OSIPP Discussion Paper : DP-2014-E-004

Labor Market Outcomes of Informal Care Provision in Japan*

April 4, 2014

Hiroyuki Yamada†
Associate Professor, Osaka School of International Public Policy (OSIPP)

Satoshi Shimizutani††
Research Fellow, Research Division, Gender Equality Bureau, Cabinet Office【Keywords】Informal care, Caregiver, Long-term care insurance, Labor
supply, Japan【JEL Classification Codes】J22, I11【Abstract】This paper examines the labor supply outcomes of family careprovision for Japanese households in 2010, ten years after the introductionof the public long-term care insurance (LTCI) program. We found thatfamily care provision for parents adversely affected labor market outcomesof main caregivers at home in terms of probability of working, employment
status and hours worked. The adverse effect was found to be more serious
for female caregivers than for male caregivers. Moreover, our results
suggest that the public LTCI program seems to only partially mitigate thedisadvantages of the main caregivers for both males and females.

* We are grateful to the Ministry of Health, Labour and Welfare forproviding us with micro-data from the Comprehensive Survey of Living
Conditions. The views expressed in this paper are completely personal andunrelated to those of the Japanese Government.
† corresponding author: E-mail: hyamada@osipp.osaka-u.ac.jp.
†† E-mail: satoshi.shimizutani@cao.go.jp.

1. Introduction
An aging population is a common phenomenon among developed countries aswell as some emerging countries especially in Asia. Together with extending
longevity, care provision for the rapidly expanding population of frail elderly is one of
the most serious challenges in the public policy arena. Since care provision at home
has been conventionally borne mostly by women, dramatic demographic change mayplace more emphasis on women’s role as caregivers, even though the capacity offamily care provision has been diminishing under the prevalence of nuclear families.
At the same time, most of those countries suffer from lower birth rates and expect
their labor force to shrink in future. Thus, many countries are targeting females ofprime age to participate in the labor force to compensate for the possible adverse
effect of the shrining labor force on the economy. These countries are confronted withthe dilemma of expecting women to be responsible both as caregivers and workers.
Japan is one of the countries that are most seriously challenged by this dilemmasince it has been experiencing an unprecedented increase in the aging population anda historically stagnant birth rate. One solution to meet those seemingly contradictory
policy requirements was to introduce the public long-term care insurance (LTCI)
program, which occurred in 2000, striving to “establish a system that responds tosociety’s major concerns about aging, the care problem, whereby citizens can beassured that they will receive care and be supported by society as a whole” by shifting
the responsibility of care from the family to the government (MHLW, 2002).
This paper provides new evidence on labor market outcomes of care provisionin Japan. To do so, it examines the labor supply effect of family care provision forJapanese households in 2010, ten years after the introduction of the public long-termcare insurance program. While many studies have examined the causal relationshipbetween care provision and work using a variety of datasets, no consensus has formedon the presence and size of the adverse effect on labor market attachment.1 Moreover,
our evidence on Japan’s experience is informative for not only Japan, but also othercountries because Japan is one of a few countries that have introduced a social
insurance-type public LTCI program.2
The adverse effect may be expected to be naturally curtailed because the public
LTCI program should substitute a part of family care provision but there is scarce

1

Van Houtven, et al. (2013) provides a decent and concise literature review. The literature
analyzing the relationship between caregiving and work is quite extensive but the results are still
mixed. While a negative relationship between family care provision and extensive margin of workin terms of work probability is found in most studies (e.g., Heitmueller (2007), Bolin, et al.
(2008)), there is less consensus on intensive margins in terms of working hours (affirmative byEttner (1996) for the U.S. and negative by Bolin, et al. (2008), Casado-Marín, et al. (2011) and
Wolf and Soldo (1994) or in terms of wage penalties (affirmative by Carmichael and Charles(2003) and Heitmueller and Inglis (2007)). McGarry (2006) shows little evidence on labor market
attachment and caregiving later in life using longitudinal data.
2 Van Houtven, et al. (2013) points out several major concerns regarding the previous literatureon the causal relationship between caregiving and work. First, much of the older literature ignored
the endogeneity problem between caregiving and work that was addressed by newer literature
producing mixed results. Second, much of the recent longitudinal literature focused on Europe andis difficult to generalize. Third, much of the literature examined either the extensive or intensive
work margin or impact on wages and a comprehensive effect of caregiving is not difficult tocapture on total impact.

evidence to support this. Surprisingly, to the best of our knowledge, there are few
studies on the effect of the public LTCI program on labor supply. Shimizutani et al.
(2008) used longitudinal data and found that the introduction of the LTCI program
had a large and positive effect on the female labor supply; the program enhanced theprobability of being employed by 30–60%, working days per week by 40–60%, andworking hours per day by 50–70%. In contrast, Tamiya et al. (2011) found that the
introduction of the LTCI program increased the probability of being employed mainly
for the high-income group and weekly working hours by 4.6 hours for the group,though they did not use longitudinal data covering before and after 2000. Note thatthose two studies focused on the effect of the introduction of the LTCI program on
female labor supply attachment. Surprisingly, virtually no empirical studies examinedthe causal relationship between caregiving and work attachment when the public
LTCI program matured despite the expanding costs of formal care provision throughthe program.3 During the decade, the use of long-term care services grewconsiderably and LTCI costs doubled from 4.0 trillion yen in FY2000 to 8.4 trillion
yen in FY2011. The National Council on Social Security (2006) estimated that LTCI
costs will increase from 19 to 24 trillion yen by FY2025 (from 3.2% to 4.1% ofGDP).
3An attempt is Sugawara and Nakamura (2013). They focus on co-residing females and do notaddress endogeneity issues between work decision and care provision. In addition, they did notprovide any analysis on male caregivers.

Empowered by micro-level data from a large-scale survey, we examine theimpact of care provision for parents at home on labor market outcomes in terms ofwork and employment status and hours worked. To do so, we focus on how labormarket outcomes of a main caregiver at home are affected by providing help for
his/her parents (Lilly, et al., 2010). We also relate those examinations with the public
LTCI program to explore whether the LTCI program could mitigate a caregiver’s
disadvantages in the labor market. While our data limits our analysis to co-residential
caregiving, there is little consensus on the effect of caregivers’ residence on labormarket outcomes; Ettner (1996) found only non-coresidential female caregivers
experience significant short-term negative work effects in the U.S. while
co-residential caregiving has stronger negative effects on work in Europe(Casado-Marin et al., 2011; Heitmueller, 2007; Heitmueller et al., 2010).
This paper proceeds as follows. Section 2 provides a concise overview of therelevant aspects of Japan’s LTCI program. Section 3 explains the dataset used in thisstudy. Section 4 describes our empirical approach and discusses the estimation results.
The final section provides our conclusions.

2. Japan’s public long-term care insurance programThis section provides a brief overview of the public long-term care insurance(LTCI) program in Japan, focusing on at-home care that is relevant to the current

paper.4 A distinct feature of Japan’s LTCI program is its “decentralized yet
centralized” approach (Mitchell, Piggott and Shimizutani, 2006, 2008).5
The LTCI program insurers are municipalities or their alliances. The
participation in the program is mandatory. The insured persons are divided into
Category 1 (individuals aged 65 and older) and Category 2 (individuals aged 40–64).
In principle, only Category 1 persons are eligible for LTC services once certified. The
program is operated as a pay-as-you-go program, financed half by premiums leviedon insured persons and half by contributions from the general tax revenue from
central and local governments. LTCI premiums differ across insurers and are subject
to revision every three years, means tested and categorized into six levels.
When a beneficiary aged 65 and older requires long-term care support, one
needs to be certified to receive services by application to the relevant municipality.
The eligibility criteria are uniform nationwide and determined by information on
physical and mental health. Any certified person is assigned one of the seven carelevels linking the necessity of support and the service allowance to be received. Carelevels 1-5 are for disabled individuals in need of LTC to help with basic activities of

4

The description of this section is largely based on Shimizutani (2013).
The centralized elements are aspects that the eligibility and certification process, type ofservices to be insured, fee for service and co-payment are determined and uniformly implemented
by the central government. In contrast, the decentralized elements are aspects where insurers andinsurance premiums vary across regions and are collected by the municipality and types of care to
be used are determined by a care manager and supplied by a variety of providers including
for-profit organizations.
5daily living (ADL) and who are eligible for “LTCI benefits” including institutionalcare services, at-home care services, and community-based services, whose provision
is based on a care plan devised by a certified care manager selected by the beneficiary.
In contrast, “Support Required” levels 1-2 are for individuals who can live
independently but are in need of care to assist with instrumental activities of dailyliving (IADL) and who are eligible for “preventive benefits”, which are based on acare plan for prevention.
A certified person can use LTC services with a flat 10% co-payment (90% is
paid by insurance) for each insured service up to a monthly ceiling amount dependingon care levels, regardless of income level. In reality, most clients use 40-60% of theceiling amounts (Tamiya et al., 2011). The fee schedule is set by the government andrevised every three years, depending on the service (fee for service) irrespective of
income level or family type (but no cash allowances), which is categorized into three
types: for at-home care services, institutional care services or community-based
services.6 Care service providers include local governments, semi-public welfare

6

A detailed description of institutional care and community-based care is provided by
Shimizutani (2013). At home care services include: home-help services (housekeeping andpersonal care), home-visiting nurse, home-visit bathing, home-visit rehabilitation, elderly careprovided in for-profit private homes, welfare device leasing, at-home medical care management
counseling, allowances for the purchase of welfare devices and for-home renovations that arereceived at home, day services (care with rehabilitation), short-stay care (service) received outside
the home.

corporations, non-profit organizations, hospitals, and for-profit companies, all ofwhich are licensed and supervised by the prefectural government.

3. Data description
This study uses micro-level data from the Comprehensive Survey of Living
Conditions, (henceforth, CSLC survey) compiled by the Ministry of Health, Labourand Welfare. A large-scale CSLC survey has been performed every three years since1986 and we use the data from 2010, which is the most recent available.7 One
advantage of the CSLC survey is the unusually rich number of households andindividuals in the sample that are selected nationwide. The sampling design in the2010 survey was to choose 5,510 survey locations randomly from the census blocks(the total number is less than a million) and then to survey all the households (289,363
households) and all household members (about 750,000 persons) for the household
survey and the health survey.8 The number of households that responded to thesurvey effectively was 228,864. We use the information that is available only in thehousehold survey related to health and long-term care. While the long-term care7

A smaller scale survey is conducted in the other years.
The survey has two additional questionnaires. The income and saving survey was submitted to
all the households (35,971 households) and their members (about 100,000 persons) in 2,000
census blocks among 5,510 that were randomly chosen for the household and health surveys. The
number of households that responded effectively was 26,115. In contrast, the long-term caresurvey asked 7,192 persons who are eligible to use public long-term care insurance services tocomplete it from 2,500 blocks who were randomly chosen among 5,510 blocks. The final sample
was 5,912 persons.
8survey contains some useful information on the use of care service, we do not use it
since the sample size is too small (5,912 persons) to perform analysis of this study andthe distribution of care levels in the survey differs dramatically from the population.9
From the household survey sample, we select individuals based on thefollowing sample selection criteria. First, we select both males and females aged
between 40 and 60 years old and who do not require any help since they are likely toprovide family care. Second, we choose individuals who co-reside with a parent
whose age is above 65 years old. To avoid complicated interaction within a household,we limit our analysis to cases where the parent is a widower, widow, or separated andthus there is no spouse who can provide informal care to the parent. Those two criteria
are employed to focus on a typical combination of at-home caregivers and at-homecare receivers in Japan in terms of age range so as to mitigate unobserved
heterogeneity in the individuals in the sample. Then, we omit any individuals from thesample if a family member needs any help that is not related to LTC since we focus
on the effect of LTC provision on labor outcome. We also eliminate any individuals
who do not live together with their parents since no information of LTC use of theparents including whether they are eligible to use LTCI services is available in thedata and we focus on the effect of LTC provision on co-resident caregivers. Finally,
9

While it is stated that the objects in the income and saving survey and the long-term care survey
are randomly chosen from the same blocks (namely 5,510 blocks), there is no household that
responded to both surveys.

information on education attainment is indispensable to the analysis of labor supply
decisions.10 We exclude individuals who have no educational attainment (less than
0.005% of the observations) while we create a dummy variable for individuals whose
educational attainment is missing (about 5% of the sample for the analyses below).
Then, our basic sample size is 9,707 for males and 9,216 for females.
Table 1 presents the summary statistics of the variables used in the analysis. In
what follows, we perform our analysis separately for males and females.11 First, welook at labor market outcomes. Whether working or not (“work”) to measureextensive margins differs between males and females; 90.8 percent of males are atwork (only 9.2 percent are not at work) while 76.7 percent of females are at work
(23.3 percent not at work). Moreover, employment status among workers is also
heterogeneous between males and females. The most dominant for males at work isregular workers (52.7 percent), followed by the self-employed (19.5 percent) andnon-regular workers (18.6 percent). In contrast, the most dominant for females is
non-regular workers (34.9 percent), followed by regular workers (28.1 percent) andthe self-employed (13.7 percent). The average working hours per week (“wh”) that10

In the CSLC survey, information of educational attainment was available in 2010 for the firsttime. In other words, any surveys prior to 2010 suffer from a lack of essential information on labor
supply.
11 Some studies find stronger work effects for female caregivers compared to males (Carmichael
and Charles, 2003) while others do not (Bolin et al., 2008; Johnson and LoSasso, 2006). Dentinger
and Clarkberg (2002) examined the effect of informal care provision on the transition to
retirement and found that wives caring for husbands have retirement odds five times greater thannon-caregiving women. However, they did not find care provision for parents or parents in-law
experience hastens the transition to retirement, which is the case for both men and women.

measure intensive margins is 47.4 hours for males and 37.4 hours for females,
respectively.12 The following four variables (“experience”, “public”, “fsize2” and“fsize3”) are available only for current workers and are missing for individuals who
are not at work. The average years of experience for the current job (“experience”) is
22.5 years and 16.5 years for males and females, respectively, which is likely to affectworking hours and thus is used in the intensive margin regressions. In addition, about
8 percent of the individuals are government employees (“public”) for both sexes.
While the share of males working for a large firm (“fsize3”, a large firm is defined as
one with more than 500 employees), 16.7 percent, is larger than that of females (13.0
percent), the share of males working for a medium size firm (“fsize2”, a medium size
firm is defined as one with more than 100 employees but less than 500 employees) is
14.4 percent, which is similar to that of females (15.2 percent).
Second, the main caregiver consists of 7.7 percent for males and 19.9 percent
for females, showing that females are more likely to provide care mainly. The main
caregiver is identified from the response to the survey and an indicator variable
allocating 1 for a main caregiver and zero otherwise is used as a key variable in theanalysis below. Third, focusing on individual characteristics, the average age (“age”)
is 51.7 and 50.8 for males and females, respectively. The proportion of married

We omitted the individuals with the top and bottom 1 percent of “wh” to mitigate
the influence of extreme value of wh.
12individuals (“marriage” is an indicator) is 70.2 percent and 79.3 percent for eachgender and the proportion of individuals having at least one child less than 18 years
old (“kid”) is 30.7 percent and 31.6 percent, respectively. Educational attainment also
differs by gender. For males, the dominant proportion is found in senior high school
graduates (“edu2”) consisting of half, followed by university or above graduates(“edu4”). The proportion of junior high school graduates (“edu1”) or of juniorcollege/training school graduates is more or less 10 percent. Health status (health) is abinary variable allocating 1 if the self-rated health status is poor or not so good while
it allocates zero if the self-rated health status is good, relatively good, or neither goodnor bad. 15.1 percent of the male respondents and 17.4 percent of the female
respondents answered that their self-rated health status is poor or not so good. About
95 percent of the individuals in the sample have their own house for both sexes. Thisis probably because our sample is restricted to individuals co-residing with theirparents. The average logarithm of monthly household expenditure per capita (“lexpc”),
which is a proxy of the living standard, is comparable between males and females.13
Finally, focusing on the characteristics of care receivers, the proportion ofparents who are approved to use LTC services from the public insurance is 14.8
percent if the respondent is male and 17.3 percent if female. The average of parent
13

While other economic variables (e.g., annual income or financial savings) are available for apart of the sample who responded to the income and saving survey, we did not use them since thesample size is reduced to about one-tenth.

age (“pa”) is 78.8 years old for the male sample and 79.9 years old for the female
sample. 15-16 percent of the parents answer that their self-rated health is poor or not
so good. Females dominate the parent’s gender (“pg”) and males constitute about
15-16 percent of single parents in the sample. The gender balance among the single
parents may be accounted for by explanations such as females live longer than males
or males are more likely to remarry. About 41-43 percent of the single parents have atleast one child living apart.

4. Empirical approach and results
This section empirically examines the effect of being a main caregiver on labormarket outcomes in terms of work status (work or not), employment status (regular,
non-regular or self-employed) and weekly working hours. The sign and size of thelabor supply effect of care provision is not ex ante determined; a caregiver may
reduce working hours or quit a job to provide care at home or may retain working
hours for salary purposes and instead decrease leisure time or may increase working
hours to compensate for the out-of-pocket burden incurred to use the service throughthe public LTCI program. Thus, the effect of care provision on labor market
attachment requires empirical investigation.
The basic specification we used is described as follows.

Yi     * Maincaregiveri   * Eligibilityi  1 * X i   i(1)where i refers to an individual in the sample. The dependent variable (Yi) takes threeforms: work status, employment status and working hours, which are explained below
in detail. The explanatory variables include a constant term (  ), an indicator that
allocates 1 for being a main caregiver (Maincaregiveri, henceforth “caregiverdummy”), the main interest of this paper and a vector of a variety of basic attributesof the respondents that are included in Table 1 (Xi); age, squared age of the
respondent, marital status (indicator that allocates 1 if married and zero otherwise),child status (indicator that allocates 1 if having at least one child aged 18 or below andzero otherwise), educational attainment (dummy variables), indicator for subjective
health status (indicator that allocates 1 if subjective health status is poor and zero if
subjective health status is good or neither good nor bad), house tenancy status(indicator that allocates 1 if having own house and zero otherwise), logarithm ofmonthly household expenditure per capita, and prefecture dummies (47 prefectures inJapan).
Moreover, we added a variable (Eligibility), which is an indicator that allocates
1 if a parent is certified to use public LTCI services and 0 otherwise (henceforth“eligibility dummy”). We include this variable on the right hand side to capture theeffect of the public LTCI program on the work decision. Ideally, we should be able toinclude the interaction term between caregiver dummy and eligibility dummy inempirical specifications. However, the correlation between this interaction term andeligibility dummy (0.53 for the male sample and 0.87 for the female sample) or
caregiver dummy (0.76 for the male sample and 0.79 for the female sample) is high,distorting the estimation results. Thus, we just include the eligibility dummy as it is to
grasp the “average” effect of the LTCI program on the work decision.

4.1 Effect of being a main caregiver on work status
First, we explore whether a main caregiver is less likely to be at work or not, so
as to measure extensive margin. The dependent variable is a binomial choice indicatorand one that allocates 1 if a respondent is at work and zero otherwise. Table 2
presents the coefficients on the main variables for males using the linear probability
model. The first two columns (Columns (1) and (2)) show the coefficients obtained by
OLS estimation. In Column (1), the coefficient on the caregiver dummy is negativeand significant, showing that being a main caregiver reduces the probability ofworking by 7.6 percent. The coefficient on age is positive and on age squared isnegative, both of which are significant. Males who are married or have kids under 18
are more likely to work. The junior high school graduates (the base case) aresignificantly less likely to work than senior high school graduates, two-year college

graduates, and university and above graduates. Males who are healthier or have theirown house or larger household expenditure per capita are more likely to work.
Column (2) shows the results by adding the eligibility dummy. The coefficienton the caregiver dummy is negative and significant, showing the probability ofworking for the main caregiver is reduced by 8.0 percent. Moreover, the coefficienton the eligibility dummy is positive but insignificant, suggesting that even if his
parent is certified to use LTC services, this does not help him work.
So far, we have not controlled for the endogeneity between being a main
caregiver and probability of working. However, it is possible that work status affects
whether a person is a caregiver and the caregiver dummy is endogenous. As such, weneed to address the endogenous relationship between the dependent variable and themain variable of interest. One way to address the issue is to employ instrumental
variable estimation. Our instrument variables are the age, health status, and gender ofa parent. In addition, we include the information on whether a parent has children
living apart or not in the set of the instrumental variables. This last instrument isfurther categorized depending on where the child closest in physical distance to theparent lives as follows: 0 if no children living apart (the base category), 1 if the childlives within the same building, 2 if living in the same site, 3 if living in theneighborhood, 4 if living in the same municipality, and 5 otherwise. The set ofinstrumental variables is strong which is evident by the fact that the F value of thefirst stage significantly exceeds 20.
Columns (3) and (4) of Table 2 present the coefficients using IV estimation(linear probability model with IV); Column (3) without the eligibility dummy andColumn (4) with the eligibility dummy. The coefficient on the caregiver dummy isnegative and significant in Columns (3) and (4), showing the probability of working is
reduced by 13.8 percent or 20.2 percent, respectively. Notably, the sizes of thecoefficients in absolute value in IV estimations are much larger than those in thecorresponding OLS estimations. The coefficient on the eligibility dummy (in Column(4)) is positive but only marginally significant at the 15 percent level. That is, if theparent is certified to use public LTCI services, LTCI may mitigate the negative impact
of being a main caregiver (or improve the probability of working) by 4.1 percent, but
this effect is only marginally significant. Furthermore, the magnitude of the mitigating
effect of LTCI eligibility is significantly smaller than the negative effect of being a
caregiver, implying the negative effect largely remains.
In sum, male main caregivers are less likely to work than male non-caregivers
by about 7.6-20.2 percent depending on the model we use. To examine the effect ofthe public LTCI program on work probability, we include the eligibility dummy since
once those needy parents are approved, they are entitled to use LTC services at 10%

coinsurance. However, the effect of mitigating the disadvantage of main caregivers ismarginal from two aspects. First, the coefficient of the eligibility dummy is positive
but is only marginally significant. Second, the magnitude of the coefficient of theeligibility dummy is positive but much smaller in absolute value than the negative
effect of being a main caregiver on work.
Next, we perform the same analysis for females. Table 3 presents thecoefficients obtained by the linear probability model using OLS (Columns (1) and (2))
and the linear probability model using the IV to address the endogeneity (Columns (3)and (4)). First, we observe negative and significant coefficients on the caregiverdummy, showing that female main caregivers are significantly less likely to work.
Although those patterns are qualitatively the same as those for males, the size of thenegative effect is significantly larger for females than for males; the probability ofworking is reduced by 9.3-58.3 percent for female caregivers (7.6-20.2 percent formale). The pattern in the remaining coefficients is also similar to those in males inTable 2 except the coefficient of LTCI eligibility. For females, the coefficient on theeligibility dummy is positive and statistically significant (Column (4)) once theindicator of being the main caregiver is instrumented. Note that the magnitude of thecoefficient of the eligibility dummy is much larger for females (33.5 percent) than formales (4.1 percent), implying that LTCI’s role of mitigating the negative effect ofbeing a main caregiver is larger for females than for males. However, the effect of
LTCI eligibility is not sufficient to cancel out the very large negative effect of being amain caregiver.
In sum, the qualitative results for females are similar to those for males, but themagnitude is larger for females than for males. Female main caregivers are less likely
to work than female non-caregivers by about 9.3-58.3 percent depending on whichmodel we use. This magnitude of the effect for females is larger than that for males.
Contrary to males, the public LTCI program seems to mitigate the disadvantage ofmain caregivers, which is evident in the positive and significant coefficient in Column(4) of Table 3. However, the positive effect is very limited so that it cannot eliminate
the large negative effect of being a main caregiver on work.

4.2 Effect of being a main caregiver on employment status
We now focus on the effect of being a main caregiver on employment status:
regular worker, non-regular worker or self-employed. The basic specification is thesame as the work status analysis but there are some differences. The dependent
variable is now a multiple choice and thus a multinomial choice indicator: 1: not work(base category), 2: non-regular worker, 3: regular worker, 4: self-employed. The
explanatory variables are exactly the same as the previous analysis; indicator forbeing a main caregiver, a variety of basic attributes of the respondents, and indicatorfor LTCI eligibility. The coefficients are obtained by multinomial logit estimation andmultinomial logit estimation with a control function method as per Kuksov andVillas-Boas (2008) and Petrin and Train (2010). The latter approach deals withendogeneity between the dependent variable and the caregiver dummy through acontrol function method with a set of exclusion restrictions. For the variables of
exclusion restrictions, we use the set of instrumental variables used in sub-section 4.1.
Concretely, first, we estimate the reduced form for an indicator of being a main
caregiver, where a variety of basic attributes of the respondents, indicator for LTCI
eligibility, and the set of exclusion restrictions are used as explanatory variables. Then,
we obtain the residual of the regression (“resid”). After that, we estimate themultinomial logit models on employment status with the explanatory variables andthe residual from the first-stage regressions. The bootstrapped standard errors are
estimated.
Table 4 presents the coefficients for males. Since none of the coefficients of“resid” are statistically significant in the multinomial logit regression and we cannot
reject the hypothesis that all the coefficients of “resid” are jointly zero, a simple
multinomial logit analysis may be sufficient in the case of males. Thus, we show only
the estimation results of simple multinomial logit in Table 4. The marginal effects ofbeing a main caregiver and LTCI eligibility dummy, together with raw estimation
results are shown.
We observe that a male main caregiver is significantly more likely to be self
employed by 4.1 percent or not at work by 4.6 percent than male non-caregivers. In
contrast, a male main caregiver is significantly less likely to be a regular worker by
8.4 percent. Put differently, a male caregiver has a significantly lower chance ofworking on a regular basis and a significantly higher chance of being self-employed
or not at work and the size of the effect is larger in the former, suggesting it isdifficult for males to be a regular worker once they are providing informal care athome mainly. The marginal effect of LTCI eligibility status is positive andstatistically significant for regular work and negative and statistically significant for
self-employed, suggesting LTCI eligibility of the parent encourages a main caregiver
to be a regular worker while discouraging a main caregiver to fall into the category ofself-employed. However, the marginal effect of being a main caregiver is larger thanthat of LTCI eligibility in absolute value, implying LTCI eligibility of the parent only
partially helps the main caregiver especially in the case of a regular worker. The
pattern in the remaining coefficients is similar to those in the results for work
probability that are examined above.

Table 5 shows the marginal effects and the coefficients for females. The resultsof the simple multinomial logit (Columns (1)-(4)) show that a female main caregiveris significantly more likely to be self-employed by 2.3 percent and not be at work by
8.8 percent. The probability of not being at work is much larger (by more than for 4
percent) for females than for males (4.6 percent). In contrast, a female main caregiveris significantly less likely to work on a regular basis by 8.8 percent. A majordifference between the case of males and females is the marginal effects of the LTCI
eligibility dummy. For females, none of the marginal effects of the LTCI eligibility
dummy is significant, implying LTCI has no influence on the employment status offemales. Next, we consider the endogeneity between the dependent variable and the
caregiver dummy using the control function method explained above (Columns(5)-(8)). All the residual terms “resid” in the estimation results are positive andstatistically different from that for the base category. Furthermore, we reject the
hypothesis that all the coefficients of “resid” terms are jointly zero at the 1 percent
level (chi-squared value is 17.41). Therefore, for females, it is more appropriate toview the results of the multinomial logit with the control function method. Comparedto the marginal effects obtained from the simple multinomial logit, there are nochanges in the qualitative results of those with the control function method, exceptthat of being a main caregiver for the self-employed. In Column (8), the marginaleffect of being a main caregiver is now negative but statistically insignificant. More
importantly from a quantitative aspect, the marginal effects of being a main caregiveron not being at work and on regular work are much larger in absolute value than thoseobtained from the simple multinomial logit model. A female main caregiver issignificantly more likely to not be at work by 27.6 percent and less likely to be a
regular worker by 19.1 percent. Thus, the negative impact of being a main caregiveron being a regular worker and positive impact on not being at work are very large.
None of the marginal effects of the LTCI eligibility dummy is significant, implying
LTCI has no influence on the employment status of females, even after controlling for
endogeneity.
In sum, the negative effect of family care on main caregivers is more serious
for females than for males; females are more likely to quit working than males once
they become a main caregiver and are less likely to be at work on a regular basis.

4.3 Effect of being main caregiver on working hours
Lastly, we explore the effect of being a main caregiver on working hours to
capture the impact on the internal margin. The dependent variable is working hours
per week. The independent variables are the same as those in the previous regressions(indicator for being a main caregiver, a variety of basic attributes of the respondents,and indicator for LTCI eligibility). In addition, the length of time at the current work
(experience), dummy for public worker (public) and medium size firm dummy(fsize2) and large firm dummy (fsize3) are included as independent variables. We use
simple OLS and IV estimation, latter of which addresses the endogeneity between
working hours and the caregiver dummy. The set of instrumental variables is the sameas that in the previous analysis.
Table 6 presents the results for males. In the results of OLS estimations(Columns (1) and (2)), weekly working hours of a male main caregiver are shorter by
1.36-1.78 hours than that of male non-caregivers. In the latter specification (Column(2)), we include the LTCI eligibility dummy, whose coefficient is positive andstatistically significant. This implies LTCI eligibility mitigates the negative effect of
being a main caregiver by 0.87 hours. However, this effect is smaller than thenegative effect of being a main caregiver. Columns (3) and (4) show the results of IV
estimations. The F-value of the first stage significantly exceeds 20. Once the indicatorof being a main caregiver is instrumented, the coefficient of the indicator still
becomes negative but statistically insignificant in Column (3). This holds even after
including the LTCI eligibility dummy in Column (4). The coefficient of the indicatorof being a main caregiver is negative but statistically insignificant. At the same time,in IV estimation of Column (4), the coefficient of the LTCI eligibility dummy is still
positive but now statistically insignificant.
Table 7 presents the results for females. In the results of OLS estimations(Columns (1) and (2)), weekly working hours of a female main caregiver are shorterby 2.14-2.38 hours than that of female non-caregivers. Note this magnitude forfemales in absolute value is larger than that for males. Contrary to the OLS result formales, the coefficient of the LTCI eligibility dummy is negative although statisticallyinsignificant, implying the LTCI program does not mitigate the negative impact on
working hours of being a main caregiver. Columns (3) and (4) show the results of IV
estimations. The F-value of the first stage significantly exceeds 20. The estimatednegative effects of being a main caregiver on working hours are much larger thanthose in OLS: 7.62-13.03 hours. Similar to the case of the IV estimation for theextensive margin, the size of the coefficients in absolute value in IV estimations aremuch larger than those in the corresponding OLS estimations. In Column (4), thecoefficient of the LTCI eligibility dummy is positive and statistically significant. Thus,
if the parent has LTCI eligibility (hence, receiving some LTC services), that helps
increase the working hours of the caregiving household’s women by 6.87 hours on
average. However, this mitigation effect of LTCI is significantly smaller than thenegative effect of being a main caregiver on working hours, leaving a female
caregiver with about 6 hours less working hours than a female non-caregiver.
In sum, if we consider the IV estimation results, the situation differs
significantly between males and females. For males, neither being a main caregiver
nor the LTCI eligibility affect working hours. In contrast, being a main caregiver hasa large negative effect on working hours in the case of females. LTCI eligibility only
partially mitigates the large negative effect. These differences between males andfemales might stem from the difference in employment status between males andfemales. Although not shown, about half of the working male main caregivers (51.3
percent) work as a regular worker, implying adjustment of working hours is moredifficult for them than for those in non-regular work or self-employed. In such a case,a parent’s LTCI eligibility would have little effect on working hours. In contrast, only
30.9 percent of the working female main caregivers work as a regular worker. The
remaining 70 percent of the female main caregivers are either non-regular workers or
self-employed, who can work more flexibly than regular workers. Then, the LTCI
eligibility of a parent becomes important as it could mitigate the negative effect of
being a main caregiver. Thus, these differences between males and females might
result in very different outcomes.

5. Conclusion
This paper examines the labor supply effect of family care provision forJapanese households in 2010, ten years after the introduction of the public long-termcare insurance program. We found that family care provision adversely affects labormarket outcomes of main caregivers such as probability of working, employment
status and hours worked.
Our empirical results show that females are more vulnerable than males once
they become main caregivers: females are less likely to work and have less working
hours even if they can work. Moreover, our results suggest that the public LTC
program seems to only partially mitigate the disadvantages of main caregivers bothfor males and females. There is no clear evidence that receiving services offered by
the public LTCI program provides main caregivers with sufficient help so that thedisadvantage of main caregivers is largely mitigated. Further research should address
why any positive effect of the LTCI program on labor market outcomes is modest
compared with the negative effect of being a main caregiver empowered by alongitudinal dataset such as Japanese Study on Aging Retirement (JSTAR).

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Table 1: Descriptive statistics
Variablework (1 if work, 0 otherwise)
Not work
Nonregular worker
Regular worker
Self-employedwh (working hours per week)
experience (years at the current work)
fsize2 (1 if the number of employees of the firm is between 100 and 500, 0 otherwise)
fsize3 (1 if the number of employees of the firm is more than 500, 0 otherwise)
public (1 if government employee, 0 otherwise)
Main caregiver dummy (1 if main caregiver, 0 otherwise)
LTCI eligibility dummy (1 if the parent is eligible, 0 otherwise)age
marriage (1 if married, 0 otherwise)kid (1 if at least one child under 18, 0 otherwise)
edu1 (1 if junior high school graduate, 0 otherwise)
edu2 (1 if senior high school graduate, 0 otherwise)edu3 (1 if junior college/training school graduate, 0 otherwise)
edu4 (1 if university or above graduate, 0 otherwise)
edu9 (1 if information on education is missing, 0 otherwise)
health (1 if subjective health condition is poor, 0 otherwise)house (1 if having own house, 0 otherwise)
lexpc (log of monthly household expenditure per capita)pa (parent's age)ph (parent's subjective health: 1 if poor, 0 otherwise)pg (parent's gender: 1 if male, 0 if female)
sep_child (1 if a parent has a child living apart, 0 otherwise)
Where does the child living apart but still closest in distance live?
pc1 (1 if living within the same building, 0 otherwise)
pc2 (1 if living in the same site, 0 otherwise)
pc3 (1 if living in the neighborhood, 0 otherwise)
pc4 (1 if living in the same municipality, 0 otherwise)
pc5 (1 if other than pc1-pc4 above, 0 otherwise)
Prefecture dummies are omitted from the table above.

Male
Obs
Mean
Std. Dev.
9707
0.908
0.289
9707
0.092
0.289
9707
0.186
0.373
9707
0.527
0.499
9707
0.195
0.396
8309 47.407 13.230
8309 22.515 12.366
8309
0.144
0.351
8309
0.167
0.373
8309
0.076
0.265

FemaleObs
MeanStd. Dev.
9216
0.767
0.423
9216
0.233
0.423
9216
0.349
0.467
9216
0.281
0.450
9216
0.137
0.344
6723 37.445 14.599
6723 16.539 11.713
6723
0.152
0.359
6723
0.130
0.336
6723
0.084
0.278

9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707
9707

0.077
0.148
51.654
0.702
0.307
0.089
0.512
0.099
0.248
0.052
0.151
0.943
1.855
78.787
0.160
0.140
0.410

0.267
0.356
5.651
0.458
0.461
0.284
0.500
0.299
0.432
0.222
0.358
0.233
0.570
6.100
0.367
0.347
0.492

9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216
9216

0.199
0.173
50.846
0.793
0.316
0.047
0.526
0.282
0.087
0.058
0.174
0.951
1.866
79.872
0.152
0.151
0.430

0.399
0.378
5.582
0.405
0.465
0.211
0.499
0.450
0.282
0.233
0.379
0.215
0.566
6.267
0.359
0.358
0.495

9707
9707
9707
9707
9707

0.023
0.007
0.057
0.113
0.210

0.150
0.083
0.233
0.316
0.408

9216
9216
9216
9216
9216

0.024
0.009
0.061
0.117
0.220

0.153
0.092
0.239
0.321
0.414

Table 2: Work or not (Male, OLS and IV)
VARIABLES
Main caregiver dummy

(1)
OLS

(2)
OLS

(3)
IV

(4)
IV

-0.076***[0.014]-0.080***[0.014]0.007[0.011]
0.064***[0.010]-0.001***[0.000]0.155***[0.012]0.017***[0.006]0.028**[0.012]0.044***[0.016]0.028*[0.014]0.029**[0.013]-0.059***[0.012]0.034**[0.016]
0.033***[0.006]-0.862***[0.254]-0.138**[0.057]-0.202**[0.091]
0.041[0.027]
0.066***[0.010]-0.001***[0.000]0.139***[0.015]0.020***[0.006]0.028**[0.012]0.046***[0.016]0.028**[0.014]0.028**[0.013]-0.054***[0.012]0.034**[0.016]
0.034***[0.006]LTCI eligibility dummyage
age squared
marriage
kid
edu2
edu3
edu4
edu9
healthhouse
lexpc
Constant

0.064***[0.010]-0.001***[0.000]0.156***[0.012]0.017***[0.006]0.028**[0.012]0.044***[0.016]0.028*[0.014]0.029**[0.013]-0.059***[0.012]0.034**[0.016]
0.033***[0.006]-0.858***[0.252]0.065***[0.010]-0.001***[0.000]0.149***[0.013]0.018***[0.006]0.028**[0.012]0.045***[0.016]0.028**[0.014]0.028**[0.013]-0.055***[0.012]0.033**[0.016]
0.034***[0.006]

Observations
9,707
9,707
9,707
R-squared
0.111
0.111
F-value of 1st stage
49.63
Prefecture dummies
Yes
Yes
Yes
Clustered standard errors at prefecture level in brackets*** P <0.01, ** P <0.05, * P <0.1

9,707
72.1
Yes

Table 3: Work or not (Female, OLS and IV)
VARIABLES
Main caregiver dummy

(1)
OLS

(2)
OLS

(3)
IV

(4)
IV

-0.094***[0.011]-0.093***[0.016]-0.002[0.019]0.113***[0.014]-0.001***[0.000]-0.021[0.013]0.018[0.012]0.028[0.025]0.044*[0.026]
0.069**[0.032]-0.017[0.028]-0.014[0.012]0.014[0.021]0.001[0.008]-1.901***[0.342]-0.282***[0.047]-0.583***[0.133]0.335***[0.095]0.109***[0.014]-0.001***[0.000]-0.041***[0.015]0.019[0.012]0.029[0.026]0.045*[0.027]0.076**[0.034]-0.011[0.029]0.014[0.016]0.007[0.022]0.005[0.008]LTCI eligibility dummyage
age squared
marriage
kid
edu2
edu3
edu4
edu9
healthhouse
lexpc
Constant

0.113***[0.014]-0.001***[0.000]-0.021[0.013]0.018[0.012]0.028[0.025]0.044*[0.026]
0.069**[0.032]-0.017[0.028]-0.014[0.012]0.014[0.021]0.001[0.008]-1.901***[0.340]0.107***[0.013]-0.001***[0.000]-0.021*[0.013]0.020*[0.012]0.027[0.026]0.043[0.026]
0.073**[0.033]-0.015[0.029]0.011[0.014]0.009[0.021]
0.004[0.008]Observations
9,216
9,216
9,216
R-squared
0.048
0.048
F-value of 1st stage
104.35Prefecture dummies
Yes
Yes
Yes
Clustered standard errors at prefecture level in brackets*** P <0.01, ** P <0.05, * P <0.1

9,216
599.63Yes

Table 4: Not work, nonregular work, regular work, or self-employed?
(Male, multinomial logit)(1)

VARIABLES

Marginal effect
Main caregiver dummy
LTCI eligibility dummy

Estimation results
Main caregiver dummy
LTCI eligibility dummyage
age squared
marriage
kid
edu2
edu3
edu4
edu9
healthhouse
lexpc
Constant(2)(3)
Multinomial logit with IV(4)

Not work(default
category)

Non-regular
workerRegularworkerSelfemployed

0.046***[0.009]-0.006[0.011]-0.002[0.018]-0.003[0.012]-0.084***[0.022]0.034**[0.015]0.041**[0.016]-0.024*[0.014]-0.591***[0.159]0.065[0.179]0.605***[0.158]-0.006***[0.002]
1.458***[0.133]0.607***[0.122]0.117[0.145]0.340[0.229]0.152[0.177]0.047[0.217]-0.584***[0.116]-0.053[0.163]0.455***[0.081]-15.631***[3.972]-0.765***[0.147]0.159[0.165]1.074***[0.108]-0.011***[0.001]
1.768***[0.128]0.810***[0.133]0.465***[0.104]0.537***[0.169]0.450***[0.155]0.389**[0.173]-0.665***[0.105]0.418***[0.130]0.564***[0.078]-26.572***[2.679]-0.365**[0.155]-0.039[0.166]0.831***[0.140]-0.008***[0.001]1.713***[0.124]0.585***[0.134]-0.036[0.124]0.624***[0.202]-0.093[0.162]0.124[0.186]-0.595***[0.140]0.563***[0.180]0.130*[0.078]-21.911***[3.441]

Pseudo R2
0.077
Observations
9522
Prefecture dummies
Yes
Clustered standard errors at prefecture level in brackets.
*** P <0.01, ** P <0.05, * P <0.1

Table 5: Not work, nonregular work, regular work, or self-employed? (Female, multinomial logit)(1)

VARIABLES

Marginal effect
Main caregiver dummy
LTCI eligibility dummy

Estimation results
Main caregiver dummy
LTCI eligibility dummyage
age squared
marriage
kid
edu2
edu3
edu4
edu9
health
house
lexpc

(2)
(3)
Multinomial logit(4)

Not work(default
category)

Non-regular
workerRegularworkerSelfemployed

0.088***[0.013]0.002[0.016]-0.022[0.015]-0.007[0.017]-0.088***[0.014]-0.001[0.017]0.023**[0.011]
0.003[0.013]-0.468***[0.093]-0.035[0.105]0.585***[0.094]-0.006***[0.001]
0.096[0.093]0.267***[0.090]0.165[0.137]0.095[0.149]-0.042[0.218]-0.118[0.162]-0.125*[0.067]0.047[0.135]0.028[0.049]-0.738***[0.086]-0.010[0.107]0.869***[0.101]-0.009***[0.001]-0.582***[0.091]-0.006[0.077]0.350*[0.179]0.657***[0.194]0.966***[0.219]-0.001[0.203]-0.022[0.082]0.079[0.141]0.118**[0.055]-0.215*[0.118]0.013[0.146]
0.385***[0.103]-0.004***[0.001]0.363**[0.150]0.048[0.106]-0.058[0.156]0.021[0.152]0.326[0.202]-0.182[0.169]-0.065[0.091]
0.144[0.195]-0.147**[0.069]-13.477***[2.329]-20.532***[2.511]-10.012***[2.615]

resid
Constant

(5)
(6)
(7)
(8)
Multinomial logit with control function method
Not work
Non-regular
Regular
Self(default
workerworkeremployedcategory)

0.276***[0.047]-0.004[0.015]-0.067[0.054]-0.006[0.020]-0.191***[0.050]0.005[0.018]-0.017[0.040]0.004[0.012]-1.444***[0.318]0.001[0.107]0.557***[0.099]-0.006***[0.001]0.090[0.081]0.274***[0.078]0.164[0.133]0.086[0.142]-0.018[0.184]-0.105[0.182]-0.000[0.089]0.025[0.144]0.039[0.058]1.018***[0.320]-12.885***[2.470]-1.970***[0.334]0.040[0.111]
0.832***[0.108]-0.009***[0.001]-0.588***[0.081]0.004[0.082]0.349**[0.159]0.646***[0.168]0.996***[0.191]
0.017[0.200]0.136[0.089]0.052[0.141]0.132**[0.060]1.284***[0.331]-19.766***[2.683]-1.318***[0.400]0.053[0.116]0.354***[0.125]-0.003***[0.001]0.355***[0.118]0.057[0.099]-0.060[0.156]
0.009[0.164]0.351*[0.202]-0.169[0.208]0.075[0.098]0.118[0.193]-0.134*[0.072]1.150***[0.405]-9.342***[3.165]

Pseudo R2
0.050
0.050
Observations
8,974
8,974
Prefecture dummies
Yes
Yes
Clustered standard errors at prefecture level (multinomial logit) and bootstrapped standard errors (multinomial logit with control function
method) in brackets*** P <0.01, ** P <0.05, * P <0.1

Table 6: Working hours (Male, OLS and IV)
VARIABLES
Main caregiver dummy

(1)
OLS

(2)
OLS

(3)
IV

(4)
IV

-1.367**[0.614]-1.783**[0.719]0.869*[0.509]0.947*[0.486]-0.013**[0.005]
3.164***[0.460]0.304[0.354]
1.922***[0.661]2.438***[0.821]2.970***[0.747]2.121**[1.010]0.915**[0.343]0.145[0.591]
0.067[0.254]0.087***[0.016]-3.787***[0.501]-1.806***[0.419]-2.903***[0.359]28.940**[12.667]-0.589[3.382]-2.766[6.060]1.103[1.479]0.969**[0.474]-0.013***[0.005]3.053***[0.809]0.325[0.382]
1.909***[0.663]2.441***[0.811]2.965***[0.742]2.101**[1.010]0.948**[0.389]0.138[0.588]0.075[0.256]0.087***[0.016]-3.777***[0.505]-1.804***[0.412]-2.905***[0.352]LTCI eligibility dummyage
age squared
marriage
kid
edu2
edu3
edu4
edu9
healthhouse
lexpc
experiencepublicfsize2
fsize3
Constant

0.914*[0.490]-0.012**[0.005]3.242***[0.459]0.294[0.355]1.920***[0.658]2.433***[0.821]2.970***[0.743]2.134**[1.010]0.947***[0.346]0.124[0.596]0.076[0.252]0.088***[0.016]-3.802***[0.503]-1.794***[0.420]-2.889***[0.361]29.667**[12.763]

0.903*[0.478]-0.012***[0.005]3.323***[0.554]0.278[0.362]
1.932***[0.662]2.432***[0.805]2.975***[0.736]
2.149**[1.006]0.910**[0.384]0.136[0.600]0.067[0.252]0.088***[0.016]-3.807***[0.500]-1.798***[0.410]-2.891***[0.357]

Observations
8,309
8,309
8,309
R-squared
0.050
0.050
F-value of 1st stage
31.86
Prefecture dummies
Yes
Yes
Yes
Clustered standard errors at prefecture level in brackets*** P<0.01, ** P <0.05, * P <0.1

8,309
46.38
Yes

Table 7: Working hours (Female, OLS and IV)
VARIABLES
Main caregiver dummy

(1)
OLS

(2)
OLS

-2.384***[0.514]-2.141***[0.616]-0.397[0.701]0.590[0.525]-0.009[0.005]-2.685***[0.602]-2.188***[0.579]1.342[0.903]1.489[1.009]4.167***[1.394]1.433[1.103]
1.164**[0.459]-2.173**[0.824]-1.463***[0.311]0.407***[0.016]
1.608**[0.632]0.373[0.396]-0.580[0.484]28.155**[12.828]

LTCI eligibility dummyage
age squared
marriage
kid
edu2
edu3
edu4
edu9
healthhouse
lexpc
experiencepublicfsize2
fsize3
Constant

0.600[0.526]-0.009[0.005]-2.708***[0.599]-2.189***[0.580]1.351[0.908]1.499[1.015]4.173***[1.401]
1.442[1.106]
1.153**[0.456]-2.174**[0.824]-1.465***[0.311]0.406***[0.016]1.610**[0.632]0.372[0.395]-0.581[0.484]27.938**[12.856]

(3)
IV

(4)
IV

-7.615*** -13.034***[1.887][4.948]6.867**[3.359]0.387
0.513[0.502][0.519]-0.006-0.007[0.005][0.005]-2.787*** -3.209***[0.587][0.660]-2.135*** -2.130***[0.589][0.612]1.180
1.311[0.883][0.934]1.340
1.482[0.995][1.035]4.276*** 4.419***[1.376][1.442]
1.557
1.742[1.093][1.142]
1.789*** 1.749***[0.539][0.595]-2.219*** -2.243***[0.817][0.828]-1.397*** -1.409***[0.309][0.310]0.404*** 0.399***[0.015][0.016]1.323**
1.287*[0.646][0.679]0.254
0.209[0.401][0.396]-0.613-0.640[0.470][0.476]

Observations
6,723
6,723
6,723
R-squared
0.151
0.151
F-value of 1st stage
46.93
Prefecture dummies
Yes
Yes
Yes
Clustered standard errors at prefecture level in brackets*** P <0.01, ** P <0.05, * P <0.1

6,723
302.65
Yes