Household Saving in China: The Keynesian Hypothesis, Life-Cycle Hypothesis, and Precautionary Saving Theory

HOUSEHOLD SAVING IN CHINA: THE KEYNESIANHYPOTHESIS, LIFE-CYCLE HYPOTHESIS, AND

PRECAUTIONARY SAVING THEORY

Shenglong LIU1* and Angang HU2

1Institute of Quantitative and Technical Economics, Chinese Academy of Social Sciences, Beijing,China, and 2School of Public Policy and Management, Tsinghua University, Beijing, China

First version received December 2012; final version accepted September 2013

We test the explanatory powers of Keynesian theory, the life-cycle hypothesis, and theprecautionary saving theory on household saving in China, based on data from 1990 to2009 from 31 provinces and autonomous regions. The results show that the precaution-ary saving motivation explains household saving better than Keynesian theory. Thestudy results also highlight the impact of life expectancy on China’s household saving.

Keywords: Household saving; Keynesian theory; Life-cycle hypothesis; Precautionarysaving; Life expectancyJEL classification: E21

I. INTRODUCTION

O ver the past 30 years, China has maintained nearly double-digit annualeconomic growth. With this rapid growth, aggregate saving has beensteadily increasing, and consumption relative to income has seen a corre-

sponding decrease. As China has the world’s largest population with more than 1.3billion people, its high saving rate has attracted the attention of numerous scholars.Kraay (2000) and Kuijs (2006) conclude that even if the determinants of saving arecontrolled, the saving rate in China is still extraordinarily high. According to datareleased by the State Statistical Bureau, China’s aggregate saving rate in 2008reached an extraordinary 52.3% (SSB 2010), one of the highest rates in the world.

The study was funded by a grant from the Innovation Program of CASS. Project: Economic Forecastand Policy Evaluation. It also received a grant by IQTE Research Project: The Relationship betweenChina’s Demography and Saving Ratio: Theory and Evidence and National Natural Science Foun-dation of China (Project no. 70903039). We are thankful for the highly constructive commentsprovided by two anonymous reviewers of this journal. Any errors that may remain are our own.* Corresponding author: Shenglong Liu, Institute of Quantitative and Technical Economics, Chinese

Academy of Social Sciences (CASS), Inner Avenue of Jian Guo Men, Beijing 100732, China.Tel: (86)-13439801450; Fax: (86)-010-65137561; Email: [email protected]

The Developing Economies 51, no. 4 (December 2013): 360–87

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doi: 10.1111/deve.12026© 2013 Institute of Developing Economies

Although China has always maintained a high investment rate, capital formation in2008 comprised 43.9% of GDP. However, because long-term saving exceedslong-term investment, China runs a net saving surplus, which has turned into acurrent account surplus. This surplus has been steadily expanding, from 1.9% ofGDP in 2000 to 4.7% of GDP in 2009, making China one of the highest capitalexporters in the world and exacerbating trade friction between China and othercountries. With China’s strong growth in the post-crisis era, scholars are increas-ingly concerned with China’s economic responsibility and the responsibility of acountry with large sums of money in savings. Saving has again become a popularsubject of study.

Scholars have examined China’s saving problem from different viewpoints,including the population structural factor (Kraay 2000; Modigliani and Cao2004; Horioka and Wan 2007; Ang 2008; Qi and Prime 2009; Wei and Zhang2009), economic development and income growth (Qian 1988; Modigliani andCao 2004; Ang 2009; Qi and Prime 2009), precautionary saving (Meng 2003;Blanchard and Giavazzi 2005; Giles and Yoo 2007; Chamon and Prasad 2010),the male/female imbalance (Wei and Zhang 2009), and sectoral contributions(Kuijs 2005; He and Cao 2007). Although these studies have provided differentperspectives on saving behavior, some factors, for example, life expectancy, havenot been as extensively studied because the determinants of saving behavior areextremely complicated.

In this study, we collect China’s provincial panel data from 1990 to 2009 and usethe life-cycle hypothesis model, the Keynesian theoretical model, and the precau-tionary saving model to test the determinants of household saving in China.

Before we present this research, three studies need to be mentioned: Kraay(2000), Modigliani and Cao (2004), and Horioka and Wan (2007). These studiesexamined the determinants of China’s household saving behavior based on thelife-cycle hypothesis but arrived at different conclusions. Modigliani and Cao(2004) stressed the significant impact of the dependency rate on household saving,but Kraay (2000) and Horioka and Wan (2007) do not confirm this. Modigliani andCao (2004) and Horioka and Wan (2007) indicated that income growth has apositive and significant impact on household saving, but Kraay (2000) held thatfuture income growth has a significant and negative effect on household saving.Modigliani and Cao (2004) proved that the deviation of short-term economicgrowth from long-term growth has a significant and positive effect on householdsaving, whereas Kraay (2000) showed that uncertainty regarding future income hasa mixed impact on household saving, although this result has not passed thesignificance test. Modigliani and Cao (2004) showed that inflation has a significantand positive effect on household saving, whereas Horioka and Wan (2007) provedthat inflation has a significant and negative effect on rural household saving, whilethe impact on urban household saving fails to pass the significance test. All of these

HOUSEHOLD SAVING IN CHINA 361

© 2013 Institute of Developing Economies

studies seem to show that different estimation methods, data with different types,and data from different time periods lead to different results. However, all of thesestudies ignored the impact of an important determinant: life expectancy.

This study improves upon the earlier studies in several ways: (1) the panel datais the most recent and is divided into four five-year periods, 1990–94, 1995–99,2000–2004, and 2005–9, and the five-year average is used to partly overcome somemeasurement errors; (2) different determinants are probed comprehensively usingthe Keynesian, life-cycle, and precautionary saving hypotheses; (3) the data in thestudy includes all household samples as well as urban and rural householdsamples; and (4) the study tests the impact of life expectancy on household savingin China.

The rest of the article is organized as follows. Section II reviews the relatedtheoretical models of saving determinants. Section III discusses the relationshipbetween the saving rate and the population’s age structure. Section IV introducesthe relationship between household saving and income. Section V explains thevariables and data used in the study. Section VI empirically tests each type ofsaving model and is followed by a conclusion.

II. THEORIES AND MODELS OF SAVING BEHAVIOR

A. The Life-cycle Model

The life-cycle model is based on the hypothesis that households will attempt tosmooth their expected consumption over their life cycle and maximize the utilitiesof consumption. Modigliani (1970) also pointed out that the saving rate is affectedby demographic structure, that is, the relation between working and nonworkingpopulations. This viewpoint has been supported by later research (Loayza,Schmidt-Hebbel, and Servén 1998; Modigliani and Cao 2004). Due to the imple-mentation of the family planning policy in the 1970s and a longer life expectancyresulting from economic and social development and improvements in publichealth, China’s young-age dependency ratio (the ratio of the 0–14 age grouppopulation to the 15–64 age group population) has dropped steadily over the past30 years, whereas the ratio of the working-age population (the ratio of the 15–64age group population to the total population) has risen steadily. The life-cyclemodel shows that when the working-age population vs. the dependent populationincreases, the saving rate increases.

The life-cycle model is:

SR y Pt t t t= − + +( ) ,1 β γ ε (1)

where SRt = saving rate in t period; yt = income growth in t period; Pt = populationstructure in t period; and εt is the error term.

362 the developing economies


B. The Keynesian Theoretical Model

The standard Keynesian model states that saving is entirely determined bycurrent income, that is, the saving rate increases as the per capita income increases.An approximate linear relationship is shown below:

S a sY s at t= + + < < <ε, , ,0 1 0 (2)

where St = saving level in t period; Yt = income level in t period; and s is marginalpropensity to save (MPS). As the MPS exceeds the average propensity to save(APS), the APS increases with income level. As people with low incomes may notbe able to accumulate sufficient savings in their youth to support consumption intheir old age, or at least may not have as much savings as people with highincomes, it is generally believed that the Keynesian saving model can be used toexplain saving behavior in relatively poor countries (Modigliani and Cao 2004).Although China has experienced more than 30 years of rapid development,it remains a developing country, ranking lower than 90th in the world for percapita GDP in 2009.1 Therefore, this study not only tests on the applicabilityof the life-cycle model to China but also compares the life-cycle model with theKeynesian model.

C. Precautionary Saving

Leland (1968) analyzed precautionary saving behavior and defined it as extrasaving motivated by uncertainty concerning future income. Leland’s results showthat a consumer’s expected marginal utility of consumption under uncertain con-ditions should be larger than the marginal utility of consumption under certainconditions. Therefore, as the uncertainty of future income increases, the marginalutility of expected future consumption increases, which makes saving moreattractive.

China had a planned economy before reform and its opening up, with either thestate or collectives providing extensive social security, albeit at a very low level. Inaddition, the planned economy also guaranteed that all laborers had jobs, so at thattime, Chinese households had very little uncertainty regarding income, expendi-ture, and employment. However, with the transition from a planned economy to amarket economy, reforms to the employment and welfare systems forced people toconfront greater uncertainty with regards to their income, expenditure, andemployment. Therefore, since reform and opening up, uncertainty in those areashas become an important factor in raising the household saving rate (Meng 2003;Chamon and Prasad 2010).

1 Data taken from the database of the World Bank.



III. THE SAVING RATE AND THE POPULATION’S AGE STRUCTURE

A. Aggregate Saving and Its Composition

Figure 1 presents the changing saving rates of the Chinese government, enter-prises, and households from 1992 to 2008. It shows the aggregate saving rate (thepercentage of total disposable income allocated to savings by the government,enterprises, and households) was at a relatively high and stable level before 2001,fluctuating between 36.38% and 38.43%. However, after 2001, the aggregatesaving rate began to increase, rising from 38.2% to an extraordinary 52.3%. Thehousehold saving rate slowly declined in the period 1992–2001, dropping from20.25% to 16.62%, respectively. After 2001, the household saving rate began toincrease and had risen by nearly six percentage points from 16.62% to 22.49% by2008.

Figure 1 also shows that China’s household savings have occupied a verysignificant share of the national aggregate savings, usually representing more than40% of national aggregate savings. During the global financial crisis of 2008, theChinese government realized the importance of stimulating domestic consump-tion. However, the consumption rate continued to decline, with consumptionspending as a portion of GDP dropping from 50.5% in 1990 to 35.3% by 2009. Incontrast, household saving has been steadily rising.

Fig. 1. Composition of China’s National Saving for 1992–2008

60

50

40

30

Agg

rega

te d

ispo

sl in

com

e (%

)

20

10

01992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Household

Government

Enterprises

Source: “Historical data on China’s Cash flow Chart” (1992–2004), China StatisticalYearbook, 2010.



B. The Household Saving Rate

China’s family survey only provides data regarding income and expenditure forurban and rural families, without distinguishing between cash spending and indi-vidual physical capital investment expenditures. Instead, we used the methodfound in Kraay (2000). To calculate the saving rate, we collected annual data onindividual investment by rural and urban households from the China StatisticalYearbook, and treated individual physical capital investment as a form of saving.Using this method we calculated the saving rates of urban households, ruralhouseholds, and all households of China’s 31 provinces from 1990 to 2009.2

Figure 2 shows the development trend of the urban, rural, and all-householdsaving rates from 1990 to 2009. It shows that urban household saving fluctuated

2 The urban household saving ratio is calculated by the ratio between per capita disposable incomeminus per capita consumption spending, and per capita disposable income. The rural householdsaving ratio is calculated by the ratio between rural per capita net income minus per capitaconsumption spending, and per capital net income. The saving ratio of all households is calculatedby the weighted average mean value of the urban and rural saving ratios with the ratio of theirrespective populations.

Fig. 2. Household Saving Rates in China for 1990–2009

0

5

10

15

20

25

30

35(%)

199219911990 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Urban

All

Rural

Source: Calculated by the authors based on data available in China Statistical Yearbook,1991–2010.



between 13.7% and 28.8%; rural household saving fluctuated between 7.5% and28.6%; and all-household saving fluctuated between 9.2% and 26.0%.

In addition, the urban saving rate rose throughout the entire sample period,whereas rural and all-household saving rates rapidly increased after 1999, slowlydeclined between 1999 to 2004, and rose gradually again until 2004.

Table 1 presents the average value of the saving rates of urban, rural, andall households in all provinces in 1990–2009. We can see that the deviation of

TABLE 1

Household Saving Rate for All Provinces (1990–2009 average value)

ProvinceSaving Rate (%)

Urban Rural All Household

Beijing 20.53 27.99 22.34Tianjin 21.79 42.65 30.30Hebei 23.12 34.46 31.31Shanxi 23.33 29.30 27.36Inner Mongolia 21.12 15.63 17.91Liaoning 17.40 25.90 21.81Jilin 19.18 26.38 21.17Heilongjiang 21.31 27.11 24.10Shanghai 22.00 19.19 21.80Jiangsu 23.46 26.04 25.19Zhejiang 22.95 21.39 22.91Anhui 20.83 19.80 19.87Fujian 22.92 22.25 22.67Jiangxi 24.97 21.66 22.74Shandong 25.09 27.51 26.99Henan 22.59 28.34 27.17Hubei 18.85 20.17 19.48Hunan 20.61 9.72 12.76Guangdong 19.08 22.34 20.83Guangxi 20.67 18.00 18.26Hainan 22.51 30.86 28.27Chongqing 14.24 25.42 19.76Sichuan 17.25 15.59 15.92Guizhou 21.27 17.26 19.06Yunnan 21.15 8.97 13.44Tibet 22.58 30.65 28.14Shaanxi 17.47 9.17 11.89Gansu 18.29 15.88 16.65Qinghai 18.12 12.98 15.09Ningxia 18.44 16.17 17.00Xinjiang 22.45 18.43 20.56Average 20.82 22.17 21.38

Sources: Calculated by the author based on data available in the China Statistical Yearbook1991–2010 and China Population Statistical Yearbook 1991–2010. Data of Chongqing is takenfrom the average of 1997–2009.



saving rates among different provinces is enormous. The urban saving rateranged from 14.24% (Chongqing) to 25.09% (Shandong), the rural saving rateranged from 8.97% (Yunnan) to 42.65% (Tianjin), and the all-household savingrate ranged from 11.89% (Shaanxi) to 31.31% (Hebei). In general, the averagesaving rate was 22.1% in rural areas and 20.82% in urban areas. This reflects ahigher level of uncertainty about income for rural households and a higher levelof vulnerability with regards to medical and old-age insurance compared withurban households.

C. Household Saving Rates and Population Age Structure

Figure 3 shows changes in the dependency ratios of urban and rural areas and allhouseholds in 1990–2009. We can see that the population dependency ratio gen-erally goes down. The main reason for this decline is the implementation of thefamily planning policy starting in 1970. This policy caused a decrease in both the

Fig. 3. Population Dependency Ratio for 1990–2009

0.00

10.00

20.00

30.00

40.00

50.00

60.00

19921990 1994 1996 1998 2000 2002 2004 2006 2008

Urban

All

Rural

Source: Calculated by the authors based on the data available in China Population StatisticalYearbook, 1991–2010.Note: The population dependency ratio is defined as the child dependency ratio (ratio ofpopulation aged 0–14 to the population aged 15–64) plus the old-age dependency ratio (ratio ofpeople aged 65 or above to the population aged 15–64). The figure for 1990 is only theall-household dependency ratio.



fertility rate and the child dependency rate. The total fertility rate dropped fromabout 6.0 in the 1960s to about 2.1 at the beginning of the 1990s. Because of thislower fertility rate, the child dependency ratio dropped from 41.49% in 1990 to25.34% by 2009. Meanwhile, the old-age dependency ratio rose steadily throughthe same period from 8.35% to 11.64%. This indicates that China’s population isgradually aging, which is a serious problem that China must address. Since thechild dependency ratio declined far faster than the increase in the old-age depen-dency ratio during the sample period, the trend of total dependency ratio is similarto the trend of the child dependency ratio.

Figure 3 shows that the rural dependency ratio is much higher than the urbandependency ratio. The former has a mean of 48.32% in the sample period while thelatter has a mean of 36.49%. This is a consequence of disparity in the implemen-tation of the population policy between urban and rural areas. The one-child policyis applied strictly in urban areas. However, in rural areas, couples are allowed tohave a second child if their first child is a daughter. This policy is known as the1.5-child policy, and it explains why the rural child dependency ratio is higher thanthe urban one. Urbanization has caused a serious labor drain in rural areas, leavingthe aged at home. This has resulted in a higher old-age dependency ratio in ruralareas and means that China’s rural areas will face a more serious aging problemthan urban areas in the future.3

Table 2 shows that, generally, there are negative correlations between the childdependency ratio/the total dependency ratio and household saving ratio, and thereis basically a positive correlation between old-age dependency ratio and householdsaving ratio. The correlation coefficient of dependency ratio and household savingratio in the time series is higher than that in the cross section. In addition, there isa significant difference in the correlation between rural/urban saving ratios and thetotal dependency ratio. In the time series, the correlation coefficient (absolutevalue) between urban household saving ratio and the dependency ratio is muchhigher than that in the rural areas. In the cross-section, the child dependency ratiois positively correlated with the urban household saving ratio but is negativelycorrelated with the rural household saving ratio; the reverse is true with the old-agedependency ratio. The total dependency ratio is negatively correlated with house-hold saving ratios, indicating that the drop in total dependency is likely one of thereasons for the rise in household saving ratio, just as the life-cycle hypothesispredicts.

3 Recent population statistics mainly indicate the population residing in an area at the end of theyear, rather than the population holding residence permits. In this study statistics pertaining to therural population also indicates the former category. This helps to show that the aging of the ruralpopulation has become an increasingly serious problem.



IV. HOUSEHOLD SAVING AND INCOME

According to Keynesian theory, household saving is solely determined by income.However, according to the life-cycle hypothesis, household saving is determinedby income growth. According to the precautionary saving theory, household savingis determined by uncertainty of income, that is, the higher the level of incomeuncertainty, the higher the saving rate.

Table 3 shows the correlation coefficients between household saving ratio andincome, income growth, and income uncertainty in the cross-sectional and timeseries.4 This table shows that the household saving ratio has a positive correlation withper capita income, income growth, and income uncertainty, both in the cross-sectionalevidence and the time series data, although the correlation coefficient of the cross-sectional evidence is lower than that of the time series evidence. All of these show thatthe Keynesian theory, the life-cycle hypothesis, and the precautionary saving theorycan be used to explain the household saving ratio in China.

In line with Kraay’s (2000) conclusion, the urban household saving ratio ispositively correlated with per capita income in the cross-provincial data, but itscorrelation coefficient is smaller than that in the time series. This means that whenhousehold income is above subsistence level, the average saving rate will rise.Following Qian (1988), we express permanent income as the moving average ofthe past five years and find that the correlation coefficient between the average

4 Following Kraay (2000), we first use the autoregression of order one (AR (1)) to calculate thecertainty trend of income, and then gauge income uncertainty using the deviation between realincome and the certainty trend.

TABLE 2

Correlation between Cross-provincial Household Saving Ratio and the Population’s Age Structure

Correlation Child DependencyRatio

Old-age DependencyRatio

Total DependencyRatio

Cross-sectional:Urban saving ratio 0.22 −0.11 0.20Rural saving ratio −0.24 0.13 −0.22All saving ratio −0.17 0.17 −0.13

Time-series:Urban saving ratio −0.92 0.85 −0.85Rural saving ratio −0.53 0.43 −0.56All saving ratio −0.83 0.79 −0.83

Source: Calculated by the authors.Note: The results are calculated using the panel data of 31 provinces, municipalities, andautonomous regions. The cross-sectional (time series) correlation coefficient is calculated basedon the average value of the calculation of all variables.



income of the past five years and the household saving rate in either cross-sectionaldata or time-series data is not lower than the correlation coefficient between currentper capita income and the household saving rate. This finding partly substantiatesthe hypothesis of permanent income.

V. ESTIMATED MODEL, VARIABLES, AND DATA

This section presents the data and sources to be used in our empirical modeland analysis. The variable to be explained is the household saving ratio (SR),which is calculated by the ratio of household saving to disposable income(family net income is used for rural areas). Following the methods of Loayza,Schmidt-Hebbel, and Servén (2000), and Horioka and Wan (2007), a simplifiedlinear equation model is used to test the determinants of China’s household savingrate. The saving models to be tested are the Keynesian model, the life-cycle model,and the precautionary saving model. The explanatory variables used in the studyare collected from these three models.

A. Variables

The standard Keynesian theory holds that household saving is determined only bycurrent income; therefore, there is only one related explanatory variable: per capitaincome (Rinc). We use real disposable income, with the benchmark year 2000 =100.5 The permanent income hypothesis holds that the saving rate is determined not

5 Rural per capita income is measured by the rural per capita real net income.The per capita real disposableincome of all-households uses the weighted average mean value of urban per capita real disposableincome and the rural per capital real net income with the ratio of their respective populations.

TABLE 3

Relation between Household Saving Rate and Income, Income Growth, and Income Uncertainty

Correlation Income PerCapita

Past 5-yearAverage Income

IncomeUncertainty

Growth of IncomePer Capita

Cross-sectional:Urban saving ratio 0.12 0.12 0.18 0.17Rural saving ratio 0.32 0.32 0.40 0.43All saving ratio 0.22 0.24 0.21 0.33

Time-series:Urban saving ratio 0.93 0.93 0.88 0.77Rural saving ratio 0.78 0.80 0.22 −0.10All saving ratio 0.74 0.79 0.55 0.70

Source: Calculated by the author based on the statistical yearbooks of various provinces,municipalities, and autonomous regions for 1991–2010.



only by current income, but also by past income level. Therefore, this study uses thepast five-year average (5-Averinc) to test the permanent income hypothesis.

The life-cycle hypothesis predicts that household saving is determined byincome growth and the population’s age structure. This study has collected thefollowing variables to test the life-cycle theory.• Young-age dependency ratio (Young): the ratio of population aged 0–14 to

population aged 15–64.• Old-age dependency ratio (Old): the ratio of population aged 65 and above to the

population aged 15–64.• Total dependency ratio (Dep): the summation of the young-age dependency ratio

and the old-age dependency ratio.• Income growth (Incgrow): five-year average real income growth (2000 = 100).

To test the explanatory power of the precautionary saving theory, we alsointroduce income uncertainty as the explanatory variable (Uncertain). FollowingKraay’s (2000) method, we first use AR (1) to calculate the determined incometrend and then use the difference between real income and the determined incometrend to measure income uncertainty.

According to the latest studies (Horioka and Wan 2007; Li, Zhang, and Zhang2007), we have also tested the impact of the following variables on householdsaving in China.• Real interest rate (Realint): the interest rate of one-year fixed deposit minus

inflation.6 When the substitution effect is higher than the income effect, the realinterest rate has a positive impact on saving.

• Inflation rate (Infl): changes in the consumer price index. The impact of inflationon household saving is also uncertain. Bailey (1956) argued that high inflation islikely to lower household purchasing power, resulting in less saving. However,Mundell (1963) supports the “inflation tax” hypothesis. He argued that a highinflation rate will have a negative impact on real monetary equilibrium. Anindividual will save more to restore his real wealth. The latest studies tend to usea high inflation rate as the proxy variable for price uncertainty and the macro-economy (Loayza, Schmidt-Hebbel, and Servén 2000; Schrooten and Stephan2005; Horioka and Wan 2007). When prices and the macro economy are notstable, individuals will be more uncertain about future income and increase theirsaving as a result.

• Population average life expectancy (Lifexp). On the one hand, when life expec-tancy increases, an individual will save enough for consumption in his/herold age. On the other hand, a longer average life expectancy means that the

6 Inflation rates are obtained from the province-specific statistical yearbooks of all years. Althoughthe interest rate for a one-year fixed saving deposit is the same for all provinces, the inflation-adjusted real interest rate is then different for different provinces.



population will be older, and the increase in the old-age dependency ratio willlead to a lower saving rate. Thus, the increase in average life expectancy appearsto have a mixed influence on household saving.

• Proportion of food consumption expenditure to total consumption expenditure(Food). Following Kraay (2000), this variable is added. Only when a person’sincome level is above the basic subsistence level will saving increase. We thusmeasure basic consumption expenditure by looking at the proportion of expen-diture on food compared to total consumption expenditure. We believe that thegreater the amount spent on food, the lower the saving rate.

B. Data Processing

We collected data for 1990–2009 from 31 provinces, municipalities, and autono-mous regions, then performed empirical tests on the panel data model. Althoughwe took family investment information into consideration in calculating the savingrate, as did Kraay (2000), we still found it very difficult to separate householdinvestment and private investment, which caused serious errors in the calculationof household saving. In addition, there are errors in the calculation of otherexplanatory variables, such as per capita disposable income and inflation. Weadopted Kraay’s (2000) method for overcoming errors by using the mean value ofthese variables in a fairly long time series. We divided the 1990–2009 data into fourfive-year periods. For instance, in the period of 1990–94, we took the mean valueof the five years for the explained variables. In the explanatory variables, weselected the 1990 initial values for per capita real income and life expectancy, andthe annual average growth rate of the real income growth. For other variables,we took the five-year mean value. Apart from life expectancy, which appears inthe all-household saving panel, the other variables are introduced into the paneldata for urban, rural, and all-households.7 For the descriptive statistics of thesevariables, see Table 4.

In this study, the variables associated with population were taken from the ChinaPopulation Statistical Yearbook, and the variables associated with the macro-economic operation were taken from the China Statistical Yearbook. The datafor the interest rates of the one-year fixed saving deposits were obtained fromthe Monetary Policy Department of the People’s Bank of China.

7 To date, we have collected only data on province-specific life expectancy for 1990, 2000, and 2010from official statistical yearbooks. Based on average life expectancy data for specific provinces for1990, 2000, and 2010, we have made a rough estimation of the province-specific life expectanciesfor 1995 and 2005. The method of estimation is to take the mean value of 1990 and 2000 as the1995 life expectancy, and the mean value of 2000 and 2010 as that of 2005. Because China’s percapita average life expectancy has been increasing steadily since 1990, such a method of estimationcan account for the changes in average life expectancy for each province.



TABLE 4

Descriptive Statistics of VariablesA. Urban household

Variable Obs Mean Std. Dev. Min Max

SR (%) 120 20.93 5.08 9.22 33.62Rinc (100 yuan RMB) 121 60.50 30.29 22.59 179.55Realint (%) 122 0.01 1.85 −5.08 4.27Infl (%) 122 5.20 4.27 0.38 14.22Incgrow (%) 121 8.06 2.50 0.53 14.00Uncertainty (100 yuan RMB) 120 2.69 7.21 −15.02 18.41Food (%) 119 43.51 7.22 31.10 61.21Young (%) 121 25.83 6.07 10.11 42.92Old (%) 121 10.55 2.57 4.97 19.03Dep (%) 121 36.38 5.16 25.25 53.665-Averinc (100 yuan RMB) 89 60.21 24.56 29.74 150.06

B. Rural household


SR (%) 122 22.14 9.34 4.06 48.78Rinc (100 yuan RMB) 122 22.64 12.78 9.05 79.43Realint (%) 122 0.14 1.60 −5.08 3.73Infl (%) 117 5.12 2.63 −0.05 11.12Incgrow (%) 122 5.77 3.17 −3.77 12.84Uncertainty (100 yuan RMB) 122 0.73 2.45 −7.15 9.07Food (%) 119 51.78 9.27 32.86 70.75Young (%) 122 37.45 10.57 12.18 58.75Old (%) 122 10.97 2.90 6.00 19.96Dep (%) 122 48.42 9.45 27.93 68.715-Averinc (100 yuan RMB) 91 22.52 11.49 9.28 66.99

C. All household


SR (%) 121 21.41 6.76 7.14 37.32Rinc (100 yuan RMB) 121 38.44 25.76 11.61 164.00Realint (%) 122 0.05 1.73 −5.08 4.15Infl (%) 122 5.16 4.05 0.46 14.22Incgrow (%) 120 8.13 3.22 −4.12 19.35Uncertain (100 yuan RMB) 119 1.83 5.82 −14.68 18.64Food (%) 118 48.97 8.56 32.43 67.96Young (%) 121 33.70 9.97 10.35 55.15Old (%) 122 10.67 2.57 5.63 18.81Dep (%) 121 44.37 8.59 25.67 63.885-Averinc (100 yuan RMB) 88 37.98 21.72 14.20 132.99Lifexp (years) 122 70.39 3.69 59.60 80.12

Note: We were unable to obtain urban-rural-specific average life expectancy data from official statistics. Wehave only collected the province-specific data for 1990, 2000 and 2010. Thus, we can only test the impactof life expectancy on the all-household saving rate.



VI. ESTIMATION RESULTS

Tables 5, 6, and 7 present the empirical results of the determinants of urban, rural,and all-household saving.8 Like Modigliani and Cao (2004), our variablesexplained in Model (1) include only per capita income level, as standard Keynesiantheory takes income as the only factor that determines the saving rate. Our empiri-cal results show that income indeed has a positive impact on the household savingrate. However, this positive impact fails to pass the significance test in the ruralhousehold saving panel model.

Based on Qian (1988), we used the mean income level of the past five yearsas an explanatory variable of the household saving rate and used it to test thepermanent income hypothesis. The empirical results show that the five-yearmean income can only explain the saving rate of urban households and fails topass the significance test in both the rural and all-household saving panelmodels.

Based on Horioka and Wan (2007), our explanatory variables in Models (3)and (4) included income growth, young-age dependency ratio, old-age depen-dency ratio, and the total dependency ratio to test the power of life-cycle theoriesin explaining the household saving rate. The empirical results show that theimpact of income growth on household saving is not significant. It is even nega-tive in both the urban and rural panel models, contrary to life-cycle theorypredictions.9 The impact of the total dependency ratio on the saving rate is oppo-site in urban and rural household data, and significant and negative in theall-household saving rate. The impact of the young-age dependency ratio is

8 We have introduced the period’s dummy variable into all the regression models. The developmenttrends of household saving rates are not identical before and after 2000 (see Figure 2), with thesaving growth flat before 2000 for urban households and picking up speed after 2000. However, theopposite is true with rural households, with saving before 2000 increasing rapidly but subsequentlydecreasing. The all-households saving rate increased significantly before 2000 and tended tobecome flat after 2000. Thus, there is only one dummy variable in these regression models, that is,0 for 1990–94 and 1995–2000 and 1 for 2000–2004 and 2005–9.

9 Both Modgliani and Cao (2000), using China’s 1953–2000 time series data, and Ang (2009), usingChina’s 1963–2005 time series data, found that income growth has a significant positive impact onChina’s household saving rate. Horioka and Wan (2007) used China’s 1995–2004 provincial paneldata and arrived at the same conclusion. Both conclusions are in line with the life-cycle theory.However, Kraay (2000) used China’s 1978–1995 provincial panel data model and found thatincome growth has a significant and negative impact on the rural household saving rate. Kraay(2000) gives full consideration to errors in measuring, dividing the data into three six-year periodsand taking the mean value of the time series. He used the instrument variable in the estimation.However, Horioka and Wan (2007) did not take the error problem into consideration and used theSystem GMM Estimation. It seems that the impact of income growth varies with differences in thetypes of data collected, the data processing methods, and the means of estimation.



significant and negative on urban saving and all-household saving, in accordancewith the life-cycle theory. However, in the rural saving panel model, the impactof the young-age dependency ratio on rural saving fails to pass the significancetest. The opposite impact can be seen for the old-age and young-age dependency

TABLE 5

Determinants of Urban Household Saving

(1) (2) (3) (4) (5) (6) (7)FE FE FE FE FE 2SLS 2SLS

Incgrow −0.20 −0.23 −0.12 −0.20 −0.39(1.40) (1.52) (0.68) (0.89) (1.86)*

Rinc 0.12(7.99)***

5-Averinc 0.14(7.70)***

Young −0.42(2.92)***

Old 0.46(1.98)*

Dep −0.20 −0.18 0.13 0.06(1.49) (1.33) (0.76) (0.38)

Uncertain 1.02 0.55(4.21)*** (1.99)**

Infl 0.13(0.13)

Realint 0.45(0.31)

Food −0.46(2.08)**

Obs. 120 89 119 119 119 87 86Provinces 31 31 31 31 31 31 31R-squared 0.76 0.76 0.66 0.62 0.62Cragg-Donald

Wald F statistic31.85 8.67

Ovrid test 0.03 0.12

Note: 5-Averinc is the per capita real income level over the past five years. The instrumentvariables of future income growth and income uncertainty include per capita income, incomeuncertainty of the period, the share of the working age population measured by using theshare of population aged 15–64 in total population and the impact of Western Develop-ment Program measured by Dwest x D2000. In Model (2), 5-Averinc is the 5-year laggedaverage income, so the time scale in the regression does not include 2005–09; in models(6)–(7), Incgrow is the predicted future income growth and income uncertainty is the pre-dicted future income uncertainty, so the time scale entering into regression does not include1990–94. All of the regression models include period’s dummy variable. Figures in bracketsare the absolute value of the t-statistic.*, ** and *** indicate significance at 10%, 5% and 1% levels.



ratio, which have a significant and positive impact on urban and all-householdsaving but an insignificant and negative impact on rural saving. These resultsregarding the impact of old-age dependency on the household saving rate wereagainst our expectations, but are identical to the empirical results of Horioka and

TABLE 6

Determinants of Rural Household Saving


Incgrow −0.02 −0.08 −0.60 −2.55 −1.67(0.11) (0.42) (2.68)*** (4.36)*** (3.02)***

Rinc 0.09(0.83)

5-Averinc −0.21(1.62)

Young 0.20(1.15)

Old −0.25(0.54)

Dep 0.15 −0.02 0.20 0.27(0.91) (0.13) (0.57) (0.95)

Uncertain 3.55 1.41(2.10)** (1.17)

Infl −0.23(1.23)

Realint 1.64(4.31)***

Food −0.48(2.41)**




Note: 5-Averinc is the per capita real income level over the past five years. The instrumentvariables of future income growth and income uncertainty include per capita income, incomeuncertainty of the period, the share of the working age population measured using theshare of population aged 15–64 in total population and the impact of Western Develop-ment Program measured by Dwest x D2000. In Model (2), 5-Averinc is the 5-year laggedaverage income, so the time scale in the regression does not include 2005–2009; in models(6)–(7), Incgrow is the predicted future income growth and income uncertainty is the pre-dicted future income uncertainty, so the time scale entering into regression does not include1990–94. All of the regression models include period’s dummy variables. Figures in bracketsare the absolute value of the t-statistic.*, ** and *** indicate significance at 10%, 5% and 1% levels.



Wan (2007).10 The effects of population structure variables on China’s householdsaving rates are not stable, which supports the study results of Deaton (1992) andHiggins (1998).11 This means that the life-cycle theory explanation of the steadyincrease in China’s household saving rate needs further study and testing.

Following Horioka and Wan (2007) and Ang (2009), we continued by introduc-ing inflation rate and real interest rate variables into Model (5) on the basis ofModel (4). The results show that the inflation rate has a negative impact on theall-household saving rate, but its impact on urban and rural saving rates fails topass the significance test.12 The real interest rate has a positive impact on thehousehold saving rate, but this positive impact fails to pass the significance test inthe rural and all-household panel models. This shows that the substitution effect ofthe real interest rate is higher than the income effect for households in China.

In a standard, forward-looking consumption and saving model, Carroll and Weil(1994) and Carroll (1994) note that expected higher future income growth wouldlower current saving or increase consumption, whereas higher future incomeuncertainty would lower current consumption or increase saving.

As in Kraay’s study (2000), the explanatory variables in Model (6) includeexpected future income growth, dependency ratio, and income uncertainty. Toestimate the impact of expected future income growth and income uncertainty onthe current household saving behavior, we set Model (6) as the following:

SR E Incgrow E Uncertain Depit t it t it it it= + + + ++ +β β β β ε0 1 1 2 1 3[ ] [ ] , (3)

where, Et[Incgrowit+1] is the expected per capita income growth of province ibetween period t and t +1 based on the information available at time t.Et[Uncertainit+1] is the expected future income uncertainty of province i betweenperiod t and t +1. The precautionary saving theory holds that the higher theuncertainty of future income, the higher the saving. Following Kraay’s method(2000), we first used AR (1) to calculate the determined income trend, then usedthe difference between real income and the determined income trend to measurethe uncertainty of income. εit is the residual term; t = 1, 2, 3, 4 represents the fourfive-year time series of the samples obtained in this study. β1 reflects the impact of

10 Chamon and Prasad (2006) used the same household survey data to verify the impact of populationstructure on microscopic household saving and found that China’s household saving rate increaseswith an increase in age. This partially explains why the impact of old-age dependency on savingdid not achieve the expected results.

11 Deaton (1992, p. 51) clearly points out: “Although the existing studies have found that populationstructure has an impact on saving, the conclusions are often unstable.”

12 Similar to the results of this study, Horioka and Wan (2007) found that in most circumstances, theimpact of the inflation rate fails to pass the significance test. However, Modigliani and Cao (2004)and Ang (2009) show that inflation rate has a significant and positive impact on China’s householdsaving rate.



expected future income on household saving. In forward-looking saving behavior,the estimated value of β1 should be negative. β2 reflects the impact of anticipatedincome uncertainty on household saving, and according to the precautionarysaving theory, the estimated value of β2 should be positive.

According to Kraay (2000), “the assumption of rational expectations suggeststhat a good proxy for expected future income growth is actual future incomegrowth, since rational expectations imply that expected income growth is equal toactual income growth plus an error term” (p. 557). In addition, we use actualuncertainty of future income as a proxy for expected uncertainly of future income.As these proxy variables have measurement errors and are correlated with thesaving rate, we need to find proper instrumental variables (IVs) for future incomegrowth and uncertainty of future income. As all variables are exogenous exceptfuture income growth and uncertainty of future income, the model is over-identified and needs to use two-stage least square (2SLS) methods to estimate itsparameters.

Based on Kraay’s (2000) method, we adopted 2SLS to estimate the param-eters of Model (6). The IVs of future income growth include initial per capitaincome level (Rinc), percentage of working age population (Workage), incomeuncertainty (uncertainty), and the dummy variables of the provinces covered bythe Western Development Program and the interactive terms of time dummyvariables of the Western Development Program (Dwest x D2000).13 Initial percapita income level may be used to capture the convergence effect across prov-inces, and in the first stage, regression is significant and negative. Many previousstudies on China’s economic growth arrive at similar conclusions that initial percapita income has a negative impact on income growth.14 The percentage ofworking age population is significant and positive in the first-stage regression,reflecting, to a certain extent, the positive impact of labor supply on incomegrowth. The income uncertainty variable is used to capture the impact of incomefluctuations on economic growth. Dwest x D2000 is primarily used to capturethe impact on income of China’s implementation of the Western DevelopmentProgram in 2000.

To simplify the problem/model, we selected the same IVs for future incomeuncertainty and income growth. Although the regression results in the first stageshow that initial per capita income level and share of the working age population

13 China started a Western Development Program in 12 provinces, municipalities, and autonomousregions in 2000. These 12 provinces are Yunnan, Sichuan, Guizhou, Shaanxi, Gansu, NingxiaXinjiang, Qinghai, Tibet, Chongqing, Inner Mongolia, and Guangxi. When the province is amongthe Western Development Program: Dwest = 1; When t = 3 or t = 4, D2000 = 1.

14 See Chen and Fleisher (1996) and Démurger (2001). They use provincial per capita GDP insteadof per capita income, with the result that when some factors affecting economic growth arecontrolled, the initial per capita GDP would have a negative impact on economic growth.



do not have much impact on future income uncertainty, the impact of currentincome uncertainty and the Western Development Program on future incomeuncertainty has passed the significance test, which indicates that instrument vari-ables have a certain capacity to predict future income uncertainty.

The last two lines of Tables 5, 6, and 7 provide diagnostic test values for theeffectiveness of the IVs. One is the Cragg-Donald Wald F-statistic, which is usedto measure weakness of IVs. We can see that, with the exception of the all-household saving panel data, in the urban-and-rural-specific household savingpanel data, IVs are not weak instruments. The other is the Sargan test statistic,which is used to test over-identification of IVs. Except for Model (6) in Table 5, theaccompanying probabilities of the over-identification test value in the other modelsare larger than 0.10. The diagnostic test result of the effectiveness of IVs shows thatin most cases, these instrument variables can pass the effectiveness test in predict-ing future income growth and future income uncertainty.

We can also see that the impact of future income growth on China’s householdsaving is negative and can pass the significance test. The impact of incomeuncertainty on household saving is significantly positive and can pass the signifi-cance test in most cases. This result is consistent with Carroll’s (1994) forward-looking hypothesis.

Model (7) introduces the variable of share of food consumption in total con-sumption on the basis of Model (6). According to some studies, when income isat or near subsistence level, a household’s average saving rate would be lowerthan the average saving rate of the whole society (Gersovitz 1983; Ogaki, Ostry,and Reinhart 1995; Atkeson and Ogaki 1996). Therefore, we expect that thisvariable would have a negative impact on household saving. Our study showsthat share of food consumption has a significant negative impact on householdsaving, indicating that when income is at subsistence level, the propensity tosave would be very low. This result is in accordance with Kraay’s hypothesis(2000).

In the urban and rural household saving panel models (6)–(7), it is surprising tosee that the population dependency ratio has a positive impact on China’s house-hold saving. This contradicts much of the evidence from cross-country empiricalstudies of current studies. In the cross-country empirical evidence, a high popula-tion dependency ratio generally leads to a low saving rate. Kraay (2000) reached asimilar result to our findings. He explained that high values for the dependencyratio reflect a large young-age population, and households have strong bequestmotives. Although the young-age dependency ratio in China has been rapidlydeclining since 1990, the old-age dependency ratio has been on the rise, so thebequest or gifting motivation can still partly explain why a higher dependency ratiowill lead to a higher saving rate. This is because parents save mainly to reduce theirchildren’s future burden of buying property. Another reason is that the wages of the



TABLE 7

Determinants of All-Household Saving


Incgrow 0.02 0.08 0.13 −1.78 −0.96(0.16) (0.67) (1.33) (1.69)* (1.42)

Rinc 0.05(1.97)*

5-Averinc 0.02(0.68)

Young −0.19(1.73)*

Old 0.61(1.72)*

Dep −0.21 −0.07 −0.88 −0.40(1.81)* (0.80) (2.39)** (1.43)

Uncertain 1.00 0.27(2.08)** (0.76)

Infl −0.49(2.24)**

Realint 0.61(1.93)*

Food −0.56(3.26)***




Note: 5-Averinc is the per capita real income level over the past five years. The instrumentvariables of future income growth and income uncertainty include per capita income, incomeuncertainty of the period, the share of the working age population measured by using theshare of population aged 15–64 in total population and the impact of Western Develop-ment Program measured by Dwest x D2000. In Model (2), 5-Averinc is the 5-year laggedaverage income, so the time scale in the regression does not include 2005–2009; in models(6)–(7), Incgrow is the predicted future income growth and income uncertainty is the pre-dicted future income uncertainty, so the time scale entering into regression does not include1990–94. All of the regression models include period’s dummy variables. Figures in bracketsare the absolute value of the t-statistic.*, ** and *** indicate significance at 10%, 5% and 1% levels.



young are quite low while compensation or pensions provided for older people arehigher and this therefore leads to a higher saving rate.15

VII. LIFE EXPECTANCY AND HOUSEHOLD SAVING

This section tests the impact of average life expectancy on all-household saving(see Table 8). As the old-age dependency and total dependency ratios have notachieved the sign expected by the life cycle theory, the empirical results inTables 5, 6, and 7 have not shown that population structure has any steady impacton China’s household saving. One possible reason is that average life expectancyis not included in these regressions. According to the expanded life cycle theoreti-cal model, the extension of life expectancy will lead to a higher saving rate (Zhang

15 We accept the suggestion from one reviewer that the gifting motivation and the income gap betweenyoung and old can explain why a higher dependency ratio leads to a higher saving rate in China.

TABLE 8

Life Expectancy and China’s Household Saving

Explained Variables: All-Household Saving

(1) (2) (3) (4) (5) (6)FE FE FE FE 2SLS 2SLS

Lifexp 1.07 1.37 1.51 1.39 1.09 0.54(2.84)*** (3.54)*** (3.74)*** (3.37)*** (1.50) (0.87)

Incgrow 0.02 0.02(0.17) (0.19)

Dep −0.02 0.08 −0.02 −0.53 −0.25(0.15) (0.66) (0.17) (1.79)* (1.16)

Realinc −0.00 −0.36 −0.20(0.11) (1.91)* (1.62)

Uncertain 2.07 1.06(2.39)** (1.91)*

Food −0.45(2.86)***

Observations 121 120 120 119 84 83Provinces 31 31 31 31 31 31R-squared 0.49 0.59 0.53 0.59Cragg-Donald



Note: Uncertainty in Models (5) and (6) is the expected future income uncertainty, so the timescale entering the regression does not include 1990–94. All of the regression models includeperiod’s dummy variables. Figures in brackets are the absolute value of the t-statistic.*, ** and *** indicate significance at 10%, 5% and 1% levels.



and Zhang 2005). To accurately estimate the impact of dependency ratios onhousehold saving, it is necessary to control average life expectancy (Li, Zhang, andZhang 2007). Much cross-country panel data has proven that increased life expec-tancy has a positive stimulating effect on saving (Bloom, Canning, and Graham2003; Li, Zhang, and Zhang 2007). However, until now, literature on the impact ofaverage life expectancy on China’s household saving has been scarce, possiblybecause of the difficulty in obtaining life expectancy data.

Following Bloom, Canning, and Graham (2003), Model (1) of Table 8 containsthe variables of average life expectancy and per capita real income growth. Itshows that average life expectancy has a significant and positive effect on China’shousehold saving, identical with the empirical results of most cross-country paneldata. Although income growth has a positive effect on China’s household saving,it fails to pass the significance test.

Model (2) introduces the variable of the total dependency ratio into Model (1).We find that the average life expectancy still has a significant and positive effect onChina’s household saving. In addition, compared with Equation (1), the coefficientof average life expectancy significantly increases. The impact of real incomegrowth on household saving still fails to pass the significance test, and the afore-mentioned coefficient remains unchanged. The total dependency ratio has a nega-tive impact on China’s household saving, but it fails to pass the significance test.

Following Li, Zhang, and Zhang (2007), Model (3) contains the variables ofaverage life expectancy and total population dependency. It shows that the coeffi-cient of average life expectancy remains positive, while the coefficient of thedependency ratio still fails to pass the significance test, and the sign is contrary towhat the life-cycle theory expects.

Model (4) introduces another saving determinant factor, initial per capita realincome, into Model (3). After the impact of initial per capita real income onhousehold saving is controlled, the average life expectancy remains significant andpositive in its impact on household saving, whereas the dependency ratio remainsunchanged as compared with Model (3). The impact of initial per capita income onhousehold saving fails to pass the significance test.

Model (5) introduces the variable of future income uncertainty to test theexplanation of China’s household saving behavior by the precautionary savingtheory. We still use the 2SLS method to estimate the parameters of Model (5), witha difference in the IVs of predicted income uncertainty, which include only currentincome uncertainty and the dummy variable of controlling the Western Develop-ment Program policy. The two bottom lines of Table 8 present the effectiveness-testing result of the IVs. We can see that the use of these two variables as IVspasses the significance test.

We found that when income uncertainty is controlled, average life expectancycontinues to positively impact household saving, but fails to pass the significance



test. The total dependency ratio has a negative impact on household saving andpasses the test at the 10% significance level, identical to the result of the life-cycle theory. Per capita income level has a significant and negative effect onhousehold saving, the opposite of the cross-country empirical evidence results ofLi, Zhang, and Zhang (2007). Expected future income uncertainty has a positiveimpact on China’s household saving and passes the test at the 5% significancelevel.

Model (6) introduces the proportion of expenditure on food compared withtotal consumption to test the impact of food consumption on household savingand the stability of the regression result of Model (5).16 It shows that the coef-ficient of the share of food consumption compared with total consumption isnegative and highly significant, a result we expected. In addition, we found thataverage life expectancy still has a positive impact on household saving, but thispositive impact nevertheless fails to pass the significance test. The estimationresult of Model (6) again revealed the negative impact of the dependency ratioon household saving, but the coefficient was not statistically significant. Thecoefficient of per capita real income remains negative and not significant, indi-cating that the explanation of China’s household saving by income level is notstable. It is worth noting that income uncertainty still has a significant and posi-tive effect on China’s household saving, again indicating the explanatory powerof the precautionary saving theory on China’s household saving.

Table 8 shows that longer average life expectancy and income uncertaintyare likely to be important reasons for the increase in China’s householdsaving.17 Although the life-cycle and Keynesian theories can theoretically explainhousehold saving behavior, the use of the dependency ratio and income growthin the life-cycle theory to explain China’s household saving behavior fails topass the significance test in most cases, and on occasion, even the signs ofthe coefficient are in conflict with the life-cycle theory. Initial per capita incomefails to achieve the expected positive impact on household saving, indicatingthat Keynesian theory seems inadequate to explain China’s household savingbehavior.

16 We also introduced the real interest rate and inflation into Model (6), and these variables fail topass the significance test while the explanatory variables in Model (6) do not have substantialchanges. In addition, we have used the GMM method to perform a re-estimation of the results ofModels (1)–(6). The results show that there are no substantial differences with the results of fixedeffect models.

17 The estimation result is slightly different from the one in Liu, Hu, and Lang (2012) because theprovincial life expectancy for 2005 is estimated using the average of the 2000 and 2010 censusdata in this study while Liu, Hu, and Lang (2012) used the estimated value of 1% of the populationsampling of 2005.



VIII. CONCLUSIONS

In this article, we collected China’s provincial panel data from 1990–2009 anddivided the data into four segments—1990–94, 1995–99, 2000–2004, and 2005–9.Based on the data, the explanatory power of the Keynesian theory, the life-cyclehypothesis, and the precautionary saving theory on China’s household savingbehavior were tested. The empirical results showed that the increase in China’shousehold saving results from the precautionary saving motivation, which is in turncaused by expected further income uncertainty. However, this article’s empiricalstudy shows that income growth does not have a positive impact on China’shousehold saving, and the dependency ratio usually does not have a significant andnegative impact on China’s household saving. These results indicate that thelife-cycle theory cannot fully explain China’s household saving behavior. Theempirical results of this study show that the precautionary saving motivation is abetter explanation than either the Keynesian theory or the life-cycle theory. Inaddition, the study examined in detail the impact of expected future incomegrowth, future income uncertainty, and average life expectancy on China’s house-hold saving behavior. The empirical results show that an increase in households’expected income growth would lower the current saving rate, whereas an increaseof future income uncertainty and longer life expectancy would increase the currentsaving rate.

Our conclusions indicate that China will continue to maintain a fairly highhousehold saving rate. Given its economic and social development, China’saverage life expectancy is increasing steadily, and this longer life expectancywill cause higher household saving. In addition, since 1992, when DengXiaoping made his “southern tour,” China has gradually been transitioning froma planned economy to a market economy. During the transition period, the pre-vious social security, healthcare, and pension systems have been dismantled,while a new system of social security has yet to be implemented. In these cir-cumstances, most Chinese families face more uncertain futures. Moreover, withthe deepening of market-oriented reforms, private spending on housing, educa-tion, medical services, and old-age insurance has been increasing. Chinesehouseholds have to increase their precautionary saving to cope with the growinguncertainty.

Since China still maintains a fairly high household saving rate, its currentaccount surplus will remain high, which will, perhaps, continue to increase tradefriction with other countries. To deal with this problem, the Chinese governmentshould introduce a policy to expand domestic demand and reduce householdsaving. China should also improve the social security, healthcare, and pensioninsurance systems; assist families in paying for education, medical services, andhousing; and stabilize citizens’ expected future income and employment. Only



when people have the confidence to spend their current income without worryingabout the future, will domestic consumption expand.

Although this study shows that precautionary saving and longer life expectancyare the important reasons for the increase in China’s household saving, there arealso some precautionary saving factors that have not been tested due to difficultyin obtaining data. These factors include private expenditures on healthcare,housing, education, and old-age security that have not been empirically tested. Inaddition, we need to collect new evidence to further test the explanatory power ofthe Keynesian theory and the life-cycle hypothesis on China’s household savingbehavior.

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Documents

Household Saving in China: The Keynesian Hypothesis, Life-Cycle Hypothesis, and Precautionary Saving Theory