Master Programme in Economic History
Household consumption in China on the rise: Can China
buy growth?
Sharon J. Riley
aeh10sri@student.lu.se
Abstract: This paper seeks to examine trends in Chinese household consumption. It is
comprised of a two-fold study, which undertakes both an exploratory and an explanatory
approach. First, an analysis into the nature of household consumption examines trends in
purchasing habits. Specifically, data on the consumption and prevalence of durable household
goods and major food commodities is gathered to establish both the yearly demand for such
products, as well as to establish whether demand in the consumer market is met by domestic
production. Second, an explanatory analysis of household consumption attempts to glean more
insight into which factors may potentially induce increases in Chinese household consumption.
This study is undertaken in response to the suggestion that the current leadership of China has
undertaken a fundamental shift in government policy which seeks to promote consumptiondriven rather than export-led growth as a means to sustained transformation and long term
economic growth.
The results suggest, coinciding with past studies, that government expenditure on social
programs (welfare, pensions, etc) has a significant impact on household consumption, supporting
theories of precautionary savings and their implicit effects on consumption levels.
Key words: China, household consumption, economic growth, domestic market, precautionary
saving
EKHR11
Master thesis (15 credits ECTS)
June 2011
Supervisor: Christer Gunnarsson
Examiner:
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Contents
1
Objective................................................................................................................................................................ 4
2
Background and justification .............................................................................................................................. 4
3
Contribution of Study.......................................................................................................................................... 8
4
China’s Economic Growth; Pre-reform to present ........................................................................................ 9
5
Theories of Household Consumption ............................................................................................................11
6
Part I - Exploratory Study of Consumption of Household Goods ...........................................................13
6.1
Data .............................................................................................................................................................14
6.1.1
Household Commodities ................................................................................................................14
6.1.2
Food Commodities ..........................................................................................................................15
6.2
Limitations of Data ...................................................................................................................................16
6.3
Methods ......................................................................................................................................................17
6.3.1
Household Commodities – durable goods ..................................................................................17
6.3.2
Food Commodities ..........................................................................................................................19
6.4
Results .........................................................................................................................................................20
6.4.1
Colour Televisions ...........................................................................................................................20
6.4.2
Bicycles ..............................................................................................................................................23
6.4.3
Cameras .............................................................................................................................................26
6.4.4
Electric Fans .....................................................................................................................................28
6.4.5
Comments .........................................................................................................................................30
6.5
7
Food Commodities ...................................................................................................................................31
6.5.1
Sugar...................................................................................................................................................31
6.5.2
Edible Vegetable Oil .......................................................................................................................32
Part II - Explanatory Study: Household Consumption on the Rise ..........................................................35
7.1
Data .............................................................................................................................................................35
7.2
Data Limitations ........................................................................................................................................36
7.3
Methods ......................................................................................................................................................36
7.4
Definitions of Variables ...........................................................................................................................37
7.4.1
Dependent Variable .........................................................................................................................37
7.4.2
Explanatory Variables .....................................................................................................................38
7.5
Modifications of Start Model ..................................................................................................................41
7.6
Results .........................................................................................................................................................43
8
Discussion and Relation to Theory .................................................................................................................46
9
Conclusion...........................................................................................................................................................50
10
Appendix .........................................................................................................................................................51
10.1
Results of Exploratory Study ..................................................................................................................51
Sharon Riley - Master Thesis – Lund University School of Economics and Management
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11
10.1.1
Bicycles ..............................................................................................................................................51
10.1.2
Cameras .............................................................................................................................................52
10.1.3
Electric Fans .....................................................................................................................................52
10.2
Descriptive Statistics .................................................................................................................................53
10.3
Alternate Model .........................................................................................................................................55
Works Cited ....................................................................................................................................................56
Sharon Riley - Master Thesis – Lund University School of Economics and Management
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1 Objective
The objective of this paper is to examine the trends in Chinese domestic household consumption
over the past three decades. Much literature has focused on the suggestion that domestic
consumption in China has not been at satisfactorily high levels, and lags behind that of other
countries at a similar development stage. It will be of interest in this thesis to look more closely at
the nature of household consumption.
This will be a two-fold study: first, an exploratory study into the nature of household demand for
durable household commodities and major foodstuffs will be completed. In this portion of this
thesis, the domestic demand will be estimated based on available data and will be compared with
data on imports and exports where possible. Two separate methods of calculating domestic
demand will be utilized and compared.
In the second portion of this paper, an attempt will be made to contribute an explanatory study
of the factors influencing increasing household consumption. Specifically, stated policy shifts of
the Chinese government, in combination with a myriad of economic and demographic factors
will be analysed. Of particular interest is the role of the government in stimulating consumption,
be it through stated policy shifts or through social spending.
This discussion of patterns in household consumption leads to a larger question surrounding
whether current consumption levels in China are at a sufficiently high level to ensure long term
economic growth.
2 Background and justification
China is on course to be one of the major consumer markets in the world, with some sources
predicting it will be the world’s third largest consumer market by 2020 (Woetzel et al., 2009). The
domestic market in China has the possibility to be a huge driver in the engine of economic
growth in both the Chinese and the world economy, based on its sheer size alone. With the
wages of even the lowest skilled labourers rising (see Figure 2-1), it seems obvious that domestic
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consumption will increase. This is not only a function of consumer purchasing power. In 2004,
China’s top political leadership made the decision to fundamentally alter the country’s growth
strategy. In place of the investment and export-led development that has been credited for the
rapid economic growth to date, they endorsed transitioning to a growth path that relied more on
expanding domestic consumption (Lardy, 2006; Aziz & Cui, 2007). It is with this policy shift in
mind that it appears relevant to undertake an analysis of changes in Chinese domestic
consumption patterns over the past three decades; during the period of rapid transformation of
the Chinese economy. First, it is of particular interest how household consumption patterns have
changed over time and what has influenced this trend. When examining household consumption
on goods, it is of specific interest whether domestic demand is met by domestic output. A
significant quantity of literature and media has focused on the Chinese consumer’s demand for
luxury goods, many of which are imported. Research into consumer preferences in the past has
unveiled, at least for some electronic goods, preferences for imported goods (Doran, 2002). It is
possible then, that rising consumerism in China could greatly affect China’s balance of trade, as
Chinese consumers may prefer imported goods (due perhaps to perception of differences in
quality). The magnitude of this effect is uncertain, as it may apply only to high-earning urban
residents and not be a trend applicable to the entire population. Regardless, the preferences of
Chinese consumers could have large impacts on the long-term sustainability of economic growth
and trade balances in China. Unfortunately, a survey of individual consumer preferences and
spending patterns in China is outside the scope of this masters thesis. Instead, the demand for
domestically produced goods will be disaggregated from numerous series of available data, and
trends examined.
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100000
Total
10000
Farming, Forestry,
Animal, Husbandry,
and Fishery
Mining and Quarrying
1000
Manufacturing
1978
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
100
Figure 2-1 - National average yearly wages (yuan) of Chinese workers in traditionally lower paid sectors and overall
total average, 1978 – 2008 (natural logarithm applied) Source: China Statistical Yearbook
Chinese households have had a historically low ratio of private consumption relative to GDP and
it appears to be declining (see Figure 2-2). This declining trend in the share of GDP that is made
up by private consumption is supported by trends in household finances, with the average
household choosing to save approximately 25% of their disposable income in 2007, compared
with only 11 percent in 1990 (Baldacci, et al., 2010). Earlier estimates of aggregate savings stated
that the national savings rate was as high as 50% of GDP in 2005 (Lardy, 2006).
Household Consumption
Government Consumption
1952
Fixed Capital
1980
2009
Changesin Inventories
Net Exportof Goodsand Services
0%
20%
40%
60%
80% 100%
Figure 2-2 – Share of GDP by expenditure approach
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Much research has been conducted to explore the origins of these behaviours, with many
researchers believing that Chinese households have traditionally high savings rates, particularly in
rural areas, due to poor social programs and uncertainty as to how a household could handle a
crisis – be it health related, due to sudden unemployment, or otherwise. Many policy makers have
suggested that initiatives on behalf of the Chinese government to increase social spending, and
thus increase the confidences of Chinese households in their ability to survive in the face of
uncertainty, would have a direct effect on rates of private consumption (Baldacci, et al., 2010;
Barnett & Brooks, 2010; Fang, 2009). Recognition of this dilemma has been evident in recent five
years plans as well as the stimulus package introduced by the government in response to the 2008
global economic crisis (Cai, Wang, & Zhang, 2010). Private consumption in China has been
increasing in recent years (see Figure 2-3). It is of interest which factors in China have led to this
increased household consumption over the past two to three decades and, also, whether these
increases have been deemed sufficient to sustain long term economic growth.
100000
10000
All Households
Rural
1000
Urban
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
100
Figure 2-3 - Average yearly household consumption (yuan) in Chinese households (natural logarithm applied), 1952 –
2009
Source: China Statistical Yearbook
China is indeed on course to become a dominant consumer market in the world in the coming
decades, as a result of both an increasing population and increasing consumerism. It is important
to note, however, that China’s private consumption accounts for only 36% of GDP (the lowest
of any major economy in the world), compared with 70% in the US (Woetzel et al., 2009). With
rising wages and subsequent increases in demand for consumer goods, particularly in urban areas,
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it seems pertinent to examine both trends in household consumption, as well as the Chinese
demand for goods manufactured in China. Certainly an ever-expanding consumer base combined
with large increases in per capita disposable income has the potential to alter the current growth
trajectory of the Chinese economy.
3 Contribution of Study
Although Chinese economic growth has been a prominent topic in both the popular media and
the world of academia since China first began to assert itself as a major player in the world
economy, relatively little literature has been published focusing on the origin of the household
goods purchased by Chinese consumers.
This is due in part to the tradition of Chinese
households to be frugal consumers who purchase relatively few goods compared to other
nations; China has not traditionally been perceived as an important consumer base and was
certainly not viewed as an important destination for exporting consumer goods. However, a
substantial hurdle to a study examining whether Chinese consumers are buying goods produced
domestically or abroad lies in the availability of data explicitly dealing with these questions. This
thesis will serve as an exploratory study of the data available, seeking to construct a data set to be
able to observe general trends in the consumption of domestically and foreign produced goods.
In addition, though the lackluster performance of Chinese domestic consumption has been
deeply analysed, with scholars citing declining consumption ratios and declining household
consumption levels when compared with the growth of investment and the GDP (Fang, 2009),
analyses have often lacked an examination of the combination of economic, demographic, and,
particularly, policy factors that may play significant roles in influencing household consumption.
As the Chinese government’s decision to officially endorse transitioning to a consumption-led
growth path occurred relatively recently, in 2004, it has not yet been possible to examine the
effects of the transition. This study commences at a time when the first steps towards this new
growth path have been taken and the effects can now be examined, at least preliminarily.
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4 China’s Economic Growth; Pre-reform to present
Consumption in the Chinese economy lagged behind those of western nations for decades after
the Second World War, leaving many with the impression that is was chronically embattled with
challenges that would hinder its ability to ever become a major economic player. In addition, per
capita GDP growth was particularly underwhelming and when the growth of the macro-economy
took off, per capita wealth increases remained lackluster. Economic reforms began in China in
the late 1970s and the country has achieved an almost unprecedented growth rate, an average of
ten percent per year, ever since (Wu, 2000). The growth pattern that was embraced throughout
the 1980s and 1990s was one that was heavily reliant on the ability of the country to attract
foreign direct investment (FDI), peaking in the mid 1990s, and the industrial policy initiatives put
into place by the government through state-owned enterprises (SOEs), often focused on heavy
industrialization and capital deepening projects (Lo, 2006). Also a major focus of Chinese
industrial policy throughout the transformation process was exports: the share of exports of
GDP increased from a negligible amount in the 1960s to nearly thirty percent in 2003, an
unprecedented rate of increase when compared to the other nations of the world (Rodrik, 2006).
As it slowly underwent a process of opening up to foreign investors, China began to attract
unparalleled levels of FDI in the 1990s; ten percent of global FDI was directed towards China by
2003 (Zhang, 2005). It is important to note that China’s openness to foreign investment has not
been without its caveats; many investors were required to form joint ventures with domestic
firms, technology sharing was often required, and protection of the domestic market has been a
priority (Rodrik, 2006). This has enabled unprecedented strengthening of domestic players, as
protectionist industrial policy measures may have enabled domestic firms to leapfrog foreign
competitors, especially in terms of the increasing Chinese activity in the area of high-tech
products. However, it is not only inward FDI that has made China a focus of international
attention: in more recent years, China’s outward FDI flows have also increased substantially,
aimed at 160 countries and increasing ten-fold between the 1980s and 2003 (Zhang, 2005),
suggesting that China’s intentions to become a dominant player in the global economy are
coming to fruition, and in unprecedented ways.
It has been the view, of many economists and policymakers alike, that China’s economic
transformation has been atypical, a special ‘China case’ made possible beginning with market
supplanting policies by a centralized government and slowly transitioning to market fostering
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policies, particularly with ascension to the World Trade Organization in 2001 (Lo, 2006; Wang &
Thomas, 1993). China’s transition began with a unique set of characteristics, largely defined by its
large surplus of agricultural labour (and the low wage demands of these workers) and dictated
mainly by the central planning of the government (influenced largely by five year plans beginning
in 1953) in both the pre-reform period and continuing into the transformation period (Cai F. ,
2008). Regardless of this atypical growth path followed by China, eschewed by other major
economies, China has been able to rise to be an important force on the world stage.
China’s successful emergence as a dominant player in the world economy has not been without
its tribulations. China’s heavy industrialization process aimed at capital deepening has involved an
intensive use of energy and raw materials, heading down a growth path which may not be
sustainable in the long run and requires vast leaps forward in terms of increasing efficiency of
resource use. Adding to the difficulties with this capital deepening growth trajectory are the issues
surrounding the necessity for capital-scarce China to rely heavily on a regulated financial sector in
order to grow; continuing on this growth path inhibits the ability of the country to transition to a
market-oriented financial industry (Lo, 2006), which is becoming increasingly important to
trading partners in the world economy. In addition, China’s transition from a centrally planned
state to a more market-oriented economy required that China change its historical, pre-reform
policy of ‘cradle to the grave’ security for its citizens (Hughes, 2002) and move to a so-called
breaking of the ‘iron rice bowl’ (Chamon & Prasad, 2008) which saw decreased priority in
government spending on expenditures for social programs, most notably in health, education,
and social programs. These factors together, capital deepening and decreased social spending,
lead to an economy for which growth is based on aggregated investment and household saving
rather than domestic consumption.
China’s government has acknowledged that the possibility of long term efficiency of a growth
trajectory relying on capital is limited (Lo, 2006). In addition, the top political and economic
leadership in China has been acutely aware of the heavy reliance on exports and investment for
growth in recent decades. Recognition that China’s trade surplus has continued to grow at a
remarkable rate, setting a global record in 2006, combined with a continued decrease in the
proportion of GDP accounted for by private consumption through 2009 (Bergsten et al., 2009, p.
106) has led the leadership to suggest an alternative growth strategy with the hope of reversing,
or at the very least, slowing, these trends. An adherence to a shift in growth policy from 2004 is
evident in the 2011 Report on the Work of Government delivered by premier Wen Jiabao at the fourth
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session of the eleventh National People’s Congress, which states, “Expanding domestic demand
is a long-term strategic principle and basic standpoint of China's economic development as well
as a fundamental means and an internal requirement for promoting balanced economic
development.” The Chinese government can promote domestic consumption in order to achieve
these long term goals through fiscal, financial, exchange rate, and price policies (Bergsten et al.,
2009, p.116). Wen Jiabao also stated in his 2011 Report on the Work of the Government numerous
promises to the people of China regarding how domestic demand would be expanded. He
specifically addresses increasing government spending that is aimed at boosting consumer
demand, increasing subsidies to rural residents and low-income urban residents (including
subsidies for rural residents to purchase home appliances), and strengthening infrastructure,
among many others (Jiabao, 2011).
China’s economy is seemingly headed down a markedly different growth path from the one that
brought about economic transformation over the past few decades; most notable here is the
increasing importance of domestic consumption as a driver of economic change and growth.
With a burgeoning population and increasing wages, policy makers in China have seemingly
recognized the potential to harness increasing household consumption as a driver of economic
growth, though to date its contribution to GDP has not increased.
5 Theories of Household Consumption
Chinese leadership and scholars agree that consumption expenditure contribution to GDP in
China is lagging behind other world economic powers (Fang, 2009; Lardy, 2006; Bergsten,
Freeman, Lardy, & Mitchell, 2009). The theories explaining this perceived lackluster contribution
can be categorized into three primary groups. The first theory relates to the growing income gap
between rural and urban households (as well as between coastal and inland households) in China.
It is hypothesized that the inability of middle and lower income groups to raise consumption
levels at a rate consistent with the ever expanding economy has led to inadequate consumption
demand (Fang, 2009; Yongbing, 2005). Income inequality is no small problem in China with the
income gap widening drastically (Yao, Zhang, & Feng, 2005); the income gap between rural and
urban areas has been debated, but it has been suggested that it has been increasing since 1995,
with the absolute incomes of the lowest income brackets have been decreasing (Benjamin,
Brandt, & Giles, 2004). Income inequality certainly has the potential to influence average per
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capita household consumption, as overall growth may be reflective of increases in income and
wellbeing enjoyed by only those at the high-end of wage earners. Thus, the majority of lowerincome workers may be tied to continued low consumption paths.
The suggestion that Chinese consumers are inherently prone to saving, that they are not lavish
consumers by tradition is offered as an alternative theory to explain relatively low levels of private
consumption. The reluctance of Chinese consumers to spend has been suggested to be linked to
a
tradition of Confucianism
which values thrift and modest economic comfort
(Watchravesringkan, 2007). In addition to a propensity for saving, the use and popularity of
consumer credit in China is lower than that observed in other countries, even in neighbouring
Asian countries with a comparable development status, further contributing to lower levels of
private consumption (Woetzel et al. 2009). Furthermore, though cultural norms and values
rooted in Confucianism may be changing, it has been suggested that changes in consumption
patterns lag behind, as habit formation may play a significant role in determining saving and may
explain continued propensity towards saving (Carroll & Weil, 1994). Growth in the savings rate
has, quite obviously, an inverse effect on household consumption.
The third theory offered as an explanation for inadequate consumption is also related to the
savings rate, but places more emphasis on the role of the state, positing that the weak welfare
system in China leads consumers to save for their futures, preparing for calamities for which the
state would not provide assistance. Government spending on healthcare, pensions, welfare, and
education has been found to have a large impact on household consumption (Baldacci, et al.,
2010); thus the criticism that China’s social programs are relatively weak may offer some
explanation of observed low consumption levels. This is of particular interest in this study, as
government expenditure on social programs is a means which has been suggested for utilization
by the Chinese government in order to attempt to induce transition to a consumption-driven
growth path. This third theory is also related to theories positing that income inequality leads to
decreased growth in household consumption, as the government possesses the ability to increase
income of the lowest wage earners by providing other social programs, such as housing subsidies
in rural areas, and thereby household consumption. A previous analysis into past rural housing
subsidy programs revealed a significant correlation with growth of household consumption
(Fang, 2009). It is logical to posit that the breaking of the ‘iron rice bowl’ which induced
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precautionary savings in weary Chinese consumers (Chamon & Prasad, 2008) must be related to
lackluster growth in consumption, as precautionary spending increased as a result.
The savings rate in China is theorized to play a crucial role in determining how factors influence
household consumption. Previous studies have looked into the role of the Life Cycle Hypothesis in
determining savings rates in China (Modigliani & Cao, 2004; Chao, LaFarrgue, & Yu, 2011).
These analyses are of interest here, as it is expected that factors which positively affect savings
will have an opposite and direct effect on consumption. The Life Cycle Hypothesis as applied by
Modiglianai and Cao (2004) suggests that savings increase during the middle and end of wage
earning years, and decrease again during retirement; leading to suggestions that, as income has
increased since the current retired group last exhibited high savings, the savings ratio computed
overall has increased (Chao, LaFarrgue, & Yu, 2011). Additionally, the one child policy is
expected to increase saving, thereby reducing consumption, as parents know that they will have
only one income to rely on for support when they retire in the future.
Part one of this thesis will seek to look at a more detailed picture of the rise in household
consumption; specifically through examining aggregate purchasing trends for important durable
goods and household commodities. It is expected that demand for these goods has been
increasing over the past three decades, as wages increased. However, as has been discussed, is it
the proportion of income that is spent on consumption that has been falling, such that, even as
income and purchases increase, the savings rate has been increasing faster; theories explaining
this have been described above. It is pertinent now to examine whether absolute levels of
consumption for basic goods have been increasing, which is completed in the following section.
6 Part I - Exploratory Study of Consumption of Household
Goods
This chapter contains findings in the exploration of trends in household consumption patterns.
This component of this study will serve as an exploratory study into the changing nature of
Chinese consumption of common household commodities and major foodstuffs. This will serve
to establish two objectives: how are levels of household consumption changing (what is the
domestic demand?) and where do goods consumed by households originate – in China or
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abroad? (is the domestic market able to meet domestic demand?) It is not possible with the
available data to trace country of origin of foreign produced goods, but an effort will be made to
establish whether or not they are produced domestically.
6.1
Data
Data has been gathered primarily from China’s Statistical yearbooks. It has been collected to
form two data sets.
6.1.1
Household Commodities
Four durable household commodities have been selected to focus analysis upon; television sets,
bicycles, electric fans, and cameras. These goods have been selected based on the assumption that
they are (1) end products, and will not go on to be processed into a more advanced commodity1
(like fabric and textiles, for example), (2) they are most likely to be consumed by households,
rather than firms (office equipment and computers for example may be more likely to be
consumed by firms than households), and (3) data is available in these specific categories.
Although it would have also been beneficial to analyse trends in other popular consumer goods
which are likely to purchased as perceptions of personal wealth increase – specifically clothing
and textiles, personal electronics, and computer related equipment - the data simply is not
available in a form where it is disaggregated to a level which could be consistently compared
across different statistical categories. These four commodities, particularly television sets and
bicycles are rather unambiguous in their interpretation, although the eventual separation of
colour and black and white television sets in the data could pose a concern. Cameras pose more
of an issue, especially as digital cameras are introduced in recent years and may have
categorical/labeling changes.
Data on the four selected commodities is available for:

Output of Major Industrial Products (in units)

Major Export Commodities in Volume and Value (units and US dollars)
A high share of china’s imports are imports for processing – 35 percent of all imports in the early 1990s
to 50 percent by 1997 (Rumbaugh & Blancher, 2004)
1
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
Number of Major Durable Consumer Goods Owned per 100 Urban Households at Year
End (units)

Number of Major Durable Consumer Goods Owned per 100 Rural Households at Year
End (units)
Data on television sets only is available for:

Major Import Commodities in Volume and Value (units and US dollars)
Data has been collected primarily from China Statistical Yearbooks. Unfortunately, yearbooks
are only available in English back to 1997 (which contains data from 1996, as well as detailed
historical data, as far back at 1952 in some cases), and several years are not available – 1998, 2000,
2001, 2004, 2006, and 20072. Data for household commodities has been collected from the
Industry, People’s Livelihood and Worker’s Wage, and Domestic Trade, Foreign Trade and Economic
Cooperation sections of the China Statistical Yearbook.
6.1.2
Food Commodities
Data has been gathered on major foodstuff commodities purchases by households in both rural
and urban areas. In these instances, complete data sets are available for sugar and edible vegetable
oil, with limited data available for grains, aquatic products, and liquor which was not sufficient to
present in this thesis. This data is obtained from annual reports on the annual consumption of
households in the People’s Livelihood and Workers’ Wage section of the China Statistical Yearbook.
Sugar and edible vegetable oil will be focused on in this study. These goods are of interest, as,
although the demand for them is not likely to expand indefinitely with increasing wealth, it is
likely to increase as households surpass the poverty line and can afford to purchase these
ingredients in sufficient quantities to fulfill dietary requirements or desires.
2
Not available from the Asia Library at Lund University
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6.2
Limitations of Data
Limitations involved with this use of China’s statistical administrative records are, unfortunately,
many. The data series used in this study were constructed from various issues and sections of the
China Statistical Yearbook; when combining data in this manner, one cannot be absolutely sure
that the categories of goods are consistent across data sets. For example, the category ”cameras”
may be more or less inclusive in the customs data used for import and export quantities than it is
in data on industrial output3.
An example of data which is not useable, but for which much data is available, is “electronics,”
which creates ambiguity in interpretation, and may refer to parts imported to then be further
manufactured into goods. As China’s trade deals heavily in the import of raw materials and the
export of components, it is occasionally ambiguous whether or not goods reported in the data are
actually finished products ready for sale to the consumer. Differing definitions and stages of
production pose a problem in this study, which is the reasoning for choosing products that are as
simple and unambiguous as possible.
This study may be said to be limited in its inability to examine trends over a long time period.
Much of the data on consumer goods does not date back further than the 1990s, giving a limited
data set, though this can be argued to be sufficient for the scope of this study, which seeks to
examine trends during the economic transformation.
Further limitations exist in the quality of the data collection by the statistical agency itself. In
some cases, it appears that the number of exports reported exceed the amount produced for a
given year, which may indicate errors in data collection, or may perhaps be an accurate
representation, as firms may hold goods produced in one year and wait to export them in
subsequent years. This has posed large complications in establishing domestic consumption, as it
yields negative results. Despite attempts to smooth data (described in section 6.3.1), exports still
exceeded output in many cases, leading to the conclusion that the problem is likely not time lags
within firms but, rather, due to differences existing internally within China’s statistical data
collection departments for these two different data sets. The data series constructed on domestic
The source for data on industrial output is not specified in the yearbooks and is presumably reported by
manufacturers or estimated by the statistical agency.
3
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output of goods was created using data on industrial output; details on the manner in which this
data was collected is not seemingly available and it may be assumed that it is voluntarily or selfreported, leading to possible faults and misrepresentations within the data itself. Criticisms of the
quality of the data found in the China Statistical Yearbooks are many, with researchers calculating
discrepancies in not only growth statistics, but also in consumption figures as well. For example,
it has been suggested that the data available on household outlays for consumptive purchases do
not agree with national data for retail sales, suggesting that data on household consumption may
be false or simply an incorrect estimate (Rawski, 2001). However, despite inherent flaws, this data
is the best and only available within the budget of this masters thesis.
With regard to food commodities, it is important to note that the demand calculated based on
per capita consumption of sugar and edible vegetable oil reflects only household consumption.
Direct consumption by households was estimated to make up approximately fifty percent of the
total sugar consumption in the 1990s (United Nations Food and Agriculture Organization, 1997),
with the remainder being utilized in industrial processing of food products. Thus, this study will
not capture aggregate domestic demand, but aggregate household demand.
It is also important to note that data is not available across consistent time periods, which
occasionally makes for confusing comparisons. An attempt has been made to display data over
the same or similar time periods as much as possible.
6.3
6.3.1
Methods
Household Commodities – durable goods
Data on consumption of domestically produced goods that are consumed within China is not
explicitly available4 and will be established, based on the following.
Domestic Consumption of good (x) in year (1) = Output (x1) – Exports (x1)
4
Where available, import data has been collected.
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Data series for output, exports, and imports (where available) have been constructed by piecing
together yearly reported statistics within the China Statistical Yearbook. Data on exports and
imports is reported only for major export commodities; therefore, some commodities appear and
disappear as their importance changes and they no longer appear in the list of top commodities.
In cases where data is unavailable for specific years (or where yearbooks are unavailable), linear
interpolations have been utilized. In some situations, when data is not available at the beginning
of the series, linear extrapolation has been used, for a maximum of four years, to avoid distortion
of actual trends. As the available data displays irregular trends that occasionally lead to negative
figures for domestic consumption, a five year moving average has been applied in an attempt to
smooth the data as well as to account for the problem of exports exceeding production in some
years (due to time lags or otherwise).
In addition, data is available on the number of durable consumer goods (specifically including
colour television sets, electric fans, cameras, and bicycles) owned per one hundred rural and per
one hundred urban households. This data is not complete, but linear extrapolations have been
utilized to fill in missing data within the series. This data is used to examine trends in the
popularity of household durable goods.
As these are durable goods, it is possible to assume that the increase from one year to the next in
the number of durable goods present per one hundred households represents the actual demand
for new products. This may be particularly true in rural households, as it is possible that many of
these goods were purchased for the first time in the past decade and have not needed
replacement. However, it must be acknowledged that, while new items may be purchased and this
will be captured in the data, some items may also be replaced (these goods are hereby referred to
as replacement goods), which would not be captured in the number of goods present in
households. However, as, particularly in rural households, it is possible that many of these goods
were purchased for the first time in the past decade. This will be further explored. In any case,
trends in the four study commodities’ popularity in Chinese households will be established using:
(𝑥𝑡 − 𝑥𝑡−1 )⁄
100) ∗ (𝑃𝑜𝑝) } + 𝑅𝑒𝑝𝑙𝑎𝑐𝑒𝑑 𝑥
𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒 𝑖𝑛 𝑑𝑒𝑚𝑎𝑛𝑑 𝑓𝑜𝑟 𝑔𝑜𝑜𝑑𝑥 = {(
𝑡−1
ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑠𝑖𝑧𝑒
Where Replaced xt-1 represents replacement goods; the number of goods that were removed from
households in year t-1 and subsequently replaced with new goods.
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If the number of replacement goods not captured in the data is assumed to be zero, this data can
be used to establish yearly demand for each of the four commodities (even without this
assumption, this figure can be used as an approximation of minimum demand). This data on
household demand for the four study goods can be compared to the number of the goods that
are produced in China (available from data on industrial output) but not exported (output minus
exports), as well as with import data (only available for television sets).
6.3.2
Food Commodities
Data series on two major food commodities were constructed using available data on per capita
consumption in urban and rural areas. All data was transformed into tons5 to maintain
consistency across measures (some data is reported in yearbooks in kilograms, others in tons,
such as exports).
The per capita household demand for both sugar and edible vegetable oil has been transformed
into a measure of aggregate household consumption of these goods, which can, in turn, be
compared with total domestic output, as well as imports and exports.
The per capita consumption was applied to the entire urban and rural population across the time
period, to get an approximation of aggregate household demand.
An approximation of annual urban per capita consumption of sugar in units is unavailable and
was created from the data available on per capita annual expenditure on sugar. The unit price was
estimated from an average of calculated import and export unit prices6 and the total expenditure
divided by this unit price to obtain an approximation of per capita demand. This estimated series
for per capita consumption of sugar was compared with an estimate of the average sugar
consumption provided by the United Nations Food and Agriculture Organization (1997)7 to
ensure accuracy.
1 kilogram = 0.001102 US short tones
Derived from data on import and export volumes and values
7 The FAO estimated sugar consumption to be approximated 5.9 kilograms(0,005 tons) in 1995/1996
overall; the method described here yielded an estimate in the order of 9 kilograms (0,01 tons) for urban
consumption, which when averaged with the much lower per capita rural consumption gives an
approximate demand of 6.3 kilograms (0,007 tons), very close to the FAO estimate. A weighted proportion
5
6
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In addition, data series were constructed on output, exports, and imports, using yearly reports on
major food commodities. This data was used to establish domestic demand for domestically
produced goods, in the same manner as it was for household commodities.
6.4
Results
The results of the exploratory examination of data available on what households consume, from
where, and in what numbers are presented in this section. Where data does not yield interesting
results, it is presented and explained in the appendix.
6.4.1
Colour Televisions
The data for colour televisions is the most interesting of the four household commodities
examined. Not only is its existence consistent over the longest time period, but it is also the most
complete, as data for imports is available. Data for black and white televisions is also available
(through the number of durable goods in households statistics), but it ceases to exist for urban
areas in the 1990s and is indicative of declining trends in the prevalence of black and white
televisions of households. This data is not utilized here, as one objective of examining statistics
on the number of televisions present per 100 households, in both rural and urban areas, is to look
at the demand for new products, not to look at the slow phasing out of old, redundant
commodities. As has been discussed, it is assumed that most colour televisions purchased during
the 1980s and 1990s were the first colour televisions to be purchased for the household, and not
replacing existing old televisions. As is expected, the number of colour televisions per 100
households has increased substantially over the past two decades (see Figure 6-1), increasing
almost seven times in urban areas, and catapulting from near non-existence in rural areas to an
average of close to one television per household. This is expected, as household income
increased over this period as well, and colour televisions quickly went from a luxury item
afforded only by the very wealthy to a prominent household item.
would yield almost identical estimates. The world average annual per capita consumption is 20 kilograms
(0,22 tons).
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The estimated yearly total demand for televisions has begun to decline in urban areas (see Figure
6-2) reaching its peak in approximately the year 2000. The demand continues to remain steady in
rural areas, which is expected as households in rural areas are expected to be generally behind in
the take-up of such goods. It is important to note that while Figure 6-2 suggests higher total
demand for colour televisions in rural areas, this is a function of a larger portion of the
population living in rural areas and not of a higher per capita demand. Per capita demand is
better approximated by examining the trends in demand8 per 100 households (Figure 6-3), where
one can see the point at which the amount by rural demand increases supersedes the amount that
urban demand increases. After this point it can be assumed that, while not satiated, the year to
year increase in demand in urban areas for colour televisions has begun to decline as the initial
rush to acquire the first television is over. This is still occurring in rural areas, though one can see
that it too is beginning to reach a plateau.
160
140
120
100
80
Rural Households
60
Urban Household
40
20
0
Figure 6-1 - Number of Colour televisions owned per 100 households at year end, 1985-2008
Demand here means the amount of new televisions that would have been bought from one year to the
next, not taking into account replacement goods.
8
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1600
1400
1200
1000
Rural
800
Urban
600
400
200
0
1995
1998
2001
2004
Figure 6-2 – Minimum estimated total yearly demand for colour televisions (10 000 televisions) in China, 1995 – 2005
(five year moving average)
10
9
8
7
6
5
Rural
4
Urban
3
2
1
0
1988
1991
1994
1997
2000
2003
Figure 6-3- Annual unit change (colour televisions) per one hundred households, 1985-2005 (five year moving averages)
An estimate of the aggregate demand for televisions is obtained by summing the rural and urban
total demand for new televisions, the results for which are given in Table 6-1. It is interesting to
note that this estimate is relatively close in its approximation of domestic demand to the
approximations yielded from statistics on domestic output and exports (plus imports). The
approximations for household demand from the former method are lower than those of the
latter until 2003. As the amount reported by the former method (establishing demand from an
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increase in prevalence of colour televisions per one hundred households) is a minimum estimate9,
it would be expected that this estimate would be lower in all cases. However, this discrepancy
may be explained by lags in the data: televisions that are produced in year(x) may not be
purchased until subsequent years, leading to a deflated or inflated approximation of domestic
consumption in that year. It is also, again, pertinent to note that the way in which data on
domestic output is measured is not clear and may not be an accurate representation of reality.
Both approximations of domestic consumption demonstrate the same trend, of increasing to a
plateau in the early 2000s and declining thereafter.
Table 6-1 - Summary of Data on Estimated Consumption of Colour Televisions in China, 1997 - 2006
Year
Output
(10 000
televisions)
Exports
Imports
Domestic
Consumption10
Household
Consumption11
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
3166,94
3506,267
3942,5
4580,85
5219,483
5889,687
6629,587
7360,305
8006,652
8299,702
1015,5
1240,2
1490,9
1942,7
2691,1
3657,7
4853,3
6338,6
6726,2
6801,4
50,5
29,3
22,7
17,5
31,5
43,6
63,9
90,5
111,3
103,9
2151,44
2266,067
2451,6
2638,15
2528,383
2231,987
1776,287
1021,705
1280,452
1498,302
1643,45
1896,848
1952,594
2037,49
2128,654
2052,684
1997,739
1897,23
1668,207
-
6.4.2
Bicycles
The data available for household consumption of bicycles is not as comprehensive in scope as
the data for televisions – noticeably as there is no data for imports of bicycles available (though it
is arguable that this amount may be negligible). The number of bicycles present per one hundred
households (see Figure 6-4) shows a trend of initial increase and eventual decline, likely indicative
of the increasing prevalence of motor vehicles, with car prevalence doubling in China in only two
It is a minimum estimate amount because it only takes into account the increase in the prevalence of
televisions, not the replacement of old televisions. Also, it includes no estimation of televisions purchased
for other purposes, i.e. for business use.
10 As estimated by subtracting exports from domestic output
11 As estimated by the increase in prevalence of colour televisions from one year to the next per 100
households
9
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years from 2003 to 2005 (Brown, 2005); this trend is observed first in urban areas and followed
by rural areas. The estimated yearly aggregate demand for bicycles has been declining since the
start of this study period (Figure 6-5)12, suggesting that bicycles have been being slowly phased out
and replaced by motorbikes and cars in many households (as is expected, this trend is first
observed in urban areas). Even in the mid 1980s, the amount by which the prevalence of bicycles
per one hundred households in both urban and rural areas grew declined (Figure 6-6)13, and in the
mid 1990s the absolute level of bicycles per one hundred also began to decline (suggesting that
no new bicycles were being purchased) and thus rendering it impossible to calculate domestic
consumption of bicycles from this data after 1995.
The trends displayed in data for bicycles is less reliable than that for televisions, as it can be
assumed that the prevalence of used bicycles in the market is much higher (as bicycles have been
available and common for much longer than households). This makes the increase (or decrease)
in the number of bicycles present per one hundred households less reflective of any real trends in
consumption, as old bicycles may be being replaced (which would not be captured in the year to
year increase) or, alternatively, an increase in the absolute number of bicycles may reflect
purchase of used bicycles rather that new ones14.
Note that rural aggregate demand for new bicycles is also a function of a higher rural population
When the amount by which the number of bicycles per one hundred households increases becomes
negative, it is then implied that ‘demand’ (not including replacement goods) is zero. The negative figures
are included in Figure 6-6 to give an idea of the amount by which the number of bicycles per one hundred
households is declining.
14 This latter effect may result be true of a transfer of bicycles from higher income urban areas to lower
income rural areas, though there is no evidence of such a transfer. Otherwise, it would likely not affect
results, as one used bicycle would simply be transferred from one household to another and not change
trends.
12
13
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250
200
150
Rural
100
Urban
50
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
0
Figure 6-4 - Number of Bicycles owned per 100 households at year end, 1985-2006 (five year moving average)
1400
1200
1000
800
Rural
600
Urban
400
200
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
0
Figure 6-5 - Minimum estimated total yearly demand for new bicycles (10 000 bicycles) in China, 1988 – 2007 (five year
moving average)
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10
8
6
4
2
Rural
-2
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
0
Urban
-4
-6
-8
-10
Figure 6-6 - Annual unit change (bicycles) per one hundred households, 1985-2006 (five year moving averages)
Further data on domestic demand and exports for bicycles in available in the appendix (see section 10.1.1)
6.4.3
Cameras
The data available to indicate the prevalence of cameras in households indicates a marked and
nearly linear increase in the number of cameras per one hundred urban households to 2004 (at
which point the level peaks and begins to unexpectedly decline), while the prevalence of cameras
in rural households is negligible and remains so (Figure 6-7). The calculated approximation of the
minimum number of new cameras purchased15 is erratic at best (see Figure 6-8). This is explained
by the fluctuating year to year increases in cameras per one hundred households (smoothed over
in Figure 6-7) by applying five year moving averages). This data is further complicated by the
possible confusion of data in the yearbooks, as digital cameras are eventually separated from
cameras, though it is not clear in the data when cameras refers to all cameras or only to film
cameras. This may explain the otherwise unexpected decline in the year to year increase in
cameras per one hundred households observed in Figure 6-9.
Calculated based on the increase in number of cameras per hundred households and translated into the
number of new cameras calculated by the entire urban population
15
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50
45
40
35
30
25
Rural
20
Urban
15
10
5
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
0
Figure 6-7 Number of Cameras owned per 100 households at year end, 1987-2006 (five year moving average)
350
300
250
200
Rural
150
Urban
100
50
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
0
Figure 6-8 Minimum estimated total yearly demand for new cameras (10 000 cameras) in China, 1988 – 2007 (five year
moving average)
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3
2.5
2
1.5
1
Rural
0.5
0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
-0.5
Urban
-1
-1.5
-2
-2.5
Figure 6-9 Annual unit change (cameras) per one hundred households, 1985-2006 (five year moving averages)
Further data on domestic demand for camera can be found in the appendix (see section 6.3.2)
6.4.4
Electric Fans
Finally, data on electric fans shows a continual increase through the 1990s and 2000s in the
number of electric fans per 100 households in both rural and urban areas, with the number of
electric fans dropping very slightly in urban areas in 2004 before data is no longer available (for
urban areas only). The rate of increase is observed to be higher in rural areas, as is shown in
Figure 6-10. Although the levels are increasing, the amount by which these levels are increasing is
diminishing, leading to a diminished aggregate demand (not including the demand for
replacement goods, which is surely substantial as old fans no longer operate and residents can
afford to upgrade), as shown in Figure 6-11. Figure 6-12 shows us that the amount by which the
number of fans present per one hundred households is increasing accelerated in urban areas at
the outset of the period, but is now diminishing in both rural and urban areas. This is possibly
indicative of an early ‘rush’ to buy electric fans in rural areas as disposable incomes allowed for
this former luxury good.
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200
180
160
140
120
100
Rural
80
Urban
60
40
20
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
0
Figure 6-10 Number of electric fans owned per 100 households at year end, 1987-2006 (five year moving average)
2000
1800
1600
1400
1200
1000
Rural
800
Urban
600
400
200
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
0
Figure 6-11 Minimum estimated total yearly demand for new electric fans (10 000 cameras) in China, 1988 – 2007 (five
year moving average)
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14
12
10
8
6
Rural
4
Urban
2
-2
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
0
-4
Figure 6-12 Annual unit change (electric fans) per one hundred households, 1985-2006 (five year moving averages)
6.4.5
Comments
These results demonstrate the expected trends in Chinese household consumption. Goods that
were perceived as luxury goods when incomes were considerably lower, in the 1980s and 1990s,
have been increasing in popularity. Of note is the peak in per capita demand for new products,
such as televisions (not including replacement goods), as the product is diffused throughout
society and prevalence is close to at least one per household. The opposite trend is present in
commodities that have since been upgraded; the decrease in per capita demand is clear and
marked. It is logical to conclude from this exploration of trends in major household commodities
that the same effects may be visible in other products as well; new technologies and products are
first largely diffused throughout urban society, where wages are higher, to a peak. A similar
diffusion occurs in rural areas, though following a lag in time and the good may never become as
prevalent as it is in urban areas if it is a more high-tech luxury good.
Of interest, then, is to also examine trends in major foodstuffs, a portion of consumption
spending that is significant and ubiquitous amongst all households, rural or urban.
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6.5
6.5.1
Food Commodities
Sugar
Per capita consumption of sugar in rural areas remained relatively low across the period (see
Figure 6-13), even declining minimally. Urban consumption of sugar, however, began to increase
in the early 2000s, which may be a factor of simultaneous increases in urban wages, or reflective
of lifestyle changes, as increasing westernization in urban areas may decrease the prevalence of
traditional eating habits, which do not include heavy sugar consumption (United Nations Food
and Agriculture Organization, 1997). Certainly, per capita sugar consumption in China has a long
way to go before it reaches even the world average of twenty kilograms (0.022 tons) per year in
1996 or the EU average of forty kilograms (0.044 tons) per year, as of 2004 (World Health
Organization, 2011). It remains to be seen if increasing household income or the prevalence of
western values will increase per capita consumption in China to these levels.
0.02
0.018
0.016
0.014
Per capita
consumption of sugar
in rural areas, tons
0.012
0.01
0.008
Per capita
consumption of sugar
in urban areas, tons
0.006
0.004
0.002
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0
Figure 6-13 per Capita Consumption of Sugar in Rural and Urban Areas, 1995-2008
It is also of interest to examine whether China is producing its own sugar or obtaining it from
elsewhere. Sugar imports have declined by 74% since data is first available in 1995 (see Table 6-2),
while total aggregate consumption has increased almost ten-fold over the same period, though
this is due almost entirely due to an increase in urban population and not necessarily due to the
increase in per capita consumption. Domestic production is higher than domestic consumption
in nearly all years, with the exception of 2001, 2005, and 2006, when it is marginally lower and it
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can be assumed that either imports or reserves made up the deficit in these cases16. It is also
worth noting that exports have also fallen considerably, by 88%, though sugar imports and
exports were never very large compared with domestic production (over this period, 1995-2008).
The sugar case is an example of a sector in which demand is rising, and output has been able to
increase by a sufficient magnitude to meet this demand. It is interesting to note that, in contrast
to household goods, China is actually a net importer of sugar.
Aggregate household consumption estimates reveal relatively similar approximations of demand
as to estimates of domestic consumption, though it is logical that aggregate household estimates
would be lower, as they do not include sugar which is utilized for processing into different food
products.
Table 6-2 Summary of Data on Estimated Consumption of Sugar in China, 1990 - 2008
Year
Output
(10 000 Tons)
Exports
Imports
Domestic
Consumption17
Household
Consumption18
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
582,00
558,64
640,20
702,58
826,00
861,00
700,00
653,10
926,00
1083,94
1033,70
912,37
949,07
1271,38
1449,48
48,00
66,00
38,00
46,00
43,50
41,00
20,00
33,00
10,32
8,52
11,98
15,44
11,05
5,84
295,00
125,00
78,00
51,00
57,50
64,00
120,00
118,00
78,00
121,00
128,00
135,00
119,00
78
510,64
574,20
664,58
780,00
817,50
659,00
633,10
893,00
1073,62
1025,18
900,39
933,63
1260,33
1443,64
139,09
119,82
580,40
587,50
593,20
660,34
690,06
739,21
746,83
783,60
859,85
947,66
1028,05
1116,21
1178,85
6.5.2
Edible Vegetable Oil
Also, it must be noted that data was not available for 2005 and this figure is a linear interpolation
As estimated by subtracting exports from domestic output
18 As estimated by the per capita demand for sugar in rural and urban households applied to entire
population
16
17
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Per capita consumption of edible vegetable oils shows a gradual increase in both urban and rural
areas (see Figure 6-14). Like the sugar case, it is unlikely that these rates will continue to rise
indefinitely. However, increasing westernization of food, particularly in urban areas may lead to a
sustained increase in the consumption of edible vegetable oil in the short run.
0.012
0.01
Per capita
consumption of edible
vegetable oil in rural
areas, tons
0.008
0.006
Per capita
consumption of edible
vegetable oil in urban
areas, tons
0.004
0.002
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0
Figure 6-14 per Capita Consumption of Edible Vegetable Oil in Rural and Urban Areas, 1995-2008
Edible vegetable oil represents a slightly different example, as domestic consumption has not
risen nearly as quickly as for sugar, only doubling over the period (see Table 6-3). However,
during this period, domestic output also doubled and imports grew by nearly 285%, while exports
remained low, hovering at or below one percent of total output since 2003 (data is not available
further back in time). Output remained well in excess of domestic consumption, except for
between 1998 and 2000. It is very likely that the types of oil exported varies greatly from the
types imported, as palm oil is an extremely important commodity in food preparation and China
is the world leader is palm oil imports, but is not a major player in exports (Koh & Wilcove,
2007).
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Table 6-3 Summary of Data on Estimated Consumption of Edible Vegetable Oil in China, 1990 - 2008
Year
Output
(10 000 Tons)
Exports
Imports
Domestic
Consumption19
Household
Consumption20
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
1144,50
946,54
893,73
602,48
718,90
835,32
1383,17
1531,22
1584,29
1235,44
1785,34
2335,23
2636,98
2419,15
5,97
6,52
23,22
39,92
16,63
24,76
213,00
153,00
275,00
206,00
192,50
179,00
165,00
319,00
541,00
676,00
672,50
669,00
838,00
816,00
1578,32
1228,92
1762,11
2295,31
2620,35
2394,39
1134,61
673,45
710,16
745,08
791,45
846,09
898,30
911,08
941,35
980,71
915,30
980,79
980,06
1035,81
1112,68
The trends observed in this section are indicative of an economy undergoing the early stages of
development, transitioning from an agrarian economy in which residents have little disposable
income available to spend even on necessities or durable goods and may struggle to buy even the
basics, to an economy with decreased market control on behalf of the government and rising real
wages. Consumers in the latter situations are actually able to make purchasing decisions for the
first time. No longer restricted by tight market restrictions or limited income, consumers in China
now, for the first time, are able to enter the market for consumer goods not necessary for daily
life. The data presented in this section shows the increase in demand as consumers are able to
afford the necessities as well as those good previously perceived as luxuries.
It could be assumed that these upward trends will continue to increase, as it appears from a basic
appraisal of the situation that consumers will only become more empowered to make purchasing
decisions. However, as has been discussed, growth in consumer spending has not been rising at
rates deemed to be sufficient to sustain long term economic growth, or even at rates similar to
As estimated by subtracting exports from domestic output
As estimated by the per capita demand for edible vegetable oil in rural and urban households applied to
entire population
19
20
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countries at a comparable stage of development. The factors potentially linked to the growth rate
of household consumption will be examined in part two of this thesis.
7 Part II - Explanatory Study: Household Consumption on the
Rise
This chapter will outline the data, methods, and results of the attempt to conduct an explanatory
study into factors influencing levels of household consumption.
7.1
Data
Data was gathered from China Statistical Yearbooks (online and at the library) on the following
variables:
-
Household Consumption (yuan per year) HshdCspn
-
Average Household Size AvgHshdSz
-
Nominal wages (yuan) reported as Real Wages RealWg, calculated using
Consumer Price Index (CPI)
-
Government Expenditures on Social Programs (Welfare, Pensions, social
programs) (100 million yuan) Gov Soc Exp
Data on the variables listed above was obtained from the Price Indices, People’s Livelihood and
Workers’ Wage, Population and Employment, and the Government Finance and Banking sections of the
China Statistical Yearbook.
In addition to data obtained from the China Statistical Yearbooks, macro-level national data on
dependency ratios DepRatio and deposit interest rates DepIntRt was obtained through records
published by the World Bank.
Based on the stated policy shift of the government, government policy change since 2004 is
reported as a dummy variable PolChng.
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7.2
Data Limitations
Data on economic and demographic factors that were examined for relationships with household
consumption is available for at least a thirty year period in most cases, making it sufficient for a
regression analysis. It is also important to note that the economic transformations that China has
undergone in the past two decades are the primary focus of this study. Household consumption,
GDP, and wages remained relatively stagnant for many years prior to the takeoff that instigated
interest in investigating this topic. It is largely unnecessary to include data from prior to this
period in this study.
In addition, limitations previously noted on the availability and quality of data are applicable in
this portion of the study.
7.3
Methods
A data set has been compiled in order to create a multivariate regression analysis on the
relationship between various economic, demographic, and policy factors on household
consumption.
This econometric component of this study will incorporate a time series analysis on Chinese data
over the period 1978-2008. This approach will be a multivariate linear regression using the
Ordinary Least Squares (OLS) approach, expressed as follows:
𝐻𝑠ℎ𝑑𝐶𝑠𝑝𝑛
= 𝛼 + 𝛽1 𝐴𝑣𝑔𝐻𝑠ℎ𝑙𝑑𝑆𝑧 + 𝛽2 𝑅𝑒𝑎𝑙𝑊𝑔
+ 𝛽3 𝐺𝑜𝑣𝑆𝑜𝑐𝐸𝑥𝑝+ 𝛽4 𝑃𝑜𝑙𝐶ℎ𝑛𝑔+ 𝛽5 𝐷𝑒𝑝𝐼𝑛𝑡𝑅𝑡+ 𝛽6 𝐷𝑒𝑝𝑅𝑎𝑡𝑖𝑜 + εi (where εi is the error term)
This method was chosen based on theoretical motivation and limited by the availability of data.
While a panel study of the effects of explanatory variables on the dependent variable over time in
each of the Chinese provinces would have been of interest, this data is not available for all
variables or, in cases where it does appear to be available, it is not available with an English
translation or in a format which enables use of an online translation service. A cross sectional
time series approach is adequate for the intent of the study, as national level phenomena are of
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interest, rather than the trends in specific cities or provinces. This is a macro-level study of
national trends.
7.4
Definitions of Variables
A comprehensive list and summary statistics of both the dependent variable and the explanatory
variables can be found in the appendix in Table 10-4.
7.4.1
Dependent Variable
The dependent variable studied in this analysis is Household Consumption (HshldCsmp). Data is
available for this variable in the form of average annual per capita household consumption,
measured in yuan, in both rural and urban areas as well as an aggregate level of household
consumption (as a component of the GDP, measured by the expenditure approach). As the focus
of this study is on the behaviour of households, the per capita household consumption measure
has been utilized. This is of particular importance as a rise in aggregate household consumption
can likely be attributed to only a rise in population.
Average annual household consumption is the best available measure of the behaviour of the
Chinese consumer. This measure includes household purchases on essential goods and services
and thus is not the ideal measure of consumer choices, as increases in household consumption
may represent forced expenditures rather than the optional ones which may be motivated by
government attempts to stimulate consumer spending. However, increased non-essential
consumerism by Chinese household is also captured in the household consumption data utilized
here and is the best measure available.
Within this model the dependent variable is transformed using the natural logarithm in order to
be able to determine the percentage change induced by a one percent change in explanatory
variables.
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7.4.2
Explanatory Variables
Within this model, the natural logarithm has been applied to all of the explanatory variables, with
the exception of the dummy variable, PolChng.
7.4.2.1
Real wages (yuan)
It was thought that some measure of income or wages should be included in this model, as it is
logical that this may be the single largest influence on household consumption, particularly as
purchasing by borrowing is not common in China, though it is on the rise (Faure & Fang, 2008).
It is unlikely that households will engage in additional spending without income to support it,
particularly in credit-skeptical Chinese consumers. Disposable income21 or wages are the best
available indicators of household inflows, but it is suspected that household consumption may be
in some way derived from income levels, as the specific method used in calculating these levels is
not reported in the China Statistical Yearbook (English version). Wage data is available on a
national level, and though it may also interact with disposable income and consumption statistics,
it is included in two (of a total of three) variations of this model and eventually removed 22. Wage
data is reported as a total average of all sectors and is in nominal terms, and has thus been
transformed using the Consumer Price Index (CPI) with base year 1978, also the start year in the
model, to obtain average annual real wages, per capita.
It is hypothesized that real wages will have a significant and positive effect on household
consumption, as higher income allows for greater expenditure on goods which could not be
purchased due to income constraints at lower wage levels. Aziz and Cui (2007) suggest that
household income is the single most important factor influencing household consumption,
having an effect notably larger than savings rate. They state that as the share of GNP accounted
for by household consumption fell, the share of household income in GNP also fell, most
markedly in wages (wages are the largest component of household income, larger than
investment returns or government transfers). Aziz and Cui (2007) thus pinpoint household
income as a significant factor explaining household consumption. In this thesis, it is posited that,
while real wages should have an effect on consumption, it is the pressures on households that
In the China Statistical Yearbook, disposable income is reported for urban areas, while net income is
reported for rural areas. Net income refers to the average total income of all rural households minus all
relevant expenses.
22 To check for interaction effects in model output
21
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induce saving rather than spending which has a larger effect on consumption levels. As real
wages increase, consumption cannot increase along with them unless the consumer is confident
that they have a solvent and financially stable future ahead of them, which is specifically
influenced by external factors, such a government spending. This theory is reinforced by the
findings of Carroll and Weil (1994) that suggest that habit formation may play a major role in
dictating the consumption patterns of households. In the China case, the previously discussed
propensity to save may not be easily overcome simply by an increase in income.
7.4.2.2
Annual Government Expenditures on Pensions, Social, and Relief Funds (100 million
yuan)
As there exists a significant quantity of literature on the effect of government spending on social
programs, as well as health care and education, on the decisions of consumers to spend or save
(hypothesizing that lower investment on behalf of the government leads to higher savings for
future misfortunes and a general reluctance to consume unnecessarily) (Baldacci, et al., 2010), it is
interesting to include some measure of government spending of this nature. Data on national
healthcare and education spending is available, though this is more likely to be infrastructural in
nature and likely represents a more indirect effect on consumer spending. The effects of
government health care expenditure on aggregate household consumption has previously been
found to be significant and a major way in which policy makers can decrease precautionary
savings and thereby increase consumption (Barnett & Brooks, 2010). The same study found the
effects of government spending on education to be negligible and did not examine the effects of
other government expenditures, most notably on social programs. Government spending on
social programs, pensions, and relief funds can be expected to have a direct impact on
precautionary savings, as they represent a transfer of income from government to households.
These expenditures may be particularly influential on the spending patterns of poorer households
and may, therefore, be of more interest in rural areas where average annual disposable household
income is lower. This data has been transformed into a per capita measure to take into account
effects of population increase that may erode the effectiveness of increased spending.
It is hypothesized that per capita government expenditures on social programs will have a
significant and positive effect on household consumption, due to the previously discussed impact
on the increased rate of savings in the presence of low government spending on social programs.
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7.4.2.3
Policy Change (dummy variable)
The official change in the Chinese government’s growth strategy and stated policy for
encouraging an accelerated increase in GDP was one of the primary motivations of the study.
This change occurred in 2004, when the government identified domestic consumption as the
desired driver of economic growth, replacing the traditional reliance on export-led growth for
long term economic sustainability (Lardy, 2006). A dummy variable has thus been included,
indicating consumption led growth policy with a zero prior to 2004 and a one in subsequent years.
It is unfortunate that relatively few years have passed since this change in government strategy,
but these five of the thirty-one total observations may be sufficient to test whether policy has had
an effect on household consumption so far. In theory, policy change aimed at increasing
consumption levels should have a positive impact on household consumption if accompanied by
actual shifts in government programs and undertakings.
7.4.2.4
Deposit Interest Rate
A major incentive for consumers to save, rather than spend their disposable in income is the
deposit interest rate23. This has been included as an explanatory variable in this model.
If the deposit interest rate increases, theoretically consumers should decrease their consumption
in favour of saving, thus deposit interest rate is expected to have a negative impact on household
consumption.
7.4.2.5
Dependency Ratios
The dependency ratio is the ratio of dependents (people older than 64 or younger than 15) to the
working-age population (those ages 15-64). Data is reported as the proportion of dependents per
one hundred working-age population (World Bank, 2011). The overall dependency ratio has been
included in this model to look for effects of the changing structure of the Chinese household –
the one child policy introduced in 1978 for example, could significantly limit the number of
dependents relying on the working population.
Deposit interest rate is the rate paid by commercial or similar banks for demand, time, or savings
deposits (World Bank, 2011)
23
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It is expected that, as the number of dependents per employed person increases, per capita
household consumption will decrease, as a precautionary reaction to the increased responsibility
placed on the income-earner. It is expected that less luxuries would be purchased as the
dependency ratio increases. However, from examining the data on dependency ratio, one can see
that the dependency ratio has been decreasing substantially (the lowest figure reported being
almost half that of the highest year, see Table 10-4), and it is thus expected that household
consumption should increase as a result of lessened responsibility to save for dependents. The
one child policy has resulted in a significant in child dependents over the past three decades.
7.5
Modifications of Start Model
The start model in this study contained many variables which were not utilized in the final model.
When modifying the start model, it was important that a number of the assumptions implicit in
regression models were taken into account. Assumptions include that the model is correct: that it
is linear, that it contains a complete list of necessary variables, and that there is no redundancy;
that is, that independent variables are also independent from each other. It is the task in this
thesis to modify this start model to create a “better” model. The justification of looking for a
better model lies in the output of the start model. In the start model not used in this thesis, the Ftest returned a significant value, the p-values for each of the independent variables did not always
register significance where it would be logical and, perhaps most importantly, the adjusted rsquared was high (unrealistically high).
In running this regression and variations on the start model, it was observed that there existed a
strong possibility of collinearity between variables. It is possible to look for collinearity between
independent variables by testing for two-variable correlation between data sets. An r-value higher
than 0.75 is generally regarded as an indicator of collinearity. In addition to looking at correlation
coefficients, it is also possible to examine the R2 or adjusted R2 of the regression24. The formal
detection-tolerance level, or the variance inflation factor25, for multi-collinearity exceeded
commonly-accepted thresholds26 in many cases. This is cause for concern, as it can lead to
incorrect estimation of coefficients – either in magnitude or in sign. In such cases, it is common
The R2 measure shows the amount of variation in the dependent variables that can be explained by the
explanatory variable.
25 Tolerance = 1 – R2; VIF = 1 / Tolerance (Hsieh, Lavori, Cohen, & Feussner, 2003)
26 A multi-collinearity problem is indicated by a tolerance level of less than 0.20 or 0.10 and/or a variance
inflation factor of 5 or 10 or above (Menard, 1995)
24
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practice to try to overcome multi-collinearity issues by either eliminating variables or combining
two variables together into one index, among other methods (O'Brien, 2007). In this study, some
explanatory variables that were initially considered for inclusion were eliminated, as they were
redundant and highly correlated with each other. These included per capita annual expenditures
on food, health care, and education, as they are implicit within the dependent variable. However,
some of the collinearity found in this model (for example, average household size and real wage
have a correlation coefficient of -0.866) cannot be explained by a theory that the two data series
contain redundant information and, rather, that perhaps they are both the results of trends in the
Chinese economy over the past three decades, family size decreasing as a result of changing
societal norms, and real wages changing as a result of the modernization of the economy.
As is stated in O’Brien (2007), it is sometimes the case that researchers incorrectly adjust models
in an attempt only to reduce multi-collinearity, thus making decisions that are no longer
theoretically motivated. When modifying the start model, it was realized that it was necessary to
remove some variables from the start model to reduce or eliminate collinearity. However, it was
felt that it is also important that when attempting to exclude redundant variables (that exhibit
strong collinearity), it is also important to include variables that are expected to have strong
explanatory ability. Both instances may result in skewed results for coefficients of independent
variables. As the purpose of this study is to test for significance of government policy on
household consumption, as compared with economic and demographic factors, it is asserted here
that the explanatory variables have been selected with a theoretically and economically sound
motivation and that they do not display redundant information, but rather are indications of
larger trends in China in the past several decades.
Further complications arose in the running of this regression as it was suspected that it is possible
that the data for household consumption may be in some way calculated using the explanatory
variables use in this model. This was taken into consideration, as the data for real wages was
found to be highly correlated with per capita household consumption (r=0.97). However, it was
deemed that this is also partially a function of a growth economy: all indicators are increasing at
rapid rates, thus displaying strong correlation. It was decided that some measure of income was
too important to leave out entirely in this regression, as it is predicted by others (Aziz & Cui,
2007) to be one of the single most important influences on household consumption.
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In seeking a more statistically sound model from which a sound interpretation of the results
could be completed, it was debated whether this study should instead focus on effects of the
rates of change (approximated by logged first differences) on the level of household
consumption. This model returns much more satisfactory R2 values (between 0,707 and 0,793)
and appears to be more statistically valid approach. However, it was decided that though this
approach yields results seemingly more appealing from a statistical approach, the economic
motivation for analysing the effects of the rates of change of the explanatory variables on the
level of the dependent variable is not sound. Results of this regression can be interpreted as the
percent change in household consumption induced by a one percent increase in the rate of
change of the explanatory variable. The theoretical motivation of this study is not to examine the
effect of a one percent increase in the rate of increase of the explanatory variables, but rather
how changing levels of the variables impact household consumption. Thus, the model using
logged first differences on explanatory variables is not utilized in this thesis, though the results
are presented in the appendix for comparison.
Therefore, the results of this study can be interpreted as the percent change in household
consumption induced by a one percent change in the explanatory variable (as the natural
logarithm is applied to all variables). Interpretation of results will be characterized using the
generally accepted significance levels of 99%, 95%, 90% (a p-value of 0,01, 0,05, 0,10
(respectively) or less). When examining the results of this regression model, it is also important to
consider the F-test, which tests if the model used is one where none of the variables are
significant (i.e. 𝛽1 = 𝛽2 = 𝛽𝑛 = 0). The significance of the F-test is the probability of this null
hypothesis being true; thus, the desired model will display a low value for the F-test; these values
are also reported in the results.
7.6
Results
The results from the statistical analysis are presented in Table 7-1. Three variations of the same
model are presented, in order to show differing interactions between variables. The columns for
each variant show the output (magnitude and sign) of coefficients and their corresponding pvalues together with an indication of significance. Results from an alternative model, using logged
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first differences and yielding a more desirable R2 value can be found in the appendix in Table 10-5,
though the results are not described or interpreted.
Table 7-1 Results of time series analysis into factors influencing dependent variable, annual per capita household
consumption (yuan)
Variable
OLS#1
OLS#2
OLS#3
DepIntRt
0,111 (0,359)
-0,009 (0,953)
0,178 (0,111)
PolChng
-0,288 (0,133)
-0,707 (0,001)***
-0,925 (5,1E-06)***
DepRat
-0,797 (0,333)
-2,595 (0,006)***
-1,776 (0,028)**
GovSocExp
0,125 (0,726)
1,354 (5,27E-06)***
0,991 (3,05E-08) ***
RealWg
-0,313 (0,538)
-1,146 (0,083)*
AvgHshdSz
-11,009 (0,0004)***
Adjusted R2
0,982
0,971
0,968
N
31
31
31
P(f)
6,47E-21
2,3E-19
6,49E-20
***p<0.01, **p<0.05, *p<0.1; p-values displayed in brackets
Note: The dependent variable in each case is the natural logarithm of the aggregate household
consumption. Percent effect is defined as the percentage induced change in y as a result of a one percent
change in x.
The first variation of the model shown in Table 7-1 refers to the modification of the start model
after most variables found to display high collinearity or redundancy were removed. This model
represents the period 1978-2008 and reports an adjusted R2 value27 of 0.982, which indicates that
98,23% of the variation in the dependent variable, household consumption, is explained by the
selected explanatory variables. Unfortunately, this high coefficient of determination (R2) is
Adjusted R2 is reported rather than R2, as adjusted R2 adjusts for the number of explanatory variables
included and will only increase to reflect upon addition of a variable if the new explanatory terms improve
the model by a greater amount than would be expected by chance.
27
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indicative of some flaw in the data, as it is likely an unrealistic approximation of the explanatory
power of the model, though previous studies have also returned models with R 2 values over 80%
or higher (Fang, 2009). As mentioned, this may be at least partially a function of the growing
nature of the Chinese economy, with many variables increasing together. As variables were
removed, the R2 value decreased, as expected, decreasing the explanatory capability of the model.
Only average household size registers significance in the OLS#1 model, while the overall
explanatory power of the model is high (the p-value of f is extremely low), suggesting that there
may be some correlation at play between variables, as well as with the dependent variable. All
variables, but one, within the model are revealed to be not significant, while the model as a whole
is significant, which can be an indicator of multi-collinearity. This model suggests that the only
significant impact is the effect of declining average household size on increasing household per
capita consumption. It was elected to remove this variable (as it is also highly related to the
dependency ratio, which contains more information on the propensity to save based on
precautionary behaviour) in subsequent models.
The variable, deposit interest rate, does not register significance in any of the three models. In
theory, as the deposit interest rate increases, household consumption should decrease (as saving
becomes a more attractive option with a higher return) but this effect does not register in the
model. This can be interpreted to relate to the notion that saving is more closely tied to
precautionary behaviour than to financial returns by means of interest. Households may elect to
save out of fear of the future, not for financial gains, and thus the deposit interest rate may not
have any real effect on consumption or saving.
Government policy change is found to have a significant role in explaining household
consumption once household size is removed from the model. However, a one unit change in
the dummy variable (i.e. a shift in government policy to one favouring consumption-driven
rather than export-led growth) is found to have a negative impact on household consumption,
displaying significance at the p<0.01 level.
This can perhaps be explained by another
phenomenon occurring simultaneously within the Chinese economy that is captured instead by
the dummy variable, which is not studied here.
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Once household size is removed from the model, overall dependency ratio appears to have a
significant effect on household consumption. In this case, a one percent decrease in the
dependency ratio will statistically induce between a one and three percent increase in household
consumption. As expected, there exists a negative correlation between dependency ratio and
household consumption size. As the number of dependents decreases, the burden of
responsibility lessens, precautionary behaviour can be eased, and increased non-essential
consumption is possible.
Interestingly, and in support of much of the published literature in this field, the rate of increase
of government spending on social programs is found to have a significant effect on household
consumption levels28, suggesting that as government spending increases, the more willing
consumers are to spend, rather than save, their incomes. This again likely relates to the historic
necessity to ensure that enough savings are available in the event of future healthcare or old-age
expenses, which the government would traditionally not have provided for since the breaking of
the ‘iron rice bowl’.
Contrary to expectations, the effects of real wage levels display a negative correlation with
household consumption in OLS #2, though this is only significant at the p<0,10 level. This
variable is subsequently removed, due to lingering suspicions of its direct interaction with the
derivation of household consumption data and the statistical anomalies that results from
problems of this nature.
OLS#3 is the model which will be utilized to interpret and discuss results.
8 Discussion and Relation to Theory
The classic question facing any household with regard to managing finances is whether to spend
or save. This question becomes increasingly important in households facing income challenges,
where a decision to consume in the present has the possibility to lead to the inability to pay future
bills, particularly when a government safety net is not guaranteed. The factors influencing this
decision have been studied before, from both angles and often yielding conflicting results. Kraay
(2000) found that perceived future income growth, related to social program existence, had a
28
Once average household size is removed from the model
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negative and significant impact on savings rates, while Modigliana and Cao (2004) found the
opposite; that future income growth had a positive and significant impact on savings rate.
Horioka and Wan (2006) found that income growth is positively related to the savings rate, and
they thus offered the prediction that as long as growth continued, savings would continue to
increase as well.
This conundrum within the published literature not only highlights the uncertainty within the
debate, but also the difficulties associated with attempting to draw conclusions on data for which
both the availability and reliability can be low. Much of the discussion on the savings rate focuses
not only on the growth of income, but on the prevalence of a social safety net. Jalan and
Ravallion (2001) make clear that when a household faces barriers to access to insurance or credit,
even a short term shock to income (such as sudden unemployment or unexpected bills, be them
for illness or otherwise) can plunge a low-income household into perpetual despair, and may
plunge even a middle-income household into a cycle of destitution. Fear of this scenario, of an
unexpected event leading to long term financial difficulties, leads to an increase in household
savings rate and is a major culprit in the accusations of lackluster private consumption growth.
How then, do public expenditures on social programs and safety nets influence consumption
behaviour?
In this study, it was found that government spending on social programs, welfare, and pensions,
had a positive and significant effect on per capita household consumption. In comparison, a
similar study focusing on Taiwan found that as health insurance coverage increased, as did
household consumption, while savings decreased (Chou, Liu, & Hammitt, 2006). Relating back
to the three primary theories of factors influencing household consumption, the results of this
study support the theory that government expenditures on social programs have the ability to
reduce precautionary savings, thereby allowing for increases in household consumption. Baldacci
et al. (2010) also find that government spending on social programs has a two fold effect on
increasing consumption: first by increasing consumer confidence and decreasing precautionary
spending, and second by directly increasing the income of households, through pension and
welfare payments. The effects of decreased precautionary spending can be observed in the results
of part one of this study, which saw increasing per capita and aggregate demand for modern
household commodities. It can be suggested that in the future these trends will continue, perhaps
increasing more rapidly.
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The life cycle hypothesis theorizes that consumption and saving depend primarily on lifetime
resources, as well as on the demographic structure of the population in study. The theory of
precautionary savings, as is popular in the literature, is in contrast to the life cycle hypothesis, as it
suggests that while income rises, expenditure risks may also rise, leading to households increasing
savings in order to prepare for adverse shocks, particularly as they age (Baldacci, et al., 2010;
Modigliani & Cao, 2004). The theory of precautionary saving is supported by the findings here
(that increases in the rate of government social spending results in an increase in consumption),
and also appears to be supported by the findings for the effect of the dependency ratio. One
would postulate that as the rate at which a worker becomes increasingly responsible for children
and the elderly, savings would increase in advance of adverse events. This is the effect observed,
which suggests that household consumption increases as the dependency ratio decreases.
The larger question involved in this study of household consumption in China is its implications
for future growth of the Chinese economy – can China buy growth? It is well known that the
contribution of domestic consumption to GDP is falling, particularly from households. This
relates to a disproportionately large reliance on net exports and investment (in GDP, by the
expenditure approach). The nature of the relatively low contribution of domestic consumption to
GDP can be rectified by increasing government consumption spending (particularly on social
programs which has been found to also increase household consumption) or by a reevaluation of
the reliance on investments and exports. Of interest in this study has been the contribution of
household purchases to China’s trade surplus.
China’s current account balance has been suggested by many to be unsustainable and increasing
domestic household consumption has been identified as a means to aid in rectifying this problem.
The trends revealed through the deeper examination of household consumption of both durable
consumer goods and major foodstuffs reveal that consumption is increasing almost across the
board, with exceptions for goods which are being phased out to be replaced by newer technology
(i.e. the substitution of bicycles for motorbikes or cars). In this study, televisions serve as an
indicator of the trends to be found in a quintessentially consumerist commodity (or the most
suitable good for which data is available). Televisions have become increasingly popular in China,
and domestic consumption has exceeded export volumes. This demonstrates that a domestic
Sharon Riley - Master Thesis – Lund University School of Economics and Management
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market does exist within China and if the government utilizes appropriate policy techniques and
ensures that lower income households have both the ability and the willingness to spend;
consumer markets have the capacity to continue to expand. The positive effects for the
contribution of consumption to growth of the Chinese economy are two-fold in this sense. Net
exports decline if a larger share of goods produced are subsequently consumed domestically
rather than exported (and preference for luxury items produced abroad also decreases net
exports). Also, a burgeoning consumer market increases the level of household spending.
Household consumption is on the rise in China; the debate surrounds whether it can keep pace
with the growth of the rest of the economy. Recognition on the part of the Chinese government
of this issue has been demonstrated through stated policy shifts, but it must now implement
further measures to continue to boost the confidence and income of consumers. Well thoughtout future policy initiatives on behalf of the government can ensure a smooth shift from an
export-led economy to one which is driven by domestic consumption, in many cases, of
domestically produced goods.
The results found in this study, and supported by previous literature, suggest that the theory of
the Chinese government is theoretically sound in its recent decision to support domestic
consumption iniatives. The findings in this study suggest that increasing spending on social
welfare programs is one way of inducing such a positive increase.
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9 Conclusion
The mixed results yielded in this study can be interpreted within the context of the broader body
of literature on the contribution of Chinese domestic consumption to GDP growth. Although
there exists no shortage of difficulties associated with the data and statistical anomalies, evidence
has still been uncovered that provides a more detailed and intimate examination of the trends in
Chinese household consumption.
An exploration of available data has been able to demonstrate trends in the increasing demand
for both consumer goods and major foodstuffs. In addition, the proportion of goods that are
produced domestically has been compared to those which are imported. To date, it has been
found that consumer goods are produced primarily at home (regardless of the origin of
investment, which is outside the scope of this study), while food commodity markets are import
oriented.
This thesis finds that household consumption is increasing, though some older technologies are
being phased out in households. As was expected, demand of rural consumers lags behind that of
their urban counterparts. A significant correlation between government spending on social
programs and the increase of household consumption was revealed, supporting existing theories
concerning the effect of precautionary savings on lackluster household consumption.
China is well on its way to the consumption-driven growth path officially endorsed by top
officials in the government. However, it remains to be seen whether government policy will
continue to recognize the precautionary behaviour of Chinese consumers and adjust social
programs and expenditures accordingly. China is on course towards becoming one of the largest
consumer markets in the world. Its transition from an investment and export led economy to one
which is propelled forward by domestic consumption is imminent, and it will undoubtedly
continue to have an unprecedented role in the global economy, as well as large implications for
China’s account balance.
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10 Appendix
10.1 Results of Exploratory Study
10.1.1
Bicycles
As with televisions, an estimate of the aggregate demand for bicycles is obtained by summing the
rural and urban total demand for new bicycles, the results for which are given in (Table 10-1)
though this method does not yield results after 1995. It is extremely difficult to make
comparisons with this data and data on domestic consumption calculated from output and export
statistics. This is due, partly do possible lags as previously discussed, possible errors and
misrepresentations in the statistics themselves, and also partially due to the declining popularity
of bicycles in households’ spending decisions. In addition, it must be reiterated that the
estimation of domestic consumption from prevalence of bicycles in households is likely a
significant underestimate, as many new bicycles would be purchased to replace old ones in
disrepair and this is not captured in this method of computation.
Table 10-1 Summary of Data on Estimated Consumption of Bicycles in China, 1986 - 2008
Year
Output
(10 000 Bicycles)
Exports
Imports
Domestic
Consumption29
Household
Consumption30
1986
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
3568,3
3141,6
4472,25
3361,18
2999,29
2312,49
2609,64
2906,79
2902,26
3957,52
5451,7
5001,26
6366,165
7731,07
7475,2
6374,87
1262
1217
1439
1761
2523,5
3286
3494
4556
5044
5175
5387
5599
5923
5659
n/a
3210,25
2144,18
1560,29
551,49
86,14
-379,21
-591,74
-598,48
407,7
-173,74
979,165
2132,07
1552,2
715,87
1821,459
1947,973
1119,279
-
29
30
As estimated by subtracting exports from domestic output
As estimated by the increase in prevalence of bicycles from one year to the next per 100 households
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10.1.2
Cameras
As with televisions and bicycles, an estimate of the aggregate demand for cameras is obtained by
summing the rural and urban total demand for new cameras, the results for which are given in
(Table 10-2). It is extremely difficult to make comparisons with this data and data on domestic
consumption calculated from output and export statistics. As cameras are a relatively new
technology, it is safe to assume that the number of replacement goods is negligible, though
difficulties related to vague differentiation between data on film cameras and digital cameras
diminished the validity of the former method of estimation.
Unfortunately, the data on output and exports of cameras is plagued with problems when applied
to calculate domestic consumption, often yielding negative results. Time lags (though these
should be accounted for when five year moving averages are applied) and data collection errors
and misrepresentations, particularly of output, are the most plausible explanations. No
conclusions on the consumption of domestically produced goods are possible with the available
data.
Table 10-2 Summary of Data on Estimated Consumption of Cameras in China, 1986 - 2008
Year
Output
(10 000
Cameras)
Exports
Imports
Domestic
Consumption31
Household
Consumption32
1986
202,54
-
n/a
-
173,0423
1990
213,22
-
-
209,1378
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
3326,15
4120,77
4686,89
5521,87
4832,29
5514,52
5962,09
5309,61
6198,14
6217,99
8199
7958,16
8689,6
8900
6150
6180
6559
6747
7910
9073
8692,5
9156
6120
5630
4570
3510
2125
12364
-2823,85
-2059,23
-1872,11
-1225,13
-3077,71
-3558,48
-2730,41
-3846,39
78,14
587,99
3629,00
4448,16
6564,60
-3464,00
128,795
278,0703
198,9889
346,9054
344,2049
129,1438
236,6018
729,4228
261,2787
328,9699
157,9036
116,5867
128,795
10.1.3
31
32
-
Electric Fans
As estimated by subtracting exports from domestic output
As estimated by the increase in prevalence of cameras from one year to the next per 100 households
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As with the other goods, an estimate of the aggregate demand for cameras is obtained by
summing the rural and urban total demand for new electric, the results for which are given in
(Table 10-3). It is again extremely difficult to make comparisons with this data and data on
domestic consumption calculated from output and export statistics, due in large part to problems
with the data on output and exports, due again to the same problems previously described for
camera data (lags and data misrepresentations). The household consumption calculated from
increases in the number of fans per one hundred households yields no noticeable trends, as the
amount of by which the prevalence of electric fans increases varies widely. No conclusions on the
consumption of domestically produced goods are possible with the available data.
Table 10-3 Summary of Data on Estimated Consumption of Electric Fans in China, 1986 - 2008
Year
Output
(10 000 Fans)
Exports
Imports
Domestic
Consumption33
Household
Consumption34
1986
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
3528,6
5799,3
12966,67
10291,68
8171,42
6724,49
6158,14
7661,61
9616,1
10761,33
12980,92
11042,64
12754,245
14465,85
15440,04
13890,74
9491
12259
13165
15185
13215
11245
12352
21285
35848
46834
50935
55036
54001
48694
n/a
3475,67
-1967,32
-4993,58
-8460,51
-7056,86
-3583,39
-2735,9
-10523,67
-22867,08
-35791,36
-38180,755
-40570,15
-38560,96
-34803,26
1999,67
2173,926
3003,046
2329,814
819,2649
1459,208
1174,455
1177,332
1426,169
2870,89
-
10.2 Descriptive Statistics
Table 10-4 Summary of descriptive statistics of variables included in explanatory model
Variable
33
34
# of Obs
Mean
Standard
Minimum
Maximum
As estimated by subtracting exports from domestic output
As estimated by the increase in prevalence of electric fans from one year to the next per 100 households
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Deviation
HsldCnspt
31
2394,74
2307,22
184,00
8349,00
DepIntRt
31
5,77
3,08
1,98
11,34
DepRat
31
52,16
8,36
39,8
72,45
PolChng
31
-
-
0
1
AvgHshdSz
31
3,83
0,42
3,16
4,41
GovSocExp
31
248,34
371,77
18,91
1538,84
RealWg
31
1106,47
666,52
343,40
2800,53
GovExpEdHlth
31
2308,44
2914,32
112,66
11691,01
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10.3 Alternate Model
Table 10-5 - Time series regression estimates of per capita household consumption using logged first differences
Variable
OLS#1
OLS#2
OLS#3
DepIntRt
-0,513 (0,303)
-0,479 (0,325)
-0,955 (0,068)*
PolChng
1,192 (0,001)***
1,200 (0,0008)***
1,447 (0,0002)***
DepRat
-35,557 (0,0006)***
37,269 (0,0002)***
32,156 (0,002)***
GovSocExp
3,599 (0,009)***
3,652 (0,007) ***
4,868 (0,0009)***
RealWg
6,564 (0,014)***
6,736 (0,010) ***
AvgHshdSz
-8,747 (0,557)
Adjusted R2
0,793
0,769
0,707
N
31
31
31
P(f)
2,59E-07
6,35E-08
3,53E-07
***p<0.01, **p<0.05, *p<0.1; p-values displayed in brackets
Note: The dependent variable in each case is the natural logarithm of the aggregate household
consumption. Percent effect is defined as the percentage induced change in y as a result of a one percent
change in the rate of change of x.
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