OSIPP Discussion Paper : DP-2008-E-007

The Response of Wives’ Labor Supply to Husbands’ Job Loss
April 11, 2008
Miki Kohara
Associate Professor, Osaka School of International Public Policy (OSIPP)
Key Words: Added worker effects, Female labor supply, Within-family risk-sharing,
Household panel data, JapanJEL Classification Number: D12, J22, C23
Abstract: This paper examines how Japanese wives react to their husbands’ involuntary
job loss, and tests the existence of complementarity of a wife’s labor supply to herhusband’s. Utilizing panel data on Japanese households from 1993 to 2004, we foundthat wives’ labor supply is stimulated when husbands suffer involuntary job loss. The
detailed statistics show that not only do working wives raise their labor hours but also
nonworking wives begin to participate in the labor market. The added worker effect isevident during the period of job insecurity in Japan following the mid-1990s.

* Osaka School of International Public Policy, Osaka University; 1-31 Machikaneyama,
Toyonaka, Osaka, 560-0043, Japan. (E-mail) kohara@osipp.osaka-u.ac.jp
The author is grateful to Kenn Ariga, Jose Luis Moraga Gonzalez, Fumio Hayashi,
Yoshio Higuchi, Charles Yuji Horioka, Yasushi Iwamoto, Akira Kawaguchi, Daiji
Kawaguchi, Franco Mariuzzo, Colin McKenzie, Naoki Mitani, Fumio Ohtake, Soichi
Ohta, Kazuaki Sakamoto, Makoto Saito, Masaru Sasaki, Ryuhei Wakasugi, Makoto
Yano, seminar participants at University of Groningen, Keio University and
Hitotsubashi University, and the members of Kansai Rodo Kenkyukai for their valuablecomments. The author is also grateful to the Ministry of Education, Culture, Sports,
Science and Technology of the Japanese Government for Grant-in-Aid for Scientific
Research number 12124207, which supported this research.

1

Introduction

Unemployment rates in Japan have skyrocketed since the mid-1990s. Not only young
unskilled workers but also middle-aged male workers have been laid oﬀ. These middleaged workers are often the main income earners in a household. How have Japanesehouseholds reacted to these changes? This paper examines their reactions to husbands’
involuntary job loss, focusing on wives’ labor supply.
When a household’s main income earner loses a job, other household members might
supply more labor either sequentially or simultaneously to compensate for the job loss.
This is called the Added Worker Eﬀect and has been examined in several countries. Heckman and MacCurdy (1985) use data from the Panel Study of Income Dynamics for 1968
to 1975 to show that the wife participates more in labor markets when the husband isunemployed. In contrast, Lundberg (1985) and Cullen and Gruber (2000) show that theadded worker eﬀect may exist but is quite small in the United States. Spletzer (1997)shows that there is the added worker eﬀect in the United States, but that this is largely explained by unobservable heterogeneity between wives whose husbands have lost their jobs
and wives whose husbands have not. The heterogeneity causes an endogeneity between
the husband’s unemployment and the wife’s labor supply. Bingley and Walker (2001)
take this endogeneity into consideration and show that the added worker eﬀect is small
but that it becomes large when the husband’s unemployment period is long. Stephens(2002) emphasizes the use of involuntary job loss as an exogenous employment shock andshows that the wife’s labor supply does not react promptly to the husband’s involuntary
job loss. Fernandes and Felicio (2005) ﬁnds that the added worker eﬀect exists in Brazil,

2

focusing on non-working wives’ reaction to their husbands’ unexpected job loss.
The fact that wives ﬂexibly change their labor supply might be surprising. Despite
the huge research on household behavior, much of it neglects households’ leisure/labordecisions and concentrates on consumption behavior. For example, many empirical investigations have examined whether households’ consumption responds to idiosyncratic
shocks (e.g. tests of the applicability of the life-cycle permanent income and full insurance hypotheses). There is little empirical research that deals with both consumption andleisure at the same time.1
The present paper clariﬁes whether or not household’s members change their labordecisions in response to unexpected shocks surrounding them. In order to examine this,
we focus on the existence of surplus labor and its reaction to shock experienced by mainincome earners. That is, the ﬁrst purpose of our paper is to reexamine the existenceof the added worker eﬀect. Empirical analysis on the added worker eﬀect has somediﬃculties such as lack of detailed information on wives’ working hours, their working
history, households’ savings/consumption patterns, and causes of husbands’ job loss. We
utilize panel data containing extensive household information so that we can specify abetter estimation model.
Another purpose of the paper is to clarify the existence of the added worker eﬀect inJapan. We not only use Japanese household data but also highlight the period when the1

Exceptionally, Low (2005) and Pijoan-Mas (2006) describe an individual’s life-cycle labor supply

behavior with precautionary motive, and shows that one can change labor supply ﬂexibly in response
to his/her uncertainty as well as wages. Attanasio et al. (2005) simulate the changes in consumption,savings and wives’ labor supply in relation to income uncertainty, and ﬁnd that female labor supply is
responsive to idiosyncratic shocks especially in those households with borrowing constraints.

3

added worker eﬀect could exist if there is any in this country. That is, our sample period(1993–2004) includes the period when Japan’s unemployment rate increased dramaticallyand stayed at a high level.
It is important to examine the existence of the added worker eﬀect in Japan for four
reasons. First, as mentioned in the last paragraph, Japan has experienced a sharp rise in
unemployment and a dramatic change in the employment atmosphere in the past decade,which provides us with a suitable context in which to examine the changes in households’
economic behavior such as labor supply and consumption. We can add another result tothe existing arguments on the added worker eﬀect.
Second, Japan is famous for having a large proportion of women who are housewives,
especially among wealthy households. Many women become housewives after marriage orat least after having a child. It is interesting to observe how Japanese labor-risk-sharing
within a household has changed (or not changed) after about a three-percentage-point
rise in middle-aged male unemployment. Third, Japan is known for its households’ highsaving rates. As drawing down savings could be one way to cope with the main earner’s
job loss, the level of savings can make a diﬀerence to a household’s labor-risk-sharing. The
Japanese data may reveal interesting diﬀerences from other countries’ results. Fourth, so
far there have been no tests of the added worker eﬀect for Japanese households, while there
is research examining the negative relationship between the wife’s labor force participationand the husband’s income (see Higuchi (2001), for example). The present paper is theﬁrst attempt to clarify the existence of the added worker eﬀect in Japan.
Our empirical investigation shows that wives’ labor supply was indeed stimulated by
the husbands’ involuntary job loss in Japan between 1993 and 2004. The detailed statistics
4

suggest not only that working wives raised their work hours, but also that nonworking
wives came to participate in the labor market. Moreover, we ﬁnd that nonworking wives
started seeking work after their husbands lost their jobs. Clearly the added worker eﬀect
was present during the high unemployment period in Japan following the mid-1990s. The
eﬀect could become large if we counted potential labor supply.
The rest of the paper consists of four sections. Section 2 explains the theoreticalbackground and the empirical model. Section 3 introduces the data used in the empiricalanalysis. Section 4 presents the estimation results. The ﬁnal section concludes the paper.

2

The Background Theory and the Estimation Model

The explanation of the added worker eﬀect (hereafter, AWE) is descriptive, but the underlying theory is summarized as a form of risk-sharing behavior within a family. A family
that maximizes their expected life time utility subject to their life time constraints faces
an intertemporally optimal condition such that today’s leisure and consumption equate to
the discounted present values of future leisure and consumption as long as the marginalutility, which is expected lifetime wealth, is unchanged over time. The so-called Euler
equations imply that a rational household does not react to temporary shocks while they
may react to unexpected permanent shocks. This smoothing behavior is a central hypothesis to be tested by the evidence of a simple life-cycle permanent income hypothesis.
A household also faces intratemporally optimal conditions such that the marginalutility of leisure weighted by wage equals the marginal utility of consumption, and that thisweighted marginal utility of leisure is equal between the wife and the husband. The wife’s

5optimal leisure/labor decision depends on complementarity or substitutability between
leisure and consumption, and between the husband’s and the wife’s leisure time.2
Thus, when the husband unexpectedly loses his job and family income decreases, thewife may raise her labor supply, partly as a reaction to unexpected permanent shocks
and partly as complementarity between the husband’s and the wife’s leisure time.3 The
literature on the AWE focuses on the shock of job loss or the displacement of the mainincome earner, and examines the response of surplus labor in a family. Because the mainincome earner is usually the husband, the question to be answered is whether or not wives

2

Household’s maximization problem is summarized as
max Et {Tc,lh ,lw

s.t. As+1s=t

β s−t u(cs , ls1 , ls2 )}= (1 + r){As + (1 − ls1 )ws1 + (1 − ls2 )ws2 − cs },where β is a discount factor (1 ≥ β ≥ 0), c is consumption (c ≥ 0), l is normalized leisure time (1 ≥ l ≥ 0),
and A is asset holdings satisfying AT = 0. The husband, denoted as subscript 1, and the wife, as 2, receive
returns on assets r and returns on labor w (wage rates). The ﬁrst-order conditions areuc (ct , lt1 , lt2 )1
2= β(1 + r)uc (ct+1 , lt+1
, lt+1),ulk (ct , lt1 , lt2 )1
2= β(1 + r)ulk (ct+1 , lt+1
, lt+1); k = 1, 2,

ul1 (ct , lt1 , lt2 )/wt1 = ul2 (ct , lt1 , lt2 )/wt2 = uc (ct , lt1 , lt2 ).
The ﬁrst two are Euler equations where uc and ul are marginal utility of consumption and leisure ineach period:∂u(ct ,l1t ,l2t )∂u(ct ,l1t ,l2t )and(k = 1, 2).
∂ct
∂lk
t

The last one is an intratemporal relationship between

consumption, the husband’s leisure and the wife’s leisure.
3

If we also take home production into consideration, the wife’s reservation wage for labor supply

could fall according to the husband’s unemployment and his increased time for home production. The
realization of the wife’s lower reservation wage raises her labor supply in the market.

6can sacriﬁce their leisure and aﬀord working time to compensate for their husbands’ jobloss.
We can thus test the existence of the AWE, examining if wives’ labor hours increaseas their husbands lose their jobs involuntarily. We can regress changes in wives’ labor
hours, ΔW Lit , on the variables including husbands’ job loss, HUit , such as
ΔW Lit = α0 HUit +j=1

αj HUit−j + ΔXit δ + εit(1)where i is a household and t is a survey year (i = 1, ...N, t = 1, ..., Ti ). W L is wives’
paid labor hours, HU is a dummy variable indicating whether or not husbands lost jobs
involuntarily, and X is a matrix of the other households’ characteristics.4 This is a general
speciﬁcation that the existing literature has examined. We mainly estimate this equation,with appropriate modiﬁcations utilizing our panel data sets.
We write the error components as εit = μi + νt + uit , where μi ∼ IID(0, σμ2 ), νt ∼IID(0, σν2), and uit ∼ IID(0, σu2 ). E(μiuit ) = 0, E(uit uis ) = 0 for all i and t = s. uit isorthogonal to all the explanatory variables in X. The null hypothesis is no existence of
the AWE; α0 = 0. That is, the wife does not alter her labor supply in the year when her
husband loses his job, so that the household can pool the shock of job loss.
Note that a husband’s resigning from a job, but not an unexpected job loss, could be
4Unless consumption and the wife’s leisure are additively separable in the utility function, changes

in a wife’s leisure are inﬂuenced by the substitutability or complementarity between her leisure andconsumption. We assume an additive separability and do not treat households’ simultaneous decisions
on consumption and leisure explicitly. As mentioned later, we cannot ﬁnd any signiﬁcant eﬀect of consumption changes on wives’ leisure changes, even if we include consumption changes as an explanatory
variable. Further consideration of simultaneous decisions between consumption and leisure remains for
future research.

7

endogenous in a decision regarding a wife’s labor supply. As previous literature points
out, the more hours a wife works, the more easily the husband may choose to resign froma job. This is not the AWE. In order to avoid this endogeneity problem, we ﬁrst take HU
as involuntary job loss but not job resignation.
As another treatment, we regress the changes in wives’ labor time but not its levels
on the husbands’ job loss. That is, we examine the wives’ reaction to the husbands’
job loss but not a simple relationship between the length of the wife’s labor time andher husband’s unemployment condition. Examining dynamic changes but not levels of
wives’ labor hours has additional merit that we can remove the problem of time-invariant
omitted variables.5
In the equation (1), there may be a time lag between husbands’ involuntary job loss,
HUit−j (j ≥ 1), and wives’ reaction. In this case, the coeﬃcients of αj (j ≥ 1) can bepositive. However, the sign of αj (j ≥ 1) can be positive even if there is no time-lagged
AWE. Suppose that wives respond to their husbands’ job loss in the same year: t is theyear in this paper, and there exists a within-one-year AWE. Also suppose that wives’labor hours are shorter in this year as their labor hours were longer in the previous year:
wives’ labor hours are negatively correlated between years.6 When a husband loses his
job in a given year, his wife will increase her labor supply in the same year, which inturn decrease her labor supply in the following year. If wives’ labor hours are positively
correlated between years, wives will raise their labor supply in the following year. That is,
without a time-lagged eﬀect of husbands’ job loss, the signs of the coeﬃcient on husbands’
5

We attempted to analyze the levels (length) of wives’ labor hours, and found the same implication

for the existence of the AWE as shown in the present paper.
6

Wives’ labor hours actually have negative time dependencies in our sample.

8

past job loss, αj , could become positive, zero or even negative depending on the existenceof a within-one-year AWE and time dependencies in wives’ labor hours. Thus, we cantest at least the existence of within-one-year AWE by α0 , while we cannot always test theexistence of the time-lagged AWE by αj (j ≥ 1).
It is important to deal with time dependency in wives’ labor hours properly if there areany, even when we test the existence of within-one-year AWE. We can include wives’ pastlabor hour changes, ΔW Lit−k (k ≥ 1), explicitly as explanatory variables in the equation(1). The coeﬃcients on ΔW Lit−k (k ≥ 1) are positive, negative or zero depending on
the relationships of wives’ labor hours between years. This estimation, however, raises a
problem of autocorrelation, since the model now includes lagged dependent variables on
the right-hand side. Following Arellano and Bond (1991), we take the ﬁrst diﬀerence toremove individual ﬁxed eﬀects and conduct GMM estimation of
ΔW Lit = α0 HUit +j=1

αj HUit−j +kβk ΔW Lit−k + ΔXit δ + Δεit(2)with appropriate instruments of Z = [Z1 , Z2 , · · · , ZN ] where E(Zi Δεi ) = 0. The possible
instruments are W Li1 , · · · , W Lit−2 , xi1 , · · · , xit for ΔW Lit−1 , where xi is a vector of all
the exogenous variables in Xit .
There are additional considerations regarding this speciﬁcation. First, there may be a
problem of multicollinearity between husbands’ past job loss, HUit−j (j ≥ 1) and wives’
past labor hour changes, W Lit−k (k ≥ 1). Therefore, we examine the case excluding
husbands’ past job loss from the explanatory variables. Second, we include the amount ofﬁnancial asset holdings one year before the time period of labor hour changes in Xit . Thiscan be important since family decisions on labor supply could be related to the behavior

9

of its precautionary savings. Households’ precautionary behaviors may diﬀer, which thenaﬀects wives’ reaction to husbands’ employment shocks. We include the amounts ofsavings accumulated by the end of the previous year, Sit , as an additional explanatory
variable in (1) and (2). The amount of savings, Sit , might be endogenous: E(Sit εis ) = 0
∀ s ≤ t, since the wife’s labor decisions may aﬀect the household saving decisions, orunobserved components in the error term may be correlated with amounts of savings. In
this case, the instruments are W Li1 , · · · , W Lit−2 , Si1 , · · · , Sit−2 , xi1 , · · · , xit for ΔW Lit−1 .
Controlled variables in Xit are mostly based on the past literature testing the AWE,
such as family needs and a wife’s age. Time-invariant variables are dropped by the ﬁrstdiﬀerence operation. The interaction terms with a wife’s educational attainments areincluded, as educational attainments may be essential factors in the Japanese household’s
economic decisions. We can simply estimate (1) if there is no time dependency in thewife’s labor hours, while we should estimate (2) if there is.7

3

The Data

The present paper uses the Japanese Panel Survey of Consumers (JPSC, hereafter) conducted by the Institute for Household Economy in Japan. This is one of the few panel
data sets in Japan. The survey questions women aged between 24 and 34 in the starting
7

In order to control for diﬀerences in risks surrounding households, we estimated the model including

income or consumption variances over the past four years within a household. Inclusion of them does
not alter the implications of the following results at all. We also controlled for consumption changes
as an endogenous explanatory variable, and the coeﬃcient on consumption changes was not statisticallysigniﬁcant while the exogeneity was accepted.

10year of 1993. The women are asked about their families and themselves in regard tolabor status including questions about job change, income, occupation and labor hours,
consumption and saving behavior, and asset holdings.
The survey collects detailed information on job changes by the husband during theprevious year. Using the survey information, we can identify those husbands who losttheir jobs at least once during the previous year between the last survey and the present
survey. The reason why we limit this to one year is that most unemployed in Japan startworking within a year after they lose a job.8 We can further distinguish whether or not
the job loss was involuntary by looking at the reasons for it. Involuntary reasons include
being laid oﬀ, the plant closing, or bankruptcy.
The JSPC asks respondents separate questions about their paid regular working hours,
their paid overtime working hours, and their unpaid overtime working hours per week.
We sum paid regular and overtime working hours because we are interested in looking at
whether the wife compensates for the husband’s negative shocks. An increase in unpaid
working hours does not mean compensation for the husband’s loss. The deﬁned working
hours are zero for non-workers. This may cause a nonlinearity in dependent variables,
since the changes in labor hours for working wives may be diﬀerent from those for nonworking wives. To check this possibility, we took the log of changes in wives’ labor hours
and conducted the same estimation, but the sign and signiﬁcance were unchanged.9
8

We do not divide a year into shorter time periods, since it decreases the number of households who

experienced the husband’s unexpected job loss. We also do not utilize the information on unemployedperiods, since the characteristics of such a limited number of households make the results unstable.
9

The present paper includes non-workers together with workers, since we do not want to loose theinformation on changes from non-workers to workers. We cannot conduct the empirical estimation for11As expected, factors other than complementarity with the husband’s job loss could also
cause an increase in the wife’s labor supply. For example, if the wife’s ﬁrm is growing or
if she is promoted as her job tenure increases, her working hours may increase. To control
for these eﬀects, we include the wife’s age and the interaction of several characteristics
with educational attainment as control variables. As mentioned, time-invariant variables,such as wives’ educational attainments and time-invariant characteristics of their working
environments, are dropped by the ﬁrst-diﬀerence operation. Other controlled variables
are the number of children, and the stock of saving (the outstanding balance of savings
accounts and holdings of securities) at each survey point.
Twelve waves of the JPSC, from 1993 till 2004, are available, but our estimation
uses (at most) nine diﬀerenced periods from 1995–96 to 2003–04. This is because twoyear lagged information is needed to instrument for the ﬁrst-diﬀerenced transformation of
one-year lagged wife’s labor hours. The sample is also limited to married women, to thenon-self-employed sample, and to the sample containing suﬃcient variables required in the
regressions. The total number of observation is 4212 (884 households) for the estimation
under an assumption of exogenous ﬁnancial assets, and 4052 (856 households) under anassumption of endogenous ﬁnancial assets. The descriptive statistics are summarized inTable 1.
Before introducing the estimation results, we ﬁrst overview the movements of husbands’ and wives’ employment rates in Japan, using our JPSC data. According to Figure
1, husbands’ and wives’ employment rates move in opposite directions, suggesting thenon-workers because of insuﬃcient observations. We will discuss the diﬀerence between non-workers andworkers later at the end of the Results section.

12

possibility that wives’ labor supply is complementary to husbands’ job loss. In our sample, about 1.5% of the households experienced the husband’s involuntary job loss during
the previous year, between 1993 and 2004. This small percentage is close to what themacro statistics show: the unemployment rate of household heads was about 1.5% in 1999
according to the National Survey of Family Income and Expenditure (Statistics Bureau).
The JPSC asks about income sources when the husband was laid oﬀ, which is listed inTable 2. Although 40% do not have unemployment periods, a quite high percentage
answers wife’s or parent’s income. The wife’s labor may be an important candidate forcomplementing the husband’s labor. Savings may be another important income source
for unemployed households. In the following regressions, we estimate the eﬀect of thehusband’s involuntary job loss on the wife’s labor supply after controlling for householdsavings.

4

The Results

4.1

Does the AWE exist in Japan?

Table 3 summarizes the results using a ﬁxed-eﬀects model including husbands’ job loss.10
Column (1) does not include their past job loss, and columns (2) to (4) do include one-,two-, and three-year lagged eﬀects. Either result shows that the husbands’ job loss during
the previous year has a positive eﬀect in the present year. This is statistically signiﬁcantmostly at the 5% signiﬁcance level. The coeﬃcient on husbands’ job loss in the previous
year (which occurred from 12 to 24 months before) is negative and signiﬁcant at the 1%
10

We also attempted a random-eﬀects model and found the same implications.

13

level, the two-year lagged job loss is negative and signiﬁcant at the 10% level, but thethree-year lagged job loss is insigniﬁcant. As mentioned before in the model, the negativeeﬀects of the husbands’ past job loss do not deny the existence of the wives’ added worker
eﬀect. If wives’ labor hours in the previous year were responsive to that year’s husbands’
job loss, and if wives’ labor hours had persistence, the negative sign would be expected.
The results in Table 3 suggest that wives’ added worker eﬀect exists in Japan.
In order to take account of persistences in wives’ labor hours over time, Table 4 shows
the results of GMM estimation including wives’ lagged labor hour changes.11 Columns(1a) and (1b) in Table 4 drop the husbands’ job loss in the previous year, while columns(2a) and (2b) include it, and columns (3a) and (3b) include additionally the two-year
lagged job loss. We do not show the results including three-year lagged job loss, since
this is not statistically signiﬁcant in any estimation. The diﬀerence between (a) and (b)is whether or not we include the previous year’s ﬁnancial asset holdings.
In either estimation, the husbands’ job loss coeﬃcient in the present year is positiveand signiﬁcant at the 1% level. On the other hand, husbands’ job loss in the previous years
mostly become statistically insigniﬁcant even at the 10% level. That is, if we control for
dynamic persistence of wives’ labor hours, we cannot ﬁnd a negative eﬀect for husbands’
past job loss, while we still have a positive eﬀect of husbands’ job loss on the wives’ labor
hours in the present year. The husbands’ job loss increases the wives’ paid labor hours

11

Simply including the lagged dependent variables in the estimation of Table 3 raises a problem of

autocorrelation: modiﬁed Durbin-Watson statistics in the case including wives’ past labor hour changes
are 1.863, 1.866, 1.865 and 1.866 respectively for columns (1), (2), (3) and (4) of Table 3, implying
existence of AR(1) serial correlation.

14

by about 2.1–2.8 hours per week. There exists at least within-one-year AWE in Japan.
As another important point, the coeﬃcients on changes in ﬁnancial asset holdingsin this year and the previous year are negative and signiﬁcant at the 1% level. Thissuggests that labor supply and savings should be substitutes, and households with moresavings supply less labor and more leisure. This may suggest the importance of taking
account of precautionary saving behaviors in explaining households’ decisions. We foundthe same implication even when taking ﬁnancial asset holdings as endogenous variables:
the coeﬃcients on the present husbands’ job loss are 2.05, 2.28 and 2.44, respectivelyin (1b), (2b) and (3b), and all of them are statistically signiﬁcant at least at the 12%level; the coeﬃcients on the previous year’s husbands’ job loss are not signiﬁcant; andthe coeﬃcients on wives’ paid labor hour changes in the past years are all the same signs,
size, and signiﬁcance, as is shown in Table 4.12
As for the speciﬁcation, the wives’ labor hour changes in the past years are negativeand signiﬁcant at the 1% level. It is important to consider dynamic persistence of wives’labor hours. We show the results including only one- and two-year lags, as we do not ﬁnd
any statistically signiﬁcant eﬀect of more lagged values. As for other signiﬁcant variables,
households with more children decrease the wife’s labor hours. This is a natural result
for mothers’ labor supply.
The estimation models in table 4 satisfy over-identiﬁcation conditions and there isno second-order serial correlation for the disturbances of the ﬁrst-diﬀerenced equation(E(Δuit Δuit−2 ) = 0). Because our estimation is derived from about 1000 households over12

The present paper points out the importance of households’ precautionary savings but does not deal

with it explicitly. More detailed investigation should be considered in future research.

15

a maximum of nine years, an Arellano-Bond GMM estimator may have a ﬁnite-sample
downward bias, as usually criticized. So, we conducted one-step GMM estimation andfound that the coeﬃcients on the husbands’ job loss are 3.55, 4.05 and 3.58, and all of them
are statistically signiﬁcant at least at the 5% level, respectively for (1b), (2b) and (3b).
The husbands’ previous year’s job loss are not statistically signiﬁcant and the wives’ pastlabor hour changes are negative and signiﬁcant, which are all the same as the implicationsin Table 4. Wives raise their labor hours in response to their husbands’ involuntary job
loss by 3–4 hours per week.
It might be surprising that wives reacted to their husbands’ job loss during a severerecession in Japan. During a recession, there may be a discouraged worker eﬀect such thatwives do not try to supply labor because of fewer employment opportunities. Even in suchstate of the economy, there are possible reasons why we found wives’ positive labor supply
responses. First, our deﬁnition of the husband’s involuntary job loss is job loss during theprevious one year, which includes not only a present condition of unemployment but also
experiences of unemployment during the previous one year between the last survey andthe next survey. Wives’ responses may look rapid but they actually include responses one
year after their husbands’ job loss. Second, the job loss of a husband, who is usually themain income earner, may give a severe shock to a household. Serious economic conditions
during this sample period made households expect the shock to last rather a long time,
lowering their life-time wealth. Third, there was indeed surplus labor in many families inJapan. As is well known, the rate of labor participation of married women is low and theirlabor hours are short in Japan. The changes in the labor market, where many full-timejobs were replaced by part-time jobs during the recession of post mid-1990, may have16

motivated them to participate in the labor market.

4.2

Who raises working hours?

We found that many Japanese wives actively reacted to their husbands’ job loss. Did
working wives raise their labor hours? Or did nonworking wives start working? Table
5 summarizes how either working wives or nonworking wives reacted to their husbands’
involuntary job loss. Although we do not have a suﬃciently large sample to examine
empirically the changes in nonworking wives’ job status, the table shows an interesting
feature. First, the wives who experienced their husbands’ involuntary job loss raised their
paid working hours much more than those who did not: the paid working hours increased
by 0.90 hours on average for those without job loss but by 4.12 hours for those withjob loss. Panel A shows the working hour changes for the wives who had worked in theprevious year. Among them, those who experienced the husbands’ involuntary job loss
raised their paid working hours by 0.75 hours (45 minutes) per week, although the wives
who did not experience their husbands’ job loss indeed decreased their working hours by
1.5 hours per week during the sample period from 1993 to 2004.
Panel B shows the changes for the wives who had not worked in the previous year.
Those who experienced their husbands’ job loss raised their working hours by 8.6 hours
per week, which is three times more than those who did not. 33% among previously
nonworking wives started working, while only 13.5% of those who did not face theirhusbands’ job loss started working. Furthermore, previously nonworking wives who faced
their husbands’ job loss started seeking work more frequently. The percentage of wives
who started working or seeking jobs is more than twice as much as those without husbands’
17involuntary job loss. These statistics emphasize that the AWE exists, and the potential
AWE could be even larger.
Japanese wives’ labor supply responds to their husbands’ job loss. During the highunemployment period following the mid-1990s, there existed the added worker eﬀect not
only for working wives’ labor hours but also for nonworking wives’ new labor supply.
The added worker eﬀect could be greater, including a larger potential labor supply in themarket.
The married female labor supply seems more ﬂexible than we expect. Although mostof the economic literature on households’ behaviors focuses on consumption but not on
the labor (leisure) decisions, we should take leisure into account when we describe thebehavior and welfare of economic agents.

5

Conclusion

In this paper, we examined how a wife’s labor supply responded to her husband’s involuntary job loss following the mid-1990s. We utilized panel data containing extensive
household information, which are indispensable for our analysis. This makes it possible
for us to investigate wives’ leisure decisions as well as households’ savings behavior against
unexpected shocks. Furthermore, our sample period (1993–2004) included a period when
Japan’s unemployment rates were very high and growing, which was advantageous whenanalyzing behavioral responses to changed economic conditions.
We found that a wife’s labor supply was actually stimulated by her husband’s involuntary job loss. The additional statistics indicate that not only working wives raised their18

labor hours but also nonworking wives began to participate in the labor market. In addition, nonworking wives started looking for jobs in response to their husband’s job loss.
There exists an added worker eﬀect during a period of job insecurity in Japan following
the mid-1990s, and the eﬀect would be larger if we included potential labor supply.

19

References[1] Arellano, M. and Bond, S.R. (1991) Some Tests of Speciﬁcation for Panel Data:
Monte Carlo Evidence and an Application to Employment Equations, Review of
Economic Studies, 58, 277-297.
[2] Attanasio, O. et al. (2005) Female Labor Supply as Insurance Against Idiosyncratic
Risk, Journal of European Economic Association, 3(2-3), 755-764.
[3] Bhargava, A., Franzini, L. and Narendranathan, W. (1982) Serial Correlation andthe Fixed Eﬀects Model, The Review of Economic Studies, 49(4), 533-549.
[4] Bingley, P. and Walker, I. (2001) Household Unemployment and the Labour Supplyof Married Woman, Economica, 68, 157-185.
[5] Cullen, J.B. and Gruber, J. (2000) Does Unemployment Insurance Crowd out Spousal
Labor Supply?, Journal of Labor Economics, 18(3), 546-572.
[6] Fernandes, R. and Felicio, F. (2005) The Entry of the Wife into the Labor Force in
Response to the Husband’s Unemployment: A Study of the Added Worker Eﬀect inBrazilian Metropolitan Areas, Economic Development and Cultural Change, 887-911.
[7] Heckman, J.J. and MacCurdy, T.E. (1985) A Life Cycle Model of Female Labour
Supply, Review of Economic Studies, 47-74.
[8] Higuchi, Y. (2001) Koyo To Shitsugyo No Keizaigaku, Nikkei Publishing Inc., Tokyo(in Japanese).

20[9] Institute for Research on Household Economics, Japanese Panel Survey of Consumers, 1993 through 2004.
[10] Low, H. (2005) Self-Insurance in a Life-Cycle Model of Labour Supply and Savings,
Review of Economic Dynamics, 8, 945-975.
[11] Lundberg, S. (1985) The Added Worker Eﬀect, Journal of Labor Economics, 3(1),11-37.
[12] Mace, B.J. (1991) “Full Insurance in the Presence of Aggregate Uncertainty,” Journalof Political Economy 99, 928-956.
[13] Pijoan-Mas, J. (2006) Precautionary Savings or Working Longer Hours?, Review of
Economic Dynamics, 9, 326-352.
[14] Spletzer,J.R. (1997) Reexamining the Added Worker Eﬀect, Economic Inquiry, 35,
417-427.
[15] Stephens, M. Jr. (2002) Worker Displacement and the Added Worker Eﬀect, Journalof Labor Economics, 20(3), 504-537.

21

Figure1. Husband's and Wife's employment rates
Hs's employment
rate
1

Wf's employmentrate
1

Husband's employment rate

0.9
0.8
0.7

0.95
0.6
0.5

Wife's employment rate

0.4
0.3

0.9
94

96

97

98

99

2000

Year

01

02

03

04

Table1. Descriptive Statistics
Mean

Standard
Deviation
16.8642
16.6856
0.12140.11650.11750.1135738.4616
4.113418.4898
0.88381.2124Min

Max

Wife's paid labor hours
14.6802013.73520
Wife's paid labor hours in the last year
Husband's job loss
0.01500
Husband's job loss in the last year
0.01380
Husband's job loss 2 years before
0.01400
Husband's job loss 3 years before
0.01310
Financial asset holdings
445.4084
0
Wife's age
36.6833
28
Wife's age * wife low education
18.68800Number of children
1.983901.05270
Number of children * wife low education[after taking the first difference]
0.945010.2127-65△Wife's paid labor hour in the last year
11.6453 398.3604-7300△Financial asset holdings
0.04270.2213-2△Number of children△Number of children * wife low education
0.01760.1515-2Note. Total number of the observations to be used for the main estimations is 4212.

65
65
1
1
1
1
8700
46
46
7
7

Table2. The Source of Living after the Husbands' Job loss
Savings
39.68%
No unemployed periods
34.92%
Retirement allowance or unemployment insurance
28.57%
Wife's income or transfers from parents
23.81%
Borrowings or use of credit card
3.17%
Others
4.76%

65
6900
2
2

Table 3. Wives' Reaction to the Husbands' Job Loss (1)Dependent variable: Wife's paid labor hour changes from the previous year
(1)
(2)
(3)
(4)
2.7639 *2.3580 **2.1240 **2.1716 **Husband's job loss
(1.5949)(1.6010)(1.6302)(1.6298)-2.8198 *** -3.0518 *** -2.9066 ***Husband's job loss in the last year
(1.8510)(1.8527)(1.8619)-1.2841 *-1.1523 *Husband's job loss 2 years before(1.9381)1.9689)0.8952
Husband's job loss 3 years before(2.1491)-0.0008 **-0.0008 *** -0.0008 *** -0.0008 ***Financial asset holdings(0.0003)(0.0003)(0.0003)(0.0003)0.09030.09240.09180.0919Wife's age(0.1313)(0.1311)(0.1311)(0.1310)-0.1607-0.1556-0.1520-0.1529
Wife's age * Wife's low educational attainment(0.1599)(0.1597)(0.1596)(0.1598)-3.3595 *** -3.4138 *** -3.3777 *** -3.3808 ***Number of children(1.0407)(1.0404)(1.0366)(1.0365)0.85250.90400.87890.8735
Number of children * Wife's low educational attainment(1.8136)(1.8145)(1.8130)(1.8128)0.47650.34220.33120.3266
Constant(3.7367)(3.7384)(3.7446)(3.7437)
Number of the observations
4212
4212
4212
4212
Number of the individuals
884
884
884
884
R-squared
0.01
0.01
0.01
0.01
F stat. for all the exclusion restrictions
2.14***
2.16***
2.04**
1.92**
F stat. for no individual effects
0.53
0.53
0.53
0.53
Notes.1. Fixed effects model is applied to all the estimations. Robust standard errors are in parentheses.
2. Asterisks, *, **, *** show that the variable is statistically significant at 10%, 5%, and 1%, respectively.
3. All the estimation models include year dummy variables for 1995-2004.
4. Wife's labor hour changes, Financial asset holdings, Number of children are changes from the last year.
5. Modified Durbin-Watson Statistics are 2.309, 2.313, 2.312 and 2.313 respectively in (1), (2), (3) and (4),which imply no AR1 serial correlations at 5% significance level (Table II in Bhargava et al. (1982)).

Table 4. Wives' Reaction to the Husbands' Job Loss (2): Dynamic model
Dependent variable: Wife's paid labor hour changes from the last year
(1a)
(1b)
2.8083 ***2.0524 ***Husband's job loss
(1.3796)(1.3019)
Husband's job loss in the previous year
Husband's job loss 2 years before(2b)2.2766 ***(1.4391)
0.4686(1.1989)(3a)2.2372 ***(1.5178)-1.1599 *(1.3712)-1.0415(1.5953)-0.2911 ***(0.0234)-0.1031 ***(0.0183)-0.0007 **(0.0003)-0.2907 ***-0.2913 ***-0.2905 ***(0.0214)(0.0234)(0.0214)-0.0897 ***-0.1030 ***-0.0897 ***Wife's paid labor hour changes 2 years before(0.0171)(0.0183)(0.0171)-0.0002 ***-0.0007 **-0.0002 ***Financial asset holdings(0.0005)(0.0003)(0.0005)-0.0004 **-0.0004 **Financial asset holdings in the previous year
(0.0007)(0.0007)0.08030.11570.08380.11740.0757
Wife's age(0.6971)(0.5564)(0.6928)(0.5525)(0.6920)0.1362
0.06330.13600.06790.1440
Wife's age * Wife low educational attainment(0.1771)(0.1683)(0.1771)(0.1684)(0.1775)-2.2351 ***-2.0300 ***-2.2489 ***-2.0267 ***-2.2551 ***Number of children(0.7459)(0.7478)(0.7447)(0.7461)(0.7437)
Number of children * wife low educational
0.40320.77200.41920.7766
0.4330 *attainment(1.3096)(1.2583)(1.3094)(1.2581)(1.3081)-0.07250.0709-0.08430.0675-0.0919
Constant(0.8381)(0.5456)(0.8347)(0.5417)(0.8334)
Number of the observations
4212
4052
4212
4052
4212
Number of the individuals
884
856
884
856
884
Wald-test for all the exclusion restrictions
210.36 ***237.46 ***211.55 ***237.27 ***212.14 ***Sargan's OID test
50.00
101.3549.88
101.48
49.73
0.51
0.11
0.52
0.11
0.53
Test for E (ΔuitΔuit-2) = 0
Notes. 1. To treat dynamic aspects of wife's labor supply, we conduct GMM estimations (Arellano- Bond (1991)).
2. All the estimations include year dummy variables for 1995-2004.
3. Instruments used in the estimation (1) are two-year-lagged wife's labor hours, and levels and lagged variables of all the other explanatory variables.
Those in the estimation (2) are two-year-lagged wife's labor hours, two or more lagged husband's job loss, and all the other explanatory variables.
4. See the notes in Table3.
Wife's paid labor hour changes in the previous year

-0.2914 ***(0.0234)-0.1027 ***(0.0183)-0.0007 **(0.0003)(2a)2.5493 ***(1.5209)-0.6182(1.3387)(3b)2.4443 ***(1.4418)0.7386(1.3121)0.1911(1.4449)-0.2896 ***(0.0213)-0.0893 ***(0.0171)-0.0002 ***(0.0006)-0.0004 **(0.0007)0.1057(0.5530)
0.0746(0.1691)-2.0573 ***(0.7456)
0.8168(1.2581)0.0779(0.5420)4052
856
236.69 ***
101.9
0.12

Table 5. Who Raised Working Hours?
The Entire Households
Mean (Standard Deviation)
Changes in wife's paid working hours from the previous year
0.945 (10.213)

Without husband's job-loss
Mean (Standard Deviation)

With husband's job-loss
Mean (Standard Deviation)

0.897 (10.179)

4.119 (11.901)-1.502 (11.308)

0.750 (8.421)

2.891 (8.645)
13.52%
8.18%
14.45%

8.611 (14.339)
33.33%
12.50%
37.04%

Panel A: for the wife who was working in the last year
Changes in paid working hours-1.460 (11.263)

Panel B: for the wife who was not working in the last year
Changes in paid working hours% Started working[as a full time worker]% Started working or Seeking Jobs

2.958 (8.750)
13.76%
8.30%
14.72%

Note. We summarize characteristics for the estimated samples; the number of the entire observations is 4212. The family who faced
the husband's job-loss is 1.50%.