New ESRI Working Paper No.54

Labor Market Impacts of Expanded Employee Social Insurance
Coverage in Japan
Atsuhiro YAMADA
Nicholas MEHR
March 2021

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “New ESRI Working Paper” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Labor Market Impacts of Expanded Employee Social Insurance
Coverage in Japan
Atsuhiro YAMADA ∗Nicholas MEHR ∗∗Abstract
As the Japanese population ages and the number of non-regular workers continue to grow, maintaining thesocial insurance system has become an integral challenge for alleviating poverty. This paper examines theimpact of the recent employee social insurance reform in October 2016, which extended benefits to a
portion of non-regular workers. Utilizing basic and special tabulations from the monthly Labor Force
Survey, we construct a longitudinal panel which covers the period 2013 to 2017, both before and after thereform. Utilizing this panel, we are able to track individuals from a nationally representative sample ofhouseholds across Japan and perform a difference in difference analysis to determine the impacts of the
expansion policy on the work hours of individuals. We find that the reform has positive effects with respect
to age and gender, and the working hours of the following groups increased following the reform: young,prime and elderly males, as well as prime and elderly females, dependent housewives and also the single
sole-heads of household. We conclude that the expansion of coverage can incentivize labor force
participation for such workers even in spite of increasing the social insurance premiums of certain workers,
i.e., dependent housewives, while decreasing that of single sole-head of households by reducing their social
insurance premiums. In light of these competing incentives on the labor force, it may be necessary toreconsider changes to the employee social insurance premiums in order to avoid potential distorting effects
on the labor market.

JEL Classification Codes: H55, J26, J32
Key words: aging, pension, social insurance, expansion of coverage, non-regular workers∗

Professor, Faculty of Economics, Keio University, a member of ESRI International Collaboration Project 2019-2020∗∗

Graduate School of Economics, Keio University

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New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

1. Introduction
Japan represents one of the oldest populations in the world. This has made sustainability a major
challenge as significant pressure has been placed in recent years on the financing of benefits to the elderly
through the social security system.
Historically, it has been compulsory for regular employees of private companies to contribute toboth their employees’ pension insurance (EPI, Kosei Nenkin) and employees’ health insurance (EHI, Kenko
Hoken), collectively this is Japan’s employee social insurance (ESI) program. However, as the share of
non-regular employment has increased, their lack of employee social insurance support has become agrowing concern. For example, one third of the participants in the National Pension Insurance (NPI,
Kokumin Nekin) program are non-regular workers whom are not covered by employee social insurance
programs (MHLW 2019a; 10). As a result, such workers bear the entire cost of their insurance premiums
while receiving a lesser benefit compared to workers enrolled in ESI. With these workers in mind, thegovernment has taken incremental steps at reforming its social insurance programs to include these nonregular workers (Nomura 2019), as we will see in the next section.
There has been a large body of literature examining the labor market impacts of such social
insurance policy reforms. Such reforms typically affect labor costs and contribute through the demand-sideof the labor market or through labor supply (i.e., changes in income, worker’s taxable status, and so on).
This paper investigates both labor and demand side effects of the recent employee social insurance
expansion (Tekiyou Kakudai) in 2016. This policy expanded coverage by requiring large firms that employ
non-regular workers to begin contributing to the worker premiums. As of October 2016, coverage of
employee social insurance increased to cover some 250,000 more non-regular workers.
This paper utilizes the Labor Force Survey (Rodoryoku Chousa, LFS) administered by the Ministryof Internal Affairs and Communications of Japan to construct a panel data set. We perform a difference-indifference analysis in order to determine outcomes on the hours worked by these newly covered employees.

2New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

We uncover positive effects on all genders and age groups including housewives who were potentiallyclassified as “No.3 insured” (No.3, Dai 3 gou hihokensha) after the reform.
The results in this paper are from research conducted between 2019-2020 for the Economic andSocial Research Institute (ESRI) for International Collaboration Project. This paper is organized as follows:in the next section, we describe the institutional background of employee social insurances, and in section
3, we discuss related literature and clarify issues that have not yet been uncovered. Section 4 describe thedata set, and in section 5, we present our empirical framework based on a difference-in-difference strategy.
In section 6 and 7, we show the results of our descriptive and econometric analysis respectively. Section 8
presents our concluding remarks.

2. Institutional Background
2.1 Pension System of JapanThe Japan pension system is composed of both a public pension system and a private-sector system,
Figure 1 (MHLW, Nomura 2019). The former system is part of the nation’s social security system andcontains NPI and the EPI. Moreover, all resident’s participation in NPI is mandatory. While the NPI is aflat-rate portion that covers everybody, the EPI is an income related portion which covers employees.

Figure 1: Japan’s Pension System

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Source: Ministry of Health, Labour & Welfare, Nomura Institute of Capital markets Research (2019)

Within the private pension plans there are sub-categories known as the defined benefit (DB) typeand also a defined contribution (DC) type. Because these private pension plans are voluntary, employers
are not mandated to offer them. Furthermore, all public employees are covered by DB plans.
National Pension Funds (NPFs) and individual DC plans are individual pension plans while NPFs
are for the self-employed and individual DC plans are for most of the working-age population, including
non-working spouses. Eligible individuals can join at their discretion.

2.2 Achieving Universal Coverage
Through a series of gradual reforms, Japan eventually achieved universal coverage of its pension
system with reforms in 1985/1986 (Takayama 2002). This was accomplished by gradually introducing theabove pension plans, which were modeled on the workplace insurance systems at the time. The mandatory
pension plan for private company workers was introduced in 1942 and this is what is now called EPI. The
pension plan for government employees and private school teachers was then established in the 1950s. By
1961, the NPI for the self-employed was introduced. At this point all workers were theoretically covered.
However, the system as a whole was fragmented into different pension plans and not integrated.
Because of this, the Basic Pension system was implemented by 1986, the NPI was rebranded as the
Basic Pension which included part of EPI and with it a requirement for non-working spouses to join thissystem (Takayama 2002). Thus, it assured that all individuals of the households regardless of their working
status were covered by a common system.

2.3 Policy Reforms to the Pension System
The advancement of ageing in Japan has exceeded what was originally projected. Simultaneously,
there has been a drop in the number of regular workers which has coincided with an increase in the ratio of
non-regular employees, from roughly 16.4% in 1985 to approximately 38% by 2014 (MHLW 2017). As

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New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japanthese non-regular workers now support a significant share of Japan’s labor force, they are more likely to
encounter job insecurity, receive a lower average wage, are less likely to receive on-the-job training incomparison to regular workers, and are at greater risk of remaining below the poverty line (Takahashi 2015).
Because of this, policy reforms have been necessary to sustain continued funding of the pension
system and in more recent years, to expand benefits to the non-regular workforce. Historically, policy has
been focused on increasing the retirement age, increasing the employer’s contribution, increasing thepremium itself, or creating alternative sources of funding. Below are some key policy reforms.
In 2001, Japan decided to raise the retirement age from 60 to 65. This is a strategy which has been
adopted by a number of developed countries. To give people sufficient time to prepare, the retirement age
changes must be introduced gradually over a long period of time. The current increase is set to continue ata staggered pace, reaching 65 in 2025 for men and 2030 for women.
In 2003, the Ministry of Health, Labour and Welfare (MHLW) changed the standard for calculationof the social insurance premium-to-salary ratio to a “total reward” system with the aim of rectifying theimbalance between monthly-salary and bonuses. Effectively, this reform changed the rate of the total
insurance premiums on bonuses from roughly 2% to 22%, and that on the monthly salary from roughly
26% to 22% (Kodama and Yokoyama 2018). This created higher insurance costs for firms with a higher
bonus-to-salary ratio provided they paid exactly the same amount in annual earnings.
In 2004, a comprehensive reform to public pension took place which raised the premiums gradually,
between 2005 and 2017, from roughly 13.5% to 18%. This policy also allocated financing half of Basic
Pension income from government revenues and in 2012, Japan decided to raise its consumption tax, firstfrom 5% to 8% and then eventually to 10%. The additional revenue generated has been contributing to thesocial security system. The consumption tax rate was raised to 8% in April 2014, and the most recent
increase to 10% occurred in October 2019 (Kodama and Yokoyama 2018).
In October 2016, EPI was expanded to cover non-regular workers working between 20-30hrs withthe aim of making firms that employ part-time workers contribute to EPI because the percentage of such
workers has steadily grown (Nomura 2019).

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

As mentioned in Figure 1, until October 2016 a large portion of such workers were treated the sameas self-employed individuals in that they paid their entire pension contribution. It is important to note thatthe conditions for EPI coverage are the same as the employees’ health insurance (EHI) coverage. Becauseof the joint requirement for EPI and EHI, this paper refers collectively to the coverage of these plans as theemployee social insurance plan (ESI). The following Figure 2 outlines the policy agenda:

Figure 2: Changes in ESI Qualifications

Source: Public Relations Office of the Government of Japan (2017)

The new ESI policy covers all workers who work 1) at least 20hrs per week, 2) make at least 88,000
JPY monthly, 3) have been employed for at least 1 year, 4) who are not students, and 5) who work in firmsof 500+ employees must be enrolled into ESI, with private firms under 500+ potentially paying
contributions after April 2017.
A critical point is that this newly covered group will experience both premium burdens and benefits,
the degree to which depends on their family structure and working style. This is illustrated in the following

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Table 1: Changes in Pension Insurance Contributions (JPY Monthly)

Source: MHLW (2012)
Note: Model case using 100,000 JPY monthly salary

In this model case, a single part time worker with a monthly income of 100,000 JPY, half of theirpension burden is transferred to their employer as a result of switching to ESI. This incentivizes such
workers’ contribution to the labor supply while it creates an additional labor cost for their employers.
In contrast, in the same model case, a part-time housewife who has a full-time working husband,
defined as a No.2 insured (No.2, Dai 2 gou hihokensha) incurs a new premium in addition to her employer.
This is because, as a No.3 dependent of her husband, she had no premium burden. Moreover, company
family allowances of such insured husbands are often tied to their wife’s No.3 status, which are also lost
upon entering the ESI system (IPSS 2011).
As for the health insurance premiums, as shown in the following Table 2, for single mothers andsole-heads of household, their health insurance premiums become reduced by the ESI expansion. On theother hand, the health insurance premiums for a self-employed individual’s housewife increase to about
900 JPY a month.

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Table 2: Changes in Health Insurance Contributions (JPY Annually)

Source: MHLW (2012)
Note: Model case using 100,000 JPY monthly salary. An estimated 65,000 JPY injury/illness (Shōbyō teate) and 21,000 JPY are maternity leave
Shussan teate) allowances.

Japan has had an overall downward trend in the number of hours worked in recent years(Kawaguchi & Mori 2019) and it has been noted that even before the 2016 reform a large majority of firms
were aware of the 1.3mil JPY ceiling for ESI. This means that firms targeted by the expansion may reduce
the hours of their workers as a result. Particularly for the No.3 females (who constitute a large proportionof the part-time workforce) who had been earning between 1.06M and 1.3M JPY (approximately 88k to
130k monthly), these limits are known colloquially as the “wall.” That is, unless a wife can earn enoughincome through part-time work to absorb the subsequent income taxes and/or forgo spousal benefits, it
makes little sense to increase her working hours.
Table 3 represents a breakdown of the pension contributions by dependent status. The legal
requirements for the No.3 status are of prime concern and details on how this paper defines this group will
be discussed in the data section.

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Table 3: Pension Contributions by Dependent Status

Source: MHLW (2019b)

3. Related literature and research question
As this paper examines the relationship between labor costs (the new employees’ social insurance
burden for the employer-side) and workers (a change in working hours for the employee-side) it follows awide body of related literature. Particularly in the case of insurance contributions, while related studies havefocused on different outcome variables, there is ample empirical evidence that increased labor costs areassociated with reduced wages, reduced working hours and displacement effects for the employees.
As for wage reductions, Komamura and Yamada (2004) analyzed the incidence of the employers’
contribution rates to health insurance and long-term care insurance. They used panel data from theindividual health insurance societies (Kenkou Hoken Kumiai Rengoukai) throughout Japan from 1995 to2001 and concluded that a majority of the employer’s contributions resulted in wage reductions.
In the case of a specific group, 40+ aged males, Sakai and Kazekami (2007) utilized a differencein-difference estimate using the Basic Survey on Wage Structure (Chingin Kousei Kihon Toukei Chousa,
BSWS) and found a decrease in this group’s wages after the introduction of Long Term Care Insurance.

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Another case of a specific group, married women, was studied by Akabayashi. In line with priorresearch by Hausman (1980) and Hausman et al. (1985) on the relationship between taxation and the labor
supply, Akabayashi uses cross-sectional data to estimate the effect on the labor supply of No.3 Insured
housewives based on a linear budget constraint imposed by taxation and the social security system,
concluding that there is a greater negative supply effect from these females in response to their premium
costs than to their tax rates.
Iwamoto (2008)’s reappraisal 1 of Tachibana and Yokoyama’s 2007 paper which used data fromthe Labor Force Survey and Yearbook of Social Security (Shakai Hoken Toukei, YSS) concluded that ESI
contributions resulted in decreased wages for employees.
As for displacement effects, Miyazato and Ogura (2010) used micro wage data from the Employee
Status Survey (Shuugyou Kousei Kihon Chousa, ESS) and insurance data and found no statisticallysignificant negative effect on regular workers wage rates, but instead found negative effects on relative
wage rates of regular workers to those of irregular workers, concluding that the employer’s costs result ina substitution of regular workers for irregular workers.
Similar to this study but focusing on an earlier reform, Kodama and Yokoyama (2018) finds firms
reduced employment after the “total coverage” pension reform of 2003. He conducts a difference-indifference analysis, fixed effects model and a DiNardo-Fortin-Lemieux decomposition using the BSWS,
utilizing variations in labor cost increases due to the 2003 reform. He concludes that those firms create
longer working hours in response to an exogenous increase in labor costs without productivity gains.
Another detailed analysis of work hour reduction has been done by Kawaguchi & Mori (2019),
who found negative but heterogeneous effects from minimum wage hikes on hours worked. Both theempirical study and results are closely related to this paper in that a panel data set is constructed from the
LFS, work hours are treated as the dependent variable, a common trend is checked between groups, and1 Although the original study found no statistically significant effect, Iwamoto modified the empirical strategy by 1) using the monthly

wage/work hour data from the LFS instead of the annual reports used in the original paper that potentially bias the employer’s contribution rate
upwards and 2) excluded observations from the YSS in the mining/real estate industries to remove short-term wage fluctuations due to sampling
errors.

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

heterogeneous effects were discovered as they conclude that while effects vary between education and sex,
there is a negative effect on hours worked.
As a relatively new reform, it remains to be seen what long term effects the employees’ social
insurance expansion will have on the labor force. For example, currently discussions have been held as towhether the requirement on the scale of the company’s workforce should be eliminated because it would
be irrational to treat employees differently in terms of pension coverage due to the size of their companies.
Considering these previous studies, it is expected that ESI expansion will also have heterogeneous
effects across gender and age. In Japan, the number of empirical studies on both the minimum wage andpensionable age are numerous, but this particular reform has not been studied. Thus, this research aims to
add new knowledge with an analysis of the effect of the 2016 expansion.

4. Data set
This paper utilizes micro data from the Labor Force Survey, which is conducted by the Statistics
Bureau each month. Samples are randomly selected from about 40,000 households using a geographicallystratified sample; the survey has a rotating, nationally representative sample structure where individual
households are surveyed 4 times over a 2 year period: consecutively in the first 2 months, then consecutively
again in the same 2 months of the second year. Additionally, during the last Basic Survey (Kiso Chousei)
an additional survey known as the Special Survey (Tokutei Chousei) is administered which records
additional variables such as education level and annual income through an additional set of questions.
Individual households can be surveyed up to 4 times (with the Special Survey administered simultaneously
alongside the final Basic Survey).
Individual respondents surveyed in different months are tracked using the following identifier
variables: 1) Survey District ID 2) Birth Year, 3) Birth Month, 4) Sex, 5) Household Category 6) Marital
Status and 7) Relationship to Head of Household to generate a unique personal identifier (hereafter, PIDs).

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

In order to add the additional variables recorded in the Special Survey, these PIDs were matchedto their corresponding key identifying variables found in the Special Survey. These additional variables
included important variables such as education and annual income.
Next, duplicate PIDs were checked. The average number of distinct PIDs in a given month is 15,000
for the Basic Survey whereas duplicate PIDs constituted, on average, about 0.2~0.3% in each month. Theseobservations were dropped. There were roughly 0.01% of cases within the overall sample that contained
PIDs which were observed over 4 times, these PIDs were also dropped.
The dataset used in our analysis contains approximately 85,000 individual observations from 20132017 (Table 4) when making use of only the Basic Survey data and roughly 70,200 observations when
making use of the additional variables in the Special Survey. January 2013 – September 2016 is defined as
before the reform, and October 2016–March 2017 as after the reform. As mentioned earlier, owing to apotential expansion of smaller sized firms in April 2017, the observation period has been limited to March2017.
The dependent variable is set as the natural log of weekly hours worked and included covariates
such as age.
The sample has been limited to the following in the pre-reform period: 1) 20-29hr weekly work
hours (assuring that such workers were previously under the 30hr requirement) and 2) potentially eligible
workers. Eligibility is defined as the following: firms over 500 workers, over 88,000 JPY in monthly wages,“Mainly working” or “Working/housework”, and neither of: “No income, going to school, in school, selfemployed, family business, side-job, director, public official, ” nor “teacher”.
Because exact wage data is not included in the LFS, 2 indicators are used as proxies to check the
ESI income requirement:
The first is a variable called the minimum possible monthly wage, which is constructed by using
prefectural-level minimum wage data (MHLW 2017) in combination with weekly, daily and monthly workhour variables from the Basic Survey:

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New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

𝑀𝑀𝑀𝑀𝑀𝑀. 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃(𝑀𝑀𝑀𝑀𝑀𝑀ℎ) =𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻(𝑊𝑊𝑊𝑊)× 𝑀𝑀𝑀𝑀𝑀𝑀. 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊(ℎ𝑟𝑟) × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷(𝑀𝑀𝑀𝑀𝑀𝑀ℎ)𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷(𝑊𝑊𝑊𝑊)As shown in the above equation, the minimum possible monthly wage is calculated by first dividing

the number of hours worked during the last seven days of the month by the number of days worked in thatsame period, then multiplying this value with the hourly minimum wage rate applicable to the individual’s
prefecture. Multiplying this value with the number of days worked during the current month gives theminimum possible wages for a PID in a given month. A minimum value of 88,000 JPY for this variable isset as the first income check.
As for the second indicator, because the annual wage recorded in the Special surveys do not makefiner distinctions between 1mil and 1.06mil and instead are categorized in 12 tiers: i) No Income, ii) Under
0.5, iii) 0.5-0.99, iv) 1-1.49, v) 1.5-1.99,vi) 2-2.99, vii) 3-3.99, viii) 4-4.99, ix) 5-6.99, x) 7-9.99, xi) 1014.99 and xii) Over 15 million JPY, a minimum value of 4 for this variable is set as the second income
check.
From this sample, the treatment and control group are adjusted such that they differ only by firm
size and wages: The control group consists of either sub-500 firm sizes or sub-88k monthly wages or sub1mil annual wages while the treatment group is defined as 500+ and 88k+ and 1mil+, respectively.
Because work hour reductions have been shown to have heterogeneous effects (Kawaguchi & Mori
2019) on individuals, due in large part to the demographic composition of the non-regular workforce, thesample is divided into sex/age groups to isolate any potential differences. Recall from Table 3 that holdersof the beneficial No.3 Insured status will have a higher take-home pay if they are not obliged to enroll in
ESI whereas workers already paying premiums under the No.2 Insured status will see a reduction in theirburden as it shifts to their employer. The former represents a worker with negative inducements to work
while the latter represents a worker with a positive inducement. Moreover, because the regular workforce
is, to date, overwhelmingly male, the irregular workforce remains predominately comprised of females inthese prime age groups.

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Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan

Dummy variables are thus created for age and sex. Namely, the sample is divided into 3 groups:
young (aged 19-24), prime (aged 25-59), and old (aged 60-64). Because the control and sample are limited
to those non-regular workers with below 30hr work weeks, the prime aged female group comprises amajority of the observations. Currently prime aged males comprise the majority of Japan’s full-time
workforce and thus are not applicable to this reform.
However, even across these groups there may be heterogeneous effects in that there will be bothindividuals who want and who do not want to increase their own work hours. In other words, the ESI
expansion increases the burden on some of these workers while decreasing it for others with respect to theirtake-home earnings. As mentioned in the Table 3, there are 3 insurance statuses, and depending on theindividual’s status, the ESI expansion may reduce, rather than increase their effective take-home earnings.
For this reason, additional groups are created for housewives with No.3 dependent status as well as for
single part time workers.
As for No.3 status, these individuals may seek to reduce their work hours to avoid their pension
costs. However, because No.3 status requires a spouse with No.2 status, it is not possible to directly confirm
this requirement using the LFS data. As an indicator of this status, No.3 status has been defined based on
the legal requirements (See section 2): i) those who were between ages 20-59, ii) were married, iii)
designated a spouse as the household head, iv) made annual wages under 1.49m and v) held a minimum
monthly wage under 108k.
As for single part timers, as the primary income earner with no no.2 support, ESI expansion serves
to reduce their insurance burden (See section 2), so they would likely not want to decrease their work hours.
This group is thus defined as ages i) 20-59, ii) single head of household, iii) unmarried and iv) “would not
like to reduce work hours”.
Table 4 lists the descriptive statistics for the key variables, hours worked (log),
weekly/daily/monthly work hours, age, minimum wage and minimum monthly wage info.

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Table 4: Descriptive Statistics of Sample

The average work hours are 24 per week, and amongst firms who are exposed to the reform, larger
firms of 1000+ workers comprise a majority. Due to the rotation sampling of the LFS, any unique ID may
be observed up to 4 times. Because not all individuals are surveyed an equal amount of times, this results
in an unbalanced panel.

5. Empirical Frame WorkTo evaluate the effects of the ESI reform on individuals working hours, the following standard
fixed-effect difference in difference model is used:

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New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in Japan𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 + 𝛽𝛽2 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛽𝛽3 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 + 𝛽𝛽4 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝑀𝑀𝑡𝑡 + 𝐹𝐹𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖(1)

yit represents outcomes for individual 𝑖𝑖 in month 𝑡𝑡, here natural logarithm of weekly hours worked

is used. 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴it is a dummy variable taking one for months after October 2016 and zero for months before

October 2016. Periods before October 2016 are treated as the “before” period and the period after October

2016 is treated as the “after” period. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇i represents being in the treatment group for an individual𝑖𝑖.
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴it・𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇i is the interaction term with the 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴it dummy variable and 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇i.

𝜷𝜷1 is the estimated coefficient of 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴it・𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇i and is of prime interest: a significant coefficient

will indicate that the 2016 reform has had an impact on working hours. Xit includes covariates; here we use

the individual characteristics of age and gender. Mt and Fi control for interaction effects common to allindividuals, Mt are monthly effects, Fi are fixed-effects, and μit represents error terms in the estimation

As mentioned in the previous section, however, in addition to age and gender, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴it ・

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇i is expected to have heterogeneous effects due to the interaction of individuals who adjust
their own work hours with employers who adjust their workers hours due to the ESI expansion. For thisreason additional groups are created for housewives with No.3 dependent status as well as for single parttime workers, the definitions of which have been covered in the data section.

6. Descriptive Analysis
To begin, we examine the time-series of mean weekly working hours for the treatment/control
groups by gender and age as well as their distribution of working hours.
For males (Figure 3), there is an average difference of 2~3hrs. among the control group andtreatment group, although the treatment groups have on average higher working hours leading up to thereform month, afterwards the there is a sharp increase across all groups. While there is a slight decline in
the months following the reform (January-February 2017), all age groups rapidly increase their work hours
with the treated groups experiencing the largest increase.

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Figure 3: Trends in Work Hours (Male)

Note: Observations (19-24) 1,449, (25-59) 7,477, (60-64) 5,788

It is instructive to look not only at the trends in time for the control and treatment groups, but also
the distribution of working hours (Figure 4). For the prime and elderly males, after the reform the
distribution of workers in the median range for the treatment group becomes more varied and condensed in
the mid to high end of the distribution while the control group has relatively an even distribution across all
bins. The younger males sample size shows a more extreme distribution in the upper bins, post reform,
while a majority of the control group is over 25hrs, the treated group maintain almost all individuals over
25hrs.

Figure 4: Trends in Work Hour Distribution (Male)

Note: Observations (19-24) 1,449, (25-59) 7,477, (60-64) 5,788

As for the female trends, there is a similar overall trend in increased working hours after the reform(Figure 5). In the prime aged female group it is noticeable that the control and treatment groups have thelargest difference in working hours of about 4.5hrs, which may be indicative of a greater proportion of
dependent part time workers in this control group than others whom experience the negative inducementsof ESI mentioned in the previous section.

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Figure 5: Trends in Work Hours (Female)Note: Observations (19-24) 2,066, (25-59) 57,476, (60-64) 10,987

For the female’s distribution (Figure 6), it is notable that all treatment groups underwent a greaterchange in distribution than the males, with the younger females working in the highest distribution after thereform, while the prime and older females maintained their distributions more evenly around the medianbin of 40hrs.

Figure 6: Trends in Work Hour Distribution (Female)Note: Observations (19-24) 2,066, (25-59) 57,476, (60-64) 10,987

As the prime female group contains a large portion of the No.3 Insured dependents, it will benecessary to investigate whether this cross section experienced a different change in distribution ofworking hours in the last figure (Figure 7). Here we examine the No.3s and single workers. For the
No.3s, there is a clear difference in the distributional changes of work hours when comparing them to theprime female group in the previous figure. In contrast, the treatment group does not exceed the medianbin of 40hrs and covers the lower half of the distribution. Contrary to the No.3s, the Single part-timer

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treatment group experienced an extreme shift to the highest distribution (over 40hrs) after the reformcompared to the control group, which maintains an relatively even distribution.

Figure 7: Trends in Work Hour Distribution (No.3 & Single Part-timers)

Note: Observations (No.3 Housewife) 31,183, (Single Part-timer) 2,994

7. Econometric Analysis
The following Table 5 presents the fixed-effects panel estimation as in equation (1), using the datafor 2013–Sept.2016 (before the reform) and Oct.2016–2017 (after the reform), including monthly effects.
These are the difference in difference estimates for individuals covered by the ESI policy (the treatment).

Table 5: Change in Working Hours (Ln) Post-October 2016 Reform (Fixed Effect Model)

Note: t statistics in parentheses. Monthly fixed effects included; observation period is between Jan.2013 and Mar.2017* p < 0.05, ** p < 0.01, *** p < 0.001

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There is a statistically positive overall effect, with the largest effects on the young/prime male andprime female groups (columns 2, 3, and 6, respectively). As mentioned in the data section, the number ofobservations for the prime females greatly outweighs that of males, covering roughly 67% of the entire
sample; in contrast the young males sample size is roughly 1.6%. With the exception of the young female
group (5), the After・Treatment coefficient is statistically significant for all groups
After・Treatment represents the average differences in the rate of change between the control andtreatment group as time changes from pre-policy to post-policy. This is 18% overall (1), and ranges between
the groups. The control variables, Age and Month represent confounding factors that should be removed
from the effect of treatment. Month has been omitted from this chart, while Age has no statisticallysignificant effects.
For the female groups, it is possible that some workers are affected differently than others
depending on their distribution of work hours. In Table 6, the same equation (1) of the Empirical Model
has been produced, this time using quantiles.

Table 6: Female Change in Working Hours (Ln) Post-October 2016 Reform (Quantile Regression Fixed
Effect Model)

Note: t statistics in parentheses. Monthly fixed effects included; observation period is between Jan.2013 and Mar.2017* p < 0.05, ** p < 0.01, *** p < 0.001

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While the Fixed Effects model in equation (1) has coefficients representing the mean effect, thecoefficients in the quantile estimates represent the effect with respect to a specified area within the group.
In this case, PIDs have been divided into quantiles representing the 20th, 40th, 80th and 90th quantiles ofthe distribution of work hours.
For the prime ages females (5) to (8), those quantiles with a higher distribution of work hours
appear to experience a smaller magnitude of increase in working hours, suggesting that those with thehighest pre-reform work hours experienced a smaller increase in work hours after the reform, however thedifference between the highest and the lowest quintile is less obvious than the elderly females. For example,
the lowest quantile experiences a 19% increase of working hours, whereas the highest quantile experiences
a 16% increase of working hours and there is only a 3% difference. In contrast, we find a more prominent
difference among the elderly females (9) to (12). The greatest increase (12%) to work hours is occurringto those with the least work hours (9), whereas the smallest increase (4%) is occurring to those with thelongest work hours (12).
In the proceeding Table 7 are the panel estimation results for equation (1) with respect to the No.3
and Single Part-Timer Groups. Recall from the data section that the No.3 group attempts to capture workers
who seek to reduce their own working hours to maintain the beneficial No.3 dependent status and has been
defined as i) those who were between ages 20-59, ii) were married, iii) designated a spouse as the household
head, iv) made annual wages under 1.49m and v) held a minimum monthly wage under 108k. The Single
Part time group attempts to capture the opposite as workers who seek to increase their own working hours
and has been defined as i) ages 20-59, ii) single sole head of household and iii) unmarried.

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Table 7: Specified Sub-Groups’ Change in Working Hours (Ln) Post-October 2016 Reform (Fixed EffectModel)

Note: t statistics in parentheses. Monthly fixed effects included; observation period is between Jan.2013 and Mar.2017* p < 0.05, ** p < 0.01, *** p < 0.001

We find a 26% increase in the working hours of the single part-timers (2), which is relatively large.
However, in contrast to our expectations in our grouping strategy, there is also a positive effect for the No.3
Status Female group of roughly 11% (1). This suggests that each group could have responded to the reformby adjusting their own working hours, whereas individuals who were the sole working household heads
engaging in part-time work (2) could have experienced a decrease in their required social insurance
contributions (section 2), the housewives in (1) would have experienced an increase in their required
contributions ceteris paribus. The fact that the No.3’s now have a tendency to work more could be explained
if they were working significantly more hours to offset the new ESI deductions and possibly to get theirown pension rights for Employees Pension, which would be an additional contribution that tops up theirbasic pension (section 2). In the next table, we confirm this possibility by examining the No.3’s quantiles.
Similar to the estimates for the gender/age groups, Table 8 represents the quantile estimates of theNo.3/Single groups in order to detect whether the magnitude of effect of After・Treatment varies based on
the distribution of working hours within these groups.

22New ESRI Working Paper No.54
Labor Market Impacts of Expanded Employee Social Insurance Coverage in JapanThe Single Part-Timers experience a similar effect regardless of an individual’s quantile, while theNo.3s workers undergo greater change in the lowest quantiles. In the case of No.3 females, those with thelowest work hours (1) may be dedicating themselves to a far greater number of work hours to offset theirincreased contributions in the case they are losing their No.3 status.

Table 8: Specified Sub-Groups’ Change in Working Hours (Ln) Post-October 2016 Reform (Quantile
Regression Fixed Effect Model)

Note: t statistics in parentheses. Monthly fixed effects included; observation period is between Jan.2013 and Mar.2017* p < 0.05, ** p < 0.01, *** p < 0.001

As for the single part timers, those with the highest work hours (8) may be experiencing the same
demand reduction the No.3s receive from their employers, but in contrast the reform may have resulted inincreased supply as these workers can increase their work hours without experiencing a reduction in theirtake home pay, the net effect of which might be offsetting some of the employer-induced work hour
reduction.

8. Conclusion
In contrast to the prior body of research, the 2016 ESI reform has resulted in overall positive effects
across age, gender and dependent status. While the overall effect is associated with working hours, it is nota straightforward task to determine which groups experience a direct reduction owing to demand-side

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employer inducements (an increased employer contribution) and which groups voluntarily adjust theirhours owing to supply-side employee inducements (a lessened employee contribution).
For this reason, this paper attempted to make distinctions between those non-regular workers who
were likely to fall into the former group (the No.3 dependent status housewives) and the latter (Single
workers who were the sole income-earners in their household).
However, there are limitations to the variables accessible in the Labor Force Survey and to this end
there is some possibility these groups could not be precisely captured owing to a lack of more detailed
monthly income, pension-insurance status (e.g. No.1, 2, 3 insured), and employment-history variables forindividuals. Nevertheless, positive effects on working hours could be confirmed within statisticallysignificant margins for the majority of groups, including the dependent housewife groups.
It is important to note that, as has been proposed in the April 2017 revision to the ESI expansion(Figure 2), the requirement on firm size is likely to be officially eliminated. In this case an estimated 1.25
million more workers will become eligible to join the ESI program.
At the time of this paper, the scope of the firm requirement is set to be reduced from 500 to 100 by
October 2022, and from 100 to 50 by 2024, in which case an additional 450,000 and 200,000 workers,
respectively, will soon be eligible for ESI (MHLW 2020). Under these conditions, business sectors thatuse large numbers of part-time workers will experience employer-related inducements on the order of
160 billion JPY more for the pension premiums of their workers (recall from the institutional
background section that each worker’s premium payment is split between the employer and employee).
Whether or not the government ultimately eliminates the “more than 50 employees” requirement
by 2024, further studies will be critical in order to determine what impact the current changes will have on
the labor market.

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