Determinants of Household Saving in ChinaMarcos Chamon

Eswar Prasad

Disclaimer: The views expressed are those of the authors and do not necessarilyrepresent those of the IMF or IMF policy.

Motivation

Chinese households save a lot!About 25% of disposable income

Historically, households main contributor to national savings

Recently, enterprises have become largest savers

But household savings are still large: about 16% of GDP

Savings as a percentage of GDP

0%

10%

20%

30%

40%

50%

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

year

Household savings (based on Modigliani and Cao 2004)

Government savings

Enterprise savings

Household savings

Gross domestic savings

High household saving rate somewhat puzzling High enterprise savings can be justified by

attractive returns on retained earnings But households typically face small real

returns on their savings (sometimes negative!)

Moreover, rapid income growth suggests households should be anticipating future consumption/delaying their life-cycle savings

Figure 9. Nominal and Real Interest Rates - 1 Year Deposit

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

%

Source: CEICNotes: Nominal one-year deposit rates (annual average) deflated by annual change in CPI (one year ahead)

Nominal

Real

Overview of presentation

Paper uses household-level data from a subset of the Urban Household Survey

Focuses on three determinants of savings:

1) Life-cycle effects

2) Transition effects from reform process

3) Credit constraints (durable good purchases)

Life-cycle effects In a fast growing economy people should:

Borrow against future income If credit constrained, at least delay

“retirement” savings Paper presents a very simple OLG model

showing that interplay of credit constraints and high income growth can actually increase savings

Model set-up

Agents live for 3 periods, earn wages in first two periods

All wages in the economy grow at a geometric rate every period:

Cohort born at t=0: w0=, w1=, w2= Cohort born at t=1: w1=, w2=w3= Cohort born at t=2: w2=, w3=w4=

Agents can only borrow up to share of their second period income in the first period

Simple example with no borrowing (e.g. Household born at t=0 has:

wt=, wt+1=, wt+2=

If ≤2, household can perfectly smooth its consumption by consuming: ct=(, ct+1=(, ct+2=(

If >2, household would like to borrow against future income in first period. Since it cannot, the best it can do is not to save at t=0. Resulting consumption path is: ct=, ct+1=, ct+2=

With borrowing constraints, income growth increases savings

Aggregating across overlapping cohorts yields:

1/ 3 2

22(1 )

if

Share of humanwealth saved for retirementif

3

2

2 2 12

3 6

1 12

4 4

if

Aggregate saving rate

if

Aggregate savings rate in an OLG economy as a function of growth rate of wages

0.0

5.1

.15

.2A

ggre

gate

sav

ing

rate

1 2 3 4 5Gamma

Relaxing borrowing constraints (>0 but still small) yields (for >2)

Aggregating across overlapping cohorts yields:

(1 ) 1max ,

2(1 ) 3Share of wealth saved for retirement

3

2

2 2 1 22

3 6 1 3

1 1 2(1 ) ( 1)

4 4 2 1 3

if

Aggregate saving rate

if

Aggregate savings rate in an OLG economy as a function of growth rate of wages and borrowing constraints

0.0

5.1

.15

.2A

ggre

gate

sav

ing

rate

1 2 3 4 5Gamma

Beta=0 Beta=2.5%

Beta=5% Beta=7.5%

Empirical Evidence on life-cycle effects Use data from urban household survey.

Entire sample for 1986-1992, subset of 10 provinces/municipalities for 1993-2001.

Limit analysis to households whose head between 25 and 70 years old

Summary of Urban Household Survey

Year Observations Income Per

Capita (in 2005 RMB)

Consumption per Capita

(in 2005 RMB)

Average Saving Rate

1986 11877 3348 2984 10.9% 1987 12700 3450 3038 11.9% 1988 13364 3433 3203 6.7% 1989 12806 3447 3036 11.9% 1990 13380 3805 3228 15.1% 1991 13508 4008 3442 14.1% 1992 16561 4337 3615 16.6% 1993 5992 5086 4306 15.3% 1994 6151 5535 4655 15.9% 1995 6159 5686 4828 15.1% 1996 6157 5884 4906 16.6% 1997 6144 6091 5078 16.6% 1998 6130 6558 5439 17.1% 1999 6135 7199 5904 18.0% 2000 5849 7620 6282 17.5% 2001 6047 8186 6504 20.6%

Age and cohort effects Following Deaton and Paxson (1994), we

compute average log(income) and log(consumption) for each age*year combination and regress on age, cohort (age in 1986) and year dummies

There is a linear relationship between age, cohort and year. Year effects are constrained to:Add to zeroBe orthogonal to a time trend

Age effects on income and consumption9

.51

01

0.5

11

11.

51

2

20 30 40 50 60 70Age

Log Y Log C

Effects shown for household that was 10 years old in 1986

Cohort effects on income and consumption7

89

10

0 20 40 60 80Age in 1986

Log Y Log C

Effects shown for 25 year old household

Age and cohort effects on savings

-.2

0.2

.4.6

0 20 40 60 80Age and Age in 1986

Age effects Cohort effects

Effects shown for 25 year old household in 2001

Time trend on income overwhelms all other effects Alternative approach:

Give up trying to identify cohort effects, and regress log (income) and log(consumption) on age dummies and unrestricted time trend

Age effects on income and consumption

9.4

9.6

9.8

10

10.

2

20 30 40 50 60 70Age

Log Y Log C

Effects shown for 2001

Age profile of savings.2

2.2

4.2

6.2

8.3

20 30 40 50 60 70Age

Effects shown for 2001

Qualitative results match our priors

Young households save substantially (possibly to self-finance purchases of durables)

Savings increase sharply around mid 40s (suggesting “retirement savings” begin around that age)

Implications for future aggregate saving patterns: Demographics

In the long run, population aging should lead to a contraction in aggregate savings

Share of population in “prime saving” age group will increase vis-à-vis “prime dissaving” group in the short- and medium-term

Share of Chinese population by age group

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0-19 20-34 35-49 50-64 65+

Age Range

1985 1995 2005 2015 2025 2035 2045

Precautionary Saving motives

Many observers emphasize role of precautionary motives and uncertainty related to reforms

Several benefits traditionally provided by State Owned Enterprises to their employees:Health; Education; Pensions; Housing;...

Provision of these benefits either lost or became uncertain

Precautionary saving motives

Households may be saving a lot not only because of higher uncertainty, but also to make-up for past savings that were not made

Different groups affected differently by this uncertainty: SOE workers have potentially a lot to lose vs

collective enterprise workers that didn’t have many benefits to begin with

Private sector workers face uncertainty but may also face better income growth prospects

Percentage of households by type of employer of head of household

Type of unit 1993 1994 1995 1996 1997 1998 1999 2000 2001 SOEs 67.7% 66.4% 67.4% 67.2% 66.8% 65.2% 63.2% 59.4% 58.3% Collective Units 13.1% 11.3% 10.3% 10.6% 10.3% 9.6% 10.0% 8.0% 7.3% Other types of units (including private) 0.5% 1.4% 1.3% 1.2% 1.8% 2.3% 3.0% 3.8% 4.3% Entrepreneurs 0.4% 0.5% 0.5% 0.7% 1.0% 1.3% 1.6% 2.6% 3.0% Employees of individuals 0.3% 0.2% 0.3% 0.2% 0.2% 0.3% 0.4% 1.1% 1.8% Re-employed retirees 3.3% 3.6% 3.4% 3.3% 2.9% 3.0% 3.4% 2.6% 2.7% Other employed 0.1% 0.1% 0.2% 0.1% 0.1% 0.2% 0.3% 0.5% 0.5% Retirees and others 14.6% 16.5% 16.6% 16.6% 16.8% 18.2% 18.0% 22.0% 21.9%

Estimated effect of employer type on saving rates

Type of unit 1993-1997 1998-2001 1993-1997 1998-2001 SOEs - - - - Collective Units

-0.018 (0.006)**

-0.043 (0.008)**

-0.018 (0.006)** -0.04 (0.009)**

Other types of units (including private) -0.015 (0.012) -0.03 (0.010)** -0.015 (0.012) -0.025 (0.010)*

Entrepreneurs 0.075

(0.022)** -0.005 (0.016) 0.075

(0.023)** -0.001 (0.016) Employees of individuals -0.065 (0.034) 0.004 (0.024) -0.066 (0.034) 0.009 (0.024) Re-employed retirees -0.020 (0.013) -0.036 (0.015)* -0.020 (0.013) -0.034 (0.015)*

Other employed -0.100 (0.051) -0.111

(0.042)** -0.100 (0.051) -0.106 (0.042)* Dummy for spouse working at SOE .000 (0.004) 0.012 (0.005)*

Implications for future aggregate saving patterns: Transition effects

Shift to a market economy and SOE reforms likely contributed to the increase in household savings

The effect may weaken over time: As households continue to accumulate savings, at

some point they will have enough assets to protect them from most adverse shocks

Eventual development of social safety net and pension system should also lower savings

Durable goods and borrowing constraints Consumer finance very limited in China Development of consumer credit should

lower savings But magnitude of the effect may be small:

If household saves 20% of income and wants to buy a new TV, it can do so just by saving less.

No need to rely on credit or even deplete past savings!

Durable good consumption

Survey has detailed data on income and consumption expenditures. We focus on 1993-2001 subsample

Exclude households with home purchasing/construction expenditures (about 8% of households)

Durable good purchases correspond on average to 6.5% of income (but distribution is very skewed due to their “lumpiness”)

Durable good purchases exceed income minus other expenditures for 33% of households (thus cannot finance purchase just by saving less)

Financing sources for durable good purchases We break down the source of funds for

durable good purchases between:(i) Income – nondurable consumption –

nonconsumption expenditures

(ii) Net financial dissavings (e.g. net saving withdrawals)

(iii) Credit

Financing sources for durable good purchases in 2001

Note: Variables expressed as share of income unless otherwise noted. Negativenet financial dissavings indicates households net financial savers

Income Age of head Durable

Consumption/ Consumption

Below Median

Above Median

Below 35

35 or older

Below 10%

Above 10%

Durable consumption 4.5% 7.5% 7.2% 6.1% 1.4% 22.3% Income - other expenditures 9.2% 23.2% 19.3% 17.1% 16.2% 21.1% Net financial dissavings -6.6% -16.5% -13.3% -12.2% -15.9% -0.4% Credit 1.8% 0.7% 1.3% 1.2% 1.1% 1.6% Per capita income (in 2005 RMB) 5134 13341 7497 8078 7533 10191 Number of households 3901 2106 686 5321 4926 1081

Are net financial dissavings related to large durable purchases? We run probit regressions of a dummy

equal to one if household is net financial dissaver (about 30% of households) on:Log (Durable good purchases/Y)Log YDummy for household head below 35

Probit regression results

Probability of Being Net Financial Dissaver

All years 2001

I II III IV

Durable Consumption/ 3.569 4.201 3.514 4.225

Income (0.064)** (0.069)** (0.194)** (0.208)**

Log (Income) -0.423 -0.479

(0.012)** (0.032)**

Age Below 35 -0.103 -0.243

(0.017)** (0.059)**

Constant Year Dummies Year Dummies -0.752 3.439

(0.020)** (0.282)**

R2 .064 .086 .059 .092

N 51746 51746 6007 6007

Marginal effects on probability of being a net financial dissaver in 2001

All variables at their means

Pr(Net Financial

Dissaver)

Durables / Income Age Below 35 Log (Income)

.278 1.415 -.077 -.160

All variables at their means, Durable/Income at 10%

.350 1.567 -.086 -1.773

All variables at their means, Durable/Income at 20%

.515 1.684 -.096 -1.908

Large durable purchases increase likelihood of net financial dissavings Magnitude of the effect non-negligible, but

relatively smallHouseholds likely to remain net financial

savers even when making very large durable purchases

Ownership of most durable goods common except cars

Durable Good 2000 2005

Washing Machine 90.8 95.5 Refrigerator 80.5 90.7 Color TV 116.7 134.8 DVD Player 37.1 68.1 Mobile Phone 18.3 137 Automobile 0.6 3.4

Source: CEIC based on NBS data covering whole sample

Home ownership rates also very high

Year 10 Province/Municipalities

Sub-Sample National Average

1993 0.206 1994 0.283 1995 0.309 1996 0.355 1997 0.477 0.557 1998 0.554 0.620 1999 0.647 0.689 2000 0.727 0.771 2001 0.767 0.810 2002 0.820 2003 0.830 2004 0.843

Implications for future aggregate saving patterns Development of consumer financing migth

only have limited impact on saving behavior (with possible exception of auto financing)

Developments in housing market should also have limited impact given very high rates of home ownership

Conclusion

Precautionary saving motives seem to play an important role

Demographic changes have contributed to aggregate savings (high income growth leverages that effect).

Demographics should continue to contribute to aggregate savings over the next two decades

Developments in consumer credit may not have a substantial effect on savings