Discussion Paper No. 636

INCOME UNCERTAINTY AND
SELF-REPORTED PRECAUTIONARY WEALTH:EVIDENCE FROM THE JAPANESE MICRO DATA

Wako Watanabe

May 2005

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

Income Uncertainty and Self-Reported Precautionary Wealth:
Evidence from the Japanese Micro Data

Wako Watanabe*

May 2005

Abstract
Using unique survey data which includes information on precautionary wealth and its
target, we analyze the precautionary saving behavior of Japanese households. Ourfindings are: 1. Measures for income uncertainty have a positive influence on the target forprecautionary wealth but not on precautionary wealth. 2. The positive influence of income
uncertainty on the target vanishes when older households with a head aged 51 or older are
included in the sample. These findings suggest that Japanese households save against
income uncertainty until around when their head is aged 50 and then save against otherrisks such as the longevity risk.

* I am indebted to Charles Yuji Horioka for many discussions and suggestions. I also thank for Naohito
Abe and Ryuichi Tanaka for helpful discussions.
†
Institute of Social and Economic Research, 6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
Tel: 81-6-6879-8686; FAX: 81-6-6878-2766
Email: watanabe@iser.osaka-u.ac.jp

1. Introduction
We are exposed to various sources of risks in our economic life.

Among them

income uncertainty is the risk that most affects an individual’s welfare through its influence
on consumption.

For an average family, the labor incomes of employed family members

are the largest part of the household income as returns on financial assets and othernon-labor incomes such as earnings from rented lands and homes are negligible.

Hence,

unexpected adverse shocks to labor incomes of working family members can havedevastating impacts on the household’s subsistence.
How do households cope with labor income risks?

Apart from partial coverage of

unemployment risks by public unemployment benefits, insurances, public or private, do not
generally protect households from such labor income risks of salaried workers as pay andbonus cuts.

Insurances aimed at volatile sales of the self-employed are almost

nonexistent.
An individual (or a household) may self-insure against unanticipated adverse events inthe future. Buffering motives for savings against future uncertainties are summarized as aprecautionary motive for saving and are distinguished from savings for specified
expenditures at various life stages such as purchasing durable goods, funding her children’s
schooling, a down payment for a home purchase, and the individual’s living expenses after
her retirement,.
In the following analysis, we run a regression of the precautionary wealth reported by
the survey respondents on measures for household labor income uncertainties.

If

individuals save not only for specified future expenditures but also against uncertainties,
individuals with a greater risk exposure should compress consumption and accumulates
wealth more than those with a smaller risk exposure.
1

A measure for income uncertainty should be more strongly (positively) correlated toself-reported precautionary wealth than (positively) to total household wealth or(negatively) to consumption.

Individuals who face imminent income uncertainty not only

reduce present consumption but may also give up some future consumption and reduce
current savings for specified future expenditures.

If such reallocation of savings is widelypracticed, higher risk exposures do not affect total wealth and present consumption as muchas the self-reported precautionary wealth.
We also use the target for precautionary wealth reported by survey respondents asanother dependent variable.

According to the buffer stock saving hypothesis proposed byCarroll (1992, 1997), an individual saves primarily against her risk exposure when she isyoung and less aware of her retirement.

Carroll derives that a consumer engaged in a

dynamic saving/consumption decision has her own target as a buffer against her risk
exposure. The target for precautionary wealth depends on the consumer’s preference andher risk exposure. Use of the self-reported target serves as a direct empirical test of the
Carroll’s hypothesis. Recently, Kennickell and Lusardi (2004) emphasized the importanceusing the self-reported wealth targets collected in the Survey of Consumer Finances intesting the precautionary saving hypothesis.
We group households into distinct homogeneous groups according to household
occupational and demographic characteristics and calculate a measure for household labor
income uncertainty as in Dardanoni (1991) and Zhou (2003). For instance, the businessincome of a self-employed is less stable than the salary of a worker of a large company
under a longer-term contract.

Such heterogeneity of income uncertainty across individuals

is captured by varied risk exposures across groups of similar households.

The logarithmof household labor income, the measure used in our analysis turns out to be empirically2more plausible than the variance used by Dardanoni (1991) and Zhou (2003). Whenrunning regressions instrumental variables are used in order to remove a potentially serious
simultaneity bias caused by a household’s occupational choice.
A dummy variable indicating whether a household head is healthy or unhealthy isemployed as an additional measure for income uncertainty.

This variable is meant tocapture the risk that a household head will go through a substantial period without labor
income due to a sick leave or a job loss caused by deteriorated health conditions.
The data we use are taken from the Survey on the Financial Asset Choice ofHouseholds (SFACH) conducted by the Institute for Posts and Telecommunications Policy(IPTP) of the Ministry of Posts and Telecommunications of the Government of Japan (thepresent Japan Post Corporation).
What makes the SFACH unique is the survey on the household’s current financial
wealth and the household’s wealth target for at least twelve saving motives in addition to
the survey on total financial assets, borrowing, and various financial and demographic
variables.

Among twelve motives surveyed, “for peace of mind but not for specificreasons” best represents the saving for unanticipated income fluctuations.

Anothersurveyed motive, “for illness, natural disasters, and other unexpected expenditures”,
captures the precautionary saving motive for uninsured and unanticipated expenses. The
precautionary saving motive for uninsured unanticipated expenses is beyond the scope of
this paper. 1
Our main findings are: 1. Measures for income uncertainty have a positive influenceon the self-reported target for precautionary wealth but not on precautionary wealth itself inyounger households. 2. The positive influence of income uncertainty on the self-reported

1

For an empirical analysis of this type of precautionary saving, see Palumbo (1999).
3

target for precautionary wealth vanishes for the sample including older households with ahead aged 51 or older. These findings suggest that Japanese households save against
income uncertainty until around when their head is aged 50 and then save against otherrisks such as the longevity risk afterward.
The paper is organized as follows.

In section 2, the relevant literature is reviewed.

In section 3, the data and econometric issues are examined.
results are reported.

In section 4, the empirical

Section 5 concludes.

2 Literature Review
We will review the literature on precautionary saving in this section.2
Carroll (1992, 1997) formally modeled the individual’s precautionary saving behavior.
Assuming the forward looking individual’s maximization of the more realistic utility
function than the one assumed by the stripped-down life cycle model, he claimed that an
ordinary individual starts to save for her retirement only in her early 50’s and that she saves
primarily for income uncertainty in the near future until then.

In her youth when her

saving motive is precautionary against uncertainties, the individual saves only as much as
her target derived from her risk exposure and preference, which serves as a buffer against
adverse realizations of her risk exposure.

Carroll’s buffer stock saving hypothesis

successfully explains the stylized fact that consumption tracks income in the individual’s
earlier life. This had remained a puzzle under the stripped-down life cycle hypothesis
according to which consumption is smoothed out over the individual’s life and isindependent of the concurrent level of income.
Results of empirical tests on the precautionary saving hypothesis are mixed.
2

See Browning and Lusardi (1996) and Murata (2003) for a more detailed literature review of theempirical research on precautionary saving.
4Thetypical regression equation testing the hypothesis has consumption, flow saving, or wealthas a dependent variable and some income risk measure as an independent variable.
Precautionary saving motive is said to exist if, controlling for demographic characteristics
and life cycle saving for retirement, the employed income risk measure is negatively related
to consumption, positively to saving, and positively to wealth.
There have been plenty of empirical works on precautionary saving motives using datafrom the US and Europe where micro household level survey data have been publicly
available. Different variables are used as a measure for income risk.

Such income risk

measures used in the literature include occupation (Skinner [1988]), some measure for alabor income risk such as a variance (Carroll [1994], Carroll and Samwick [1997, 1998]),
some measure for a risk of consumption growth (Dynan [1993]), an estimated probabilityof job loss (Carroll, Dynan and Krane [2003]) and some subjective measure of income risks
reported by a survey respondent (Lusardi [1998]).
On the other hand, it is only recently that empirical investigation on precautionarysaving began with the Japanese micro household level data.

With the 1994 wave of the

SFACH, Horioka and Watanabe (1997) find that 14 percent of the gross household flow ofsavings in financial assets is for a “peace of mind” motive. Using the 1996 wave of thesame survey, Zhou (2003) group households in more homogeneous subgroups ofhouseholds based on the household head’s age group, occupation, and educational
attainment.

She then calculates the group variances of household labor income, and findthat household consumption is lower for those who belong to a group with a higher groupvariance of labor income.

Murata (2003) uses the yearly panel of the Japanese Panel

Survey of Consumers from 1993 through 1998, and shows that a subjective measure forincome uncertainty based on the respondent’s self-report does not account for their
5

financial wealth.
Studies on household level data from the US, Europe and Japan differ considerably in
the importance of the precautionary saving motive to insure against income uncertainty.
Some studies find a statistically significant coefficient on an employed income risk measureand others do not.

3. Data and Empirical Methodology
3.1. Data
The Survey on the Financial Asset Choice of Households has been conducted every
two years since 1988.

The most recent 9th wave of the SFACH was conducted in 2004.

The sample of households of each wave is selected through a two-stage stratified sampling
method.

In conducting the survey, interviewers visit the selected sample households andasked them to fill out the questionnaire, and several days later, interviewers revisit themand collect the questionnaire.

Similar questions were asked repeatedly but they were notnecessarily identical across waves.
In this paper, the data from four waves of the SFACH, the 1994 wave (the fourthwave), the 1996 wave (the fifth wave), the 1998 wave (the sixth wave), and the 2000 wave
(the seventh wave) are pooled and analyzed. For every wave used in our study, 6000
households including single-person households were surveyed and the response rate wasover 60 percent (65.4% in 1994, 61.6% in 1996, 62.6% in 1998 and 62.1% in 2000).
Every wave but the 2000 wave was conducted between the late November and the early
December of the survey year, and the remaining 2000 wave was conducted between the late
January and the early February of 2001.3
3

Examining the 1994 wave, Horioka and Watanabe (1997) ensure reliability of the SFACH, based on its

comparison with the Family Saving Survey, the most widely used saving survey in Japan.
6

In constructing the pooled data set of four waves of the SFACH, households with ahead aged 29 or younger or households with a head aged 60 or older are dropped so that the
study focuses on households whose head is of a working age.4

Single-person households

are dropped since they are underrepresented in the SFACH.

Households whose before-tax

non-capital income is zero or missing are dropped.5, 6

In addition, households with afemale head, a head who is not working, who is working part-time, and who is a student,
are dropped.7
Dropping further households whose relevant demographic information such as thenumber of children and the age of the household head, the constructed sample pooling fourwaves of the SFACH includes 3903 households.

3.2. Estimation of labor income uncertainty
In this study, following Dardanoni (1991) and Zhou (2003), the measure for household

4

The primary age of retirement in Japan is 60.

Households with a head in her 20’s are dropped since

many such households are yet to enter the workforce.
5

We use after-tax income for our analysis, since data on before-tax income are not available for 1998

and 2000 waves of the SFACH.
6

In the earlier two waves (1994 and 1996 waves), the survey collects information on whether individual

household members such as a household head and his spouse have any capital income and if so howmuch.

In the later two waves (1998 and 2000 waves), the survey collects information on whether anentire household has any capital income and if so how much.

In calculating non-capital income, ifhouseholds answer that they have capital income but do not answer the amount of capital income, we
regard their capital income to be zero. Such way of processing the data is less likely to lead tounderestimation of capital income because 1) only a fraction of households say they have capital income
but do not report the amount of capital income (13 percent of household heads in 1994, 24 percent ofhousehold heads in 1996, 15 percent of households in 1998, and 7 percent of households in 2000), and 2)
those who say they have capital income but who do not report the amount of capital income are likely tohave little capital income compared to those who report the amount of capital income.
7

There are very few households with a female head in the SFACH data.
7

labor income uncertainty is calculated for each group of homogeneous households sharing
the same demographic characteristics.

More precisely, following the grouping ofhouseholds employed by Zhou (2003) in spirit but improving on it, the sample is groupedby the age group, cohort, occupation, and educational attainment of the household head.
Each age group and cohort considered have a 10 year window based on a household
head’s age and his cohort: i.e., 30’s, 40’s, 50’s for age groups, and those born in the period
from 1931 to 1940, in the period from 1941 to 1950, in the period from 1951 to 1960, andin the period from 1961 to 1970.

Occupations considered are employment in a private

entity that employs less than 30 persons, employment in a private entity that employs 30 ormore but less than 500 persons, employment in a private entity that employs 500 persons or
more, public service, and self-employment that includes farming, forestry, and fishery.
Educational attainments considered are junior high school, high school, and advanced
education (junior college, college, or graduate school).8
As we emphasized in the previous section, using of the appropriate measure of (labor)
income uncertainty is crucial in our empirical analysis.

Table 1 presents the variance ofand the standard error of the logarithm of after tax non-asset household income.

The

variance of the after tax total household non-capital income is higher for households whose
heads are employed in medium sized (entities that employ 30 or more but less than 500
persons) and large private entities (entities that employ 500 or more persons) than for those
whose heads are public servants. This is contradictory to common sense with regard to
the stability of earnings for each of the three occupations.
Zhou (2003) uses the variance of the level of household labor income as a measure forlabor income uncertainty for the regression of household consumption, and finds that the8

Zhou (2003) does not consider a cohort effect in grouping households since she uses only the 1996

wave.
8

coefficient of this risk measure is negative and statistically significant, and thereby claims
that precautionary saving motives exist in Japan.

Her finding may be due to the fact that

her income risk measure does not appropriately reflect the actual household labor income
uncertainty.
The variance of the logarithm of total non-asset household income is larger for those
employed in large and medium sized private entities than public servants.

The standard

intertemporal optimization model on consumption assumes that shocks (persistent ortransitory) are specified as multiplicative shocks to the predictable component of labor
income and hence they enter additively in the equation for the logarithm of the labor
income process (see Hubbard, Skinner and Zeldes [1995], Carroll [1992, 1997], Gourinchas
and Parker [2002]).
In this paper, following Carroll and Samwick ([1997], [1998]), we employ thelogarithm of the variance of the logarithm of non-asset income, what Carroll and Samwick(1997, 1998) call LVARLY, as a measure for labor income uncertainty.

In calculating

LVARLY for the groups of households considered, groups with less than ten households are
dropped for fear of inaccurate estimates of variances for these groups.
We employ an indicator (dummy) variable that takes the value of 1 if a respondent
answers that the head of her household is healthy as an additional measure for householdincome risk.

This variable is meant to capture the risk that a household will go through aperiod of little or no income.

A household head with health related problems is more

likely to leave his job and to lose income or is forced to take a sick leave and to go througha period of a substantial fall in his income than a healthy household head is.9
9

Ideally if the data collected information on previous occupations of a household head who is currently

not working, we would be able to estimate the job loss risk in addition to earning uncertainty of theemployed worker.
93.3. Questions on precautionary saving motives
The SFACH surveys not only amounts of total financial assets held and total debts
owed but also collects information on at least 12 saving motives such as “for one’s
children’s educational expenses” and “for living expenses during retirement” in the fourwaves used for our analysis.10

Two of the motives are consistent with precautionarysaving motives in a broader definition.

The two such motives are “for illness, disasters,

and other foreseen expenditures” and “no specific motive but for peace of mind” (hereafter“the peace of mind motive”).

While the former is the precautionary saving motive forunexpected expenses and is not the focus of this paper, the latter is the most consistent withthe theoretical concept of precautionary saving for income uncertainty. Since this is the
only saving motive for unspecified expenses at present and in the future, any household thathas precautionary wealth for income uncertainty should choose “the peace of mind motive”
as one of its saving motives. “The peace of mind motive” is not listed as one of thesurveyed borrowing motives, as households are unlikely to borrow for no specific expenses.
Use of saving for the peace of mind motive rather than a more broad measure forsaving such as total wealth is important. Since saving for the peace of mind motive does
not include saving for life cycle related motives, which are dealt with separately in at least
9 listed motives in the SFACH, the effect of the measure of income uncertainty on saving
for the peace of mind motive should be stronger than that on other traditionally used10

The 12 saving motives surveyed in all four waves of the SFACH are, (1) for living expenses during

retirement, (2) for illness, disasters, and other unforeseen expenditures, (3) for one’s children’s
educational expenses, (4) for one’s children’s marriage expenses, (5) for the acquisition (including
rebuilding and replacement purchase) of owner-occupied housing (including land), (6) for purchase of
consumer durables, (7) for leisure expenses, (8) for the payment of taxes, (9) for an independent business,(10) no specific motive but for peace of mind, (11) in order to leave a bequest, and (12) other.
10dependent variables.

The empirical results using total financial wealth which give

unfavorable results for the precautionary saving hypothesis do not necessarily reject the
precautionary saving hypothesis if the results with saving for the peace of mind motive
support the hypothesis.11
The survey questionnaire is structured such that a respondent is first asked to answer
whether her household has currently savings for each motive, and then the household’s
current financial wealth for that motive, the household’s wealth target for that motive, andthe number of years the household thinks it will need to meet that target.12

3.4. The empirical model
The main empirical model in this study is the same as the model analyzed by Lusardi(1998), Carroll and Samwick (1997, 1998), and Murata (2003) in structure, but thedependent variable is the logarithm of precautionary wealth, PEACE.

ln PEACEit = α 0 + α σ LVARLYit + α h H it + α y ln Pit + X it β it + uit
H it is a dummy variable that takes on the value of 1 if the respondent answers that thehousehold head is healthy and zero otherwise (hereafter “the health dummy”).
estimated after-tax permanent income excluding returns on assets.

11

Pit is the

X it contains age andSpecifying a functional form of a utility function, Parker and Preston (2002) decompose consumption

growth into the negative effect of precautionary saving and the rest, and regresses the estimated negative
effect of precautionary saving on consumption on income uncertainty.
12

In the 1998 and 2000 waves of the SFACH, respondents are asked to report the proportion of thecurrent financial wealth for the peace of mind motive to total financial wealth rather than the current
financial wealth for that motive itself.

Thus, in these two waves, the current financial wealth for thepeace of mind motive is obtained by multiplying this proportion by the total financial wealth for eachhousehold.
11

other demographic variables to control for the predictable life cycle effects on wealth.
Variables included in X it are the age of the household head, the square of the age of thehead, the age of the head in 1990 as a cohort effect, the number of children in the household,and time dummy variables. 13

u it is an error term.

The measure for income uncertainty,

LVARLYit , is supposed to be measured with an error.

Two variables are tested for PEACE, the household’s current financial wealth for thepeace of mind motive, PEACE_CURRENT and the household’s wealth target for thismotive, PEACE_TARGET. For households that answer they do not have savings for thepeace of mind motive, missing values for PEACE_CURRENT and PEACE_TARGET are
replaced with the value zero, but households that answer they have savings for the peace ofmind motive and leave PEACE_CURRENT or PEACE_TARGET unanswered are dropped
from the sample when the corresponding variable is used as a dependent variable. Such atreatment of missing values leaves households without the precautionary saving motive
overrepresented in the sample.

However, as long as the underlying structural modelcharacterizing the precautionary saving behavior is unaltered no matter whether adependent variable is positive or zero, which is our model specification described in thenext subsection, and households are not dropped non-randomly, such overrepresentation ofhouseholds with a dependent variable being zero does not bias the estimates.

3.5. Endogeneity of the measure for income risk and use of instrumental variables

Endogeneity of the measure for income risk LVARLY would bias its estimatedcoefficient. The error in the measure for labor income risk LVARLY could be large since
it is not a directly observable variable but an estimated one.

13

We followed Carroll and Samwick (1997, 1998) in selecting variables included in X it .
12

Even more serious is likely the reverse causality from the individual’s saving behavior
to her income risk through her risk averseness that would result in underestimating thecoefficient of LVARLY if an OLS regression were run. Risk-averse individuals save more
precautionary wealth for income risks, and are also likely to choose a more stable
occupation such as the public service or larger companies. On the other hand, risk loving
individuals save less precautionary wealth, and are likely to choose unstable occupations
such as self-employment or smaller venture companies where chances and opportunities
may be plenty but risks are higher. Thus, (labor) income uncertainty and saving can be
negatively related through an individual’s risk-averseness.
In order to correct the bias on the estimate of the coefficient of LVARLY, we estimate
the equation using an instrumental variable regression. As instrumental variables, we use
exogenous variables in the equation to be estimated (the health dummy H, and the variables
included in X), dummy variables for regions (regional dummies), dummy variables for city
size (city size dummies), dummy variables for educational attainment (education dummies,
the base group being advanced degree holders), the square of the age of the head in 1990
(the square of the cohort effect), age interacted with education dummies, age squaredinteracted with education dummies, age interacted with a health dummy, age squaredinteracted with a health dummy, a cohort effect interacted with education dummies, and acohort effect squared interacted with education dummies.

We do not include dummy

variables indicating the occupation of the household head, since the occupation of thehousehold head is likely correlated with savings through risk averseness of family members
in a household.
Indeed estimating the equation with instrumental variables is equivalent to a two-stepestimation by running an OLS regression with predicted values of independent variables
13

obtained through regressing them on instrumental variables. Thus, the value of ln Pit
predicted by instrumental variables is used in running the regression for precautionarysaving. This is equivalent to saying that the permanent non-asset income estimated using
age, education, the health condition, and the cohort effect of the household head, is used as
an independent variable.

4. Results
4.1. Descriptive Statistics and Preliminary Analysis

Figure 1 displays the profiles of gross financial wealth, net financial wealth (grossfinancial wealth less debts), current financial wealth for the peace of mind motive and theshare of current financial wealth for the peace of mind motive within gross financial wealthby the age group of the household head controlling for the birth year of the household head.
As the household head ages, gross and net financial wealth increase monotonically,
implying that households are accumulating financial wealth for their retirement as the life
cycle hypothesis suggests. The savings for the peace of mind motive, on the other hand,
do not increase as the household head ages.
As a result, the share of precautionary wealth within the household financial wealthremains high and ranges from 20 percent to 30 percent before 55, but declines to about 15
percent afterward. This feature of the age profile of our direct measure for precautionary
wealth is consistent with the finding by Cagetti (2003), who simulates the share of liquid
wealth attributable to a buffer against income uncertainty based on the estimates ofpreference parameters of a dynamic optimizing household using the wealth data on
American households in the Survey of Consumer Finances.14
14

Parker and Gourinchas (2002) and Abe and Yamada (2005) estimate preference parameters using

consumption data on American and Japanese households respectively and conduct a similar simulation
14

Descriptive statistics of dependent variables and independent variables are presented
in Table 2. In the following analysis, for the baseline regressions, households with a head
aged 50 or younger are selected in the sample as in Carroll and Samwick (1997, 1998).
This is intended to exclude from the sample life cycle savers who primarily save for theirretirement.

4.2. Estimation results using general measures for financial wealthRows one and two of Table 3 show the regression results with financial wealth used asa dependent variable. The dependent variables are gross financial wealth in the first row
and net financial wealth in the second row. Net financial wealth used in the second row isfound by subtracting the balance of all debts but housing loans from gross financial wealth.
This is because the same amount of real assets appears in the household’s balance sheet
when it holds housing loans, and the net value of debts for housing loans is approximately
zero.15, 16
Regardless of the dependent variable used, estimated coefficients of income
uncertainty and the health dummy are not statistically significant.

The results withmeasures of income and health uncertainty are not consistent with the Carroll andexercise to Caggeti’s one. They find that the share of precautionary wealth starts to decline a little
earlier in the mid 40’s than in our sample.
15

In the SFAHC, respondents are asked first whether they are borrowing and then they are asked theamount of debts they owe.

In order to keep the sample size large enough, we replace missing values

for the amount of debts with zero even if respondents answer that they are borrowing.

We apply thesame principle to housing loans held by households.
16

In the 1998 and 200 waves of the SFACH, respondents are asked to report the proportion of housing

loans borrowed to total debts owed rather than the amount of housing loans itself. Thus, in these two
waves, housing loans borrowed is obtained by multiplying the proportion by total debts owed for eachhousehold.
15

Samwick’s results for American households but are consistent with the results for Japanesehouseholds found by Murata (2003).

4.3. Estimation results with self-reported financial wealth and wealth target

Dependent variables used in rows three and four are self-reported precautionary
wealth and the wealth target for a precautionary motive.
Since many households in the sample are not saving for the peace of mind motive, andhence, variables PEACE_CURRENT and PEACE_TARGET take on zero for many
households, treating an observed dependent variable as a continuous variable is likely tolead to biased estimates. Thus, we estimate a Tobit model with instrumental variables
assuming that the model is censored at zero for the dependent variable. We use predictedvalues for the independent variables from the first stage OLS regression in the maximum
likelihood estimation of a second-stage Tobit model.17

Neither the estimated coefficient ofincome uncertainty (LVARLY) nor that of the health dummy is statistically significant
when the self-reported current precautionary wealth is used as the dependent variable.
The coefficient of LVARLY is positive and statistically significant at the five percent
level when the self-reported target for precautionary wealth is used.

Our coefficientestimate implies that an increase in LVARLY by one percent would increase the target
precautionary wealth by 0.68 percent. This means that, for instance, the self-employed,
the riskiest occupation, target 28 percent more of precautionary wealth than public servants,
17

We estimate a Tobit model with the two-step estimation method described in Amemiya (1979) andSmith and Blundell (1986).

In the second step Tobit regression, the dependent variable is regressed onvalues of endogenous variables predicted by instrumental variables as well as on estimated residuals for
endogenous variables obtained from the first step OLS regressions.

Correction of biases in standard

errors of parameter estimates resulting from the first stage OLS regressions for endogenous variables
follows the way suggested by Smith and Blundell (1986).
16the safest occupation.
The coefficient of the health dummy is negative and statistically significant at the one
percent level for the regression with the target as the dependent variable. The effect of thehealth dummy on the wealth target is large. The estimated coefficient of -0.84 means thata household with an unhealthy head targets 2.3 times as much precautionary wealth as ahousehold with a healthy head does. As saving for unexpected medical expenditures issurveyed as a separate saving motive in the SFACH, households with an unhealthy headattempt to insure against a large income loss caused by job loss or sick leave.
Our finding is consistent with the recent findings on precautionary saving by Americanhouseholds. Kennickell and Lusardi (2004) find that the target for precautionary wealthsurveyed in the Survey of Consumer Finances is correlated to various measures for
uncertainty. Parker and Preston (2002), on the other hand find that the coefficient of themeasure for income uncertainty is not statistically significant in the regression for theestimated flow of precautionary saving. Like American households, Japanese households
follow the buffer stock saving behavior. What is relevant is the target for wealth not thecurrent amount of wealth itself.
Our point estimate of the coefficient of LVARLY on the self-reported target wealth ofJapanese households is about twice as large as the coefficient of the same variable on grossfinancial wealth for American households found by Carroll and Samwick (1997) (theirpoint estimate is 0.368), suggesting that our use of the self-reported target is more
appropriate in capturing the precautionary saving motive of households.

4.4. Endogenous income uncertainty and use of a censored regression

Table 4 compares the baseline result obtained using a two-stage Tobit model with17

instrumental variables for non-asset income and the measure for income uncertainty
LVARLY with results obtained using the OLS, a Tobit model without instrumental variables,
a two-stage Tobit model with instrumental variables for non-asset income only, and the twostage least square (2SLS).
As it turns out, the coefficient of LVARLY is always negative and statisticallysignificant when LVARLY is not instrumented (OLS, Tobit, two-stage Tobit when
instrumental variables for non-asset income only are used), suggesting that the use of
instrumental variables for LVARLY is crucial in identifying the effect of income risk on thetarget for precautionary wealth with the opposite effect through differences in households’
risk averseness.
When censoring at zero for the dependent variable is not taken into account but
LVARLY is instrumented, the coefficient of LVARLY is indistinguishable from zero,suggesting that the use of a censored regression model (Tobit) is essential.

4.5. Robustness checks

Table 5 presents the robustness checks of our findings with the self-reported target forprecautionary financial wealth as the dependent variable by estimating regressions withdifferent samples of households.

Age and the buffer stock saving behavior: the US-Japan comparison
We change age groups included in the baseline sample and see if our findings still hold.
The coefficient of LVARLY remains positive and statistically significant for the sample ofhouseholds with a head aged 45 or younger. In contrast, the coefficient of LVARLY is nolonger statistically significant when households with a head older than 50 are included in18the sample.

The coefficient of the health dummy remains negative but is no longer

statistically significant for the sample of households with a head aged 45 or younger. The
coefficient of the health dummy is not statistically significant for the sample including
households with an older head.18
Our findings that income uncertainty does not influence the target for precautionary
wealth suggest that our measures for income uncertainty, LVARLY and the health dummy,
may not capture the risks faced by older households. Examples of such risks include thelongevity risk. Though the retirement motive for saving is surveyed as a separate saving
motive, respondents may choose the peace of mind motive rather than the retirement
motive for vague anxieties over their longevity.
Using the US – Japan survey conducted in 1996 by the IPTP whose questionnaire issimilar to the SFACH, Horioka et al (2000) find that Japanese households are found to be
not strongly motivated for leaving behind bequests and that 70 percent answer “I will notmake any particular efforts to leave behind a bequest but will leave to my child or children
whatever assets happen to be left over.”They, however, find that the proportion ofhouseholds that have received bequests in the past and/or that expect to receive them in the
future is 40 percent. They further show that the average bequest of households which
received bequests in the past is huge (54,110,000 yen or 475,609 dollars (exchanged at therate at the end of November 1996)) or 9.6 times the average annual household disposable
income.)They show that American households in contrast have a stronger willingness to leave

18

The sample of households with a head aged 40 or younger was also examined.

Estimates are

implausible and one of the standard errors for coefficients estimated according to Smith and Blundell(1986)’s method was an imaginary number.

These may be caused by lack of an adequate sample size(882 households).
19

behind bequests but end up leaving smaller bequests (46 percent of American households
answer “I want to make efforts to leave behind a bequest” and the average bequest left is
74,756 dollars.). Accidental bequests left behind by American households are thus small.
It may be due to this difference in preference toward the longevity risk between
American and Japanese households that the evidence by Carroll and Samwick (2002) and
our finding in the sample including older households are not consistent with each other(Carroll and Samwick find that the coefficient of LVARLY remains statistically significant
even in the sample including older households.).

Excluding the self-employed
The coefficient of LVARLY is estimated to be larger and that of the health dummy isestimated to be smaller for the sample excluding self-employed households (which includes
households in agriculture or in forestry) than for the baseline sample. This may suggestthat staying healthy is particularly important for the self-employed, particularly agricultural
workers whose work includes more manual labor than that of salaried workers.

5. Conclusion

Using the Survey on the Financial Asset Choice of Households (SFACH) conductedby the Institute for Posts and Telecommunications Policy of the Ministry of Posts andTelecommunications of the Government of Japan (the present Japan Post Corporation), we
analyzed the relationship between income uncertainty and precautionary wealth.
What makes the SFACH unique is the survey on the household’s current financial
wealth and the wealth target for at least twelve saving motives in addition to the survey on
total financial assets, borrowing, and various financial and demographic variables.
20

Weconsider saving “for peace of mind but not for specific reasons” as (self-reported)
precautionary saving against income uncertainty.
Our main findings are: 1. Measures of income uncertainty have a positive influence on
the self-reported target for precautionary wealth but not on precautionary wealth itself. 2.
The positive influence of income uncertainty on the self-reported target for precautionary
wealth vanishes for the sample including older households with a head aged 51 or older.
These findings suggest that Japanese households save against income uncertainty until
around when the head is aged 50 and then save against other risks such as the longevity
risk.

21

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Carroll, Christopher D. (1992), “The Buffer Stock Theory of Saving: Some Macroeconomic
Evidence,” Brookings Papers on Economic Activity, 1992 (2): 61-156.

Carroll, Christopher D. (1997), “Buffer Stock Saving and the Life Cycle/Permanent Income
Hypothesis,” Quarterly Journal of Economics, 107 (1): 1-56.

Carroll, Christopher D. and Andrew Samwick (1997), “The Nature of Precautionary
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Carroll, Christopher D. and Andrew Samwick (1998), “How Important is Precautionary
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Risk and Precautionary Wealth: Evidence from Households’ Balance Sheets,” Review of
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Dynan, Karen E. (1993), “How Prudent Are Consumers?” Journal of Political Economy,
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Horioka, Charles Yuji, Hideki Fujisaki, Wako Watanabe and Takatsugu Kouno (2000). “Are23

Americans More Altruistic than the Japanese? A U.S.-Japan Comparison of Saving andBequest Motives,” International Economic Journal, 14 (1): 1-31.

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24

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25

Table 1 The measure for uncertainty of non-asset income by occupation
The logarithm of thevariance of thelogarithm

Occupation

The standard error

Private entity, less than 30 employees

0.5120313.2

Private entity, 30 or more but less than 500 employees

0.4771

404.6

Private entity, more than 500 employees

0.4959398.9Public service

0.4365415.0

Self-employment

0.7146

826.5

3903

3903

N
A unit of non-asset income is 10,000 yen
Table 2. Descriptive statistics
Continuous variables
N

Mean

Std

MIN

MAX

The current wealth for the PM motive

2983

170.2

479.2

0

8100

The wealth target for the PM motive

2825

242.6

771.0

0

12000

Gross financial wealth3351

1120.71369.7

1

21100

Net financial wealth

3351

993.3

1555.3-2601321100

After tax non-asset income

3903

765.2

439.8

4

7700

LVARLY

2983-1.5290.5485-2.9460.080

Age of the household head in the survey year

3903

45.5

8.27

30

59

Age of the household head in 1990

3903

38.6

8.7

20

55

Number of children

3903

1.70

0.97

0

7

Note:
1. Amounts are in 10,000 yen.
2. In calculating summary statistics for the current wealth for the peace of mind (PM) motive andthe wealth target for the peace of mind motive, respondents who reported they have savings for thepeace of mind motive but do not give the amount are excluded.
3. LVARLY is the logarithm of the variance of the logarithm of non-asset household income.
26

Binary variables
Frequency Proportion
RegionCity size

Hokkaido and Tohoku

444

11.35

Kanto

1260

32.28

Chubu and Kinki

1267

32.46

Chugoku and Shikoku

424

10.86

Kyushu

509

13.04

Total

3903

Tokyo 23 boroughs

314

8.05

11 large cities

669

17.14

Cities of population over 150,000

1195

30.62

Cities of population over 50,000

726

18.60

Cities of population below 50,000

244

6.25

Counties (rural)

755

19.34

Total

3903

Occupation of the head Private entities, less than 30 employees

540

13.84

Private entities, less than 500 employees

1031

26.42

Private entities, 500 or more employees

1052

26.95

Public service

479

12.27

Agriculture, forestry, and self-employment

801

20.52

Total

3903

Educational attainment Junior high schoolof the headHigh school

Health condition of
the household head

505

12.94

1897

48.60

Advanced education

1501

38.46

Total

3903

Healthy

3729

95.54

Sick

174

4.46

Total

3903

27

Table 3. Estimation results
Dependent variable
Financial wealth

LNINCOME (log of
non-asset income)LNVARLY
Health dummy
Head age
Head age squared
Head age in 1990
The number of children
Year dummy for 1996
Year dummy for 1998

Peace of mind motive
Current
wealthGross

Net1.568***

2.482***

4.552***

5.150***(0.188)(0.533)(0.291)(0.327)0.291

0.683**(0.249)(0.268)-0.220(0.168)0.051-0.100(0.476)-0.021

Target wealth-0.839***-0.253(0.138)(0.389)(0.203)(0.245)

***

***

***

-3.092***-2.179-3.994-4.035(0.384)(1.087)(0.585)(0.668)

***

**

***

0.00530.0101***-0.0032-0.0048(0.0008)(0.0022)(0.0012)(0.0014)

***

***

***

2.447

4.376

3.430

2.021***(0.432)(1.221)(0.655)(0.749)-0.136***-0.274***0.009-0.132*(0.026)(0.072)(0.039)(0.046)

***

***

***

4.655

8.434

8.174

5.820***(0.840)(2.377)(1.271)(1.453)

***

***

***

10.561***

9.932

17.45614.990(1.742)(4.927)(2.641)(3.018)14.838***25.986***21.032***14.321***(2.599)(7.353)(3.942)(4.507)

The predicted residual
for LNINCOME

1.174***

1.507***(0.290)(0.327)

The predicted residual
for LVARLY**-0.960***(0.249)(0.268)

2014

1902

Year dummy for 2000

N

-0.562
2267

2267

Note
1. *, ** and *** show that a coefficient is statistically significant at 10 %, 5% and 1% respectively.
2. LNVARLY is thea logarithm of the variance of the logarithm of non-asset household income.
3. In the text, current wealth for the peace of mind motive and the target wealth for the peace of mind
motive are sometimes abbreviated as PEACE_CURRENT and PEACE_TARGET respectively.
28

Table 4. Results of alternative econometric models for the target precautionary wealthTwo-step Tobit(baseline from
Table 3)logarithm of non-asset
incomeLVARLY
Health dummy

***

5.150

Head age squared

The number of children

Year dummy for 2000

0.617

***

5.118***

1.747***(0.521)1.777(0.425)(0.292)

**

***

0.683

-0.261-0.836-0.4940.066(0.268)(0.133)(0.393)(0.076)(0.427)-0.839***-0.171-0.598-0.748***(0.245)(0.382)(1.098)(0.219)***-0.231(0.390)-3.092

0.918

0.119

-4.225(0.668)(0.722)(2.285)(0.572)0.0101***0.00350.00860.0087***0.0037*(0.0014)(0.0021)(0.0060)(0.00120)(0.0022)

***

2.021

-1.249(0.749)(0.885)(2.554)(0.640)-0.274***-0.081-0.217-0.268***(0.046)(0.071)(0.209)(0.041)-0.960

3.291

***

5.820

-1.569

0.831

8.341

(1.453)(1.7847)(5.1284)(1.238)10.561-4.381-1.7040(3.557)(10.227)(2.585)-6.864-3.60721.967***(5.327)(15.317)(3.854)(3.018)14.321***(4.507)15.623***The predicted residual
for LNINCOME

1.507***

1.505***(0.327)(0.292)The predicted residual
for LVARLY

***

N

2SLS

exogenous
***

**

***

Year dummy for 1998

LNVARLY is(0.138)***Year dummy for 1996

Tobit

**

***

Head age in 1990

OLS

(0.327)***

Head ageTwo-step Tobit,-0.361(1.066)-0.021(3.009)-0.274(1.194)0.556(2.316)
0.639(4.813)0.519(18.117)-0.960(0.268)
1902

1902

1902

1902

1902

Note
1. *, ** and *** show that a coefficient is statistically significant at 10 %, 5% and 1% respectively.
2. LNVARLY is the logarithm of the variance of the logarithm of non-asset household income.

29

Table 5. Robustness checks
Exclude45 or younger 50 or younger 55 or younger 59 or younger
agriculture, forestry(baseline fromandTable 3)self-employment
LVARLY
Health dummyN

0.803**

0.683**

1.090

0.054

0.956***(0.407)(0.268)(0.868)(0.645)(0.258)***-0.054-0.563**-0.8390.206(0.423)(0.245)(0.248)(0.178)(0.248)

1363

1902

2403

2817

1597

-0.233Note
1. *, ** and *** show that a coefficient is statistically significant at 10 %, 5% and 1% respectively.
2. LVARLY is the logarithm of the variance of the logarithm of non-asset household income.

30

Figure 1-1 Age profile of gross financial wealth
2000
1800
1600
(10,000 yen)

1400
1200
1000
800
600
400
200
0
30-34

35-39

40-44

45-49

50-54

55-59

1956-1960

1961-1965

Age group of the household head
1936-1940

1941-1945

1946-1950

1951-1955

Note: Each curve tracks households in the same cohort.

Figure 1-2 Age profile of net financial wealth
1800
1600

(10,000 yen)

1400
1200
1000
800
600
400
200
0
30-34

35-39

40-44

45-49

50-54

55-59

1956-1960

1961-1965

Age group of the household head

1936-1940

1941-1945

1946-1950

1951-1955

Note: Each curve tracks households in the same cohort.
31

Figure 1-3 Age profile of financial wealth for the peace of mind motive
600
500(10,000 yen)

400
300
200
100
0
30-34

35-39

40-44

45-49

50-54

55-59

1956-1960

1961-1965

Age group of the household head
1936-1940

1941-1945

1946-1950

1951-1955

Note:
1. Each colored curve tracks households in the same cohort.
2. Households that save for the peace of mind motive but do not report current financial
wealth accumulated for the peace of mind motive are assumed to have the average forhouseholds who report current financial wealth for the peace of mind motive.

32

Figure 1-4 Age profile of the share of financial wealth for the peace of mind motive
0.6
0.5
0.4
0.3
0.2
0.1
0
30-34

35-39

40-44

45-49

50-54

55-59

1956-1960

1961-1965

Age group of the household head
1936-1940

1941-1945

1946-1950

1951-1955

Note:
1. Each colored curve tracks households in the same cohort.
2. Households that save for the peace of mind motive but do not report current financial
wealth accumulated for the peace of mind motive are assumed to have the average forhouseholds who report current financial wealth for the peace of mind motive.
3. The share is taken against gross financial wealth.

33