How China Achieved World's Highest Household Savings Rate

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 a growth rate in excess of 9% in 2006 for the fifth

consecutive year, thereby serving as the engine of growth in the Asian-Pacific region (Nihon Keizai Shimbun, evening edition of April 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. This is 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 saving rate without worrying about endogeneity issues. Moreover, because the one-child policy 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, and the saving rate has increased even further since 2000--to nearly 50% of GDP. Gross capital 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. This 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, "A Survey of the World Economy," page 13). (1) Thus, it is important to understand the 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 as over space.

In this paper, we conduct a dynamic panel analysis of the determinants of the household 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 the determinants of the saving rates of rural and urban households during the 1978-83 and 1984-89 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 that neither 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 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 time-series data for the 1953-2000 period and find that the long-term growth rate, the reciprocal of the dependency ratio (proxied by the ratio of the employed population to the number of minors), the deviation of growth from the long-term growth rate, and inflation all have positive and significant impacts on the household saving rate. Thus, the two studies obtain somewhat conflicting results. Kraay finds that the dependency ratio does not have a significant impact on the household saving rate, whereas Modigliani and Cao find that it does. Moreover, Kraay finds that future income growth has a negative and significant impact on the household saving rate, whereas Modigliani and Cao 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 the household saving rate.

The current study improves upon these earlier studies in a number of respects: (i) the data are much newer; (ii) the dependent variable (the household saving rate) is defined more carefully and includes household investments in real assets; (iii) the dependency ratio is defined more carefully and the young dependency ratio and the old dependency ratio are entered separately; (iv) we include variables not included by previous authors such as the lagged saving rate and the interest rate; (v) 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 and Cao 2004, who obtain results only for all households); and (vi) we use superior estimation techniques.

This paper is organized as follows. In Section 1, we present data on household saving rates and related variables; in Section 2, we discuss the estimation model and data sources; in Section 3, we discuss the estimation method; in Section 4, we present the estimation results; and Section 5 is a concluding 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 over space therein are the lagged saving rate, the income growth rate, (in many cases) the real interest rate, and (in some cases) the inflation rate. However, we find 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 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.

1. 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 have been 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 (the ratio 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 by 2004 (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 older 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" and other 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 in particular that the dependency ratios will have a negative impact on the saving rate, and if we compare trends over time in the national saving rate with trends over time in the dependency ratios, the upward trend in the saving rate that has been observed since the 1960s 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.

[FIGURE 1 OMITTED]

Looking next at the age structure of China's population in international comparison, China's young dependency ratio was higher than the worldwide level in 1975 (0.74 vs. 0.67) but fell at an unprecedented rate due to the one-child policy and other population control measures. As a result, it was far less than the worldwide level by 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 in life 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 the worldwide 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 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, and all households for the 1995-2004 period from China's household survey, and as can be seen 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 17.3-22.98%, 15.78-29.77%, and 16.29-25.17% 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 of urban 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 all households, 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 and total dependency ratios, and thus it is possible that the latter are one of the causes of the 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.

[FIGURE 2 OMITTED]

Table 1 shows data on the average saving rates of urban, rural, and all households 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 the saving 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 all households 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 urban households, 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 urban households, 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 2 to 4 to see if variations in the household saving rate correlate with variations in the age structure of the population.

2. THE ESTIMATION MODEL AND DATA SOURCES

In this section, we discuss the estimation model and data sources we use in our empirical 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 (net household income in the case of rural households) and where household saving is calculated as household disposable (or net) income minus household consumption.

Following Loayza, Schmidt-Hebbel, and Serven (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, e.g., Modigliani 1970, Deaton 1992, ch. 2), we include the following explanatory variables:

CHY = the income growth rate, defined as the real rate of growth of per household disposable income (net household income in the case of rural households).

YOUNG = the young dependency rate, defined as the ratio of the population aged 0-14 to the population aged 15-64. (3)

OLD = the old dependency rate, defined as the ratio of the population aged 65 or older to the population aged 15-64.

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:

SR(-1) = the one-year lag of the saving rate.

RINT = the real interest rate, defined as NINT-INFL, where NINT = the nominal interest rate on 1-year bank deposits and INFL = the rate of change of the consumer price index.

INFL = the rate of change of the consumer price index.

RURAL = a dummy variable that equals 1 in the case of rural households and 0 otherwise (included only when the pooled sample of urban and rural households is used).

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 effect more than offsets the income effect. The inflation rate is included as a proxy for price uncertainty and/or macro-economic stability more generally (as done by Loayza, Schmidt-Hebbel, and Serven 2000, 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 3), is included in some 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 taken from the China Statistics Yearbook, all demographic data are taken from the China Population Statistics Yearbook, and data on nominal interest rates are taken from the International Monetary Fund's International Financial Statistics.

Data were available for all 31 provinces for the 10-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 and consumption were not available for Tibet Province during the 1995-98 period. These 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 9 years (1996-2004). Finally, because the lagged real growth rate of per capita gross provincial product was used 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.

3. ESTIMATION METHOD

In this section, we briefly describe our estimation method. Following Loayza, Schmidt-Hebbel, and Serven (2000) and Schrooten and Stephan (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: (i) inertia is likely to be present in annual data, and it seemed desirable to use a dynamic specification to allow for it; (ii) 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; (iii) there is the possibility of unobserved province-specific effects correlated with the regressors, and it seemed desirable to control for such effects.

Following Loayza, Schmidt-Hebbel, and Serven (2000) and Schrooten and Stephan (2005), we use the alternative "system GMM estimator" proposed by Arellano and Bover (1995) and Blundell and Bond (1998), which reduces the potential biases and imprecision 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 the efficient 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 by Windmeijer (2005). (4)

Following Loayza, Schmidt-Hebbel, and Serven (2000) and Schrooten and Stephan (2005), the demographic 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 were regarded 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 product was 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 4-7 show the results of these tests and also show which instruments were used in each equation.

4. ESTIMATION RESULTS

In this section, we present our estimation results concerning the determinants of the household saving rate. The estimation results for urban, rural, and all households and for a pooled sample of urban and rural households are shown in Tables 4-7, respectively.

Looking first at the coefficient of SR(-1) (the lagged saving rate), this 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 effect that is 1.91-2.69 times the short-run effect, in the sample of urban households; from 0.476 to 0.844, implying a long-run effect that is 1.91-6.41 times the short-run effect, in the sample of rural households; from 0.622 to 0.721, implying a long-run effect that is 2.65-3.58 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 2.54-3.45 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, and from 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 0.192-0.536 percentage point increase in the household saving rate. Moreover, the long-run impact of the income growth rate is 1.91-6.41 times these figures.

Looking next at the coefficient of RINT (the real interest rate), it is insignificant and usually positive in the sample of urban households, positive and significant in two out of four cases in the sample of rural households, and positive and significant 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 the permanent income hypothesis.

Looking next at the impact of the demographic variables (YOUNG, OLD, and DEP), their coefficients are never significant in the sample of urban households and the sample of rural households, the coefficients of YOUNG and DEP are positive and sometimes significant but the coefficient of OLD is insignificant in the sample of all households, and the coefficients of YOUNG and DEP are negative and at least marginally significant but the coefficient of OLD is insignificant in the pooled sample of urban and rural households. Thus, the only case in which the coefficients of the demographic variables are significant with the expected sign is in the case of the coefficients of YOUNG and DEP in the pooled sample of urban and rural households, (6) and the coefficients of YOUNG and DEP are sometimes significant with the wrong sign in the sample of all households. The reasons for the mixed performance of the demographic 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 of urban and rural households, but it is negative and significant in three out of four cases in the sample of rural households. These results suggest that the impact of inflation is not always significant but that it is sometimes negative and significant.

Looking next at the coefficient of the RURAL dummy in the pooled sample of urban and rural households, it is positive and significant in all four cases, which suggests 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 time trend, it is positive in all cases and is significant in five out of eight cases, which suggests that there is an upward trend in China's household saving rate.

We also tried adding year dummies and the level of per household disposable income as additional explanatory variables, but we dropped them from the final 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 at variance with Kraay's (2000) finding that (future) income growth has a negative and significant 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 and Cao'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, we tried estimating all of our equations using two-stage least squares, the same estimation method used by Kraay, and found that the results 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 are due largely to differences in estimation method and underscore the importance of using dynamic panel techniques when using panel data.

5. CONCLUSION

In this paper, we conducted a dynamic panel analysis of the determinants of the household 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. To summarize our main findings, we found that China's household saving rate has been high and rising and that the main determinants of variations over time and over space therein are the lagged saving rate, the income growth rate, (in many cases) the real interest rate, and (in some 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 is 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 remain high 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, China may well suffer from current account deficits rather than current account surpluses.

Turning finally to directions for further research, there are a number of factors that we were not able to consider in this analysis due to data limitations, such as borrowing constraints, precautionary saving, bequest motives, the distribution of income, 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.

APPENDIX: DATA SOURCES

Central Intelligence Agency, CIA Worm Factbook. http://www.indexmundi.com/g/g.aspx?c=ch&v=30

Department of Population, Social, Science and Technology Statistics, National Bureau of 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.

Data Appendix for Figure 1 and Other Related Variables

             Young              Old              Total
          dependency        dependency        dependency
Year         ratio             ratio             ratio

1949
1950         0.57              0.13              0.70
1951
1952
1953         0.64              0.13              0.77
1954
1955         0.67              0.14              0.81
1956
1957
1958
1959
1960         0.72              0.13              0.86
1961
1962
1963
1964         0.77              0.13              0.89
1965         0.76              0.13              0.89
1966
1967
1968
1969
1970         0.74              0.13              0.87
1971
1972
1973
1974
1975         0.74              0.13              0.87
1976
1977
1978
1979
1980         0.62              0.13              0.75
1981
1982         0.57              0.13              0.70
1983
1984
1985         0.49              0.13              0.62
1986         0.47              0.13              0.61
1987         0.46              0.14              0.59
1988
1989         0.42              0.14              0.56
1990         0.43              0.13              0.57
1991         0.44              0.15              0.59
1992         0.44              0.15              0.59
1993         0.43              0.15              0.58
1994         0.43              0.15              0.58
1995         0.42              0.16              0.58
1996         0.41              0.17              0.57
1997         0.39              0.17              0.56
1998         0.38              0.17              0.55
1999         0.37              0.17              0.54
2000         0.34              0.16              0.50
2001         0.34              0.17              0.51
2002         0.32              0.18              0.49
2003         0.30              0.18              0.48
2004         0.28              0.18              0.46

             Life
          expectancy           Total
           at birth         population
Year      (in years)       (in millions)

1949                          541.67
1950         40.80            551.96
1951                          563.00
1952                          574.82
1953         40.30            587.96
1954         42.40            602.66
1955         44.60            614.65
1956         47.00            628.28
1957         49.50            646.53
1958         45.80            659.94
1959         42.50            672.07
1960         24.60            662.07
1961         38.40            658.59
1962         53.00            672.95
1963         54.90            691.72
1964         57.10            704.99
1965         57.80            725.38
1966         58.60            745.42
1967         59.40            763.68
1968         60.30            785.34
1969         60.80            806.71
1970         61.40            829.92
1971         62.00            852.29
1972         62.30            871.77
1973         63.00            892.11
1974         63.40            908.59
1975         63.80            924.20
1976         64.20            937.17
1977         64.60            949.74
1978         65.10            962.59
1979         65.00            975.42
1980         64.90            987.05
1981         64.80            1000.72
1982         64.70            1016.54
1983         64.63            1030.08
1984         64.55            1043.57
1985         66.60            1058.51
1986                          1075.07
1987                          1093.00
1988                          1110.26
1989                          1127.04
1990         68.55            1143.33
1991                          1158.23
1992                          1171.71
1993                          1185.17
1994                          1198.50
1995         69.70            1211.21
1996                          1223.89
1997                          1236.26
1998                          1247.61
1999                          1257.86
2000         71.38            1267.43
2001         71.62            1276.27
2002         71.86            1284.53
2003         72.22            1292.27
2004         71.96            1299.88

NOTE: Young dependency ratio is defined as the ratio of the population
aged (1-14 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.

Data Appendix for Figure 2

                 Saving rate of urban)
Year               households (in %)

1995                     17.33
1996                     19.19
1997                     18.13
1998                     19.61
1999                     20.59
2000                     19.87
2001                     21.88
2002                     21.19
2003                     22.31
2004                     22.98
Mean                     20.31

                 Saving rate of rural
Year               households (in %)

1995                     15.78
1996                     18.38
1997                     22.65
1998                     27.30
1999                     29.77
2000                     25.93
2001                     26.51
2002                     27.12
2003                     27.11
2004                     26.29
Mean                     24.69

                  Saving rate of all
Year               households (in %)

1995                     16.29
1996                     18.79
1997                     20.35
1998                     23.40
1999                     25.17
2000                     22.77
2001                     24.05
2002                     23.67
2003                     24.28
2004                     24.29
Mean                     22.31

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

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 and Emerging Asia: Reorganizing the Global Economy," the Seattle Conference of the Asia-Pacific Economic Association, the Summer Institute of the National Bureau of Economic Research, the Annual Pacific Basin Conference of the Federal Reserve Bank of San Francisco, and the fall meeting 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 Research number 18330068, which supported this research.

Received November 14, 2006; and accepted in revised form December 20, 2006.

LITERATURE CITED

Alessie, Rob, and Annamaria Lusardi. (1997) "Consumption, Saving and Habit Formation." Economics Letters, 55, 103-08.

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, 277-97.

Arellano, Manuel, and Olympia Bover. (1995) "Another Look at the Instrumental Variable Estimation of Error-Components Models." Journal of Econometrics, 68, 29-51.

Banister, Judith. (1984) "An Analysis of Recent Data on the Population of China." Population and Development Review, 10, 241-71.

Banister, Judith. (1987). China's Changing Population. Stanford, CA: Stanford University Press.

Blundell, Richard, and Stephen Bond. (1998) "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models." Journal of Econometrics, 87, 115-43.

Bond, Stephen. (2002) "Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice." Department of Economics, Institute for Fiscal Studies, London, CEMMAP Working Paper CWP09/02.

Chamon, Marcos, and Eswar Prasad. (2006) "Determinants of Household Saving in China." Mimeo, International Monetary Fund, Washington, DC.

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." Worm Bank Economic Review, 14, 545-70.

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?" World Bank 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, 165-81.

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. E G. Scott, and J. N. Wolfe, pp. 197-225. Oxford: Clarendon Press.

Modigliani, Franco, and Shi Larry Cao. (2004) "The Chinese Saving Puzzle and the Life-Cycle Hypothesis." Journal of Economic Literature, 42, 145-70.

Roodman, David. (2005) "xtabond2: Stata Module to Extend xtabond Dynamic Panel Data Estimator." Center for Global Development, Washington, DC. http://econpapers.repec.org/software/bocbocode/s435901.htm.

Roodman, David. (2007) "How to Do xtabond2: An Introduction to 'Difference' and 'System' GMM in Stata," Working Paper 103, Center for Global Development, Washington, DC. 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, 13, 287-309.

Windmeijer, Frank. (2005) "A Finite Sample Correction for the Variance of Linear Efficient Two-Step GMM Estimators." Journal of Econometrics, 126, 25-51.

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

(2.) The demographic data in this paragraph and the two following paragraphs are based on 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 in the 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 other samples.

(7.) Chamon and Prasad (2006) analyze micro-data from the same household survey we use 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 saving rate.

CHARLES YUJI HORIOKA is Professor at the Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka 567-0047, Japan (E-mail: horioka@iser.osakau.ac.jp). JUNMIN WAN is Associate Professor, Faculty of Economics, Fukuoka University, Japan (E-mail: wan@econ.fukuoka-u.ac.jp).

TABLE 1
HOUSEHOLD SAVING RATE BY PROVINCE (AVERAGES FOR THE 1995-2004 PERIOD)

                                   Saving rate (in %)

                      Urban            Rural             All
Province            households       households       households

Beijing                19.4             27.2             20.8
Tianjin                22.4             43.7             27.6
Hebei                  23.5             41.7             35.1
Shanxi                 21.7             35.5             28.8
Inner Mongolia         22.5             17.4             20.2
Liaoning               17.6             29.0             21.5
Jilin                  19.3             27.7             22.5
Heilongjiang           22.6             31.1             25.6
Shanghai               22.4             19.6             22.1
Jiangsu                23.4             31.0             27.3
Zhejiang               21.9             23.3             23.0
Anhui                  21.1             26.7             24.5
Fujian                 22.8             26.1             24.7
Jiangxi                25.7             22.6             24.0
Shandong               24.5             30.3             27.5
Henan                  22.8             31.9             28.7
Hubei                  17.0             24.9             21.0
Hunan                  18.5             10.4             13.7
Guangdong              19.4             24.8             21.6
Guangxi                19.6             20.8             20.6
Hainan                 22.7             34.5             28.9
Chongqing              10.7             24.4             17.7
Sichuan                16.6             18.2             17.5
Guizhou                19.9             20.3             20.2
Yunnan                 19.5             10.0             14.1
Tibet                  19.1             31.2             25.1
Shaanxi                15.9             10.9             13.5
Gansu                  18.2             21.0             19.6
Qinghai                17.9             16.1             17.2
Ningxia                17.0             17.8             17.4
Xinjiang               22.5             16.6             21.1

Mean                   20.3             24.7             22.4

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

TABLE 2
AGE STRUCTURE OF THE POPULATION BY PROVINCE
(AVERAGES FOR THE 1995-2004 PERIOD)

                                  Urban households

                      Young            Old            Total
                   dependency      dependency      dependency
Province              ratio           ratio           ratio

Beijing               0.167           0.123           0.290
Tianjin               0.204           0.136           0.340
Hebei                 0.264           0.092           0.357
Shanxi                0.304           0.088           0.392
Inner                 0.269           0.078           0.347
  Mongolia
Liaoning              0.207           0.119           0.325
Jilin                 0.216           0.094           0.310
Heilongjiang          0.227           0.080           0.308
Shanghai              0.175           0.185           0.360
Jiangsu               0.236           0.122           0.358
Zhejiang              0.223           0.123           0.347
Anhui                 0.300           0.107           0.407
Fujian                0.272           0.103           0.375
Jiangxi               0.303           0.099           0.402
Shandong              0.265           0.100           0.365
Henan                 0.290           0.098           0.388
Hubei                 0.266           0.092           0.358
Hunan                 0.257           0.109           0.366
Guangdong             0.315           0.098           0.412
Guangxi               0.290           0.120           0.410
Hainan                0.342           0.080           0.422
Chongqing             0.231           0.133           0.364
Sichuan               0.255           0.129           0.384
Guizhou               0.313           0.101           0.414
Yunnan                0.270           0.111           0.381
Tibet                 0.389           0.093           0.481
Shaanxi               0.280           0.107           0.387
Gansu                 0.247           0.090           0.337
Qinghai               0.265           0.077           0.342
Ningxia               0.276           0.071           0.347
Xinjiang              0.282           0.075           0.357

Mean                  0.265           0.104           0.369

                                  Rural households

                      Young            Old            Total
                   dependency      dependency      dependency
Province              ratio           ratio           ratio

Beijing               0.276           0.125           0.401
Tianjin               0.338           0.109           0.447
Hebei                 0.355           0.104           0.459
Shanxi                0.419           0.101           0.519
Inner                 0.326           0.088           0.414
  Mongolia
Liaoning              0.278           0.098           0.375
Jilin                 0.280           0.078           0.357
Heilongjiang          0.291           0.068           0.359
Shanghai              0.182           0.160           0.341
Jiangsu               0.328           0.158           0.486
Zhejiang              0.278           0.145           0.423
Anhui                 0.396           0.115           0.511
Fujian                0.427           0.119           0.546
Jiangxi               0.428           0.101           0.529
Shandong              0.321           0.130           0.450
Henan                 0.407           0.110           0.518
Hubei                 0.409           0.111           0.520
Hunan                 0.360           0.117           0.477
Guangdong             0.525           0.134           0.659
Guangxi               0.427           0.118           0.545
Hainan                0.486           0.121           0.607
Chongqing             0.356           0.127           0.483
Sichuan               0.355           0.110           0.465
Guizhou               0.479           0.092           0.572
Yunnan                0.423           0.095           0.518
Tibet                 0.497           0.083           0.580
Shaanxi               0.412           0.095           0.507
Gansu                 0.433           0.078           0.511
Qinghai               0.460           0.069           0.529
Ningxia               0.509           0.068           0.577
Xinjiang              0.494           0.074           0.568

Mean                  0.385           0.106           0.492

                                  All households

                      Young            Old            Total
                   dependency      dependency      dependency
Province              ratio           ratio           ratio

Beijing               0.188           0.123           0.311
Tianjin               0.245           0.127           0.372
Hebei                 0.333           0.101           0.435
Shanxi                0.378           0.096           0.474
Inner                 0.303           0.084           0.387
  Mongolia
Liaoning              0.239           0.109           0.349
Jilin                 0.248           0.086           0.333
Heilongjiang          0.257           0.075           0.332
Shanghai              0.176           0.181           0.357
Jiangsu               0.287           0.138           0.425
Zhejiang              0.255           0.135           0.390
Anhui                 0.369           0.112           0.481
Fujian                0.368           0.110           0.479
Jiangxi               0.388           0.100           0.488
Shandong              0.299           0.117           0.417
Henan                 0.381           0.108           0.488
Hubei                 0.353           0.103           0.456
Hunan                 0.330           0.114           0.444
Guangdong             0.429           0.116           0.545
Guangxi               0.393           0.118           0.511
Hainan                0.436           0.106           0.542
Chongqing             0.305           0.129           0.435
Sichuan               0.321           0.117           0.437
Guizhou               0.431           0.095           0.526
Yunnan                0.391           0.098           0.490
Tibet                 0.479           0.086           0.565
Shaanxi               0.371           0.099           0.470
Gansu                 0.383           0.081           0.465
Qinghai               0.394           0.072           0.466
Ningxia               0.427           0.069           0.496
Xinjiang              0.402           0.075           0.477

Mean                  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 or older to
the population aged 15-64; the total dependency ration is defined as
the ratio is defined as the ratio of the population aged 0-14 or 65
or older to the population aged 15-64.

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

TABLE 3
DESCRIPTIVE STATISTICS

Variable         Obs.    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     -0.033       0.116
INFL (urban)     272      0.016      0.032     -0.034       0.116
INFL (rural)     272      0.017      0.031     -0.037       0.116
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)     272   3844.672   2097.599   1511.344   14573.670
INCOME (urban)   272   6643.530   2341.563   3353.940   16682.820
INCOME (rural)   272   2521.854   1126.045   1100.590    7066.330
CONS (all)       272   2938.318   1591.129   1323.966   11248.800
CONS (urban)     272   5239.805   1771.079   2767.840   12631.030
CONS (rural)     272   1848.667    839.803    880.650    6328.849
RURAL RATIO      272      0.692      0.151      0.219       0.864
CPI (all)        272    101.663      3.081     96.700     111.600
CPI (urban)      272    101.645      3.168     96.600     111.600
CPI (rural)      272    101.736      3.078     96.300     111.600
CHY (all)        272      0.073      0.034     -0.037       0.191
CHY (urban)      272      0.073      0.042     -0.039       0.231
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,
1995-2005 editions.

TABLE 4
THE DETERMINANTS OF THE HOUSEHOLD SAVING RATE IN CHINA
(URBAN HOUSEHOLDS)

Dependent variable = SR

SR(- 1)                    0.628         0.624
                        (0.055) ***   (0.056) ***
CHY                        0.212         0.260
                        (0.087) **    (0.086) ***
RINT                       0.209         0.233
                          (0.294)       (0.292)
YOUNG                      0.062
                          (0.064)
OLD                        0.080
                          (0.079)
DEP                                      0.058
                                        (0.064)
INFL                       0.272         0.307
                          (0.189)       (0.194)
Constant

Number of                   272           272
  observations
Number of groups            31             31
Hansen test of             0.737         0.540
  over-identification
  (p-value)
Test for 1st-order         0.000         0.000
  serial correlation
  (p-value)
Test for2nd-order          0.121         0.131
  serial correlation
  (p-value)

Transformation used                 First differences

Instruments only for     GMM (SR(-1),        GMM (SR(-1),
  first difference         CHY RINT, INFL,     CHY, RINT, INFL,
  equation                 (2.) collapse)      (2.) collapse)

Instruments for both     YOUNG, OLD          DEP
  first difference and
  level equations

  Instruments only for                       CHGDP(-1)
  level equation

Dependent variable = SR

SR(- 1)                    0.544         0.476
                        (0.056) ***   (0.084) ***
CHY                        0.270         0.192
                         (0.148) *     (0.093) **
RINT                       0.198         -0.043
                          (0.373)       (0.340)
YOUNG                      0.002
                          (0.059)
OLD                       -0.009
                          (0.082)
DEP                                      -0.012
                                        (0.070)
INFL                       0.255         0.075
                          (0.308)       (0.227)
Constant                   0.042         0.076
                          (0.030)     (0.022) ***
Number of                   272           272
  observations
Number of groups            31             31
Hansen test of             0.978         0.522
  over-identification
  (p-value)
Test for 1st-order         0.000         0.001
  serial correlation
  (p-value)
Test for2nd-order          0.124         0.126
  serial correlation
  (p-value)

Transformation used                First differences

Instruments only for     GMM (SR(-1),        GMM (SR(-1),
  first difference         CHY, RINT,          CHY RINT
  equation                 INFL, (3,3))        INFL, (2.)
                                               collapse)

Instruments for both     YOUNG, OLD          DEP
  first difference and
  level equations

Instruments only for     CHGDP(-1)
  level equation

NOTES: Standard errors are in parentheses; *, **, *** denote
significant at the 10%, 5%, and 1 % levels, respectively.

TABLE 5
THE DETERMINANTS OF THE HOUSEHOLD SAVING RATE IN CHINA
(RURAL HOUSEHOLDS)

Dependent variable = SR

SR(-1)                              0.774                0.844
                                   (0.068) ***          (0.042) ***
CHY                                 0.495                0.536
                                   (0.107) ***          (0.149) ***
RINT                                0.591                0.593
                                   (0.163) ***          (0.185) ***
YOUNG                               0.030
                                   (0.033)
OLD                                 0.200
                                   (0.157)
DEP                                                      0.025
                                                        (0.024)
INFL                               -0.356               -0.338
                                   (0.180) *            (0.232)
Constant

Number of observations              272                  272
Number of groups                     31                   31
Hansen test of                      0.596                0.538
  over-identification
  (p-value)
Test for 1st-order serial           0.000                0.000
  correlation (p-value)
Test for 2nd-order serial           0.680                0.802
  correlation (p-value)

Transformation used                    First differences

Instruments only for          GMM(SR(-1),          GMM(SR(-1),
  first difference              CHY, RINT, INFL,     CHY, RINT, INFL,
  equation                      (2.) collapse)       (2.) collapse)

Instruments for both          YOUNG, OLD DEP
  first difference and
  level equations
Instruments only for                               CHGDP(-1)
  level equation

Dependent variable = SR

SR(-1)                              0.481                0.476
                                   (0.168) ***          (0.169) ***
CHY                                 0.332                0.331
                                   (0.181) *            (0.155) **
RINT                                0.069               -0.022
                                   (0.369)              (0.492)
YOUNG                              -0.088
                                   (0.075)
OLD                                -0.001
                                   (0.243)
DEP                                                     -0.078
                                                        (0.069)
INFL                               -0.773               -0.843
                                   (0.284) **           (0.370) **
Constant                            0.164                0.173
                                   (0.052) ***          (0.063) **
Number of observations              272                  272
Number of groups                     31                   31
Hansen test of                      0.636                0.613
  over-identification
  (p-value)
Test for 1st-order serial           0.013                0.014
  correlation (p-value)
Test for 2nd-order serial           0.496                0.506
  correlation (p-value)

Transformation used                    First differences

Instruments only for          GMM(SR(-1),          GMM(SR(-1),
  first difference              CHY, RINT, INFL,     CHY, RINT,
  equation                      (2.) collapse)       INFL, (2.)
                                                     collapse)
Instruments for both          YOUNG, OLD           DEP
  first difference and
  level equations
Instruments only for          CHGDP(-1)
  level equation

NOTES: Standard errors are in parentheses; denote significant at
the 10%, 5%, and 1% levels, respectively.

TABLE 6
THE DETERMINANTS OF THE HOUSEHOLD SAVING RATE IN CHINA
(ALL HOUSEHOLDS)

Dependent variable = SR

SR(-1)                              0.721             0.711
                                   (0.028) ***       (0.034) ***
CHY                                 0.201             0.204
                                   (0.073) ***       (0.097) **
RINT                                0.513             0.622
                                   (0.157) ***       (0.154) ***
YOUNG                               0.058
                                   (0.026) **
OLD                                 0.070
                                   (0.068)
DEP                                                   0.058
                                                     (0.025) **
INFL                                0.113             0.145
                                   (0.119)           (0.115)
Constant

Number of observations               272               272
Number of groups                     31                31
Hansen test of                      1.000             0.187
  over-identification
  (p-value)
Test for 1st-order serial           0.000             0.000
  correlation (p-value)
Test for 2nd-order serial           0.262             0.251
  correlation (p-value)

Transformation used                   First differences

Instruments only for first     GMM(SR(-1),       GMM(SR(-1),
  difference equation            CHY, RINT,        CHY, RINT,
                               INFL, (2 4))      I NFL, (2, 5)
                                                   collapse)
Instruments for both first     YOUNG, OLD        DEP
  difference and level
  equations
Instruments only for level                       CHGDP(-1)
  equation

Dependent variable = SR

SR(-1)                              0.658             0.622
                                   (0.097) ***       (0.119) ***
CHY                                 0.211             0.240
                                   (0.097) **        (0.109) **
RINT                                0.435             0.500
                                   (0.144) ***        (0.270) *
YOUNG                               0.053
                                   (0.034)
OLD                                 0.077
                                   (0.078)
DEP                                                   0.034
                                                     (0.025)
INFL                                0.048            -0.003
                                   (0.120)           (0.193)
Constant                            0.018             0.034
                                   (0.033)           (0.039)
Number of observations               272               272
Number of groups                     31                31
Hansen test of                      1.000             0.209
  over-identification
  (p-value)
Test for 1st-order serial           0.000             0.001
  correlation (p-value)
Test for 2nd-order serial           0.277             0.226
  correlation (p-value)

Transformation used                  First differences

Instruments only for first     GMM(SR(-1),       GMM(SR(-1),
  difference equation            CHY               CHY, RINT,
                               RINT, INFL,       1 NFL, (2, 5)
                                 (2, 4))           collapse)
Instruments for both first     YOUNG, OLD        DEP
  difference and level
  equations
Instruments only for level     CHGDP(-1)
  equation

NOTES: Standard errors are in parentheses; *, **, *** denote
significant at the 10%, 5%, and 1% levels, respectively.

TABLE 7
THE DETERMINANTS OF THE HOUSEHOLD SAVING RATE IN CHINA
(POOLED SAMPLE OF URBAN AND RURAL HOUSEHOLDS)

Dependent variable = SR

SR(-1)                               0.700                0.710
                                    (0.044) ***          (0.038) ***
CHY                                  0.385                0.396
                                    (0.125) ***          (0.130) ***
RINT                                 0.738                0.726
                                    (0.139) ***          (0.138) ***
YOUNG                               -0.054
                                    (0.035)
OLD                                 -0.006
                                    (0.093)
DEP                                                      -0.049
                                                         (0.025) *
INFL                                 0.203                0.195
                                    (0.125)              (0.129)
RURAL                                0.067                0.068
                                    (0.007) ***          (0.007) ***
Constant

Number of observations                544                  544
Number of groups                      62                   62
Hansen test of                       0.249                0.279
  over-identification
  (p-value)
Test for 1st-order serial            0.000                0.000
  correlation (p-value)
Test for 2nd-order serial            0.923                0.853
  correlation (p-value)

Transformation used                    First differences

Instruments only for first     GMM(SR(-1),          GMM(SR(-1),
  difference equation            CHY,                 CHY,
                               RINT, INFL,          RINT, INFL,
                                 (2, 2))              (2, 2))
Instruments for both first     YOUNG, OLD,          DEP, RURAL
  difference and level
  equations                    RURAL
Instruments only for level                          CHGDP(-1)
  equation

Dependent variable = SR

SR(-1)                               0.604                0.606
                                    (0.080) ***          (0.083) ***
CHY                                  0.337                0.338
                                    (0.119) ***          (0.115) ***
RINT                                 0.563                0.569
                                    (0.164) ***          (0.156) ***
YOUNG                               -0.091
                                    (0.045) **
OLD                                 -0.098
                                    (0.110)
DEP                                                      -0.092
                                                         (0.042) **
INFL                                 0.070                0.071
                                    (0.138)              (0.139)
RURAL                                0.070                0.070
                                    (0.009) ***          (0.008) ***
Constant                             0.053                0.052
                                    (0.028) *            (0.029) *
Number of observations                544                  544
Number of groups                      62                   62
Hansen test of                       0.339                0.336
  over-identification
  (p-value)
Test for 1st-order serial            0.000                0.000
  correlation (p-value)
Test for 2nd-order serial            0.895                0.897
  correlation (p-value)

Transformation used                     First differences

Instruments only for first     GMM(SR(-1),          GMM(SR(-1),
  difference equation            CHY,                 CHY,
                               RINT, INFL, (2,      RINT, INFL, (2,
                                 2))                  2))
Instruments for both first     YOUNG, OLD,          DEP, RURAL
  difference and level
  equations                    RURAL
Instruments only for level     CHGDP(-1)
  equation

NOTES: Standard errors are in parentheses; *, **, *** denote
significant at the 10%, 5%, and 1% levels, respectively.