Silviase/jgov_v1.4_full_ann huggingface dataset:
Table 1. Five-year job retention rate for university graduates

1990	1995	Five-year job retention rate (A)	1995	2000	Five-year job retention rate (B)	2000	2005	Five-year job retention rate (C)	2003	2008	Five-year job retention rate (D)	(B)-(A)	(C)-(B)	(D)-(C)
Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	(iv) 2003-2008	(iv) 2003-2008	(iv) 2003-2008
Large firms
20-24	91.5%	25-29	65.8%	71.9%	20-24	89.9%	25-29	55.9%	62.2%	20-24	88.7%	25-29	50.8%	57.3%
25-29	63.3%	30-34	56.2%	88.8%	25-29	65.8%	30-34	59.7%	90.7%	25-29	55.9%	30-34	47.1%	84.2%
30-34	58.1%	35-39	52.7%	90.7%	30-34	56.2%	35-39	53.3%	94.9%	30-34	59.7%	35-39	57.9%	97.0%
35-39	54.7%	40-44	54.0%	98.7%	35-39	52.7%	40-44	53.9%	102.4%	35-39	53.3%	40-44	50.7%	95.1%
40-44	58.3%	45-49	55.9%	95.8%	40-44	54.0%	45-49	50.7%	93.9%	40-44	53.9%	45-49	48.4%	89.7%
45-49	57.5%	50-54	52.3%	91.0%	45-49	55.9%	50-54	55.9%	100.0%	45-49	50.7%	50-54	47.0%	92.7%
Small to medium-sized firms
20-24	90.5%	25-29	55.6%	61.4%	20-24	89.7%	25-29	51.2%	57.0%	20-24	89.0%	25-29	48.4%	54.4%
25-29	55.7%	30-34	40.7%	73.0%	25-29	55.6%	30-34	39.8%	71.7%	25-29	51.2%	30-34	36.8%	72.0%
30-34	41.7%	35-39	35.6%	85.4%	30-34	40.7%	35-39	35.4%	87.1%	30-34	39.8%	35-39	34.6%	86.8%
35-39	34.1%	40-44	29.9%	87.6%	35-39	35.6%	40-44	32.0%	90.0%	35-39	35.4%	40-44	30.4%	85.9%
40-44	34.4%	45-49	32.3%	93.9%	40-44	29.9%	45-49	29.1%	97.6%	40-44	32.0%	45-49	31.6%	98.6%
45-49	31.1%	50-54	31.3%	100.5%	45-49	32.3%	50-54	30.2%	93.7%	45-49	29.1%	50-54	27.8%	95.3%

Source: See Figure 1.

Notes: The five-year job retention rate is calculated by dividing the lifetime employee share in age group i in year t by that in age group $ i+1 $ in year $ t+5 $ . Large firms are firms with 1,000 or more indefinite-contract employees. Small to medium-sized firms are firms with fewer than 1,000 indefinite-contract employees. The three rightmost columns report the change in the retention rate between two neighboring periods.
Table 2. Five-year job retention rate for high school graduates

1990	1995	Five-year job retention rate (A)	1995	2000	Five-year job retention rate (B)	2000	2005	Five-year job retention rate (C)	2003	2008	Five-year job retention rate (D)	(B)-(A)	(C)-(B)	(D)-(C)
Lifetime employee share	Lifetime employee share	(i) 1990-1995	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	Lifetime employee share	(iv) 2003-2008	(i)→(ii)	(ii)→(iii)
2a. Large-sized firm
20-24	60.4%	25-29	47.4%	78.5%	20-24	62.4%	25-29	56.1%	90.1%	20-24	60.7%	25-29	50.9%	84.0%
25-29	52.4%	30-34	47.6%	90.8%	25-29	47.4%	30-34	44.5%	93.8%	25-29	56.1%	30-34	55.0%	98.0%
30-34	51.7%	35-39	49.7%	96.1%	30-34	47.6%	35-39	45.7%	96.1%	30-34	44.5%	35-39	41.6%	93.4%
35-39	51.7%	40-44	49.8%	96.4%	35-39	49.7%	40-44	48.0%	96.6%	35-39	45.7%	40-44	44.3%	97.0%
40-44	39.7%	45-49	39.5%	99.6%	40-44	49.8%	45-49	50.0%	100.3%	40-44	48.0%	45-49	47.8%	99.5%
45-49	35.0%	50-54	34.1%	97.6%	45-49	39.5%	50-54	37.0%	93.7%	45-49	50.0%	50-54	46.3%	92.7%
2b. Small- to medium-sized firm
20-24	35.1%	25-29	19.3%	54.9%	20-24	34.6%	25-29	21.6%	62.5%	20-24	36.1%	25-29	21.6%	59.9%
25-29	22.0%	30-34	15.1%	68.9%	25-29	19.3%	30-34	16.5%	85.4%	25-29	21.6%	30-34	18.4%	85.1%
30-34	17.7%	35-39	13.8%	78.1%	30-34	15.1%	35-39	15.4%	101.6%	30-34	16.5%	35-39	14.8%	89.6%
35-39	15.6%	40-44	12.8%	82.1%	35-39	13.8%	40-44	13.8%	99.5%	35-39	15.4%	40-44	14.2%	92.3%
40-44	12.6%	45-49	11.3%	90.2%	40-44	12.8%	45-49	12.8%	99.9%	40-44	13.8%	45-49	13.1%	95.1%
45-49	10.8%	50-54	9.7%	89.5%	45-49	11.3%	50-54	10.4%	91.7%	45-49	12.8%	50-54	13.1%	102.6%

Source: See Figure 1.

Notes: See Table 1.
Appendix Table 1. Sample statistics for the male indefinite-contract employees

Year	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998
Monthly wage/CPI (100 yen)	4,152.0	4,253.9	4,314.6	4,385.2	4,561.0	4,614.3	4,660.5	4,619.4	4,614.5	4,570.8
(2,035.5)	(2,076.8)	(2,082.9)	(2,102.7)	(2,230.1)	(2,232.3)	(2,233.9)	(2,166.8)	(2,148.0)	(2,154.6)
Age	39.48	39.77	40.01	40.16	39.50	39.56	39.77	40.03	40.27	40.31
(11.58)	(11.75)	(11.93)	(12.03)	(12.05)	(12.00)	(11.99)	(12.05)	(12.07)	(12.09)
Educational attainment
Junior high school	0.239	0.229	0.221	0.206	0.161	0.149	0.143	0.142	0.134	0.127
High school	0.520	0.524	0.529	0.533	0.519	0.519	0.518	0.530	0.531	0.526
Junior college	0.037	0.040	0.041	0.044	0.051	0.054	0.058	0.062	0.065	0.070
University	0.204	0.207	0.209	0.216	0.268	0.277	0.281	0.265	0.270	0.277
Firm size (no. of indefinite-contract employees)
1,000≤	0.315	0.317	0.317	0.329	0.379	0.383	0.370	0.325	0.321	0.324
300≤ & ≤999	0.136	0.139	0.141	0.142	0.163	0.164	0.164	0.165	0.169	0.164
30≤ & ≤299	0.350	0.349	0.346	0.343	0.312	0.306	0.314	0.356	0.358	0.357
5≤ & ≤29	0.200	0.195	0.196	0.186	0.146	0.147	0.151	0.154	0.152	0.155
Observations	762,126	758,632	758,516	759,437	796,410	760,491	800,212	814,247	822,308	803,000

Year	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008
Monthly wage/CPI (100 yen)	4,553.1	4,583.7	4,639.0	4,605.3	4,567.7	4,513.0	4,564.4	4,593.3	4,590.8	4,495.0
(2,151.7)	(2145.0)	(2,182.7)	(2,203.5)	(2190.0)	(2,156.5)	(2,287.2)	(2,302.1)	(2,337.1)	(2,281.4)
Age	40.51	40.67	40.88	40.83	41.04	41.16	41.12	41.12	41.08	41.06
(12.01)	(11.9)	(11.82)	(11.79)	(11.70)	(11.65)	(11.54)	(11.62)	(11.64)	(11.62)
Educational attainment
Junior high school	0.118	0.111	0.103	0.093	0.085	0.073	0.068	0.063	0.059	0.054
High school	0.523	0.522	0.520	0.517	0.515	0.510	0.486	0.489	0.489	0.502
Junior college	0.072	0.075	0.079	0.083	0.085	0.089	0.087	0.085	0.087	0.088
University	0.287	0.292	0.298	0.307	0.314	0.328	0.360	0.362	0.365	0.356
Firm size (no. of indefinite-contract employees)
1,000≤	0.319	0.322	0.325	0.320	0.315	0.285	0.278	0.286	0.303	0.316
300≤ & ≤999	0.163	0.163	0.167	0.166	0.167	0.175	0.165	0.163	0.159	0.157
30≤ & ≤299	0.360	0.356	0.351	0.352	0.355	0.405	0.388	0.370	0.368	0.358
5≤ & ≤29	0.157	0.159	0.157	0.162	0.164	0.135	0.169	0.181	0.170	0.169
Observations	792,656	756,490	739,097	722,613	713,736	715,765	567,072	596,717	548,022	554,980

Source: See Figure 1.

Notes: The rows "Monthly wage/CPI" and "Age" report mean values. The monthly wage is the sum of monthly contractual cash earnings plus one-twelfth of the annual special cash earnings of the previous year. The figures in parentheses are standard deviations. The rows "Educational attainment" and "Firm size" report the share for each sub-category.
ESRI Discussion Paper Series No.93

Outsourcing At-home Elderly Care and Female Labor Supply:

Micro-level Evidence from Japan’s Unique Experience

by

Satoshi Shimizutani, Wataru Suzuki and Haruko Noguchi

April 2004

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# Outsourcing At-home Elderly Care and Female Labor Supply: Micro-level Evidence from Japan’s Unique Experience $ ^{1} $

By

Satoshi Shimizutani $ ^{2} $ , Wataru Suzuki $ ^{3} $ and Haruko Noguchi $ ^{4} $

April 2004
## Abstract

This is the first study to take advantage of Japan’s public long-term care insurance as a unique and natural experiment to evaluate how outsourcing long-term care spurs female labor supply. We utilize our unusual and rich panel data from households with an elderly person who needs care and who became eligible to use care services through the public insurance after 2000, and those who are not eligible to use care service.

Our empirical results based on the difference-in-difference estimates demonstrate that the stimulating effect of the public elderly care insurance on female labor supply was observed only for households with a less-needy care receiver in care-level 1 in FY 2001, one and a half years after the initiation. However, we clearly find a large and positive effect on the female labor supply in households in FY 2002, two and a half years after the implementation. The new public scheme enhanced the probability of being employed by 30 percent to 60 percent, working days per week by 40 percent to 60 percent and working hours per day by 50 percent to 70 percent.
### 1. Introduction

This is the first study to take advantage of Japan’s public long-term care insurance as a unique and natural experiment to evaluate how outsourcing long-term care spurs female labor supply.

Japan’s public elderly care insurance was introduced in the spring of 2000. One of the most important purposes of this new scheme was to diminish the onerous care burden at home, which traditionally has depended heavily on women. Socializing the care burden was expected to spur the female labor supply in Japan, whose citizenry is experiencing rapid aging and declining fertility.

Under the new insurance system, market-oriented reform was implemented on the demand side $ ^{5} $ . Now, not only households in lower income classes—who were eligible to use near-free care—but all insured eligible people are entitled to receive necessary care services. To receive LTC benefits, an individual applies to an expert municipality committee and a screening determination is made within 30 days. When certified in need of LTC, the person is further categorized into one of six health conditions $ ^{6} $ , which determines benefit entitlements. Based on the care plan provided by a care manager, a user is free to contract care services with any provider under the “contract system,” and must pay a 10-percent co-insurance payment of the officially fixed prices for each insured service $ ^{7} $ .

Other purposes of the insurance system on the supply side have so far been relatively well examined, including stimulating the supply of care services by allowing for-profits into the at-home care market, and improving the quality of care in response to the expanding number of elderly in Japan (Shimizutani and Suzuki (2002)). On the contrary, surprisingly, how the public
elderly care insurance stimulated female labor supply has remained unexplored, despite the relevance to academics and pragmatic policymakers. This is very surprising since the introduction of the public insurance was partly justified by the externality to family, especially women, who were saddled with onerous care burdens at home and who were discouraged to work.

To address this empirical issue, we take advantage of an unusual and rich panel data set from two types of households in Japan. The first category is households with an elderly person who needs care and who, in most cases, became eligible to use care services through public insurance after 2000. The other type is households with chronically diseased patients, many of whom were eligible to receive care before or after 2000. The data contains information on the labor supply before and after the introduction of the public insurance scheme in 2000. Thus, we employ the difference-in-difference approach to examine the effect of the introduction of public elderly care in Japan.

Since the introduction was implemented exogenously for individual households, we were exempt from the endogeneity problem between labor supply and outsourcing care, something that previous studies suffered from. In other words, Japan’s unique experience provides us with an unusually clean opportunity to examine the relationship between care-giving at home and female labor supply.

The main empirical results are summarized as follows. The introduction of public elderly care insurance stimulated the female labor supply by 10 percent in probability of being employed, 20 percent in working days per week, and 30 percent in working hours per day, which was observed only for households with a less-needy care receiver in care-level 1 in FY 2001, one and a half years after the initiation. However, we clearly find a large and positive effect on the female labor supply in households in FY 2002, two and a half years after the implementation. The new public scheme enhanced stimulated the probability of being employed by 30 percent to 60 percent, working days per week by 40 percent to 60 percent, and working hours per day by 50 percent to 70 percent.
The outline of this paper is as follows. The next section reviews previous studies. We rely on empirical studies outside Japan since this is the first study on the topic in Japan. Section 3 briefly describes the data set used in this study. Section 4, the main part of this study, employs the difference-in-difference approach to evaluate the effect of the public elderly insurance on female labor supply. The final section concludes and discusses policy implications.

### 2. Previous Studies

Since this is the first study to evaluate the effect of elderly care insurance on the female labor supply in Japan, we rely on empirical studies performed in countries outside Japan.

In the United States, the Family Medical Leave Act of 1993 and the Older Americans Act Amendments of 2000 address the concern that caregivers may reduce hours or quit altogether to provide elderly care needs; the loss of trained workers is likely to negatively affect productivity in the economy as a whole $ ^{8} $ . However, empirical results on the relationship between work and caregiving, primarily relying on cross-sectional data, are ambiguous. A number of early studies found a strong negative relationship between the two (Dwyer and Coward (1991), Brody and Schoonover (1986), Boaz and Muller (1992), Stone, Caferata, and Sangl (1987)). However, several more recent papers have not found a trade-off (Ettner (1996), Stern (1995), Wolf and Soldo (1994)).

McGarry (2003) attributes this inconclusiveness to a reduced form specification frequently used in this literature. Some studies put the decision to provide care as a dependent variable and labor market status as an explanatory variable. On the contrary, others treat labor supply decisions such as labor force participation and hours worked as functions of care-giving. Since labor status and care-giving are decided simultaneously, one cannot infer causality from these results.
Market work and care-giving are certainly competing uses of one’s time, suggesting a trade-off between the two. However, it is not clear whether those who provide care do so because they are working fewer hours, or if they work fewer hours because of their care-giving burden. Thus an investigation of causality is critically important in assessing the labor market effects and the potential policy response.

Even though we assume causality by observing that informal care provision affects labor supply, the expected sign of the effect on labor supply is undetermined. We should consider two competing effects. One is the substitution effect. Given scarce time, care-giving will increase shadow wage rates and depress labor supply. At the same time, care-giving demands both money and time from caregivers (Carmichael and Charles (1998)).

McGarry (2003) takes advantage of a longitudinal panel of observations drawn from the Health and Retirement Study (HRS) to examine labor market behavior prior to care-giving, and noted how it differs for those who subsequently provide care and those who do not. She finds surprisingly little relationship between previous employment and later care-giving in both short-term and longer-term periods (six years).

In the United Kingdom, Carmichael and Charles (1998, 2003) address the relationship between informal care and female labor supply. Carmichael and Charles (1998) find care-giving may both constrain female labor supply and adversely affect earnings. Carmichael and Charles (2003) apply a new data set to their extended model to evaluate the relevant social security benefits (Attendance Allowance and Invalid Care Allowance) on the labor supply. They also confirm the adverse effects of care-giving on labor supply and earnings.

### 3. Data

We take advantage of two different data sets to employ the difference-in-difference approach explained in the next section; the data set is on elderly who need long-term care services as a
treatment group and those with chronically diseased patients as control group.

The dataset on treatment group is the micro-level data from “Survey on Long-term Care Users.” This survey was performed twice, in 2001 and 2002. The first survey was conducted from November to December 2001, by the Price Policy Division of the Cabinet Office. The sample comes from a research company that collected data from 136,349 households from all areas of Japan. Those households were randomly chosen based on Japan’s household registration system; the distribution resembles the census data.

A care receiver is any elderly person who needs care in daily life. In other words, a care receiver need not be approved by local governments of eligibility to use public insurance. In addition, a care receiver in this survey is any person who lives at home together with family members; those living in institutions separately from family members are excluded from the sample. Among all samples, we screened households with a care receiver and chose samples with only one receiver randomly $ ^{9} $ . We mailed our questionnaires to 1,300 households, and received responses from 1,005 households (a response rate of 77.1 percent).

The second survey was implemented by the Economic and Social Research Institute, Cabinet Office, in October and November, 2002. The notable merit of this survey was to perform a similar survey on the same households in the 2001 survey. Of the 1005 households, we obtained responses from 822. Among those, 617 samples still lived at home with family to receive at-home care $ ^{10} $ .

The respondent is the main caregiver in individual households $ ^{11} $ . The questionnaire covers a variety of items, including care use over several years, health conditions of caregivers and receivers, and household demographics. In the first survey, labor status included working days per
week and hours per day in FY1999 (just before the introduction of the public insurance) and in November 2001. The second survey contains that information in November 2002. Thus, we have information on labor status at three different times.

On the other hand, data on those with chronic diseases were collected by the “Survey on Medical Insurance in the Elderly” implemented in February 2002. The total sample is exactly the same as those with “Survey on Long-term Care Users.” In this case, a sample was randomly chosen from households with an elderly person older than 70 with a chronic disease. The survey was mailed to 1,500 respondents; we received responses from 1,095 households, for a response rate 73 percent.

The variables included contain various information on living-will or terminal care as well as on chronic disease, self-burden of medical treatment, subjective opinions on public insurance, and patient fees. In addition, the survey asked about health conditions, financial information on annual income and assets, labor status, and working days per week and hours per day.

In November 2000, some variables were collected again from the same sample, including financial information on annual income and assets and labor status. The sample size was 872, with a response rate of 79.6 percent.

Table 1 reports the basic statistics of major variables. The share of those employed was above 60 percent in FY 1999 in both types of households. It declined in FY 2001 for those who with a care-receiver, but recovered to more than 70 percent for both categories. We should note that working days and hours are much shorter for households with a care receiver. In FY1999, working days and hours per day were 2.5 and 3.3 respectively for households with a needy elderly, while those were 4.3 and 5.8 for the other type of household. However, we should keep in mind that the figures decreased between FY 2001 and FY 2002 for households with a patient, but the situation for households with a needy care receiver remained basically unchanged. As expected, the share of those in need of care or entitled to receive care is much smaller for households with a chronic patient.
The demographics of caregivers are relatively homogeneous between the two types of households, though average age is slightly younger for households with a care-receiver. All caregivers are female in the households with a chronically-diseased patient, and the share of females is almost 95 percent for those with an elderly person needing care. There is no distinct difference in education level or financial status. Moreover, chronic conditions do not differ from each other except a higher share in cardiovascular diseases and a lower share in chronic diseases related to digestive organs for households with chronic disease.

In sum, those two types of households have an elderly person in poor health condition, and the characteristics of caregivers and care receivers are relatively homogenous. This fact can justify utilizing households with a chronically-diseased patient as a control group to estimate the effect of introduction of public aged care support insurance on female labor supply.

In the next section, we turn to estimate the female labor spurring effect of the public elderly care insurance.

### 4. Empirical Specification and Estimation Results

In this section we examine the effect of the introduction of the public elderly insurance in 2000 on female labor supply. Prior to 2000, long-term care was provided through by each municipality under the name of social welfare policy. The characteristic of “social welfare” was confined to target to care-receivers in lower income households with no monetary burden. However, after 2000, once an elderly person is approved to be eligible to use care services, he/she is free to use care at a rate of 10 percent of co-payments. Since this reform was totally exogenous to each household, we are exempt from the causality problem discussed in section 2. In this sense, Japan’s unique experience serves a clean and natural experiment to evaluate the relationship between female employment and care-giving.

A theoretical background behind our empirical specification is frequently used in the
standard labor supply literature to examine (McGarry (2003)). A caregiver (an altruistic child) is assumed to face the following utility maximization problem.

$$ Max\;U_{c}=U(\;C_{c},L_{c},\;U_{p}(C_{p},H_{p})) $$

$$ subject to:\ wT=wL_{c}+pC_{c}+wHC $$

where C is consumption of market goods and L is that of leisure. The subscripts c and p stand for child and parent respectively.

A caregiver’s utility includes his/her own utility as well as his/her parent utility which depends on consumption of market goods $ C_{p} $ and health status $ H_{p} $ . Parent’s health status is enhanced through the provision of home health care, HC. If all else is constant, an hour of care will be more valuable if the parent needs more care.

The child has a finite number of hours T which he can allocate to employment (income), leisure, or care-giving. w is the child’s wage rate (i.e. the opportunity cost of his time), and p is the price of the consumption good. Providing care is costly to the child, however, and it requires time and thus enters into the child’s budget constraint with a price equal to the opportunity cost of the child’s time. Thus time spent helping a parent comes at the expense of reduced consumption of goods and/or hours of leisure. By shifting the first term in the right hand side to the left, the wage received is the sum of expenditure for market goods and costs for long-term care.

Assuming that care offered by family members is a substitute for that provided outside the home through the public insurance, one can use a larger amount of care services at a lower cost. In other words, the introduction of the public care insurance offers the possibility that a household could increase the labor supply through outsourcing at-home long-term care.

Based on those backgrounds, the empirical specifications we employ in this section are as follows.

$$ \left(\mathrm{A}\right)LS_{i,99,0I}=\mathrm{a}_{1}*Dummy_{0I}+\mathrm{a}_{2}*CareNeed_{99,0I}+\mathrm{a}_{3}*Eligible_{99,0I}+\mathrm{a}_{4}*(Dummy_{0I}*CareNeed_{0I})+\mathrm{a}_{5} $$