissuggests that the restrictions inherent in the Hukou system are the key determinants ofconsumption heterogeneity.

VI. Shrinking effects of Hukou on consumption?

The data we used were the best we could find for estimating how the Hukou system hasconstrained migrants’ consumption. The analyses above lend strong support to the theory of
the restrictions that Hukou imposes on migrants’ consumption. However, due to thecross-sectional nature of the data, one may worry that, as time goes by, the effects of Hukou
on migrants’ consumption may become less and less important. In this section, we argue
that there are at least three factors that strengthen the constraints of Hukou on consumption
at the aggregate level.
First, the number of cross-region migrants, mainly from rural to urban areas, has risen
continually in the past. This means that an increasing number of people are constrained by
the Hukou system in terms of their consumption (see Table VIII for the time series data
concerning the number of migrants). This number amounted to around 221 million in2010. 19 In big coastal cities, the number of migrants grows faster than the national total.
The proportion of migrants without local Hukou in the total population is as high as 39.0%
in Shanghai and 35.9% in Beijing, as reported by the sixth population census in 2010. In

19

Data source: State Statistical Bureau of China: The First Report on the Main Statistics of the

Sixth Population Census, http://www.stats.gov.cn/tjfx/jdfx/t20110428_402722253.htm
20

Shanghai, the number of migrants without local Hukou increased from 3.46 million in 2000
to 8.98 million in 2010, with an annual growth rate of 9.99%. 20 As previously shown, in
big cities, the effect of Hukou is even greater. Therefore, this implies that the constraints of
Hukou on aggregate consumption will increase as time goes by.
[Table VIII about here]
Second, if the income level keeps rising and the constraints of Hukou on consumption
are stronger for those with high incomes, aggregate consumption will be constrained by agreater amount as migrants get richer. CHIPS in 1999 and 2002 showed that migrants' percapita real income had risen from 707 yuan/month in 1999 to 784 yuan/month in 2002,
which implies that the annual growth rate of migrants' income is 3.4%. In recent years,
migrants' income level has risen even faster (Zhang et al., 2011; Ge and Yang, 2011). In
order to test the possible effects of rising incomes on consumption under Hukou constraints,
we added an interaction term of per capita income and the migrant dummy to the baselinemodel in column 1 of Table IX. We find that the coefficient of the interaction term wassignificantly negative, which implies that the effects of Hukou constraints on consumption
will increase as migrants' incomes rise.
Third, if migrants have stronger precautionary savings motivation and face a higher
probability of cross-region mobility, they would reduce their consumption of
non-necessities. If the consumption gap between urban residents and migrants is greater for
non-necessities, the aggregate constraints of Hukou on consumption will become greater as

20

The numbers for Beijing and Shanghai are from their Reports on the Main Statistics of the Sixth

Population Census http://www.stats.gov.cn/tjfx/jdfx/t20110428_402722253.htm
21

migrants get richer and should be consuming more non-necessities. In order to test thishypothesis, we examined the gaps between the two groups in terms of the consumption
ratio for sub-category consumption. We also included the interaction term between ln (percapita income) and migrant status in the regression in order to capture the income effects
on different categories of consumption. Columns 2-7 of Table IX report regression results
on food, clothing, household equipment, health and medicine, communication, educationand culture. The results show that all of the interaction terms are negative and significant,which implies that migrants have a lower propensity to consume on all of the
sub-categories when their income rises.
[Table IX about here]
First, as regards food, clothing and communication, migrants’ propensity to consume
over income is lower than that of urban residents by 9.7%, 24.9% and 18.3%, respectively.
The relative magnitudes of the coefficients are consistent with the nature of consumption –as food is a necessity, the gap is minimum; in contrast, as clothing and communication are
not necessities, the gap is larger. The subsistence consumption levels of migrants for foodand clothing are seemingly higher than those of urban households. However, a simple
calculation tells us that the per capita income level which equalizes urban residents andmigrants as regards their food and clothing consumption is 969 and 211 yuan. In our
sample, only nine out of 6784 urban households had a per capita income of less than 969
yuan, which implies that the consumption levels of almost all urban households are higher
than migrants’ given income levels.
As regards household equipment and health and medicine, migrants’ marginal
22

propensity to consume is lower than that of urban residents by 57.3% and 59.8%. However,
the migrant dummy is significantly positive. As household equipment is durable, its
consumption should be higher for people who move less frequently. Therefore, due to theirincreased mobility, migrants have a lower propensity to consume durable goods when theirincome rises. Meanwhile, migrants have to buy some necessary durables when they moveto a new place, which leads to higher subsistence durable consumption. As regards healthand medicine consumption, higher subsistence consumption levels are due mainly to thelower medical insurance coverage for migrants. Without medical insurance, migranthouseholds have to pay most medical expenses by themselves. However, with risingincomes, migrants are unwilling to receive more medical services that are not necessary.
Therefore, we can observe both a higher level of subsistence consumption and a lower
marginal consumption ratio. A calculation tells us that the per capita income level whichequalizes urban residents and migrants as regards their consumption of household
equipment and medical and health goods is 314 and 827 yuan respectively. This implies
that the consumption of these two categories by almost all urban households is higher thanmigrants’ given income levels.
The gaps for education and culture consumption are the largest, as they reach 132.4%.
Expenditure on education is an investment in human capital, which is more sensitive tochange in one’s future income and job stability. As the lack of a local urban Hukou reduces
income stability but increases interregional mobility, it is not surprising that Hukou
constraints have the greatest impact on educational expenditure. Furthermore, a lack of
local urban Hukou would limit the probability that migrants’ children will be able to enter
23

state schools, as children are entitled to subsidized state education only in the area of theirlegal permanent residency (e.g., Afridi, Li, and Ren, 2009). This leads to much lower
educational expenditure for migrant households. However, migrants have to pay more foran education in urban areas because of Hukou restrictions, which implies that subsistence
educational expenditure will be higher for migrants. A simple calculation tells us that the
per capita income level which equalizes urban residents and migrants as regards theireducational expenditure is 1180 yuan, which implies that almost all urban households
consume more in terms of education than the migrants’ given income levels.
In recent years, there has been no substantive change in migrants’ struggle for equality
and the right to city welfare; moreover, recent reforms to the Hukou system have actually
made the permanent migration of rural peasants to cities harder than it was before (Chanand Buckingham, 2008). Given the increasingly strong restrictions of Hukou, coupled with
the increasing number of migrants and their total income, the effects of Hukou on migrants’consumption are unlikely to shrink.
VII. Conclusions and policy implications
It is estimated that the total number of migrants in China has already reached 221 million,
and it is still growing. At the same time, China’s household consumption-to-GDP ratio has
remained fairly low, and it is still falling. Using CHIPS 2002 data, this paper finds that,compared with urban residents, migrants have a higher level of mobility and lower social
safety net coverage. We find that migrants’ consumption is lower than that of urban
residents by 30.7%, after controlling for other important factors. Careful analysis suggeststhat consumption heterogeneity is explained mainly by the Hukou system, rather than24

factors such as household characteristics, culture or habits.
The number of migrants is still growing, and they are getting richer and therefore
likely to consume more non-necessities. Consequently, the constraints imposed by the
Hukou system on migrants’ consumption are growing in magnitude. The policy implicationof this paper is that, for a successful structural change and balanced growth, it is necessary
for China to reform the Hukou system. The threshold for Hukou should be lowered, andpublic services, including social security status, should be equalized between migrants andurban residents. The Hukou system currently presents a major obstacle to China’s quest to
become a modern economy (Chan, 2009), and removing the restriction would be aneffective way to significantly stimulate China’s aggregate consumption and domestic
demand, as well as to achieve global economic balance.

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29

Table I. Consumption expenditure as a percentage of GDP (2009)
Country
Final consumption
expenditure (%)
Household final
consumption expenditure(% of GDP)

U.S.

U.K. German Japan Korea India Brazil China

89

89

79

79

70

68

84

48

71

65

59

60

54

56

62

35

Data source: World Development Indicator (WDI), available at:
http://data.worldbank.org/indicator/NE.CON.TETC.ZS andhttp://data.worldbank.org/indicator/NE.CON.PETC.ZS

Note: Final consumption expenditure (formerly total consumption) is the sum of household final consumption
expenditure (private consumption) and general government final consumption expenditure (general government
consumption).

30

Table II. Summary statistics of major household characteristics 21

Mean:

Mean:

t-statistics:

Urban residents

Migrants

Difference

Total consumption (yuan)

18163.1

11561.2

21.6

Total income (yuan)

24368.016573.1

19.6

Age of the head

47.9

36.0

43.4

Years of education of the head

10.7

8.1

31.6

No. of family members

3.0

2.7

15.6

137655.1

37331.8

25.2

Total wealth (yuan)

21

We removed observations with outlier values for some variables, i.e., observations with zero

household consumption and income, and for which the head of the family was older than 80 or
younger than 20. In total, 88 observations were excluded.
31

Table III. Consumption behavior of migrants and urban residents:
Baseline results

VARIABLE

(1)

(2)

(3)

lncon

Ln (con+Adj-remittance)

Ln (con+(4)(5)

Per capita con 1-(con/hhinc)+remittance)
Migrant
Ln (income)Age-0.307***-0.245***-0.177***-1,274.961***0.130***[0.019][0.019][0.018][95.799][0.023]0.625***

0.616***

0.641***

0.349***

0.342***[0.013][0.015][0.013][0.015][0.031]-0.001-0.001-0.001**-3.276-0.001[0.001][0.001][0.001][2.998][0.001]0.011***

0.011***

0.010***

57.642***-0.013***[0.002][0.002][0.002][9.808][0.002]-0.030***-0.044***-0.057***-159.024***0.039***[0.006][0.006][0.006][34.134][0.006]0.020***

0.020***

0.016***

0.005***-0.018***[0.004][0.004][0.004][0.002][0.006]

Health dummy

Yes

Yes

Yes

Yes

Yes

Ownership dummy

Yes

Yes

Yes

Yes

Yes

Education
Family size
Ln (asset_per)

Occupation dummy

Yes

Yes

Yes

Yes

Yes

Industry dummy

Yes

Yes

Yes

Yes

Yes

Contract dummy

Yes

Yes

Yes

Yes

Yes

2.889***

3.028***

2.915***

3,580.999***-2.711***[0.130][0.144][0.130][413.240][0.264]

Observations

8,750

8,750

8,750

8,750

8,750

R-squared

0.657

0.641

0.650

0.585

0.188

Constant

Note: The figures in brackets are robust standard errors; *, ** and *** indicate significance levels of 10%,
5% and 1% respectively.

32

Table IV. Labor risk of urban residents and migrants
Migrant (%) Non-migrant (%)
Job and contract
Fixed or long-term contract
Temporary or short-term contract
Individual business
Other

5.24
25.78
65.91
3.07

77.52
14.03
5.88
2.57

Major sources of income
Wage or salary income
Individual business income
Other

42.76
53.68
3.56

70.37
4.14
25.49

33

Table V. Consumption heterogeneity across different regions
(1)

(2)

(3)

(4)

(5)

lncon

lncon

lncon

lncon

lncon

Migrant-0.717***-0.468***-0.365***-0.284***-0.231***[0.090][0.056][0.041][0.033][0.033]

Lninc

0.413***

0.592***

0.594***

0.659***

0.655***[0.048][0.032][0.026][0.027][0.023]-0.003-0.0010.001-0.002*-0.004***[0.003][0.001][0.001][0.001][0.001]0.019**

0.013***

0.014***

0.005

0.006**[0.008][0.005][0.003][0.003][0.003]-0.072***-0.015-0.021*-0.015-0.043***[0.027][0.016][0.012][0.011][0.010]0.058***

0.054***

0.012*

0.028***

0.016***

VARIABLE

Age
Edu.
Population
Lnasset[0.017][0.010][0.007][0.010][0.005]

Health dummy

Yes

Yes

Yes

Yes

Yes

Ownership dummy

Yes

Yes

Yes

Yes

Yes

Occupation dummy

Yes

Yes

Yes

Yes

Yes

Industry dummy

Yes

Yes

Yes

Yes

Yes

Contract dummy

Yes

Yes

Yes

Yes

Yes

4.466***

2.524***

3.009***

2.536***

2.448***[0.518][0.312][0.281][0.243][0.211]

Observations

582

1370

2378

1762

3240

R-squared

0.671

0.673

0.609

0.668

0.587

Constant

Notes:(a) The results in each column are estimates for sub-samples consisting of:(1) Beijing;(2) Coastal capital cities of Beijing, Liaoning, Jiangsu and Guangdong;(3) Non-coastal capital cities of Shanxi, Anhui, Henan, Hubei, Chongqing, Sichuan, Yunnan and Gansu;(4) Coastal non-capital cities;(5) Non-coastal non-capital cities.

(b) The figures in brackets are robust standard errors; *, ** and *** indicate the significance levels of 10%,
5% and 1% respectively.

34

Table VI. Consumption heterogeneity and family structure

Dependent var.: Ln (con)

(1)

(2)

(3)

(4)

(5)

Family size≤3

Age≤50Not owning

No. of children

No. of boys

a houseMigrant
Ln (income)
Age
Education
Family size
Ln (asset_per)-0.323***-0.299***-0.289***-0.296***-0.296***[0.022][0.020][0.027][0.019][0.019]0.637***

0.603***

0.530***

0.629***

0.629***[0.014][0.017][0.024][0.013][0.013]-0.0010.002**

0.001-0.000-0.000[0.001][0.001][0.001][0.001][0.001]0.012***

0.014***

0.016***

0.011***

0.011***[0.002][0.002][0.003][0.002][0.002]-0.002-0.027***-0.037***-0.040***-0.040***[0.011][0.009][0.011][0.006][0.006]0.018***

0.019***

0.017***

0.020***

0.020***[0.004][0.004][0.005][0.004][0.004]0.019***

0.020***[0.005][0.006]

No. children
No. boys-0.001[0.005]

Health dummy

Yes

Yes

Yes

Yes

Yes

Ownership dummy

Yes

Yes

Yes

Yes

Yes

Occupation dummy

Yes

Yes

Yes

Yes

Yes

Industry dummy

Yes

Yes

Yes

Yes

Yes

Contract dummy

Yes

Yes

Yes

Yes

Yes

2.721***

2.932***

3.535***

2.839***

2.839***[0.149][0.168][0.252][0.132][0.132]

Observations

7145

6157

3222

8750

8750

R-squared

0.653

0.657

0.590

0.657

0.657

Constant

Note: The figures in brackets are robust standard errors; *, ** and *** indicate the significance levels of 10%,
5% and 1% respectively.

35

Table VII. Consumption heterogeneity and culture
(1)

(2)

(3)

(4)

Independent var.:

Urban residents

Condition (1)Hukou due toYears living in

Ln (con)born in rural

education≤9

joining the army or landurban areas

expropriated by gov.

areas
Migrant
Ln (income)
Age
Education
Family size
Ln (asset_per)-0.259***-0.245***-0.221***[0.027][0.033][0.042]0.531***

0.476***

0.457***

0.404***[0.021][0.024][0.025][0.027]

0.000

0.001

0.001

0.001

[0.001][0.001][0.001][0.001]0.015***

0.015***

0.016***

0.017***[0.003][0.004][0.004][0.004]-0.034***-0.035***-0.032***-0.038***[0.009][0.010][0.011][0.013]0.023***

0.026***

0.028***

0.031***[0.005][0.006][0.006][0.007]

Years in urban areas

0.000[0.000]

Health dummy

Yes

Yes

Yes

Yes

Ownership dummy

Yes

Yes

Yes

Yes

Occupation dummy

Yes

Yes

Yes

Yes

Industry dummy

Yes

Yes

Yes

Yes

Contract dummy

Yes

Yes

Yes

Yes

3.485***

3.880***

3.964***

4.258***[0.214][0.265][0.287][0.311]

Observations

3741

2628

2376

1966

R-squared

0.587

0.490

0.497

0.422

Constant

Note: The figures in brackets are robust standard errors; *, ** and *** indicate the significance levelsof 10%, 5% and 1% respectively.

36

Table VIII. The number of rural-to-urban migrants

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005

Rural labor force(million)

Non-agricultural
rural labor force(million)

Share ofnon-agricultural
rural labor force
(%)

Rural
migrant
labor force(million)

Share of rural
migrants(%)

370.65379.90390.00400.76409.39420.10430.93438.02442.56446.54450.42452.88459.62464.32468.97479.62482.29484.72488.84496.76503.87

62.33
66.82
70.50
73.61
75.58
76.94
79.16
83.80
92.09
97.98
102.57103.78106.10108.04109.55112.24115.32118.73120.80127.53134.80

16.8
17.6
18.1
18.4
18.5
18.3
18.4
19.1
20.8
21.9
22.8
22.9
23.1
23.3
23.4
23.4
23.9
24.5
24.7
25.6
26.7

8.00
9.00
10.50
12.50
15.00
18.00
21.40
25.92
27.52
28.88
30.00
34.00
38.90
49.36
52.40
76.00
90.50
104.70113.90118.23125.78

2.2
2.4
2.7
3.1
3.7
4.3
5
5.9
6.2
6.5
6.7
7.5
8.5
10.6
11.1
15.8
18.8
21.6
23.3
23.8
24.2

Data source: Sheng (2008, p. 9 Table 1-4, p. 73, Table 4-1).

37

Table IX. Consumption behavior of migrants and urban residents:
Sub-category consumption
(1)

(2)

(3)

Lncon

Food

Clothing

2.165***

0.667***[0.202]Ln (income)Migrant*Ln (income)[0.023]

Independent variable:
Ln (sub-category consumption)
Migrant

Age
Education
Family size
Ln (asset_per)

(6)

(7)

Household Health andCommu-

Education andequipment

medicine

nicationculture1.333**

3.295***

4.307***

0.587

9.365***[0.203][0.549][0.744][0.786][0.672][0.855]0.713***

0.500***

0.953***

1.056***

0.941***

1.079***

1.139***[0.011][0.011][0.030][0.037][0.047][0.033][0.046]-0.289*** -0.097*** -0.249***-0.573***-0.598***-0.184**-1.324***[0.063][0.088][0.092][0.078][0.100]-0.002*** 0.003*** -0.027***-0.0030.017***-0.014***-0.030***[0.001][0.001][0.002][0.002][0.002][0.002][0.002]0.009***

0.001

0.026***

0.035***

0.026***

0.030***

0.045***[0.002][0.002][0.005][0.006][0.007][0.005][0.007]-0.030*** -0.090*** 0.079***

0.069***

0.153***

0.058***

0.695***[0.006][0.006][0.018][0.023][0.027][0.019][0.029]0.024***

0.006

0.089***

0.117***

0.029*

0.145***

0.142***[0.024](4)(5)[0.004][0.005][0.012][0.014][0.016][0.014][0.018]

Health dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ownership dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Occupation dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Industry dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Contract dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Health dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Constant

2.143***

3.466*** -2.381***-5.824***-5.274*** -4.635***-6.378***[0.114][0.114][0.428][0.516][0.499][0.324][0.367]

Observations

8750

8750

8750

8750

8750

8750

8750

R-squared

0.671

0.533

0.381

0.407

0.243

0.412

0.400

Note: The figures in brackets are robust standard errors; *, ** and *** indicate significance levels of 10%,
5% and 1% respectively.

38

Figure I
Consumption ratio and saving ratio of China’s households (1992~2008)

70.00
60.00
50.00
40.00
30.00
20.00
10.00

Consumption/GDP（%）
Houseshold Consumption/GDP(%)
Houseshold Saving/Household Disposable Income(%)

0.00

Data sources:
National Bureau of Statistics, China Statistical Yearbook, 2005~2010;
National Bureau of Statistics, Data of Flow of Funds of China, 1992~2004, Beijing:
China Statistics Press, 2008.

39