Discussion Paper No. 676

THE DETERMINANTS OF
HOUSEHOLD SAVING IN CHINA:A DYNAMIC PANEL ANALYSIS OFPROVINCIAL DATACharles Yuji Horioka
andJunmin Wan

November 2006
Revised September 2007

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

THE DETERMINANTS OF HOUSEHOLD SAVING IN CHINA:A DYNAMIC PANEL ANALYSIS OF PROVINCIAL DATA*Charles Yuji Horioka**
Professor, Institute of Social and Economic Research, Osaka University, andNational Bureau of Economic Research, Inc.
horioka@iser.osaka-u.ac.jp
andJunmin Wan
Associate Professor, Faculty of Economics, Fukuoka University
wan@econ.fukuoka-u.ac.jp
September 5, 2007
*We are grateful to Barry Bosworth, Hideki Hayashi, Takako Fujiwara-Greve, Teh-Ming Huo,
Insang Hwang, Shinsuke Ikeda, Junichiro Ishida, Miki Kohara, Justin Yifu Lin, Ronald I.
McKinnon, Kazuo Ogawa, Hugh Patrick, Masaya Sakuragawa, Shizuka Sekita, Katsuya Takii,
Midori Wakabayashi, Xiaoping Wang, Calla Weimer, Tongsheng Xu, Zhihao Yu, Yaohui Zhao,
and especially Christopher Carroll, Galina Hale, Aart Kraay, Louis Kuijs, Colin McKenzie,
Masao Ogaki, and Etsuro Shioji, and participants of the Seoul Conference on “China andEmerging Asia: Reorganizing the Global Economy,” the Seattle Conference of the Asia-Pacific
Economic Association, the Summer Institute of the National Bureau of Economic Research, theAnnual Pacific Basin Conference of the Federal Reserve Bank of San Francisco, and the fallmeeting of the Japanese Economic Association, seminars at the Osaka School of International
Public Policy (OSIPP ) of Osaka University, the Asian Public Policy Program of Hitotsubashi
University, the China Center for Economic Research of Peking University, the School of
Economics of Jiangxi University of Finance and Economics, the Faculty of Economics of Keio
University, the Faculty of Economics of Fukuoka University, and the Brookings Institution for
their valuable comments, and Horioka is grateful to the Ministry of Education, Culture, Sports,
Science and Technology of the Japanese Government for Grant-in-Aid for Scientific Researchnumber 18330068, which supported this research.
**Address for correspondence: Charles Yuji Horioka, Institute of Social and Economic
Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka 567-0047, JAPAN
Tel.: 81-6-6879-8586, Fax: 81-6-6878-2766, Email: horioka@iser.osaka-u.ac.jp
Forthcoming in Journal of Money, Credit and Banking, vol. 39, no. 8 (December 2007).

Abstract
In this paper, we conduct a dynamic panel analysis of the determinants of the householdsaving rate in China using a life cycle model and panel data on Chinese provinces forthe 1995-2004 period from China’s household survey.

We find that China’s householdsaving rate has been high and rising and that the main determinants of variations overtime and over space therein are the lagged saving rate, the income growth rate, (in manycases) the real interest rate, and (in some cases) the inflation rate. However, we findthat the variables relating to the age structure of the population have the expected
impact on the household saving rate in only one of the four samples.

These results

provide mixed support for the life cycle hypothesis as well as the permanent income
hypothesis, are consistent with the existence of inertia or persistence, and imply that
China’s household saving rate will remain high for some time to come.
Journal of Economic Literature classification numbers: D12, D91, E21, J10.
Key words: Age structure, China, demographics, dependency ratio, habit formation,
household saving, household saving rate, household, inertia, life cycle hypothesis, life
cycle model, permanent income hypothesis, persistence, saving, saving rate.

11. Introduction
China has attracted increasing attention because it is the world’s most populous
nation and because it has maintained phenomenal rates of economic growth in recent
years.

For example, the Asian Development Bank now projects that China will attain agrowth rate in excess of 9% in 2006 for the fifth consecutive year, thereby serving as theengine of growth in the Asian-Pacific region (Nihon Keizai Shimbun, evening edition ofApril 6, 2006, page 1).
Moreover, another reason for being interested in China is that China introduced
a so-called “one-child policy” in 1979 as a way of controlling population growth.

Thisis an interesting natural experiment that makes fertility largely exogenous and enables
us to assess the impact of the age structure of the population on the household savingrate without worrying about endogeneity issues.

Moreover, because the one-childpolicy was applied more leniently to ethnic minorities, the policy also led to substantial
variations among provinces in the age structures of their populations, and this will
enable us to more sharply estimate the impact of the age structure of the population on
the household saving rate.
Yet another noteworthy aspect of China’s economy is its high saving rate.
China has had by far the highest overall saving rate in the world since at least 2000, andher saving rate has increased even further since 2000—to nearly 50% of GDP.

Grosscapital formation (investment) is also high in China, but because saving exceeds
investment, China has been running a net saving surplus, which translates into a current
account surplus, and that surplus has been growing sharply--from 1.9% of GDP in 2000
to 3.6% in 2004 and a remarkable 7.2% in 2005--even though China is investing at a
staggering rate of 43-46% of GDP and even though China is still relatively poor. This2

has made China one of the world’s largest capital exporters and has exacerbated trade
frictions with the United States and other countries.

Moreover, China’s net saving

surplus shows no signs of abating (The Economist, September 24-30, 2005, edition,
page 13 of “A Survey of the World Economy”).１

Thus, it is important to understandthe determinants of, and future trends in, China’s saving rate, and the obvious
candidates are the rapid rates of economic growth alluded to earlier and the age
structure of the population, which has shown tremendous variation over time as well asover space.
In this paper, we conduct a dynamic panel analysis of the determinants of thehousehold saving rate in China using a life cycle model and panel data on Chinese
provinces for the 1995-2004 period from China’s household survey.
At least two previous studies have conducted similar analyses.

Kraay (2000)

uses panel data on Chinese provinces from China’s household survey to analyze thedeterminants of the saving rates of rural and urban households during the 1978-83 and1984-89 periods and finds that, in the case of rural households, future income growth
has a negative and significant impact on their saving rates, that the share of food in total
consumption has a negative and significant impact on their saving rates, presumably
because households closer to the subsistence level have less ability to save, and thatneither the dependency ratio (proxied by the ratio of population to employment) nor
future income uncertainty has a significant impact on their saving rates. However,Kraay (2000) finds that virtually none of the explanatory variables has a significant
impact on the saving rates of urban households. Modigliani and Cao (2004) conduct a
regression analysis of the determinants of the household saving rate using times series
data for the 1953-2000 period and find that the long-term growth rate, the reciprocal of3the dependency ratio (proxied by the ratio of the employed population to the number ofminors), the deviation of growth from the long-term growth rate, and inflation all havepositive and significant impacts on the household saving rate.
obtain somewhat conflicting results.

Thus, the two studies

Kraay (2000) finds that the dependency ratio

does not have a significant impact on the household saving rate, whereas Modigliani
and Cao (2004) find that it does. Moreover, Kraay (2000) finds that future income
growth has a negative and significant impact on the household saving rate, whereas
Modigliani and Cao (2004) find that the long-term growth rate and the deviation of
growth from the long-term growth rate have a positive and significant impact on thehousehold saving rate.
The current study improves upon these earlier studies in a number of respects:(1) the data are much newer, (2) the dependent variable (the household saving rate) isdefined more carefully and includes household investments in real assets, (3) the
dependency ratio is defined more carefully and the young dependency ratio and the olddependency ratio are entered separately, (4) we include variables not included byprevious authors such as the lagged saving rate and the interest rate, (5) we obtain
results for the sample of urban households, the sample of rural households, the sample
of all households, and a pooled sample of urban and rural households (unlike Kraay(2000), who obtains results only for urban and rural households, and Modigliani andCao (2004), who obtain results only for all households), and (6) we use superior
estimation techniques.
This paper is organized as follows: In section 2, we present data on householdsaving rates and related variables; in section 3, we present our theoretical model; insection 4, we discuss the estimation model and data sources; in section 5, we discuss the4

estimation method; in section 6, we present the estimation results; and section 7 is aconcluding section.
To preview our main findings, we find that China’s household saving rate has
been high and rising and that the main determinants of variations over time and overspace therein are the lagged saving rate, the income growth rate, (in many cases) thereal interest rate, and (in some cases) the inflation rate. However, we find that thevariables relating to the age structure of the population have the expected impact on thehousehold saving rate in only one of the four samples.

These results provide mixed

support for the life cycle hypothesis as well as the permanent income hypothesis, areconsistent with the existence of inertia or persistence, and imply that China’s householdsaving rate will remain high for some time to come.

2. Data on Saving Rates and Other Related Variables
In this section, we present data on household saving rates and other related
variables.
First, Figure 1 shows data on trends over time in the age structure of the
population during the 1949-2004 period, and as can be seen from this figure, there havebeen pronounced trends over time in both the young dependency ratio (the ratio of the
population aged 0-14 to the population aged 15-59) and the old dependency ratio (theratio of the population aged 60 or older to the population aged 15-59).

The former

increased from 0.57 in 1950 to 0.77 in 1964 before starting to decline, falling to 0.28 by2004 (due in large part to the “one-child policy” and other population control measures),while the latter increased more or less steadily from 0.13 in 1950 to 0.18 in 2004.
Finally, the total dependency ratio (the ratio of the population aged 0-14 or 60 or older5to the population aged 15-59) showed more or less the same trends over time as the
young dependency ratio, increasing from 0.70 in 1950 to 0.89 in 1964 before starting to
decline, falling to 0.46 by 2004 (also due in large part to the “one-child policy” andother population control measures).

The life cycle hypothesis predicts that the age

structure of the population will have a significant impact on the saving rate and inparticular that the dependency ratios will have a negative impact on the saving rate, andif we compare trends over time in the national saving rate with trends over time in thedependency ratios, the upward trend in the saving rate that has been observed since the1960s coincides with a downward trend in the young and total dependency ratios during
the same period, suggesting that the latter may be a cause of the former.
Looking next at the age structure of China’s population in international
comparison, China’s young dependency ratio was higher than the worldwide level in1975 (0.74 vs. 0.67) but fell at an unprecedented rate due to the one-child policy andother population control measures.

As a result, it was far less than the worldwide levelby 2005 (0.32 vs. 0.46).２
By contrast, the old dependency ratio was somewhat lower than the worldwide
level in 1975 (0.13 vs. 0.16) but has gradually increased due to the steady increases inlife expectancy and was just under the worldwide level by 2005 (0.16 vs. 0.17).
However, because trends over time in the young dependency ratio have been
more pronounced than trends over time in the old dependency ratio, trends in the total
dependency ratio mirror trends in the youth dependency ratio: it was just over theworldwide level in 1975 (0.87 vs. 0.83) but declined sharply thereafter, falling to far
less than the worldwide level by 2005 (0.48 vs. 0.63).
The fact that the young and total dependency ratios were formerly relatively

6high by international standards can explain why China’s saving rate was formerly
relatively low by international standards, and the fact that the young and total
dependency ratios are now relatively low by international standards can explain why
China’s saving rate is now relatively high by international standards.
Figure 2 shows data on trends over time in the saving rates of urban, rural, andall households for the 1995-2004 period from China’s household survey, and as can beseen from this figure, the saving rates of the three categories of households are roughly
comparable not only with respect to their levels but also with respect to trends over time
therein.

Looking first at the level of the saving rate, the saving rates of urban, rural,and all households fluctuated in the 17.33% to 22.98%, 15.78% to 29.77%, and 16.29%
to 25.17% ranges, respectively, and averaged 20.3%, 24.7%, and 22.4%, respectively,
during the 1995-2004 period.

The fact that the saving rate of rural households is

considerably higher than that of urban households even though their income levels are
so much lower is surprising, but it could be due to the greater income volatility of rural
households, the vast majority of whom are farmers, as a result of which they save more
for precautionary purposes, or to the fact that differences in income levels largely reflect
differences in price levels, as a result of which the purchasing power of the incomes ofurban and rural households is not nearly as different as their nominal incomes.
Turning to trends over time in the saving rates of urban, rural, and allhouseholds, the saving rate of urban households showed an upward trend throughout the
1995-2004 period, while the saving rates of rural and all households showed upward
trends until 1999 before leveling off slightly. The upward trends in the saving rates of
all three categories of households coincide with the downward trends in the young andtotal dependency ratios, and thus it is possible that the latter are one of the causes of the7former.

Thus, the evidence presented thus far suggests that the age structure of

China’s population can explain not only the high level of China’s household saving rate
but also the upward trend therein.
Table 1 shows data on the average saving rates of urban, rural, and allhouseholds during the 1995-2004 period by province, and as can be seen from this table,
there has been enormous variation among provinces in their saving rates, with thesaving rate of urban households ranging from 10.7% to 25.7%, that of rural households
ranging from 10.0% to 43.7%, and that of all households ranging from 13.5% to 35.1%.
Finally, Table 2 shows data on the age structure of urban, rural, and allhouseholds by province during the 1995-2004 period, and as can be seen from this table,
there has been enormous variation among provinces in the age structure of their
populations as well.

For example, the young dependency ratio ranged from 0.17 to

0.39 for urban households, from 0.18 to 0.52 for rural households, and from 0.18 to 0.48
for all households, the old dependency ratio ranged from 0.07 to 0.18 for urbanhouseholds, from 0.07 to 0.16 for rural households, and from 0.07 to 0.18 for all
households, and the total dependency ratio ranged from 0.29 to 0.48 for urbanhouseholds, from 0.34 to 0.66 for rural households, and from 0.31 to 0.56 for all
households.

We will conduct a regression analysis in sections 5 through 7 to see if

variations in the household saving rate correlate with variations in the age structure of
the population.

3. Theoretical Model
From the above sections, we know that the Chinese economy can becharacterized as showing a high saving rate, rapid economic growth, and a dramatic8change in the age structure of her population.

In this section, we present atheoretical model that provides the justification for our estimation model.

Inparticular, we present a two-period overlapping generations model based on the life
cycle hypothesis and use this model to show the impact of income growth and theage structure of the population on the saving rate.
Suppose that there is an endowment economy.
to T.

The economy starts from time 0.

The time horizon is from 0

At time 0, n0 (=1) persons with anendowment or human capital of y 0 (=1) enter the economy, and at time 1, they
become old and die.

This person earns no wages at time 1 but she has to consume

at both time 0 and time 1, so she has to save at time 0 to finance her consumption attime 1.

The person has no bequest motive.

population

growthandincomegrowth,The variables n and y denoterespectively,

and

are

positive.

Consequently, at period t, nt persons newly enter the economy with endowment
nt y t and live together with nt −1 old persons.

It is also assumed that the nt new

persons cannot lend to or borrow from the nt −1 old persons.

All persons

homogenously have a log linear utility function with a subtractive stock of habits
and/or durability.

For simplicity, the rate of time preference and the interest rate

are assumed to be zero, but we have confirmed that the qualitative findings remainunchanged even when a positive rate of time preference and a positive interest rate
are assumed.

Thus, the new generation solves the following individual

maximization problem at time t:maxlog(ct ) + log(ct +1 − γct )s.t.

ct + ct +1 = y t

where ct , ct +1 denotes consumption at time t and time t+1, respectively.

9

γ is aparameter that represents the degree of durability or habit formation, with a negativevalue of gamma indicating the existence of durability, a positive value indicating theexistence of habit formation, and a zero value indicating the absence of bothdurability and habit formation.
ct > 0 , ct +1 > 0 .

We also assume that γ>-0.5 in order to insure that

Given these assumptions, we obtain the following solutions for

consumption at time t and time t+1:
ct =

yt
,
2 + 2γ(1)(1 + 2γ ) y t.
2 + 2γct +1 =

(2)

Next, we derive the aggregate saving rate in this economy at time t (t>0) as
follows:⎛yt(1 + 2γ ) y t −1 ⎞⎟n t y t − ⎜⎜ n t+ n t −1
2 + 2γ
2 + 2γ ⎟⎠
⎝
SR =
,
nt yt
=

(3)

1 ⎞
1 + 2γ ⎛
⎜⎜1 −
⎟.
ny ⎟⎠
2 + 2γ ⎝The impacts of y, n and γ on the aggregate saving rate are as follows:∂SR
1 + 2γ=> 0,∂y( 2 + 2γ ) ny 2

(4)

∂SR
1 + 2γ=> 0,∂n( 2 + 2γ ) n 2 y(5)ny − 1∂SR=,∂γ2(1 + γ ) 2 ny
> 0 for ny > 1,=

6)

for ny = 1,

< 0 for ny < 1.

These results imply that income growth and an increase in the working-age
10population will raise the saving rate and that the strengthening of habits or the
weakening of durability will raise the saving rate in a booming economy but lower it
in a declining economy.

These implications from the theoretical model suggest that

China’s high and rising household saving rate may be due to rapid income growth,
the decline in the dependency ratio, and/or the uniqueness of utility parameters.
Compared with the previous literature, the impact of γ on individual saving(individual saving = ct +1 , ∂c t +1 / ∂γ = ( 2 + 2γ ) −2 > 0 ) is the same as that of, forexample, Lahiri and Puhakka (1998).

However, the impact of γ on aggregate

saving as expressed in equation (6) cannot be found in previous studies.

4. Estimation Model and Data Sources.
In this section, we discuss the estimation model and data sources we use in ourempirical analysis.
The dependent variable we use in our analysis is SR = the household saving
rate, defined as the ratio of household saving to household disposable income (nethousehold income in the case of rural households) and where household saving is
calculated as household disposable (or net) income minus household consumption.
Following Loayza, et al. (2000) and Schrooten and Stephan (2005), we
estimate a reduced-form linear equation rather than adhering to a particular, narrow
structural model, but the theoretical literature offers guidance regarding what variables
should be included as explanatory variables. Since the life cycle hypothesis predicts
that the household saving rate will be a function of the growth rate of per capita income
and the age structure of the population (see section 3 above and also Modigliani (1970)
and Deaton (1992), Chapter 2), we include the following explanatory variables:11(1) CHY = the income growth rate, defined as the real rate of growth of percapita household disposable income (net household income in the case of rural
households)(2) YOUNG = the young dependency rate, defined as the ratio of the
population aged 0-14 to the population aged 15-64３(3) OLD = the old dependency rate, defined as the ratio of the population aged
65 or older to the population aged 15-64(4) DEP = the total dependency rate, defined as the ratio of the population aged
0-14 or 65 or older to the population aged 15-64

In addition, we include the following explanatory variables:(5) SR(-1) = the one-year lag of the saving rate(6) RINT = the real interest rate, defined as NINT – INFL, where NINT = thenominal interest rate on one-year bank deposits and INFL = the rate of change of theconsumer price index(7) INFL = the rate of change of the consumer price index(8) RURAL = a dummy variable that equals 1 in the case of rural households
and zero otherwise (included only when the pooled sample of urban and rural
households is used)(9) A constant term

The lagged saving rate is included to test for the presence of inertia or
persistence.

The real interest rate is included to test for the impact of financial

variables, and we would expect its coefficient to be positive if the substitution effect12

more than offsets the income effect. The inflation rate is included as a proxy for priceuncertainty and/or macroeconomic stability more generally (as done by Loayza, et al.
(2000) and Schrooten and Stephan (2005)), a rural dummy is included to see if there are
any systematic differences between urban and rural households in trends over time in
the household saving rate, and a constant term, which corresponds to the coefficient of
the time trend in the regressions in differences (see section 6 below), is included insome variants
Finally, the real growth rate of per capita gross provincial product is used as an
instrument in the level equation, as discussed below.
The data we use in our analysis are panel data for 1995-2004 on Chinese
provinces. All variables are available for urban, rural, and all households with the
exception of the nominal interest rate, which is available only for the country as a whole,and the real growth rate of per capita gross provincial product, which is available only
for each province as a whole. Thus, we are able to obtain separate results for urban,
rural, and all households and for a pooled sample of urban and rural households.
All data from China’s household survey and national accounts data are takenfrom the China Statistics Yearbook, all demographic data are taken from the China
Population Statistics Yearbook, and data on nominal interest rates are taken from theInternational Monetary Fund’s International Financial Statistics.
Data were available for all 31 provinces for the ten-year period from 1995 to
2004 with the following exceptions: data were not available for Chongqing Province
during the 1995-96 period because this province did not become independent of
Sichuan Province until 1997, and data on the CPI and/or on household income andconsumption were not available for Tibet Province during the 1995-98 period. These13

missing values caused the number of observations to decline from 310 to 304.
Moreover, one year’s worth of data were lost because the income growth rate was used
as an explanatory variable. This reduced the number of observations further from 304
to 273 and means that the sample period for most provinces was nine years (1996-2004).
Finally, because the lagged real growth rate of per capita gross provincial product wasused as an instrument, yet another observation for Chongqing Province (that for 1998)
had to be dropped, causing the final number of observations to be 272.
Descriptive statistics on the variables used in our analysis for the final sample
of 272 observations are shown in Table 3.

5. Estimation Method
In this section, we briefly describe our estimation method. Following Loayza,etal.

(2000)

andSchrooten

andStephan(2005),

we

use

a

generalized-method-of-moments (GMM) estimator applied to dynamic models using
panel data. We use this estimator for at least three reasons: (1) Inertia is likely to bepresent in annual data, and it seemed desirable to use a dynamic specification to allow
for it. (2) Some of the explanatory variables (such as RINT and CHY) are likely to be
jointly determined with the saving rate, and it seemed desirable to control for the
potential joint endogeneity of the explanatory variables. (3) There is the possibility ofunobserved province-specific effects correlated with the regressors, and it seemed
desirable to control for such effects.
Following Loayza, et al. (2000) and Schrooten and Stephan (2005), we use thealternative “system GMM estimator” proposed by Arellano and Bover (1995) and
Blundell and Bond (1998), which reduces the potential biases and imprecision14

associated with the usual difference estimator by combining, in a system, the regression
in differences with the regression in levels.
As Windmeijer (2005) notes, the estimated asymptotic standard errors of theefficient two-step GMM estimator will be severely downward biased in small samples,
and thus we correct the standard errors for this bias using the method proposed byWindmeijer (2005).４
Following Loayza, et al. (2000) and Schrooten and Stephan (2005), thedemographic variables (YOUNG, OLD, and DEP) are the only explanatory variables
that we treated as being strictly exogenous and included as instruments in the level
equation as well as the first-difference equation. All other explanatory variables wereregarded as being weakly exogenous, and lagged values thereof were included as“internal instruments,” with Bond’s (2002) method being used to select instruments.５
Finally, the one-period lag of the real growth rate of per capita gross provincial productwas used as an instrument only in the level equation. All of the instruments we use
passed all of the commonly used tests: the Hansen test, the AR(1) test, and the AR(2)test. Tables 4-6 show the results of these tests and also show which instruments wereused in each equation.

6. Estimation Results
In this section, we present our estimation results concerning the determinantsof the household saving rate.

The estimation results for urban, rural, and allhouseholds and for a pooled sample of urban and rural households are shown in Tables
4, 5, 6, and 7, respectively.
Looking first at the coefficient of SR(-1) (the lagged saving rate), this15

coefficient is always positive and highly significant, indicating strong inertia or
persistence. This coefficient ranges from 0.476 to 0.628, implying a long-run effectthat is 1.91 to 2.69 times the short-run effect, in the sample of urban households; from
0.476 to 0.844, implying a long-run effect that is 1.91 to 6.41 times the short-run effect,in the sample of rural households; from 0.622 to 0.721, implying a long-run effect thatis 2.65 to 3.58 times the short-run effect, in the sample of all households; and from
0.604 to 0.710, implying a long-run effect that is 2.54 to 3.45 times the short-run effect,in the pooled sample of urban and rural households.
Looking next at the coefficient of CHY (the income growth rate), it is always
positive and highly significant (which is consistent with the life cycle hypothesis),ranging from 0.192 to 0.270 in the sample of urban households, from 0.331 to 0.536 in
the sample of rural households, from 0.201 to 0.240 in the sample of all households, andfrom 0.337 to 0.396 in the pooled sample of urban and rural households. These figures
imply that a one percentage point increase in the income growth rate causes a 0.192 to
0.536 percentage point increase in the household saving rate. Moreover, the long-run
impact of the income growth rate is 1.91 to 6.41 times these figures.
Looking next at the coefficient of RINT (the real interest rate), it isinsignificant and usually positive in the sample of urban households, positive andsignificant in two out of four cases in the sample of rural households, and positive andsignificant in all cases in the sample of all households and the pooled sample of urban
and rural households. Thus, the real interest rate has a significant positive impact on
the household saving rate for every sample except for the sample of urban households,
which suggests that the interest elasticity of saving is positive and is consistent with thepermanent income hypothesis.

16

Looking next at the impact of the demographic variables (YOUNG, OLD, andDEP), their coefficients are never significant in the sample of urban households and thesample of rural households, the coefficients of YOUNG and DEP are positive andsometimes significant but the coefficient of OLD is insignificant in the sample of allhouseholds, and the coefficients of YOUNG and DEP are negative and at least
marginally significant but the coefficient of OLD is insignificant in the pooled sampleof urban and rural households. Thus, the only case in which the coefficients of thedemographic variables are significant with the expected sign is in the case of thecoefficients of YOUNG and DEP in the pooled sample of urban and rural households,６and the coefficients of YOUNG and DEP are sometimes significant with the wrong signin the sample of all households.

The reasons for the mixed performance of thedemographic variables is a topic for future research.７
Looking next at the coefficient of INFL (the inflation rate), it is insignificant in
the sample of urban households, the sample of all households, and the pooled sample ofurban and rural households, but it is negative and significant in three out of four cases inthe sample of rural households. These results suggest that the impact of inflation is notalways significant but that it is sometimes negative and significant.
Looking next at the coefficient of the RURAL dummy in the pooled sample ofurban and rural households, it is positive and significant in all four cases, whichsuggests that the saving rate of rural households have a more pronounced upward trend
than that of urban households after controlling for other factors.
Looking finally at the constant term, which represents the coefficient of a timetrend, it is positive in all cases and is significant in five out of eight cases, whichsuggests that there is an upward trend in China’s household saving rate.

17

We also tried adding year dummies and the level of per capita household
disposable income as additional explanatory variables, but we dropped them from thefinal specification because their coefficients were not statistically significant.
Lastly, we compare our results to those of previous studies. Our finding that
income growth has a positive and significant impact on the household saving rate is atvariance with Kraay’s (2000) finding that (future) income growth has a negative andsignificant impact on the saving rate of rural households and does not have a significant
impact on the saving rate of urban households but is consistent with Modigliani andCao’s (2004) finding that (long run) income growth has a positive and significant
impact on the household saving rate. In order to shed light on why our results differ
from those of Kraay (2000), we tried estimating all of our equations using two-stage
least squares, the same estimation method used by Kraay (2000), and found that theresults are substantially different.

For example, the coefficients of the variables

relating to the age structure of the population, which had previously been insignificant,are now significant, whereas the coefficient of income growth, which had previously
been positive and significant, becomes totally insignificant (which is consistent with
Kraay’s (2000) results for urban households) when two-stage least squares are used.
These findings suggest that the differences between our results and those of Kraay(2000) are due largely to differences in estimation method and underscore theimportance of using dynamic panel techniques when using panel data.

7. Conclusion
In this paper, we conducted a dynamic panel analysis of the determinants of thehousehold saving rate in China using a life cycle model and panel data on Chinese

18

provinces for the 1995-2004 period from China’s household survey. To summarize ourmain findings, we found that China’s household saving rate has been high and risingand that the main determinants of variations over time and over space therein are thelagged saving rate, the income growth rate, (in many cases) the real interest rate, and (insome cases) the inflation rate. However, we found that the variables relating to the age
structure of the population have the expected impact on the household saving rate in
only one of the four samples. These results provide mixed support for the life cycle
hypothesis (with the positive and significant coefficient of income growth supporting
the life cycle hypothesis and the mixed performance of the demographic variables being
unfavorable to the life cycle hypothesis), provide some support for the permanent
income hypothesis (with the positive and significant coefficient of the interest rate
supporting this hypothesis), and are also consistent with the existence of inertia or
persistence.
Turning to the implications of our findings, our finding that inertia or
persistence are strong implies that there will not be a dramatic decline in China’s
household saving rate, and our finding that the income growth rate has a positive impact
on the household saving rate implies that China’s household saving rate will remainhigh as long as the growth rate remains high. However, if the growth rate tapers off,
we can explain a gradual decline in the household saving rate.
Thus, it seems likely that China’s household saving rate will remain high in the
short to medium run, and to the extent that this causes China’s current account surplus
to remain high, this may cause continued frictions with the United States and China’s
other trading partners. In the long run, however, China’s household saving rate can be
expected to taper off assuming the growth rate tapers off, and thus, in the long run,

19

China may well suffer from current account deficits rather than current accountsurpluses.
Turning finally to directions for further research, there are a number of factorsthat we were not able to consider in this analysis due to data limitations, such as
borrowing constraints, precautionary saving, bequest motives, the distribution ofincome, and old-age pensions, health insurance, and other social insurance programs,
and we hope to be able to incorporate these factors in our future research.

20

References
Alessie, Rob, and Annamaria Lusardi. (1997) “Consumption, Saving and Habit
Formation.” Economics Letters, 55:1 (August 15), 103-108.
Arellano, Manuel, and Stephen Bond. (1991) “Some Tests of Specification for Panel
Data: Monte Carlo Evidence and an Application to Employment Equations.” Review of
Economic Studies, 58:2 (April), 277-297.
Arellano, Manuel, and Olympia Bover. (1995) “Another Look at the Instrumental
Variable Estimation of Error-Components Models.” Journal of Econometrics, 68:1
(July), 29-51.
Banister, Judith. (1984) “An Analysis of Recent Data on the Population of China.”
Population and Development Review, 10:2 (June), 241-271.
Banister, Judith. (1987) China’s Changing Population. Stanford, California: Stanford
University Press.
Blundell, Richard, and Stephen Bond. (1998) "Initial Conditions and Moment
Restrictions in Dynamic Panel Data Models." Journal of Econometrics, 87:1 (August),
115-143.
Bond, Stephen. (2002) “Dynamic Panel Data Models: A Guide to Micro Data Methods
and Practice.” CEMMAP Working Paper CWP09/02, Department of Economics,
Institute for Fiscal Studies, London.
Chamon, Marcos, and Eswar Prasad. (2006) “Determinants of Household Saving in
China.” Mimeo., International Monetary Fund, Washington, D. C.
Deaton, Angus. (1992) Understanding Consumption. Oxford: Clarendon Press.
“The Frugal Giant.” The Economist, September 24-30, 2005, issue, pp. 12-14 of “A
Survey of the World Economy.”
Kraay, Aart. (2000) “Household Saving in China.” World Bank Economic Review, 14:3

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(September), 545-570.
Kuijs, Louis. (2005) “Investment and Saving in China.” World Bank Policy Research
Paper Series No. 3633 (June). Available at SSRN: http://ssrn.com/abstract=756985.
Kuijs, Louis. (2006) "How Will China's Saving-Investment Balance Evolve?" WorldBank Policy Research Working Paper No. 3958 (July 1). Available at SSRN:
http://ssrn.com/abstract=923265.
Loayza, Norman; Klaus Schmidt-Hebbel; and Luis Serven. (2000) “What Drives
Private Saving across the World?” Review of Economics and Statistics, 82:2 (May),
165-181.
Modigliani, Franco. (1970) “The Life Cycle Hypothesis of Saving and Intercountry
Differences in the Saving Ratio.” In Induction, Growth and Trade: Essays in Honour of
Sir Roy Harrod, edited by W. A. Eltis, M. FG. Scott, and J. N. Wolfe, pp. 197-225.
Oxford: Clarendon Press.
Modigliani, Franco, and Shi Larry Cao. (2004) “The Chinese Saving Puzzle and theLife-Cycle Hypothesis.” Journal of Economic Literature, 42:1 (March), 145-170.
Roodman, D. (2005) “ xtabond2: Stata module to extend xtabond dynamic panel data
estimator.” Center for Global Development, Washington, D.C.
http://econpapers.repec.org/software/bocbocode/s435901.htm
Roodman, D. (2007), “How to do xtabond2: An introduction to ‘difference’ and‘system’ GMM in Stata,” Working Paper 103, Center for Global Development,
Washington, D.C.
http://www.cgdev.org/content/publications/detail/11619
Schrooten, Mechthild, and Sabine Stephan. (2005) “Private Savings and Transition:
Dynamic Panel Data Evidence from Accession Countries.” Economics of Transition,
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Efficient Two-Step GMM Estimators.” Journal of Econometrics, 126:1 (May), 25-51.

22

Central Intelligence Agency,
com/g/g.aspx?c=ch&v=30

Data Sources
CIA World Factbook.

http://www.indexmundi.

Department of Population, Social, Science and Technology Statistics, National Bureauof Statistics of China, ed., China Population Statistics Yearbook, 1991-2005 editions.
Beijing: China Statistics Press.
International Monetary Fund，International Financial Statistics, 1995-2005.
National Bureau of Statistics of China., ed., China Statistical Yearbook, 1988-2005
editions. Beijing: China Statistics Press.
United Nations (2002), World Population Prospects: The 2002 Revision. New York:
United Nations.

23

F igu re 1 : Age S tru c tu re of th e Popu lation in Ch in a, 1 9 4 9 - 2 0 0 4
1
0.9
0.8

Ratio of the
population
aged 0-14 to
the population
aged 15-59

0.7
0.6

Ratio of the
population
aged 60 orolder to the
population
aged 15-59

0.5
0.4

Ratio of the
population
aged 0-14 or
60 or older tothe population
aged 15-59

0.3
0.2
0.1

2004

1999

1994

1989

1984

1979

1974

1969

1964

1959

1954

1949

0

Sources: China Population Statistics Yearbook, 1988-2005 editions, and World Population
Prospects: The 2002 Revision (United Nations)24

Figure 2: Household Saving Rate in C hina, 1995-2004
35.00

30.00

25.00

Saving rate ofurban households(in percent)

20.00
Saving rate ofrural households(in percent)

15.00
Saving rate of allhouseholds (inpercent)

10.00

5.00

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

0.00

Source: Authors' calculations based on China Statistics Yearbook, 1996-2005 editions

25

Table 1: Household Saving Rate by Province (Averages for the
1995-2004 Period)
Saving rate (in percent)

Province
Beijing
Tianjin
Hebei
Shanxi
Inner Mongolia
Liaoning
Jilin
Heilongjiang
Shanghai
Jiangsu
Zhejiang
Anhui
FujianJiangxi
Shandong
Henan
Hubei
Hunan
Guangdong
Guangxi
HainanChongqing
Sichuan
Guizhou
YunnanTibet
Shaanxi
Gansu
Qinghai
Ningxia
Xinjiang
Mean

Urban households

Rural households

All households

19.4
22.4
23.5
21.7
22.5
17.6
19.3
22.6
22.4
23.4
21.9
21.1
22.8
25.7
24.5
22.8
17.0
18.5
19.4
19.6
22.7
10.7
16.6
19.9
19.5
19.1
15.9
18.2
17.9
17.0
22.5
20.3

27.2
43.7
41.7
35.5
17.4
29.0
27.7
31.1
19.6
31.0
23.3
26.7
26.1
22.6
30.3
31.9
24.9
10.4
24.8
20.8
34.5
24.4
18.2
20.3
10.0
31.2
10.9
21.0
16.1
17.8
16.6
24.7

20.8
27.6
35.1
28.8
20.2
21.5
22.5
25.6
22.1
27.3
23.0
24.5
24.7
24.0
27.5
28.7
21.0
13.7
21.6
20.6
28.9
17.7
17.5
20.2
14.1
25.1
13.5
19.6
17.2
17.4
21.1
22.4

Source: Authors' calculations based on China Statistics Yearbook, 1996-2005 editions, andChina Population Statistics Yearbook, 1996-2005 editions.

26

Table 2: Age Structure of the Population by Province (Averages
for the 1995-2004 Period)
Urban households
Province

Beijing
Tianjin
Hebei
Shanxi
Inner
Mongolia

Rural households

All households

Young
Old
Total
Young
Old
Total
Young
Old
Total
dependency dependency dependency dependency dependency dependency dependency dependency dependency
ratio
ratio
ratio
ratio
ratio
ratio
ratio
ratio
ratio

0.167
0.204
0.264
0.304

0.123
0.136
0.092
0.088

0.290
0.340
0.357
0.392

0.276
0.338
0.355
0.419

0.125
0.109
0.104
0.101

0.401
0.447
0.459
0.519

0.188
0.245
0.333
0.378

0.123
0.127
0.101
0.096

0.311
0.372
0.435
0.474

0.269

0.078

0.347

0.326

0.088

0.414

0.303

0.084

0.387

0.207
0.119
0.325
0.278
0.098
0.375
0.239
0.109
0.349
0.216
0.094
0.310
0.280
0.078
0.357
0.248
0.086
0.333
0.227
0.080
0.308
0.291
0.068
0.359
0.257
0.075
0.332
0.175
0.185
0.360
0.182
0.160
0.341
0.176
0.181
0.357
0.236
0.122
0.358
0.328
0.158
0.486
0.287
0.138
0.425
0.223
0.123
0.347
0.278
0.145
0.423
0.255
0.135
0.390
0.300
0.107
0.407
0.396
0.115
0.511
0.369
0.112
0.481
0.272
0.103
0.375
0.427
0.119
0.546
0.368
0.110
0.479
0.303
0.099
0.402
0.428
0.101
0.529
0.388
0.100
0.488
0.265
0.100
0.365
0.321
0.130
0.450
0.299
0.117
0.417
0.290
0.098
0.388
0.407
0.110
0.518
0.381
0.108
0.488
0.266
0.092
0.358
0.409
0.111
0.520
0.353
0.103
0.456
0.257
0.109
0.366
0.360
0.117
0.477
0.330
0.114
0.444
0.315
0.098
0.412
0.525
0.134
0.659
0.429
0.116
0.545
0.290
0.120
0.410
0.427
0.118
0.545
0.393
0.118
0.511
0.342
0.080
0.422
0.486
0.121
0.607
0.436
0.106
0.542
0.231
0.133
0.364
0.356
0.127
0.483
0.305
0.129
0.435
0.255
0.129
0.384
0.355
0.110
0.465
0.321
0.117
0.437
0.313
0.101
0.414
0.479
0.092
0.572
0.431
0.095
0.526
0.270
0.111
0.381
0.423
0.095
0.518
0.391
0.098
0.490
0.389
0.093
0.481
0.497
0.083
0.580
0.479
0.086
0.565
0.280
0.107
0.387
0.412
0.095
0.507
0.371
0.099
0.470
0.247
0.090
0.337
0.433
0.078
0.511
0.383
0.081
0.465
0.265
0.077
0.342
0.460
0.069
0.529
0.394
0.072
0.466
0.276
0.071
0.347
0.509
0.068
0.577
0.427
0.069
0.496
0.282
0.075
0.357
0.494
0.074
0.568
0.402
0.075
0.477
0.265
0.104
0.369
0.385
0.106
0.492
0.341
0.106
0.446
Notes: The young dependency ratio is defined as the ratio of the population aged 0-14 to the
population aged 15-64; The old dependency ratio is defined as the ratio of the population aged 65 orolder to the population aged 15-64; The total dependency ratio is defined as the ratio of the
population aged 0-14 or 65 or older to the population aged 15-64.
Liaoning
Jilin
Heilongjiang
Shanghai
Jiangsu
Zhejiang
Anhui
FujianJiangxi
Shandong
Henan
Hubei
Hunan
Guangdong
Guangxi
HainanChongqing
Sichuan
Guizhou
YunnanTibet
Shaanxi
Gansu
Qinghai
Ningxia
Xinjiang
Mean

Source: Authors' calculations based on China Population Statistics Yearbook, 1996-2005 editions.

27

Table 3: Descriptive Statistics
VariableObs.
Mean
Std. Dev.
Minimum
Maximum
SR (all)
272
0.230
0.057
0.087
0.390
SR (urban)
272
0.207
0.041
0.077
0.313
SR (rural)
272
0.255
0.097
-0.044
0.494
YOUNG (all)
272
0.312
0.086
0.116
0.527
YOUNG (urban)
272
0.257
0.053
0.110
0.420
YOUNG (rural)
272
0.376
0.093
0.136
0.596
OLD (all)
272
0.102
0.027
0.043
0.219
OLD (urban)
272
0.106
0.028
0.027
0.225
OLD (rural)
272
0.108
0.029
0.063
0.314
DEP (all)
272
0.414
0.084
0.220
0.655
DEP (urban)
272
0.363
0.048
0.245
0.539
DEP (rural)
272
0.483
0.088
0.262
0.771
NINT (all)
272
0.033
0.018
0.020
0.075
INFL (all)
272
0.017
0.031
-0.0330.116
INFL (urban)
272
0.016
0.032
-0.0340.116
INFL (rural)
272
0.017
0.031
-0.0370.116
RINT (all)
272
0.016
0.022
-0.041
0.068
RINT (urban)
272
0.016
0.022
-0.041
0.067
RINT (rural)
272
0.015
0.024
-0.041
0.072
CHGDP (all)
272
0.094
0.050
-0.272
0.228
POP
272
4126.225
2601.504
262.000
11430.000
CHPOP
272
8.613
18.023-49.865
188.721
INCOME (all)2723844.672
2097.599
1511.344
14573.670
INCOME (urban)
272
6643.5302341.563
3353.940
16682.820
INCOME (rural)
272
2521.854
1126.045
1100.590
7066.330
CONS (all)272
2938.318
1591.1291323.966
11248.800
CONS (urban)
272
5239.805
1771.079
2767.840
12631.030
CONS (rural)272
1848.667
839.803880.6506328.849
RURAL̲RATIO
272
0.692
0.151
0.219
0.864
CPI (all)272
101.6633.081
96.700111.600
CPI (urban)272
101.645
3.168
96.600111.600
CPI (rural)272
101.7363.078
96.300111.600
CHY(all)
272
0.073
0.034
-0.0370.191
CHY(urban)
272
0.073
0.042
-0.0390.231
CHY(rural)
272
0.060
0.052
-0.101
0.331
Source: Authors' calculations based on China Statistics Yearbook, 1996-2005 editions, China
Population Statistics Yearbook, 1996-2005 editions, and International Financial Statistics, 19952005 editions.

28

Table 4: The Determinants of the Household Saving Rate in China (Urban Households)
SR(-1)
CHY
RINT
YOUNG
OLD

0.628
(0.055)***0.212(0.087)**0.209(0.294)0.062(0.064)0.080(0.079)

DEP
INFL

0.272
(0.189)Constant
Number of observations
Number of groups
Hansen test of overidentification (p-value)
Test for 1st-order serial correlation (p-value)
Test for 2st-order serial correlation (p-value)
Transformation used
Instruments only for first difference equation

272
31
0.737
0.000
0.121
GMM(SR(-1), CHY,
RINT, INFL, (2 .)collapse)

0.624(0.056)***0.260(0.086)***0.233(0.292)

Dependent variable = SR
0.544
(0.056)***0.270(0.148)*0.198(0.373)0.002(0.059)-0.009(0.082)0.058(0.064)0.307(0.194)0.255(0.308)0.042(0.030)
272
272
31
31
0.540
0.978
0.000
0.000
0.131
0.124
first differences
GMM(SR(-1), CHY,
GMM(SR(-1), CHY,
RINT, INFL, (2 .)RINT, INFL, (3, 3))collapse)

Instruments for both first difference and level
YOUNG, OLD
DEP
YOUNG, OLD
equations
CHGDP(-1)
Instruments only for level equation
Notes: Standard errors are in parentheses; *, **, *** denote significant at the 10%, 5% and 1% levels, respectively.

29

0.476
(0.084)***0.192(0.093)**-0.043(0.340)-0.012(0.070)0.075(0.227)0.076(0.022)***
272
31
0.522
0.001
0.126
GMM(SR(-1), CHY, RINT,
INFL, (2 .) collapse)
DEP

Table 5: The Determinants of the Household Saving Rate in China (Rural Households)
SR(-1)
CHY
RINT
YOUNG
OLD

0.774
(0.068)***
0.495
***
(0.107)0.591***(0.163)0.030(0.033)0.200(0.157)-0.356*(0.180)

272
31
0.596
0.000
0.680

272
31
0.538
0.000
0.802

GMM(SR(-1), CHY, RINT,
INFL, (2 .) collapse)

GMM(SR(-1), CHY, RINT,
INFL, (2 .) collapse)

Constant
Number of observations
Number of groups
Hansen test of overidentification (p-value)
Test for 1st-order serial correlation (p-value)
Test for 2st-order serial correlation (p-value)
Transformation used
Instruments only for first difference equation

Dependent variable = SR
0.481
(0.168)***0.332*(0.181)
0.069(0.369)-0.088(0.075)-0.001(0.243)0.025(0.024)-0.338(0.232)

DEP
INFL

0.844
(0.042)***
0.536
***
(0.149)0.593***(0.185)-0.773**(0.284)0.164(0.052)***
272
31
0.636
0.013
0.496
first differences-0.078(0.069)-0.843**(0.370)0.173(0.063)**
272
31
0.613
0.014
0.506

GMM(SR(-1), CHY, RINT, GMM(SR(-1), CHY, RINT,
INFL, (2 .) collapse)
INFL, (2 .) collapse)

Instruments for both first difference and level
YOUNG, OLD
DEP
YOUNG, OLD
equations
CHGDP(-1)
Instruments only for level equation* ** ***
denote significant at the 10%, 5% and 1% levels, respectively.
Notes: Standard errors are in parentheses; , ,

30

0.476
(0.169)***0.331**(0.155)-0.022(0.492)DEPTable 6: The Determinants of the Household Saving Rate in China (All Households)
SR(-1)
CHY
RINT
YOUNG
OLD

0.721
(0.028)***0.201(0.073)***0.513(0.157)***0.058(0.026)**0.070(0.068)DEP
INFL
Constant
Number of observations
Number of groups
Hansen test of overidentification (p-value)
Test for 1st-order serial correlation (p-value)
Test for 2st-order serial correlation (p-value)
Transformation used
Instruments only for first difference equation

0.113(0.119)0.711(0.034)***0.204(0.097)**0.622(0.154)***

Dependent variable = SR
0.658
(0.097)***0.211(0.097)**0.435(0.144)***0.053(0.034)0.077(0.078)0.058(0.025)**0.145(0.115)0.048(0.120)0.018(0.033)
272
272
272
31
31
31
1.000
0.187
1.000
0.000
0.000
0.000
0.262
0.251
0.277
first differences
GMM(SR(-1), CHY, RINT, GMM(SR(-1), CHY, RINT, GMM(SR(-1), CHY,
INFL, (2 4))
INFL, (2, 5) collapse)
RINT, INFL, (2, 4))

Instruments for both first difference and level
YOUNG, OLD
DEP
YOUNG, OLD
equations
CHGDP(-1)
Instruments only for level equation
Notes: Standard errors are in parentheses; *, **, *** denote significant at the 10%, 5% and 1% levels, respectively.

31

0.622
(0.119)***0.240(0.109)**0.500(0.270)*0.034(0.025)-0.003(0.193)0.034(0.039)
272
31
0.209
0.001
0.226
GMM(SR(-1), CHY, RINT,
INFL, (2, 5) collapse)
DEP

Table 7: The Determinants of the Household Saving Rate in China(Pooled Sample of Urban and Rural Households)
SR(-1)
CHY
RINT
YOUNG
OLD

0.700
(0.044)***0.385(0.125)***0.738(0.139)***-0.054(0.035)-0.006(0.093)

DEP
INFL
RURAL

0.203
(0.125)0.067(0.007)***

Constant
Number of observations
544
Number of groups
62
Hansen test of overidentification (p0.249
value)
Test for 1st-order serial correlation
0.000(p-value)
Test for 2st-order serial correlation
0.923(p-value)
Transformation used
Instruments only for first difference GMM(SR(-1), CHY,
equation
RINT, INFL, (2, 2))
Instruments for both first difference YOUNG, OLD,
and level equations
RURAL DUMMY

Dependent variable = SR
0.710
0.604
(0.038)***(0.080)***
0.396
0.337
(0.130)***(0.119)***
0.726
0.563
(0.138)***(0.164)***-0.091(0.045)**-0.098(0.110)-0.049(0.025)*
0.195
0.070
(0.129)(0.138)0.068
0.070(0.007)***(0.009)***0.053(0.028)*
544
544
62
62

-0.092(0.042)**0.071(0.139)0.070(0.008)***0.052(0.029)*
544
62

0.279

0.339

0.336

0.000

0.000

0.000

0.853

0.895

0.897

0.606
(0.083)***0.338(0.115)***0.569(0.156)***

first differences
GMM(SR(-1), CHY, GMM(SR(-1), CHY,
RINT, INFL, (2, 2))
RINT, INFL, (2, 2))

GMM(SR(-1), CHY,
RINT, INFL, (2, 2))

DEP, RURAL
DUMMY

DEP, RURALDUMMYYOUNG, OLD,RURAL DUMMYCHGDP(-1)
Instruments only for level equation
Notes: Standard errors are in parentheses; *, **, *** denote significant at the 10%, 5% and 1% levels, respectively.

32

Data Appendix for Figure 1 and Other Related Variables
Year
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

Young Dependency Old Dependency Total Dependency
Ratio
Ratio
Ratio

Life expectancy atbirth (in years)

0.57

0.13

0.70

40.80

0.64

0.13

0.77

0.67

0.14

0.81

0.72

0.13

0.86

0.77
0.76

0.13
0.13

0.89
0.89

0.74

0.13

0.87

0.74

0.13

0.87

0.62

0.13

0.75

0.57

0.13

0.70

0.49
0.47
0.46

0.13
0.13
0.14

0.62
0.61
0.59

40.30
42.40
44.60
47.00
49.50
45.80
42.50
24.60
38.40
53.00
54.90
57.10
57.80
58.60
59.40
60.30
60.80
61.40
62.00
62.30
63.00
63.40
63.80
64.20
64.60
65.10
65.00
64.90
64.80
64.70
64.63
64.55
66.60

0.42
0.43
0.44
0.44
0.43
0.43
0.42
0.41
0.39
0.38
0.37
0.34
0.34
0.32
0.30
0.28

0.14
0.13
0.15
0.15
0.15
0.15
0.16
0.17
0.17
0.17
0.17
0.16
0.17
0.18
0.18
0.18

0.56
0.57
0.59
0.59
0.58
0.58
0.58
0.57
0.56
0.55
0.54
0.50
0.51
0.49
0.48
0.46

68.55

69.70

71.38
71.62
71.86
72.22
71.96

Total population (inmillions)
541.67551.96563.00574.82587.96602.66614.65628.28646.53659.94672.07662.07658.59672.95691.72704.99725.38745.42763.68785.34806.71
829.92852.29871.77892.11908.59924.20937.17949.74962.59975.42987.051000.721016.541030.08
1043.57
1058.511075.071093.00
1110.261127.04
1143.33
1158.231171.711185.171198.50
1211.211223.89
1236.26
1247.61
1257.86
1267.43
1276.27
1284.53
1292.271299.88

Note: Young Dependency Ratio is defined as the ratio of the population aged 0-14 to the population aged 15-59; Old
Dependency Ratio is defined as the ratio of the population aged 60 or older to the population aged 15-59; Total
Dependency Ratio is defined as the ratio of the population aged 0-14 or 60 or older to the population aged 15-59.
Sources: China Population Statistics Yearbook, 1988-2005 editions; Banister (1987); World Population Prospects: The
2002 Revision (United Nations); and U.S. CIA Factbook.

33

Data Appendix for Figure 2
Year

Saving rate of urbanhouseholds (inpercent)

Saving rate of rural
Saving rate of allhouseholds (in percent) households (in percent)

17.33
15.78
16.29
1995
1996
19.19
18.38
18.79
1997
18.13
22.65
20.35
1998
19.61
27.30
23.40
1999
20.59
29.77
25.17
2000
19.87
25.93
22.77
2001
21.88
26.51
24.05
2002
21.19
27.12
23.67
2003
22.31
27.11
24.28
2004
22.98
26.29
24.29
Mean
20.31
24.69
22.31
Source: Authors' calculations based on China Statistics Yearbook, 1996-2005 editions.

34

Endnotes
１

See Kuijs (2005, 2006) for data on the overall level and sectoral composition ofsaving and investment and on the saving-investment balance in China.
２

The demographic data in this paragraph and the two following paragraphs are basedon United Nations data and hence do not coincide precisely with the earlier data.
３

It would have been preferable to use the population aged 15-59 since the retirement

age in China (for males) is 60, but we could not do so due to data limitations.
４

All calculations were done using Stata, version 10. We used Roodman’s (2005,2007) ”xtabond2” program in Stata to correct the standard errors.
５

The “collapse” command in Stata was used to select instruments.

６

The fact that the coefficients of YOUNG and DEP are negative and significant only inthe pooled sample is presumably due to the fact that the one-child policy has been
enforced much more strictly in urban areas than in rural areas, as a result of which there
is much greater variation in YOUNG and DEP in the pooled sample than in the othersamples.
７

Chamon and Prasad (2006) analyze micro data from the same household survey weuse and find that saving increases with age and is highest for the elderly. This can help
explain why OLD and DEP do not have the expected impacts on the household savingrate.

35