Discussion Paper No. 632

WHY DID JAPAN’S HOUSEHOLD SAVINGS RATEFALL IN THE 1990S?

Kazuo Ogawa

March 2005

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

August 2004
Revised February 2005

Why Did Japan’s Household Savings Rate Fall in the 1990s?

Kazuo Ogawa
Institute of Social and Economic Research
Osaka University

Abstract

This paper investigates empirically why Japan’s household savings rate fell in the 1990s. We
constructed an economic model consisting of two types of household: unconstrained life-cycle
households and liquidity-constrained households. Unconstrained households generally save, but
liquidity-constrained households consume all of their disposable income. We found that the
proportion of liquidity-constrained households increased sharply in the late 1990s, which led to a
decline in Japan’s household savings rate. Our simulation analysis demonstrated that if the
proportion of liquidity-constrained households in the 1990s had stayed at the level as that of the late1980s, the household savings rate would have increased by four percent points in 2001 and 2002.

JEL Classification Number: D11,D31 and E21
Keywords: Household saving rate, Life cycle theory, Liquidity constraint and Aging

Correspondence to:
Institute of Social and Economic Research, Osaka University,
6-1 Mihogaoka, Ibaraki, Osaka, 567-0047 JAPAN
Tel: +81- 6- 6879-8570

Fax: +81- 6-6878-2766

E-mail: ogawa@iser.osaka-u.ac.jp

1. Introduction
The Japanese economy remained stagnant throughout the 1990s, a period that has often been
described as the “lost decade.” The household savings rate exhibited a declining trend during thisperiod.1 Figure 1 shows Japan’s household savings rate since 1970. After the savings rate reached its
peak (20%) in the middle of the 1970s, it declined steadily over the 1980s and 1990s and fell to a levelof 5% in 2001.2 Japan’s household savings rate, once prominent for its strength, is now approximately
one-fourth of its peak.
One theory explaining the household savings rate decline focuses on Japan’s aging population.
According to the celebrated Life Cycle theory of consumption, consumers save to provide for their old
age while they work, and spend their savings after retirement to maintain consumption levels.
Therefore, if the Life Cycle theory is at all applicable when it comes to explaining the saving behavior
of Japanese households, aging can indeed account for the fall of Japan’s household savings rate in the
1990s.3 However, it is questionable whether the pace of aging in the late 1990s was fast enough to
produce the rapid reduction in the household savings rate that occurred at that time. Thus, the obvious
question is: do any other factors exist that could have caused the fall of the household savings rate in
the late 1990s? The aim of this study was to answer this question empirically by providing anothertheory for the decline of the household savings rate in the late 1990s.
Through our study we found that an increase of liquidity-constrained households in the late
1990s contributed to the fall in the household savings rate. We put forward the idea that when Life
Cycle savers are trapped into liquidity constraint, possibly via unemployment, their savings drop to
zero. Therefore, as the proportion of liquidity-constrained households increases, the householdsavings rate falls. In a later section of our study, we provide evidence demonstrating that this wasindeed the case for Japan in the late 1990s. Simulation analysis shows that without an increase of
liquidity-constrained households, the household savings rate would not have declined so rapidly in thelate 1990s.
First we examined the effect of liquidity constraints on the household savings rate based on asimple theoretical model. Then, we undertook empirical analysis to evaluate quantitatively the
relationship between liquidity constraints and household savings rates derived from the theoreticalmodel. Finally, we drew conclusions from our analysis.

12. A Simple Model to Determine the Household Savings Rate
We assumed two types of household in the economy. Some households make their consumption
and saving plans in order to maximize utility over their lifetime. They save a positive portion of theirincome while they work, and spend from the wealth they have accumulated during their working
period after retirement, in order to attain a smooth path of consumption over their lifetime. We called
this household type “Life Cycle households,” abbreviated as LCY households. The other households
we grouped together are liquidity-constrained or “rule of thumb” households. Consumption ofliquidity-constrained households hinges entirely upon current disposable income, either because they
do not hold any liquid assets or because they are unable to borrow to sustain the consumption level thatmaximizes lifetime utility.4
Consumption of the i-th LCY household is determined under certainty equivalent assumption as
follows:

C it = α it TWit
where TWit = Ait +

(1)
∞

Yit

∑ (1 + r )

t

t =0

Cit ：consumption of the i-th household at year t
TWit ：real total wealth of the i-th household at the beginning of year t
Ait ：real non-human wealth, sum of net financial wealth and tangible wealth, of
the i-th household at the beginning of year t

Yit ：real labour income of the i-th household at year tr ：real discount rate

α it ：marginal propensity to consume from total wealth

Consumption is based on total wealth, which is composed of human wealth and is defined as
discounted present value of labour income, net financial wealth, and tangible wealth. Savings isdefined as the difference between real disposable income （ YDit ） and consumption:

S it = YDit − Cit(2)2

Conversely, the savings of liquidity-constrained households amount to zero, because theirconsumption is equal to their disposable income. Current consumption and savings ofliquidity-constrained households are given as follows:

Cit = YDit

(3)

S it = YDit − Cit = 0(4)Household savings as a whole ( S t ) are the sum of savings by the LCY households andliquidity-constrained households, the latter of which is zero. Formally, S t may be written as:

S t ≡ ∑ S it = ∑ S iti∈W , L

(5)i∈W

where W ：an index standing for LCY households

L ：an index standing for liquidity-constrained households

The household savings rate（ st ） is defined as follows:

YDit − ∑ Cit∑ Sit i∑
St
i∈W
∈W
i∈W
=
=
st ≡
YDt
YD
YD
∑ it
∑ it
i∈W , L

(6)i∈W , L

where YDt : disposable income of households as a whole YDt ≡∑ YDi∈W , Lit

Let λt （ 0 < λt < 1 ） be the proportion of disposable income held by liquidity-constrained households.
Substituting eq.(1) into eq.(6), we obtained the following expression for the household savings rate:

st = 1 − λt −∑ α TW

i∈W

it

YDt

it

= 1 − λt −α t ∑ TWit
i∈W

YDt

⎛
⎞
⎜ TWit ⎟where α t ≡ ∑ ⎜⎟α iti∈W ⎜ ∑ TW jt ⎟
⎝ j∈W
⎠

3

(7)

We can simplify eq.(7) by making certain assumptions about the distribution of human andnon-human wealth in LCY households and liquidity-constrained households. Specifically, we madetwo different assumptions. Under the first assumption, the proportion of human wealth, as well as
non-human wealth, held by liquidity-constrained households is the same as that of disposable income.5
There exists less inequality of wealth distribution between liquidity-constrained households and the
LCY households under this assumption, because liquidity-constrained households hold the same
proportion of human wealth and non-human wealth as disposable income. Under this assumption, thetotal wealth held by the LCY households is written as:∑ TW = (1 − λ )TW

i∈W

it

t

where TWt ≡

(8)

t

∑ TW

i∈W , Lit

Substitution of eq.(8) into eq.(7) yields the following expression:

⎛
TWt ⎞
⎟
st1 = (1 − λt )⎜⎜1 − α t
YDt ⎟⎠⎝(9)

The proportion of liquidity-constrained households affects household savings rate changes in thefollowing manner:

⎛
TWt ⎞
∂st1
⎟⎟
= −⎜⎜1 − α t
YD
∂λt
t ⎠
⎝

(10)

When the household savings rate is positive, we obtained the following result:

∂st1
<0
∂λt

When the household savings rate is positive, the rise in the proportion of liquidity-constrained4

households reduces the household savings rate. For example, a positive household savings rate meansthat the LCY households, as a whole, save positive amounts of savings. Therefore, when some of the
LCY households are trapped into liquidity constraints, their savings are reduced to zero; thus,
aggregate household savings decrease, leading to the fall of the household savings rate.6
An alternative assumption regarding the distribution of human and non-human wealth is statedas follows: the proportion of human wealth held by liquidity-constrained households is the same asthat of disposable income, but LCY households hold, exclusively, the non-human wealth. Under thisassumption, there exists a large inequality in wealth distribution between the LCY households andliquidity-constrained households, because the LCY households hold all the tangible assets, as well as
financial assets. Under this assumption, the total wealth held by the LCY households is written as:∞∑ TWit = (1 − λt )∑

Yt
+ At
t
t = 0 (1 + r )

i∈W

∞

Yt
= TWt − λt ∑tt = 0 (1 + r )where Yt ≡

(11)

∑Y

i∈W , Lit

At ≡ ∑ Aiti∈W , LLet φt be the ratio of non-human wealth to human wealth for the households as a whole. Then, it iseasily shown that:
∞

Yt

1

∑ (1 + r ) = 1 + φ TW
t =0

t

t

(12)

t

Substituting eq.(12) into eq.(11), which is further substituted into eq.(7), and arranging the terms, we
obtained the following expression for the household savings rate:⎛
λ ⎞ TWt
st2 = 1 − λt − α t ⎜⎜1 − t ⎟⎟⎝ 1 + φ t ⎠ YDt

(13)

5

The effect of the proportion of liquidity-constrained households on the household savings rate is given
by:

∂st2
α t TWt= −1 +∂λt
1 + φt YDt(14)Comparing eq.(10) with eq.(14), it is shown that:0>∂st1 ∂st2>∂λt ∂λt

The more concentrated the wealth holding in the LCY households, the larger in absolute value
is the effect of the proportion of liquidity-constrained households on the household savings rate. For
example, suppose that the proportion of disposable income held by liquidity-constrained households
increases, keeping the total disposable income intact. Under the first assumption, the proportion of
non-human and human wealth of the LCY households decreases in the same proportion. Alternatively,
when LCY households exclusively hold non-human wealth, the total wealth of the LCY households
does not decrease proportionately, nor does consumption of LCY households decrease proportionately.
The implication is that the decrease of consumption is smaller than that of disposable income for the
LCY households, leading to a larger fall of the household savings rate.
These two assumptions on wealth distribution are at two extremes. One assumption implies
that the income distribution is the same as the wealth distribution, and less inequality of wealthdistribution exists between the LCY households and liquidity-constrained households. Under the otherassumption, the LCY households exclusively hold the non-human wealth, and liquidity-constrainedhouseholds do not hold any non-human wealth. The actual situation will lie somewhere in between
these two assumptions.

3. Liquidity Constraints and Household Savings Rate: Quantitative Evaluation
In this section we applied our theoretical model of household savings rate determination to theJapanese aggregate data and evaluated quantitatively to what extent a change in the proportion of6liquidity-constrained households affects the household savings rate. In order to translate thetheoretical model to the real world, we needed the time series data of wealth, income, consumption,and savings, and we had to assign certain values to the parameters underlying the model.
Specifically, in order to characterize the model, we needed the time series data of the householdsavings rate（ st ）, proportion of liquidity-constrained households（ λt ）, disposable income（ YDt ）,∞human wealth（Yt∑ (1 + r ) ）, non-human wealth（ A ）, and total wealth（ TW ）. Next, we explained
t =0

t

t

t

the procedure to construct each data series.

Specification of the Size of Liquidity-Constrained Households
As for the proportion of liquidity-constrained households, we used “the proportion ofhouseholds with no saving balance” reported in Public Opinion Survey on Household Financial Assets
and Liabilities compiled by The Central Council for Financial Services Information.7 Figure 2 shows
the proportion of households with no savings balance in the period from 1970 to 2002. The data
exhibited an increasing trend in the 1990s, notably in the late 1990s to 2000s. The increase was 4.3%
from 2000 to 2001, reaching its peak (16.7%) in 2001.
Table 1 shows the proportion of households with no savings balance in the period from 1995 to2002 by occupation. From 2000 to 2001 a large increase of the proportion of households with nosavings balance was observed in “other” households (6.3-percent-point increase), managers(4.9-percent-point increase), households of agricultural, forestry, and fisheries (4.5-percent -point
increase), and business proprietors (4-percent-point increase). Note that the proportion of householdswith no savings balance totaled 20% in “other” households, households of agricultural, forestry, andfisheries, and business proprietors in 2001. “Other” households included unemployed households,
which are likely to be liquidity-constrained, given that unemployed households are more likely to run
through all of their liquid assets in order to maintain consumption levels while unemployed. The
unemployment rate rose rapidly in the late 1990s, which might have been responsible for the increasein the proportion of liquidity-constrained households. To examine this supposition, we regressed the
proportion of liquidity-constrained households on the unemployment rate. Preliminary examination
indicates that the two variables have unit roots and are not co-integrated. Therefore, we applied the
Cochrane-Orcutt method to estimate the relationship.8 The estimation results are follows:7λt = −0.0044 + 3.1903 AVEUNEMP(-0.20)ρ = 0.6382

R 2 = 0.7877(4.29)(15)

where λt : proportion of households with no saving balance

AVEUNEMPt ：unemployment rate averaged over the current year and thepast three years.

ρ : coefficient of first order serial correlation
The values in parentheses are t-values.

The unemployment rate exerts a significantly positive effect on the proportion of liquidity-constrainedhouseholds. An increase of the averaged unemployment rate by one percent point raises the proportionof liquidity-constrained households by 3.2 percent points.9

Construction of Income, Wealth, and Savings Time Series Data
Time series data of income, wealth, and savings are taken from the 2004 Annual Report on
National Accounts (Economic and Social Research Institute, Cabinet Office). However, the items in
the Income and Outlay Accounts based on 93SNA are available only from 1980 forward, and those in
the Closing Balance Sheet Account are available from 1990 forward. Therefore, we constructed a newseries from the period 1970 to 2002 by linking the 93SNA-based series with the 68SNA based series.
Both of the 93SNA-based and the 68SNA-based series of the items in income and outlay accounts areavailable for 1980 to 1984. Thus, we calculated the average ratio of 93SNA-based series to
68SNA-based series over this period, and we multiplied by this ratio the 68SNA-based series prior to1980. As for the items in the Closing Balance Sheet Account, the link coefficient is the average ratio of
93SNA-based series to 68SNA-based series in the period from 1990 to 1994.10 As for disposable
income, we used an adjusted disposable income series that is consistent with the treatment of socialtransfer in kind in 68SNA.11 Accordingly, we also adjusted the household savings rate.
In constructing our wealth series, we defined non-human wealth as tangible assets (land, fixed
assets, and inventories) and net financial assets at the beginning of period. Human wealth is defined as
the expected discount value of after-tax labour income ( LYt ). After-tax labour income is the sum ofcompensation of employees, receivable and net mixed income minus taxes on income. In other words,8⎡ ∞ LYt +i⎤
HWt ≡ E ⎢∑Ωt ⎥i
⎣ i =0 (1 + r )⎦(16)

where r : subjective discount rate including risk premium

Et [ Ω t ] : expectation operator conditional on the information set
available to households in period t

Stochastic process of the after-tax labour income should be specified to calculate humanwealth based on eq.(16). We first conducted the unit root test of after-tax labour income, and could not
reject the null of unit root. Therefore, we estimated the AR process of the first-differenced series ofafter-tax labour income.12 Akaike Information Criteria chose the order of the AR process. It turns outthat the first difference of the after-tax labour income is generated by the following AR(1):∆LYt = a0 + a1∆LYt −1 + ut

(17)

where ut : stochastic disturbanceThen it can be shown that the human wealth is computed as follows:

HWt =

1+ r
(LYt + HW 2 t )rwhere HW 2 t =

(18)

1
⎛1+ r
⎞
a0 + a1∆LYt ⎟⎜1 + r − a1 ⎝ r⎠

To calculate the time series of human wealth based on eq.(18), we needed parameter estimates of
eq.(17) and the subjective discount rate. As for the subjective discount rate, we used 6.2% per annum,
which is taken from Takenaka and Ogawa (1987). Eq,(17) is estimated by annual time series covering
the period from 1970 to 2002. In estimation, after-tax labour income is divided by the deflator of final
consumption expenditure of households and converted into a per household basis by dividing by the
number of households. The estimated results are as follows:9∆Yt = 0.0190 + 0.4756∆Yt −1(0.91)(2.87)

R 2 = 0.1949
Durbin h = −0.2340

(19)

2

where R : coefficient of determination adjusted for degree of freedom
Durbin h：Durbin’s h-statistics

The time series of human wealth thus calculated is multiplied back by the number ofhouseholds to obtain aggregate human wealth. Table 2 shows the estimated aggregate human wealth
together with tangible wealth, net financial wealth, and the share of each wealth. Human wealth,2559.511 trillion in 1971, exhibited a gradual upward trend and reached its peak (5181.52 trillion yen)in 1997. The share of human wealth, 83% in 1971, declined steadily over the 1970s and 1980s andreached its lowest point (64%) in 1990. In the 1990s, the share of human wealth rose and stayedaround 69% after the mid-1990s.
Tangible wealth is the second largest component of total wealth. It was 396.888 trillion yen in1971 and increased substantially during the bubble period in the late 1980s, reaching 1794.963 trillion
yen in 1991. However, after the bubble burst, it fell consistently, reaching 1234.956 trillion yen in
2002. The share of tangible wealth shows a similar trend. The share rose by 11.9 percent points from1970 to 1990, but it declined by 8 percent points in the 1990s.
Net financial wealth increased steadily over the sample period. The net financial wealth in 2002
(1076.408 trillion yen) is 7.6 times as large as that in 1971 (140.961 trillion yen). Accordingly, theshare of net financial wealth also rose from 5% in 1971 to 15% in 2002.

Estimates of α t Series
Given the time series of wealth variables, we can obtain the ratio of non-human wealth tohuman wealth ( φt ), which in turn is used, together with other data series, to calculate the marginal
propensity to consume from total wealth for LCY households ( α t ) from eq.(9) or eq.(13). The

α t series thus obtained is shown in Table 3. It should be noted that the two series, albeit obtained under
two different assumptions on wealth distribution, exhibit a similar trend. The α t series obtained fromeq.(9) and eq.(13) takes its minimum and its maximum in the same year. Two α t series also increased
gradually in the 1990s.
We expected that the population composition would affect the α t series, because it

10

represents the marginal propensity to consume of the LCY households as a whole. In particular, as the
population enrolled at school and/or that after retirement increases, the α t series will rise. To examine
this supposition, we regressed the α t series on the population who were younger than 15 years(POP15) and older than 65 years (POP65). It turns out that these three variables have unit roots and are
not co-integrated, so we applied the Cochrane-Orcutt method to estimate the relationship. The
estimation result, using the α t series obtained from eq.(9) (abbreviated as the α 1t series), is given as
follows:

α1t = −0.0049 + 0.1277 POP14 + 0.1803POP65(-0.46)
ρ = 0.5082(3.92)

R 2 = 0.8033(5.07)(20)

where ρ : coefficient of the first order serial correlation

Conversely, the estimation result using the α t series, obtained from eq.(13) (abbreviated as the α 2 tseries), is given as follows:

α 2 t = 0.0008 + 0.1137 POP14 + 0.1468POP65(0.07)
ρ = 0.5893(2.90)

R 2 = 0.6932(3.45)(21)

We can see from both of the estimation results that the population increase of those who were younger
than 15 years and/or older than 65 years has a significantly positive effect on the marginal propensity
to consume with respect to the LCY households, as predicted by Life Cycle theory.

Liquidity Constraints and Household Savings Rate
Next we evaluated quantitatively the effect of liquidity-constrained households on thehousehold savings rate based on eq.(10) or eq.(14). Table 4 shows the partial derivatives of householdsavings rate with respect to the proportion of liquidity-constrained households. As was shown
theoretically in the previous section, the absolute value of partial derivative calculated from eq.(14) islarger than that calculated from eq.(10). The partial derivative calculated from eq.(14) ranges from11

-0.4647and -0.3181, while that calculated from eq.(10) ranges from -0.2213 and -0.0646. The average
is –0.3892 in the former series and –0.1453 in the latter series. The increase in the proportion ofliquidity-constrained households by five percent points leads to the fall of the household savings rateby 1.9 percent points in the former case, while it leads to the fall of the household savings rate by 0.73
percent points in the latter case. It is clear that change in the proportion of liquidity-constrainedhouseholds exerts a non-negligible effect on the household savings rate.
As was seen at the outset, the proportion of liquidity-constrained households increased
substantially in the late 1990s. We calculated by simulation technique an alternative path for thehousehold savings rate in the situation that the proportion of liquidity-constrained households had notincreased so much. We also evaluated the impact of demographic change on the household savings
rate to identify the relative importance of the change in liquidity constraint and demographic
composition.
The simulation analysis took the following steps. First, we calculated the base solution of thehousehold savings rate for the period from 1990 to 2002. Actual values of population younger than 15
years and older than 65 years are substituted into eq.(20) or (21) to obtain the α t series for the period
1990 to 2002, which is further substituted into eq.(9) or (13) together with actual series of disposable
income, wealth, and the proportion of liquidity-constrained households. Then we obtained the basesolution of the household savings rate.
Next, we calculated the path of the household savings rate under the alternative assumptions on
demographic composition and the proportion of liquidity-constrained households. The alternative
scenario of demographic composition assumes that the average of POP15 and POP65 for the period
from 1987 to 1989 lasts until after the period 1990 to 2002. Under this assumption, we calculated theα t series to obtain the path of the household savings rate from eq.(9) or (13). Comparison of the basesolution with this solution showed the extent to which the household savings rate is affected bydemographic factors, especially low fertility and aging.
Similarly, we evaluated quantitatively the effects of liquidity constraints on the householdsavings rate. Specifically, we calculated the alternative path of the household savings rate under theassumption that the averaged proportion of liquidity-constrained households in the period 1987 to
1989 lasted from 1990 to 2001. Comparing the solution thus obtained with the base solution, we
evaluated the effects of liquidity constraints on the household savings rate.

12

Table 5 shows the base solution of the household savings rate, as well as the divergence of thehousehold savings rate from the base solution under alternative scenarios, on demographic factors andliquidity constraints for two different assumptions of wealth distribution. When the wealth distribution
and the income distribution are the same for the LCY households and liquidity-constrained households,
the effects of liquidity-constraint on the household savings rate are not large. The household savings
rate increases by only 0.6-0.7 percent points, even in 2001 and 2002 when the household savings rate
precipitated. Contrasted with the small impact of liquidity constraints on the household savings rate,
the effects of demographic factors on the household savings rate are quite large. The householdsavings rate rose by 9.4 and 10.7 percent points in 2001 and 2002, respectively.
However, when the LCY households exclusively hold the non-human wealth, the mitigation ofliquidity constraints has a much larger effect on the household savings rate. The household savings
rate rose by 4.1 and 3.9 percent points in 2001 and 2002, respectively. Judging from the size of thedemographic effects on the household savings rate, which are 7.2 and 8.3 percent points in 2001 and2002 respectively, the effect of liquidity constraints on the household savings rate is not small.
Because it is quite likely that the actual wealth distribution is between our two extreme assumptions on
wealth distribution, it is fair to say that severe liquidity constraints in the late 1990s had anon-negligible impact on the household savings rate.

4. Concluding Remarks
Japan’s investment-saving balance changed dramatically in the 1990s. In the household sector thesavings rate fell, which reduced financial surplus substantially. Some fear that Japan’s aging
population might further reduce the surplus. Our study showed that the investment-saving balance ofthe household sector was also greatly affected by cyclical factors, such as the unemployment rate,
factors that are closely linked to the size of liquidity constraints. The unemployment rate is partly
determined by demand for labour by the corporate sector, which in turn is affected negatively by thedebt burden of the corporate sector.13 The upshot is that, as the corporate sector reduces its debt
outstandings, it will lead to mitigation of liquidity constraints by way of the improvement of labour
market conditions. Thus, it will contribute to the deceleration of the decline in the household savings
rate. It is true that aging exerts a negative effect on the household savings rate. However, it should be
remembered that demographic change is structural and slow in nature, so that a corresponding fall in13the household savings rate will be gradual; in addition, the actual change in the household savings rate
will be compounded by cyclical factors that are associated with liquidity constraints.

14

Acknowledgements

I am grateful to Charles Yuji Horioka and Keiko Murata for extremely helpful comments andsuggestions on earlier draft. This research was partially supported by Grants-in-Aid for Scientific
Research 12124207 of the Ministry of Education. Any remaining errors are the sole responsibility of
the author.

15

Footnotes
1

Horioka (2004) has analyzed the factors underlying the stagnancy of consumption in the 1990s.

2

See Section 3 for the procedure to construct a consistent series of household savings rates from 1970
to 2002.

3

Horioka and Watanabe (1997) have presented evidence to show that a large proportion of householdsavings in Japan is to provide for people’s old age, lending support to the Life Cycle theory.

4

Formulation of aggregate consumption as the sum of consumption of the LCY households andliquidity-constrained households dates back to Flavin (1981), Hall and Mishkin (1982) and Hayashi(1982). For empirical studies supporting this formulation, see Campbell and Mankiw (1989,1991),
Jappelli and Pagano (1989), Ogawa (1990) Cushing (1992), Carroll, et al. (1994), and Chyi and Huang(1997).

5

Liquidity-constrained households do not hold any liquid assets by definition, so that under thisassumption the non-human wealth of liquidity-constrained households consists of illiquid financial
wealth and tangible wealth.

6

When the household savings rate is negative, increase in the proportion of liquidity-constrainedhouseholds raises the household savings rate. This is because LCY households, maintaining highlevels of consumption by negative savings, are forced to reduce consumption by raising savings tozero owing to liquidity constraints.

7

The Family Saving Survey, integrated into The Family Income and Expenditure Survey in 2002 andconducted by the Ministry of Public Management, Home Affairs, Posts and Telecommunications, also
reports on the proportion of households with no savings balance. In the period from 1970 to 2000 it
attains its maximum (1.3%) in 2000. In the same year, the proportion of households with no savingsbalance is reported as 12.4% in The Public Opinion Survey on Household Financial Assets andLiabilities. The difference might reflect the income size of sampled households. In The Family Saving
Survey the averaged annual income in the period 1970 to 2000 is 7.21million yen, while it is 5.57
million yen in The Public Opinion Survey on Household Financial Assets and Liabilities. The former
exceeds the latter by 26%, so that the estimate of the proportion of liquidity-constrained households inthe former is smaller than that in the latter.
8

See Hamilton (1994) pp.561-562 for justification in applying the Cochrane-Orcutt method toestimate the relationship among the variables with unit roots.

9

Horioka et al. (2002) and Horioka and Kohara (2004) show the evidence that Japanese households
break into their savings when facing unexpected events such as unemployment. Our estimation result
is consistent with their findings.
10

Link coefficients for each item are shown in Appendix.

11

I thank Charles Yuji Horioka for suggesting this point.

12

We conducted a unit root test for the first-differenced series of after-tax labour income and rejected
the null of unit root at the conventional significance level.

13

See Ogawa (2003) for the evidence of negative effects of debt outstanding on demand for labour.

16Appendix
Procedure to Construct Time-series Data of
Income, Savings and Wealth of the Household Sector
The time-series data of income, savings and wealth of the household sector come from 2004 Annual
Report on National Accounts (Economic and Social Research Institute, Cabinet Office). However, mostof the data series based on 93SNA are available only up to 1980 or 1990. Therefore we have to link the
68SNA-based data with the 93SNA-based data to construct the data series from 1970. To link the
93SNA-based data series with the 68SNA-based data series in a consistent manner, we calculate theratio of 1980-84 average of each variable based on 93SNA to that based on 68SNA and multiply the
68SNA-based data series between 1970 and 1979 by this ratio. As for the link multiplier of wealthvariables, the ratio of 1990-94 average based on 93SNA to that based on 68SNA is calculated and the
68SNA-based data series between 1970 and 1989 is multiplied by the ratio. The link multipliers forincome, savings and wealth series are shown below.
Variables

Link

Data source

multiplier
Saving, net

0.9319

Income and outlay accounts of households

Adjusted disposable income, net

1.0451

Income and outlay accounts of households

Compensation

0.9916

Income and outlay accounts of households

Mixed income, net

1.0537

Income and outlay accounts of households

Taxes on income

0.9869

Income and outlay accounts of households

Labour income--------

Compensation of employees, receivable +ofemployees,

receivable

Mixed income, net - Taxes on income
Inventories

3.1552

Closing balance sheet account of households

Fixed assets

1.0047

Closing balance sheet account of households

Land

1.0151

Closing balance sheet account of households

Real assets--------

Inventories + Fixed assets + Land

Financial assets

1.0792

Closing balance sheet account of households

Liabilities

1.0203

Closing balance sheet account of households

Net financial assets--------

Financial assets - Liabilities

Deflator of final consumption

0.9388Gross domestic expenditure (deflators)

expenditure of households（1995=1）
The data series of savings, income and wealth are deflated by deflator of final consumption expenditureof households

17References[1] Campbell, J.Y. and N.G. Mankiw(1989).” Consumption, Income and Interest Rates:
Reinterpreting the Time Series Evidence,” in Blanchard, O.J. and S. Fischer (eds.) NBER
Macroeconomic Annual, MIT Press, pp.185-216.

[2] Campbell, J.Y. and N.G. Mankiw(1991).” The Response of Consumption to Income: A
Cross-Country Investigation,” European Economic Review 35, pp.723-767.

[3] Carroll,C.D., Fuhrer,J.E. and D.W. Wilcox(1994).” Does Consumer Sentiment Forecast
Household Spending? If So, Why? “ American Economic Review 84, pp.1397-1408.

[4] Chyi, Y. and C. Huang(1997).” An Empirical Study of the ‘Rule of Thumb’ Consumption Modelin Five East Asian Countries,” Applied Economics 29, pp.1271-1282.

[5] Cushing,M.J.(1992)” Liquidity Constraints and Aggregate Behavior,” Economic Inquiry 30,
pp.134-153.

[6] Flavin,M.(1981).” The Adjustment of Consumption to Changing Expectations about Future
Income,” Journal of Political Economy 89, pp.974-1009.

[7] Hall,R.E. and F.Mishkin(1982).” The Sensitivity of Consumption to Transitory Income:Estimates from Panel Data on Households,” Econometrica 50, pp.461-481.

[8] Hamilton, J.D.(1994). Time Series Analysis, Princeton University Press.

[9] Hayashi,F.(1982).” The Permanent Income Hypothesis: Estimates and Testing by Instrumental
Variables,” Journal of Political Economy 90, pp.895-916.

[10] Horioka,C.Y.(2004).” The Stagnation of Household Consumption in Japan,” Osaka University,
ISER Discussion Paper No.599.

[11] Horioka,C.Y. and M. Kohara(2004).” Defure Jidai niokeru Risuku Taisho Ho (How to Copewith Risks in deflation),” in Higuchi,Y.and K.Ota (eds.) Jyoseitachi no Heisei Fukyo (Women in
Heisei Recession), Nippon Keizai Shimbunsha, pp.235-260.

18

[12] Horioka,C.Y., Murakami,A. and M. Kohara(2002).” How Do the Japanese Cope with Risk?”
Seoul Journal of Economics 15, pp.1-30.

[13] Horioka,C.Y. and W. Watanabe(1997).” Why Do People Save? A Micro-Analysis of Motives
for Household Saving in Japan,” Economic Journal 107, pp.537-552.

[14] Jappelli,T. and M.Pagano(1989).” Consumption and Capital Market Imperfections: An
International Comparison,” American Economic Review 79, pp.1088-1105.

[15] Ogawa,K.(1990).” Cyclical Variations in Liquidity-Constrained Consumers :Evidence fromMacro Data in Japan," Journal of the Japanese and International Economies 4, pp.173-193.

[16] Ogawa,K.(2003).” Financial Distress and Employment: The Japanese Case in the 90s," NBER
Working Paper 9646.

[17] Takenaka,H. and K.Ogawa(1987). Taigai Fukinko no Makuro Bunseki (A Macroeconomic
Analysis of External Imbalance), Toyo Keizai Shinposha.

19Figure 1 Hosehold Saving Rate in Japan
25
23
21
19
17
％ 15
13
11
9
7
5
1970

1972

1974

1976

1978

1980

1982

1984

1986

Data Source: Economic and Social Research Institute, Annual Report on National Accounts

1988

1990

1992

1994

1996

1998

2000

2002

Figure 2 Proportion of Hoseholds without Saving Balance
18

16

14

12

10
％
8

6

4

2

0
1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

Data Source: The Central Council for Financial Services Information, Public Opinion Survey on Household Financial Assets and Liabilities

2000

2002

Table 1 Proportion of Households without Saving Balance by Occupation
year

Agriculture,
forestry andfisheries

Business
proprietors

1995
1996
1997
1998
1999
2000
2001
2002

9.2
9.9
18.4
14.2
15.5
14.9
19.4
20.2

8.0
14.4
11.6
12.7
14.2
15.3
19.3
18.1

White-collar
workers

Blue-collar
workers

Managers

5.1
5.0
6.0
5.3
6.9
7.8
8.4
6.9

9.0
10.6
12.4
13.7
13.9
15.3
17.6
18.8

3.5
3.8
2.7
3.7
5.2
4.5
9.4
7.2

Professional Others
workers
14.0
13.6
11.6
18.9
17.2
20.4
23.4
27.8

Average

9.3
12.3
12.8
12.5
13.2
13.3
19.6
18.6

Data Source: The Central Council for Financial Services Information, Public Opinion Survey on Household Financial Assets
and Liabilities

7.9
10.1
10.2
10.8
12.1
12.4
16.7
16.3

Table 2 Composition of the Household Wealthyear

Human wealth

1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

balance(billion yen)25595112846035
3231750
31245783251270
332114533770923544324361631534332273601885365164836797703762268
39040313925257
4012970
431198044527464482922475749148171514839883
500177350648665126283
518152051697115073878512327650627785097788

Tangible wealthsharebalance%(billion yen)
82.6
39688882.445094980.756941879.561124681.0
57119581.555906981.1
56958781.1
58952779.864637777.3
72356376.780167375.387483674.690816474.591207674.3
93334373.2
98393070.4116264667.8
145889066.9154549664.2
1720524
65.6
179496366.8
166560568.3
152471569.2
1463406
69.1
143717669.5
138727869.6
136551469.4
135732669.2
133155468.5
131494568.3
129081768.8
1234956

share
%
12.8
13.1
14.2
15.5
14.2
13.7
13.7
13.5
14.3
16.3
17.1
18.0
18.4
18.1
17.8
18.3
20.4
22.9
23.2
24.7
24.7
23.1
21.5
20.3
19.6
18.8
18.3
18.2
18.2
17.6
17.4
16.7

Net financial wealth

Total wealthbalance(billion yen)
140961
157945202429195783
193393
19574521889623755226947128369429370732115034306737577441377645349852415858742566174377587869999572560472675976117283212186312489597292321592890810436781061838
1076408

balance(billion yen)
30973593454928
40035963931606
401585840759594165575
4371404
4532163444048446972654847634
4931001
50501195251150
5362685
56997746358295
665998669793247252449
7208360
70913577226351
7334163
73766857443006
7450251733434074818997415433
7409152

share
%
4.6
4.6
5.1
5.0
4.8
4.8
5.3
5.4
5.9
6.4
6.3
6.6
7.0
7.4
7.9
8.5
9.2
9.2
9.9
11.1
9.7
10.1
10.2
10.5
11.3
11.7
12.0
12.4
12.7
13.9
14.3
14.5

Table 3 Estimates of α t Series

year
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

series１

series２

0.04130.0406
0.03810.03900.03990.04020.0406
0.0411
0.0421
0.04340.04160.04200.04260.04250.04250.0431
0.04230.03950.03930.03910.03880.0401
0.04140.04200.04240.04330.04340.04320.04420.04420.0456
0.04630.04080.04040.03780.03870.03950.03970.03990.04070.04170.04290.0411
0.04140.0421
0.04170.04200.0426
0.04190.03870.03810.03780.03780.03880.03990.04070.04130.04190.04190.04170.04240.04230.04290.0437

Notes: Series 1 is calculated under the assumption that the distribution of non-human wealth as well as
human wealth is the same across the LCY households and liquidity-constrained households, while
series 2 is calculated under the assumption that the non-human wealth is exclusively held by the LCY
households.

Table 4 Effect of Liquidity-Constrained Households on Household Saving Rate

year
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

∂st1
∂λt

∂st2
∂λt

-0.1689-0.1673-0.1907-0.2153-0.2125-0.2213-0.2128-0.1957-0.1706-0.1632-0.1719-0.1593-0.1525-0.1537-0.1447-0.1379-0.1200-0.1292-0.1331-0.1371-0.1455-0.1397-0.1357-0.1225-0.1137-0.0959-0.0971-0.1083-0.1058-0.0938-0.0681-0.0646-0.3206-0.3181-0.3529-0.3818-0.3678-0.3734-0.3732-0.3547-0.3453-0.3612-0.3732-0.3758-0.3762-0.3803-0.3717-0.3771-0.3866-0.4228-0.4381-0.4647-0.4542-0.4440-0.4313-0.4101-0.4038-0.3925-0.3924-0.4034-0.4066-0.4060-0.4018-0.3933

Table 5

Effects of Liquidity Constraints on Household Saving Rate:
Quantitative Evaluation(1) Case where wealth distribution and income distribution are the sameacross the LCY households and liquidity-constrained households(%)
Base solution

No change

No changeof householdin fertility andin the proportion ofsaving rate

aging since the 90s

liquidity-constrainedhouseholds since the 90s

1990

11.15

-0.30

0.34

1991

10.86

-0.05

0.12

1992

11.92

0.46

0.40

1993

13.21

1.00

0.63

1994

12.38

1.87

0.35

1995

11.65

2.70

0.21

1996

10.39

3.75

0.45

1997

9.43

4.89

0.42

1998

8.93

5.98

0.46

1999

9.31

6.78

0.62

2000

6.58

8.45

0.46

2001

5.23

9.39

0.66

2002

4.78

10.71

0.58

(2) Case where non-human wealth is exclusively heldby the LCY households(%)
Base solution

No change

No changeof householdin fertility andin the proportion ofsaving rate

aging since the 90s

liquidity-constrainedhouseholds since the 90s

1990

10.81

-0.51

1.25

1991

11.13

-0.42

0.47

1992

11.70

-0.08

1.34

1993

12.84

0.31

1.86

1994

12.72

0.96

1.08

1995

12.40

1.56

0.70

1996

10.74

2.42

1.58

1997

9.99

3.33

1.59

1998

9.51

4.22

1.84

1999

9.66

4.90

2.40

2000

7.05

6.28

2.45

2001

4.51

7.21

4.11

2002

4.50

8.32

3.89