Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2014-009
March, 2015
Impacts of an Increased Legal Overtime Rate on Overtime Work Hours andProvision/Acquisition of Paid Leave—Analysis on Effects of Partial Revision of Labour Standards Act in 2008—
Ryotaro Fukahori＊
Risa Hagiwara＊＊【Abstract】

To reduce long working hours, the revised Labour Standards Act of 2008 was executedon December 12, 2008, and went into effect on April 1, 2010. In this revision, thefollowing amendments were implemented: (1) the legal overtime rate to be paid for 60 or
more hours of overtime per month was increased from 25% to 50%; (2) once alabor-management agreement was executed, it was possible to provide paid leave forthose employees who worked overtime more than 60 hours per month, in place of thestandard 25% increase for overtime; and (3) instead of paid leave that was analternative to the overtime rate referenced in Section (2), after the revision of the law,when a business executed a labor-management agreement, an employee could use any
existing paid leave by the hour up to five days per year.
In this study, to re-verify the effects of the revision, utilizing the existence of
employees who are not covered by the revision, we perform Difference-in-Differences(DID) analysis by using the Keio Household Panel Survey. We estimate that a total of 55
working hours per week corresponds to 60 hours of overtime work per month, and thenwe perform the analysis. As a result, we confirm that the revision of the law reduced theovertime working hours of the employees who worked more than 55 hours per week on
average before the revision (2004 to 2009). However, we confirm that there is no
statistically significant impact on the provision and acquisition of annual paid leave of
employees who worked more than 55 hours per week each period.
＊

Ph.D student of the Faculty of Business and Commerce, Keio University, Research

Fellow of the Japan Society for the Promotion of Science＊＊

Ph.D student of the Faculty of Business and Commerce, Keio University, AssistantProfessor of Faculty of Business and Commerce, Keio University

Panel Data Research Center at Keio University
Keio University

Impacts of an Increased Legal Overtime Rate on Overtime Work
Hours and Provision/Acquisition of Paid Leave—Analysis on Effects of Partial Revision of Labour Standards Act in2008 1 —Ryotaro Fukahori *1
Risa Hagiwara *2
Abstract
To reduce long working hours, the revised Labour Standards Act of 2008 was executed on
December 12, 2008, and went into effect on April 1, 2010. In this revision, the following
amendments were implemented: (1) t he legal overtime rate to be paid for 60 or more hours ofovertime per month was increased from 25% to 50%; (2) once a labor -managementagreement was executed, it was possible to provide paid leave for those employees who
worked overtime more than 60 hours per month, in place of the standard 25% increase forovertime; and (3) instead of paid leave that was an alternative to the overtime rate referenced
in Section (2), after the revision of the law, when a business executed a labor -managementagreement, an employee could use any existing paid leave by the hour up to five days peryear.
In this study, to re-verify the effects of the revision, utilizing the existence of employees
who are not covered by the revision, we perform Difference -in-Differences (DID) analysis
by using the Keio Household Panel Survey. We estimate that a total of 55 working hours per
week corresponds to 60 hours of overtime work per month, and then we perform the analysis.
As a result, we confirm that the revision of the law reduced the ov ertime working hours of
the employees who worked more than 55 hours per week on average before the revision(2004 to 2009). However, we confirm that there is no statistically significant impact on theprovision and acquisition of annual paid leave of emplo yees who worked more than 55 hours
per week each period.
Keywords
overtime work hours, annual paid leave, Labour Standards Act of 2008

We were coached by Professor Yoshio Higuchi of the Faculty of Business and Commerce , Keio
University, in the first stage of this research when necessary, and received valuable comments from
Professors Takanobu Nakajima, Mitsuhiro Fukao, and Isamu Yamamoto of the Faculty of Business andCommerce, Associate Professor Michio Naoi of the Faculty of Economics, and from an anonymous refereeof this document. We also received the individual data of the “ Keio Household Panel Su rvey” from the Panel
Data Research Center at Keio Universit y. We sincerely appreciate their support. Note that any errors in thisdocument are attributable to the authors.
*1
Ph.D student of the Faculty of Business and Commerce , Keio University, Research Fellow of J apan
Society for the Promotion of Science .
*2
Ph.D student of the Faculty of Business and Commerce , Keio University, Assistant Professor of Facultyof Business and Commerce , Keio University.

11.

Introduction
In Japan, routinely working long hours is a serious problem. It has been pointed out thatworking long hours not only interferes with the work -life balance, 1 ) but also affects
employees’ physical and mental health, and in the worst case, can cause death fromoverwork. 2)
One of measures that may make working long hours reasonable is an increase in theovertime rate, as defined in the Labour Standards Act of 2008. Conventionally, the overtime
rate in Japan was a standard 25% for hours worked over the legal limit. Compared to the rate
in Europe and the United States, which was 50%, 3 ) the rate in Japan was half, and manyargued that it was too low. However, on December 12, 2008, the Law for Partial Amendment
to the Labour Standards Act of 2008 (Law No. 89, 2008) was executed, and became effective
on April 1, 2010.
In that law, the following amendments were implem ented: (1) the legal overtime rate 4 ) tobe paid for 60 or more hours of overtime per month was increased from 25% to 50%(however, medium and small companies did not have to comply right away 5) 6) ); (2) once a
1)
In order to support diverse work habits, the W ork-li fe Balance Charter refers to working long hours andthe utilization of annual paid holidays as follows: “In order to secure the health of workers and provide a
working environment where they can work safely, it is important to promote the utilization of annual paid
holidays to prevent working long hours and to make efforts to prevent mental health problems.”
2)
Iwasaki (2008), Ma (2009), Yamaoka (2012), and Yamamot o & Kuroda (2014) pointed out that working
long hours might increase the risk of cerebropathy and heart disease, and also might affect sleep, fatig ue,
and physical and mental disorders . Related to this, the Ministry of Health, Labour, and Welfare released thenumber of determined cases of compensation, and each number of determined cases of compensation by
overtime work hour (monthly average) concern ing cerebropathy, heart disease, and mental disorders in areport entitled “Compensation for Worker’s C erebropathy, Heart Disease, and Mental Diseases in 2013.”
The number of determined cases of compensation means the number of cases where the ministry determinedjob-related or not in the relevant year, and the number of cases of allowance determination means thenumber of job-related cases among them. The approval rate is a percentage of the number of cases ofcompensation from the total number of cases. We consider compensation determination for cerebropathy,
heart disease, and mental diseases where the ministry finds a relationship between long workd ays and these
diseases. The number of determined cases regarding cerebropathy and heart diseases was 683 (he number ofcases involving death among them: 290), and among them, the number of cases of 60 or more working hours
was 285 (the number cased involvi ng death among them: 123). The approval rate was 45% (The rate of deathcases: 46%). Concerning mental diseases, the number of determined cases was 1193 (cases involving suicideand suicide attempts among them: 157), the number of cases of 60 or more worki ng hours was 171 (cases
involving suicide and suicide attempt s among them: 37), and the approval rate was 23% (Suicide cases
including suicide attempts: 29%). The approval rate indicates an upward trend. For more details, refer to“Compensation for Worker’ s Cerebropathy, Heart Disease, and Mental Diseases.” However, the number ofcases in this report show only the number of applications, and it is estimated that there are more potential
cases.
3)
For more details on each company, refer to t he Japan Institut e for Labour Policy and Training (2014)
and the Japan External Trade Organization (2013).
4)
Ordinance for Enforcement of the Labour Standards Act ( Health and Welfare Ministry Ordinance No. 23,
1947) “Article 21: According to the definition in Clause 5, Article 37, in addition to family allowance andcommuting allowance, the wages mentioned below shall not be calculated as the wage that is the base of theextra wages defined in Clause 1 and Clause 4 of Article 37: 1) family separation allowance ; 2) childeducation allowance; 3) house allowance; 4) special wages; and 5) wages paid at regular intervals over a
period exceeding one month .”5)
This is applied to companies of which amount of capital or total amount of investment is 50,000,000 yen
or less in the case of a retail business , 50,000,000 yen or less in the case of a service business , 100,000,000
yen or less in the case of a wholesale trade, and 300,000,000 yen or less in the other cas es, or the number of2

labor-management agreement was executed, it was possible to provide paid leave for those
employees who worked overtime more than 60 hours per month, in place of the standard 25%
increase for overtime; and (3) instead of the paid leave that was an alternative to theovertime rate referenced in Section (2), after the revision of the law, when a businessexecuted a labor-management agreement, an employee could use any existing paid leave by
the hour up to five days per year (this applied to every company regardless of size 7 ) ).
Employees could freely select whether they would take paid leave by the day or by thehour. 8)
According to the amendments referenced above, in case of overtime of more than 60 hours
per month, employees have the following three new options: (1) take the additional overtime
rate in the form of a monetary bonus; (2) alternate part of the overtime rate with one -dayleave or a half-day leave and take any remaining time with the overtime rate in the form of amonetary bonus; (3) alternate the total additional overtime for one -day leave or a half-day
leave. Here, the unit for alternate leave is one -day or a half-day leave. This is because Item 2,
Clause 1, Article 19-2 of the Law for Partial Amendment to the Labour Standards Act
defines that a period of rest for employees shall be provided in a comprehensive time scale. 9 )Was the increase in the overtime rate and the alternatives for paid leave as defined in the2008 revised law really effective for reducing long workdays? As will be explained in
Chapter 3, if the Labour Demand Model 1 0 ) (Hamermesh, 1993) is applicable, the increase in
the overtime rate is considered to be effective for reducing long working hours. On the other
hand, as estimated in the Employment Contract Model 1 1) (Trejo, 1991), in cases where thetotal amount of wages and labo r hours are part of a package contract, even if the overtime
rate is increased, when an adjustment is performed due to a package contract by a reductionin the scheduled wages (possibly for an annual bonus or other allowances), the labor hours(and corresponding paid leave as a result of the law revision) may not change. 1 2 ) To analyze
workers regularly employed is 50 or less in the case of a retail business, 100 or less in the case of a service
business, 100 or less in a wholesale trade, and 300 or less in the other cases. Applicability is determined
individually for companies (corporation or sole proprietor ).
6)
The overtime rate for work on a scheduled day-off (35%) and late-night labor (25%) do not change. The
overtime rate of medium and small companies will be discussed after three years of implementation.
7)
Even part-time workers with short, fixed working days can take paid leave after they conclude alabor-management agreement with employers. Employers decide how many hours equals one -day of annual
paid leave based on the scheduled working hours of workers.
8)
For example, when workers want to take paid leave by the day, employers cannot change it to the paid
leave by the hour.
9)When a labor-management agreement approves that workers can bring fractional hours together withpaid leave such as existing holidays and annual paid leave, it is permitted to alternate leave together withthe existing paid leave and provide one -day or half-day leave. Please see the following URL for moredetails: http://www.mhlw.go.jp/topics/2008/12/dl/tp1216 -1l.pdf (accessed August 1, 2014).
10)
It is also called the Fixed-Wage Model as used by Trejo (1991).
11)
It is also called the C ompensati on Hypothesis or Fixed-Job Model, of which the latter name was usedby Trejo (1991).
12)Effectiveness of the overtime rate increase varies according to whether hours as defin ed in a package
contract mean the total number of actual working hours or the hours including rest periods and paid leave.

3the effectiveness of the revisions from the point of views of both models, it is desirable tocontrol even observable properties and unobservable fixed effects (such as person alpreference and company characteristics 1 3 ) ), and also to focus precisely on any changes inwages. For that purpose, it was necessary to use data that allowed us to understand theworking hours, wages, and other personal characteristics of a person who wo rked for thesame company before and after the revision.
In this study, we verify how the revised Labour Standards Act of 2008 affected overtime
work hours and the provision and acquisition of allowances for annual paid leave. 1 4 ) We useddata from the Keio Household Panel Survey (KHPS), which contains follow -up surveys. The
KHPS provides data for not only working hours and wages, but also the provision andacquisition of paid leave, concentration on work, and working styles such as discretionary
work, de facto work, and flextime working without a set schedule. Therefore, we can analyze
the impact on paid leave and the ability to concentrate on the job. Moreover, we can analyze
impacts due to the overtime rate increase on each category such as working hours, wages,
paid leave, and the ability to concentrate on the job, excluding categories where employees
work under a system that includes discretionary work, de facto work, and flextime working
without a set schedule, which may not allow overtime pay.
Our results show that the incidence of more than 60 hours overtime work per month in
employees was decreased after the revision , and accordingly, the wages for those employees
decreased. But overall this impact was small because more than 60 hours overtime work wasrare. We cannot determine if the provision and acquisition of paid leave were affected.
Please note that this result contains s ome limitations.
This paper will proceed as follows. In the next chapter, we confirm whether the revision of
the law was effective for matters concerning the number of working hours and annual paid
leave using government statistics. In Chapter 3, we introd uce research that verifies theeffects of labor hour laws on matters regarding the number of working hours and annual paid
leave, a theoretical model indicating the effects of the revisions of the Labour Standards Act
of 2008 in Chapter 4, and the data and analysis method used for this research in Chapter 5.
We interpret the estimated results in Chapter 6, explain additional limitations in Chapter 7,
and then we present the conclusions of this research in the final chapter.

For this study, we assume the total number of actual working hours.
13)
The DID analysis used in this study cannot control differences in individual characteristics generated
as a result of the revisions to the law. The analysis of this research is based on the assumption that theeffects of the revision were fixed changes. For more details on the restrictions of the DID analysis, refer toBertrand and Mullaitahan (2004) and Imbens and Wooldridge (2009).
14)
We focus on the heterogeneity of individuals for analysis. Thus, because the KHPS does not allow
identification of companies, it is difficult to consider a mutually dependent relationship on labor demandactions such as the hiring of replacements. There is currently no ideal data, so we must address this theme atsome point in the future.

4

2.

Confirmation of the effects of the revisions to the Labour Standards Act of 2008,
according to government statistics
Before performing DID analysis with the information from the KHPS, we confirmed

whether the revision of the law was effective for reducing the number of working hours an dthe acquisition and provision of annual paid leave using government statistics.
First, we confirmed the working hours. Table 1 shows the transition of the index of thetotal average actual working hours per month of a full -time worker who belongs to acompany with five or more employees in the total industrial group, the index of scheduledworking hours, and the index of non -scheduled working hours, according to Ministry of
Health, Labor, and Welfare’s Monthly Labour Survey. In the table, the index of the total
actual working hours, the index of scheduled working hours, and the index of non -scheduledworking hours dropped in 2009 (most notably was that the index of non -scheduled working
hours dropped significantly). We consider the main cause of this to be the impact of thebankruptcy of Lehman Brothers in 2008. In 2010, the index of total actual working hours, theindex of scheduled working hours, and the index of non -scheduled working hours all
increased. After 2010, although the index of scheduled workin g hours increased anddecreased frequently, the index of non -scheduled working hours was on the rise. However,the possibility cannot be denied that working hours could have increased more, if it were notfor revisions to the Labour Standards Act. 1 5 )15)According to Ministry of Health, Labour , and Welfare’s General Survey on Working Conditions, in
2013, the percentage of companies that applied a 50% or higher overtime rate for workers who worked morethan 60 hours per month of overtime was 52.8%. Also, the report shows that individual percenta ges of thecompanies that applied a 50% or higher overtime rate by company size were as follows: 30 to 99 employees– 35.7%; 100 to 299 employees – 58.6%; 300 to 999 employees – 78.0%; 1,000 or more employees – 91.8%.
Thus, there was a significant differen ce in the overtime rates according to company size. From the existingstatistics, it is difficult to confirm the actual conditions where employees worked overtime for free,disregarding the rule. However, from what we gathered, unpaid overtime work does ex ist.

5

Table 1: Transition of the index of the total average actual working hours per month of afull-time worker who belongs to a company with 5 or more employees in the total industrial
group, the index of scheduled working hours, and the index of non-scheduled working hours
Company size
Research items
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

Index of total actual
working hours
101.6
101.0
101.6
101.7
100.8
98.2
100.0
100.0
100.8
100.2

Five or more employees
Index of scheduled
Index of non-scheduledworking hours
working hours
101.6
100.6
101.0
101.2
101.4
104.3
101.2
106.6
100.4
105.2
98.9
89.9
100.0
100.0
99.9
101.2
100.7
103.2
99.8
106.1

Source:Monthly
LabourSurvey,MinistryofHealth,Labour, and Welfare.
Note 1: The research target is“full-time employees.” The scale
is based on the number of
employees per business.
Note 2: The basis of the indexes
is from the 2010 data, defined as
100.

Table 2 shows the transition of the ratio of non -agricultural employees’ total working
hours of the last week of a month by the number of employees (by company size), as reported
in the Labour Force Survey from the Stat istics Bureau of the Ministry of Internal Affairs andCommunications. On the assumption that the number of scheduled working hours is 8 hours
per day and the number of working days per week is five, the working hours totaled 40 hours
per week. The non-scheduled working hours to which the increased overtime rate applied
according to the revised law was over 60 hours per month, and when converted to weekly
hours, the total was over 15 hours per week (4 weeks in one month). As a result, employees
who worked more than 55 hours per week qualify for the increased overtime rate. However,
the released document did not separate the data for those who worked more than 55 hours per
week or those who worked less than 55 hours per week. Therefore, we focused on the chang ein the ratio of people who were required to work more than 60 hours per week. Further, theLabour Force Survey shows working hours including hours for side businesses, work done
from home, and temporary work, and does not always show whether the hours we re worked
in the same place. Since the data show the work hours of the last week of a month, there is aconcern that in cases of companies where their busy season is concentrated at the end of themonth, the data reported may be extremely different from th e typical working hours.
However, the Labour Force Survey organizes the statistics by company size precisely, and
we also used the Labour Force Survey in this study to confirm changes in the ratio of people
who worked for more than 60 hours per week. By lo oking at the information in Table 2, we
can confirm that the data shows a downward trend. Concerning 2010, when the revised
Labour Standards Act of 2008 was implemented, the application range focused on companies
with 100 or more employees, thus we cannot confirm that the ratio of people who worked formore than 60 hours per week decreased significantly after 2010. To see the number
employees who worked for more than 60 hours per week in each category, please refer to the6ratio of people who worked less tha n 35 hours per week as shown in Table 2. This data shows
an increase.
Table 2: Transition of the ratio of non -agricultural employees’ total working hours in the last
week of a month by company size in the total industrial group
Total working
hours of the last
week of a monthCompany size
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

Employees who work 60 hours or more per week
1 to 29
employees
12.5%
11.9%
11.0%
10.5%
10.6%
10.0%
10.2%
10.0%
9.6%
9.3%

30 to 99
employees
12.6%
11.9%
11.1%
10.6%
10.3%
9.5%
9.8%
9.7%
9.4%
9.3%

100 to 499
employees
13.0%
12.0%
11.2%
10.5%
9.9%
9.1%
9.4%
9.0%
8.8%
8.7%

500 to 999
employees
13.3%
12.8%
11.6%
10.2%
10.5%
9.2%
9.4%
9.8%
9.9%
8.9%

Employees who work less than 35 hours per week1000 or more
employees
12.8%
12.2%
11.2%
10.4%
9.5%
8.5%
8.5%
8.4%
8.6%
8.0%

1 to 29
employees
27.4%
28.1%
27.3%
29.4%
30.2%
31.1%
31.6%
31.9%
31.9%
33.6%

30 to 99
employees
22.9%
23.3%
22.2%
24.2%
25.1%
26.7%
26.3%
26.7%
26.6%
28.0%

100 to 499
employees
21.2%
21.8%
19.7%
22.2%
23.4%
24.6%
23.4%
24.3%
23.6%
25.8%

500 to 999
employees
20.8%
21.5%
19.3%
21.7%
23.9%
24.6%
23.8%
23.7%
23.7%
25.9%

1000 or more
employees
22.0%
22.1%
20.6%
23.2%
25.0%
25.7%
25.1%
25.7%
25.2%
28.1%

Source: Statistics Bureau, Ministry of Internal Affairs and Communications “Labour Force Survey”(data from 2011 does not contain information for Iwate, Miyagi, or Fukushima).
Note: Research target is limited to “non -agricultural employees.” The scale is based on the numberof employees per company.

To determine whether the revised law affected the acquisition and provision of annual
paid leave, we indicated a transition of the number of provided and acquired days of annual
paid leave according to the size of the company in tota l industrial group as reported in the
Ministry of Health, Labour, and Welfare’s General Survey on Working Conditions as shownin Figure 1. Because the data concerning annual paid leave in the General Survey on
Working Conditions shows the average number of days per worker, a comparison of thetime-series is difficult. Therefore, we compared the trends of the companies with 1000 or
more employees, which are considered to be the most significantly affected companies, andcompanies with 30 to 99 employees, whi ch are considered to be the least affected. Although
in Figure 1, the number of provided days and the number of acquired days increased in casesof companies with more than 1000 employees in 2009, both numbers transitioned similarly
from 2004 to 2008. From 2010, the number from companies with more than 1000 employees
was slightly increased, but there was no significant shift. 1 6

16)
The number of days for paid leave shows the average number of days per worker. Because it is difficult
to estimate how the statistics were interpreted, it is necessary to note this as a limitation of the study .

7

Figure 1: Transition of the number of days of provided and acquired annual paid leave bycompany size in total industrial group
Number of days

quired days (1,000 or more employees) 25.0
20.0

18.8

18.8

16.5

16.4

16.3

19.0
16.9

18.9
16.8

19.3
17.1

19.5

16.6

19.1
16.8

10.4

9.9

10.2

9.7

10.0

10.6

10.2

10.5

10.9

10.6

7.2

7.2

7.2

7.1

7.0

6.5

6.9

7.0

7.2

6.8

15.0

10.0

19.8

19.1
16.8

19.2

16.9

Number ofprovided days

Number ofacquired days

5.0
0.0
2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Number of provided days (1,000 or more employees)
Number of provided days (30 to 99 employees)
Number of acquired days (1,000 or more employees)
Number of acquired days (30 to 99 employees)

Source: “General Survey on Working Conditions,” Ministry of Health, Labor, and Welfare.
Note 1: The research target is “full -time workers.” The company size is based on the number of
employees per business.
Note 2: “The number of provided days” does not include the number of days that were carried -over.
“The number of acquired days” is the number of days actually acquired for paid leave in one year.
Note 3: The research target prior to and including 2007 was “private companies with more than 30
regular employees at HQ,” but from 2008, it was expanded to include “private companies with morethan 30 regular employees.”

As previously mentioned, when comparing the working hours and annual paid leave withthe government statistics, we could not confirm that th e working hours and the number ofacquired and provided days for annual paid leave changed drastically from 2010. However,
the working hours and the progression of the acquisition and provision of annual paid leavethat we have reviewed thus far did not al low us to identify the effects of the revisions to thelaw, but it did include other effects. Therefore, in this study, to control these “other effects”
and then to verify the effects of the revision of the law, we used the KHPS and verified theinformation using DID analysis.

3.

Research precedents on the effects of working -hour laws
Effects of the revised Labour Standards Act of 2008 vary according to what mechanism isworking in the background. In this section, we introduce the Labour Demand Model(Hamermesh, 1993) and the Employment Contract Model (Trejo, 1991), to consider what8

mechanism works in each case where the revision of the law proves to be either effective or
ineffective. Then, we confirm the verified research results on the effects of the workin g-hourlaws.
First, if the Labour Demand Model is at play, companies supposedly reduce the working
hours to respond to the increase of expenses when the overtime rate is increased. In otherwords, in the Labour Demand Model, the scheduled wages are constan t, and only when there
are costs for the adjustment 17) of the number of employees, do the companies respond to theincreased overtime rate by changing the working hours. This explanation is primarily
derived from the reports of Trejo (1991). For more detai ls, refer to the research of Trejo(2003) and Bell and Hart (2003).
Hamermesh and Trejo (2000) analyzed the effects of an increase in the overtime rate in
California, USA, with the Current Population Survey from 1973, 1985, and 1991. In
California, people could ask for payment of overtime when they worked more than 40 hours
per week, as defined by the Fair Labor Standards Act. After the revision, they could also ask
for payment of overtime when they worked for more than 8 hours per day. This provisionapplied to women only at first, and beginning in 1980, it also applied to men. In the study,
they compared the working hours of male employees and female employees in California andemployees in other states to see if the working hours of male employees decreas ed. As aresult of the analysis, they confirmed that the number of male employees who worked morethan 8 hours in a day was lower in California than in other states, and accordingly consideredthat the Labour Demand Model was applied.
On the other hand, if the Employment Contract Model is established in Japan, even when
the overtime rate is increased, other employment costs are reduced and working hours do notchange, and accordingly, the effects of the revised Labour Standards Act of 2008 disappear.
In the Employment Contract Model, employment contracts include both wages and working
hours in a package contract. Under the hypotheses, even if the overtime rate is increased,that amount is set off by a reduction in fixed wages (or in bonuses or other allowanc es), andthe wage is adjusted in the package contract. Therefore, working hours and the acquisitionand provision of annual paid leave supposedly do not change. Trejo (1991) analyzed theeffects of the overtime rate with the results of the Current Populati on Survey from May 1974,
1976, and 1978, in order to verify the Employment Contract Model. The results demonstrated
that scheduled wages were reduced to set off the increased overtime rate, but the effects
were not significant enough to completely set off the increase. However, there are someissues with this analysis, as there was no differentiation in the application of the overtime
17)
Trejo (2003) explained the costs for the adjustment of quasi-labor fixed costs as required for humanresources and training, fringe benefit s, insurance premiums , and so on.

9rate, and the industries and job types were largely different, so it was not clear whether theeffects were brought on by th e increase in the overtime rate or were due to the different
industries and job types. With that, Trejo (2003) used the panel data of 11 industries from1970 to 1989, and again verified the effects of the increase in the overtime rate derived fromthe revised Fair Labor Standards Act and a Supreme Court ruling. As a result, it was
confirmed that overtime hours did not change in response to the increase in the overtime rateduring the sampling period.
The other research precedent on the Employment Contract M odel and the Labour Demand
Model had different conclusions about which model was consistent. Bhattacharya, DeLeire,and MaCurdy (2000) verified the effects of restoring provisions of the above -referenced law
from allowing employees who worked for more than 8 hours per day to ask for payment ofovertime to the former working-hour restriction where employees could ask for payment ofovertime for working more than 40 hours per week. Mitchell (2005) verified the effects of
the white-collar exemption conducted in 1999 in California. Results from both studies wereconsistent with the conclusions of the Labour Demand Model. On the other hand, Bell andHart (2003), and Kalwij and Gregory (2005) who used data from England, concluded that the
Employment Contract Model was more consistent.
Although there were other verifications of the impacts of reduced legal hours, theseverifications of clearly the same results were not generally accepted. Friesen (2002)
confirmed that the impact on working hours was limited and also that wages for legal hours
increased. Skuterud (2007) verified the effects of the policy in the province of Quebec in
Canada to reduce weekly working hours from 44 hours to 40 hours and demonstrated that the
number of full-time employees who worked more t han 40 hours per week decreased by 20%.
It was further confirmed that the decrease in the number of working hours did not lead tomore job opportunities. Kawaguchi, Naito, and Yokoyama (2008) analyzed the effects ofreduced legal hours from 48 hours to 40 hours in Japan from 1987 to 1997, from theperspectives of the number of working hours, wage rate, and employment opportunities. As aresult of the analysis using the Basic Survey on Wage Structure, they demonstrated that evenwhen legal hours were reduced by almost one hour, reduction in the actual time worked wasjust 0.14 hour. On the other hand, it was confirmed that the monthly earnings increased, eventhough the working hours were slightly reduced.
Research on the overtime rate in Japan has demonstrat ed that the increase in the overtime
rate generally contributed to the reduction of working hours. Hayami (1995) used the KEO
Model II developed by the Keio Economic Observatory at Keio University for simulation of
the impacts of the increase in the overtime rate, and reported the results. According to thereport, when the overtime rate was increased from 25% to 35%, the total actual working

10hours decreased by 2 to 2.3%, and the number of employees went up by 1.2 to 1.3%.
Moreover, Kuroda and Yamamoto (2012) studied the impacts of the system that provided noovertime, such as the system for white -collar exemptions and nominal managers (to which noworking-hour restriction was applied). According to their analysis, compared to theemployees who were covered by working-hour restrictions, the employees who were notcovered by such restrictions tended to work longer especially in difficult economic times,
and they stated that the Labour Demand Model was more consistent.
Was the revision of the overtime rate in 2008 really effective for reducing long workdays?
In order to confirm whether it changed as indicated by the Labour Demand Model, or
whether it did not change the working hours and paid leave as indicated by the Employment
Contract Model, we must use the data that provides for a distinction of the effects derived
from the increase in the overtime rate and the effects of demand derived from the different
industries and job types, similar to the research that Trejo (2003) performed. W e should also
control the properties of individual preferences and types of companies. For that purpose,
analysis with the panel data is necessary. Before our research, Asai (2014) utilized the panel
data from the Japanese Life Course Panel Survey (JLPS), from the Institute of Social Scienceat the University of Tokyo and performed a DID analysis concerning the effects of therevised Labour Standards Act of 2008. In that analysis, she compared the data from January
2010, immediately before the revision, and the data from every January after the revision.
She also analyzed requests of employees regarding whether they wanted to increase or
decrease their working hours, considering their preferences. As a result, she clarified thatthe increase in the overtime rate had no impact on working hours and overtime of more than
60 hours per month, which was valuable information. However, we should discuss anadditional issue regarding her research. To be more precise, the research did not clarify theeffects of alternating overtime premiums with paid leave, brought by the revisions in 2008,
as well as the shift from the normal business hours system to the discretionary working hours
system, de facto work, or flextime working without a set schedule. In addition, the oldest
employee in the JLPS respondents at this time was in his/her mid -40s at most, and if older
employees are included in the analysis, the results may be different from the results ofAsai’s research (2014). 18) In this study, we consider these iss ues and re-verify how thecompanies responded to the effects of the revised Labour Standards Act of 2008.
18)
Based on the data of the number of workers (n on-agricultural industries ) in the Labour Force Survey b y
the Statistics Bureau of the Ministry of Internal Affairs and Communications , we calculated the ratios ofworkers who worked for more than 60 hours per week out of all workers in 2009 and 2012 (the data of 2011
did not include continuous data due to the Great East Japan Earthquake) and compared them. Employees
aged 45 to 49, aged 50 to 54, and aged 55 to 59 changed by -0.49 to -0.15%, and employees aged 20 to 24,
aged 25 to 29, aged 30 to 34, aged 35 to 39, and aged 40 to 44 changed by -0.18 to 0.04%. Therefore, there isa possibility that the working hours of employees that are older than mid -40s are reduced. To confirm what
factor may be affected most by the revision, we should increase variation in ages and job tenure years

11

4.

Theoretical model
In this study, we verify the effects of the revised Labour Standards Act of 2008 using

examples from the theoretical model introduc ed by Trejo (2003). 1 9 ) First, a profit isindicated as 𝛱. Turnover and expenses that make up the profit are indicated as follows:
turnover 𝑓(𝑁, 𝐻) is indicated as a production function consisting of the number of
employees (𝑁) and working hours per person (𝐻), for simplification. Expenses include one
factor 𝑣𝑁 that is required for the employment adjustment and another part that varies
according to number of hours worked. The latter part is divided further into three parts,
including one part where the overtime rate is 0% and the working hours are within the legal
̅ 𝑝25 , requiring the expense ratio 𝑤𝑁𝐻 where 𝑤 is thehours (this is represented by 𝐻 ≤ 𝐻
rate of scheduled wages), a part of where the overtime rate is 25% and the working hours are̅ 𝑝25 < 𝐻 ≤ 𝐻
̅ 𝑝50 , that requires (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻 −over the legal hours (this is represented by 𝐻
̅ 𝑝25 ) 1 + 𝜃 𝑝25 = 1.25 times the overtime rate. Note 𝜃 𝑝25 = 0.25.), and a part where the𝐻overtime rate is 50% and the working hours are over the legal hours (this is repr esented by̅ p50 < 𝐻, indicating where overtime hours are more than 60 hours per month. The expenseH̅ 𝑝50 ), in other words, the expense of 1 + 𝜃 𝑝25 + 𝜃 𝑝50 = 1.5 timesis (1 + 𝜃 𝑝25 + 𝜃 𝑝50 )𝑤𝑁(𝐻 − 𝐻
overtime rate. Note 𝜃 𝑝50 = 0.25.). Therefore, the profit is indicated as follows according to
the pattern of working hours:̅ 𝑝25 :
In case of 𝐻 ≤ 𝐻
𝛱1 (𝑤, 𝑣) = 𝑚𝑎𝑥 𝑓(𝑁, 𝐻) − (𝑤𝑁𝐻 + 𝑣𝑁)
̅ 𝑝25 < 𝐻 ≤ 𝐻
̅ 𝑝50 :
In case of 𝐻
̅ 𝑝25 ) = 𝑚𝑎𝑥 𝑓(𝑁, 𝐻) − [𝑤𝑁𝐻̅ 𝑝25 + (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻 − 𝐻̅ 𝑝25 ) + 𝑣𝑁]
𝛱 2 (𝑤, 𝑣, 𝜃 𝑝25 , 𝐻
̅ 𝑝50 < 𝐻:
In case of 𝐻
̅ 𝑝25 , 𝐻̅ 𝑝50 )
𝛱 3 (𝑤, 𝑣, 𝜃 𝑝25 , 𝜃 𝑝50 , 𝐻
̅ 𝑝25 + (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻̅ 𝑝50 − 𝐻
̅ 𝑝25 ) + (1 + 𝜃 𝑝25 + 𝜃 𝑝50 )𝑤𝑁(𝐻 − 𝐻
̅ 𝑝50 ) + 𝑣𝑁]= 𝑚𝑎𝑥 𝑓(𝑁, 𝐻) − [𝑤𝑁𝐻

The Labour Standards Act that was revised in 2008 defined a new pattern of working
̅ 𝑝50 < 𝐻). As a result,
hours, of which the overtime rate was 50% (as represented by 𝐻
19)
Asai (2014) also presented a theoretical model that was similar to this study concerning the impact on
working hours.

12companies that permitted their employees to work overtime of more than 60 hours per month
were under the obligation to pay additional expenses calculated by (1 + 𝜃 𝑝25 + 𝜃 𝑝50 )𝑤𝑁(𝐻 −̅ 𝑝50 ). The part of the equation represented by 𝜃 𝑝50 , 𝜃 𝑝50 depends on the scheduled wage rate
𝐻(𝑤), the number of employees ( 𝑁), and the number of working hours per person (𝐻). We
assume that companies change 𝐻 when 𝑤 and 𝑁 are fixed (the Labour Demand Model), orchange 𝑤 when 𝑁 and 𝐻 are fixed (the Employment Contract Model), in order to pursue
profit. 20)
The revised Labour Standards Act of 2008 allowed compan ies to provide paid leave totheir employees who worked overtime of more than 60 hours per month if they executed alabor-management agreement. The part of the equation that can be alternated with paid leave̅ 𝑝50 ), and the profit function is indicated as follows:
is expressed as 𝜃 𝑝50 𝑤𝑁(𝐻 − 𝐻
̅ 𝑝50 < 𝐻, alternated with paid leave:
In case of 𝐻
̅ 𝑝25 , 𝐻̅ 𝑝50 )
𝛱 4 (𝑤, 𝑣, 𝜃 𝑝25 , 𝜃 𝑝50 , 𝐻
̅ 𝑝25 + (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻̅ 𝑝50 − 𝐻
̅ 𝑝25 ) + (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻 − 𝐻
̅ 𝑝50 )= 𝑚𝑎𝑥 𝑓(𝑁, 𝐻) − [𝑤𝑁𝐻̅ 𝑝50 ) + 𝑣𝑁]+ 𝜃 𝑝50 𝑤𝑁(𝐻 − 𝐻When the profit function is as shown above, the sum totals of the profit function and̅ 𝑝50 < 𝐻 are the same. Therefore, the total expense the company
expenses in the case of 𝐻
bears does not change if the paid leave is acquired by the day or by the hour or if overtime
premiums are paid. In any case, when employees work overtime more than 60 hours permonth, companies shall bear additional expenses.
In this section, we consider a case where a cost represented by ∅(∅ ≥ 1) is required foralternating overtime payments with paid leave. When a worker takes paid leave, it isnecessary to assign a person to take the place of that worker in some cases. We assume thecost for this exchange. The symbols below indicate the case wher e one of the variables that̅ 𝑝50 ):
can be alternated with paid leave is changed to ∅𝜃 𝑝50 𝑤𝑁(𝐻 − 𝐻̅ 𝑝25 , 𝐻̅ 𝑝50 )
𝛱 4 (𝑤, 𝑣, 𝜃 𝑝25 , 𝜃 𝑝50 , 𝐻
̅ 𝑝25 + (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻̅ 𝑝50 − 𝐻
̅ 𝑝25 ) + (1 + 𝜃 𝑝25 )𝑤𝑁(𝐻 − 𝐻
̅ 𝑝50 )= 𝑚𝑎𝑥 𝑓(𝑁, 𝐻) − [𝑤𝑁𝐻̅ 𝑝50 ) + 𝑣𝑁]+ ∅𝜃 𝑝50 𝑤𝑁(𝐻 − 𝐻
In the case where ∅ = 1, the impact on the profit of a company does not change even when
overtime premiums are paid or alternated with paid leave. However, in the case where ∅ > 1,
20)
In this study, it is difficult to strictly identify both hypotheses with specific parameters. The precedent
research tends to have this pr oblem. Also, the Labour Demand Model ignores actions of the labor suppler.
This point should be considered in future research .

13since the cost is necessary when the overtime premiums are alternated with paid leaves, there
is no incentive for the company. We consider that ∅ varies according to whether employees
acquire their paid leave by the day or by the hour. We assume that the first case incurs ahigher cost because a substitute employee is necessary. Although ∅ cannot be observed
directly in this study, we perform positive analysis on how the provision and acquisition of
paid leave changes due to the increase in the overtime rate to confirm the impacts of theincrease of the overtime rate on the provision and acquisition of paid leave. Due to special
circumstances in Japan, people actually hesitate to take paid leave even though the system of
paid leave is available. If paid leave is not fully utilized, the provisions for paid leave may
be even more cost effective that the p ayment of overtime. This is also why we analyze thetwo aspects of paid leave—provision and acquisition.

5.

Data and analysis methods
The individual data used in this research was derived from the KHPS of the Panel Data

Research Center at Keio University. Th e KHPS is given every year to respondents tofollow-up on the initial survey and has been conducted every January since 2004, andincludes several questions concerning occupation, earnings, and family structure. The
research target includes 4000 respondent s from 20 years old to 69 years old at the time of theinitial survey (the end of January) including men and women (the number of respondents inthe data provided to us was 4005 including extra respondents). In addition, the center
similarly sampled and added 1400 respondents in 2007 (1419 including the extra
respondents), and 1000 responders in 2012 (1012 including the extra respondents). We wereable to utilize data collected for 10 years from 2004 to 2013. This data allowed us tounderstand the variation in working hours of individuals of the overall working generations
around 2010, when the revised Labour Standards Act of 2008 went into effect. In this study,
we analyzed initial respondents and additional respondents in 2007.
Our research target was regular employees under 60 years of age. We only analyzed
employees who worked for the same companies before and after the implementation of therevised law (Survey in 2010 and Survey in 2 011). With these KHPS respondents, we madehistograms showing distributions of working hours before and after the revision as shown in
Figure 2. They showed not only distributions before and after the revision, but also whether
or not each employee’s working hours were averaging over 55 hours per week before the14revision. 21) The reason why we focused on 55 hours per week, is that the overtime rate went
up to 50% when overtime hours were more than 60 hours per month, and cases where
employees worked 55 hours per week corresponded to cases of overtime of 60 hours permonth, on the assumption that the scheduled working hours were eight hours per day and thenumber of workdays per week was five. We continuously focused on the number 55 hours
per week in the later estimates. However, the KHPS Survey in January 2010 was conducted
immediately before the revision, and it was difficult to determine whether the revised law
affected the working hours system to be discussed later in this study, except for overtime andpaid leave systems. Therefore, we excluded the data from this period.
In the overall respondents, the data is concentrated near 40 hours per week prior to therevision, and most subparts (81.55%) are found within the area of 55 hours per week o r less.
Therefore, it can be stated that a limited number of employees were affected by the revised
law. The overall figure after the revision also showed no significant change. Therefore, we
selected a respondent of employees whose average working h ours were over 55 hours per
week before the revision (2004 to 2009) and compared their data both before and after therevision. Although there were concentrations around 60 hours per week before the revision,
it was mitigated after the revision, and workin g hours tended to be reduced. On the other
hand, when the average before the revision was less than 55 hours, no significant change
appeared, similar to the overall data. In the following sections, we make estimations
regarding additional factors and analyze the impact on working hours, wages, and theprovision and acquisition of paid leave.

21)
Also in the following sections, we focus on whether the average working hours before the revision were
55 hours or more. This is an average value, calculated by dividing the total observed working hours of eachperiod before the revision by the number of times of observations. In our calculations, we only used theperiod in which the workers belonged to the same companies both before and after the revision.

15

Figure 2: Distributions of working hours before and after the revision of the law(concentrating on employees who worked less than 55 hours per week and who worked over
55 hours per week, before and after the revision)

Total (2011 to 2013)

55 or less hours (2004 to 2009)

55 or less hours (2011 to 2013)

Over 55 hours (2004 to 2009)

Over 55 hours (2011 to 2013)0

10 20 300Percent

10 20 30

0

10 20 30

Total (2004 to 2009)

0

50

100

0

50

100

Hours worked per week
Percent
kdensity

Source: Created by the authors based on data from the KHPS.
Note: We used the data of employees younger than 60 years of age who worked for the same
companies before and after the revisions. We excluded employees under the irregular working hours
systems, discretionary or de facto work, flextime working without a set schedul e, and employees andowners from companies with 4 or less employees.

In this study, we perform Difference in Difference (DID) analysis, by comparison between
a treatment group of employees whose legal overtime rate was increased, and a control groupof employees whose rate was not increased. In particular, concerning working hours, real
wage rates, the number of days of provided and acquired annual paid leave, and working
hours systems, we prepared the following nine formulas:

𝐻1′

𝐻1
𝐻1
𝐻1
𝐻1𝑖𝑡 = 𝛽1𝐻1 + 𝛽2𝐻1 𝑌𝑖𝑡𝐻1 + 𝛽3𝐻1 𝐴𝐻1
𝑖𝑡 + 𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

16𝐻1
𝛽5𝐻1 + 𝛼𝑖𝐻1 + 𝜀𝑖𝑡

(1)

H2
𝐻2
𝐻
𝐻2
𝐻2𝑖𝑡 = 𝛽1𝐻2 + 𝛽2𝐻2 𝑌𝑖𝑡H2 + 𝛽3𝐻2 𝐴H2𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡2′

𝐻2
𝛽5𝐻2 + 𝛼𝑖𝐻2 + 𝜀𝑖𝑡

′

𝑉1
𝑉1
𝑉1
𝑉1
𝑉1
𝑉1𝑖𝑡 = 𝛽1𝑉1 + 𝛽2𝑉1 𝑌𝑖𝑡𝑉1 + 𝛽3𝑉1 A𝑉1
𝛽5𝑉1 + 𝛼𝑖𝑉1 + 𝜀𝑖𝑡𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

′

𝑉2
𝑉2
𝑉2
𝑉2
𝑉2
𝑉2𝑖𝑡 = 𝛽1𝑉2 + 𝛽2𝑉2 𝑌𝑖𝑡𝑉2 + 𝛽3𝑉2 A𝑉2
𝛽5𝑉2 + 𝛼𝑖𝑉2 + 𝜀𝑖𝑡𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

′

W1
𝑊1
𝑊1
𝑊1
𝑊1
𝑊1𝑖𝑡 = 𝛽1𝑊1 + 𝛽2𝑊1 𝑌𝑖𝑡W1 + 𝛽3𝑊1 𝐴𝑊1
𝛽5𝑊1 + 𝛼𝑖𝑊1 + 𝜀𝑖𝑡𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

W2
𝑊2
𝑊
𝑊2
𝑊2𝑖𝑡 = 𝛽1𝑊2 + 𝛽2𝑊2 𝑌𝑖𝑡W2 + 𝛽3𝑊2 𝐴𝑊2
𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

2′

𝑊2
𝛽5𝑊2 + 𝛼𝑖𝑊2 + 𝜀𝑖𝑡′

𝐶𝐶𝑖𝑡 = 𝛽1𝐶 + 𝛽2𝐶 𝑌𝑖𝑡𝐶 + 𝛽3𝐶 𝐴𝐶𝑖𝑡 +𝛽4𝐶 (𝑌𝑖𝑡𝐶 × 𝐴𝐶𝑖𝑡 ) + 𝑋𝑖𝑡𝐶 𝛽5𝐶 + 𝛼𝑖𝐶 + 𝜀𝑖𝑡

′

𝑁1
𝑁1
𝑁1
𝑁1
𝑁1
𝑁1𝑖𝑡 = 𝛽1𝑁1 + 𝛽2𝑁1 𝑌𝑖𝑡𝑁1 + 𝛽3𝑁1 𝐴𝑁1
𝛽5𝑁1 + 𝛼𝑖𝑁1 + 𝜀𝑖𝑡𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

′

𝑁2
𝑁2
𝑁2
𝑁2
𝑁2
𝑁2𝑖𝑡 = 𝛽1𝑁2 + 𝛽2𝑁2 𝑌𝑖𝑡𝑁2 + 𝛽3𝑁2 A𝑁2
𝛽5𝑁2 + 𝛼𝑖𝑁2 + 𝜀𝑖𝑡𝑖𝑡 +𝛽4 (𝑌𝑖𝑡 × 𝐴𝑖𝑡 ) + 𝑋𝑖𝑡

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Here, the subscript 𝑖 indicates a person, and 𝑡 indicates a year. 𝐻1𝑖𝑡 is a dummy
variable indicating whether or not weekly working hours go over 55 hours. 𝐻2𝑖𝑡 is acontinuous variable regarding the surplus of hours over 55 in a week. This variable is 1 when
the weekly working hours are 56 hours, and cases where the weekly working hours are lessthan 56 are not included in the variable. 𝑉1𝑖𝑡 indicates the number of days of provided
annual paid leave, and 𝑉2𝑖𝑡 indicates the number of days of acquired annual paid leave. 𝑊1𝑖𝑡
indicates annual earnings from primary work (log in real term), and 𝑊2𝑖𝑡 indicates the wage
rate per hour (log in real term). 22) 𝐶𝑖𝑡 is a dummy variable for the ability to concentrate on
the job. 23) 𝑁1𝑖𝑡 is a dummy variable, which indicates 1 when the working hour system
includes discretionary or de facto work, and indicates 0 when the working hour system is a
general or flexible schedule system. 𝑁2𝑖𝑡 too is a dummy variable, which indicates 1 when
the working hour system includes flextime working without a set schedule (managers andothers who do not receive overtime premiums), and indicates 0 when the working hoursystem is a general or flexible schedule system. 𝑌𝑖𝑡 is a dummy variable, which indicates 1
when the period is 2011 to 2014, and indicates 0 when the period is 2005 to 2009. However,22)
It was adjusted using the “Consumer Price Index” (sum total excluding imputed rents of owned houses)by the Statistics Bureau, Ministry of Internal Affairs and Communications.
23)
Dummy variable for the ability to concentrate on the job : Question “Have you ever lost yourconcentration on the job?” Answer: “Frequently/Sometimes/Almost never/Never.” The variable is 1 when a
responder selects “Never.”17

concerning formulas (3), (4), (8), and (9), we only analyze data after 2008. Thi s is because
the surveys contain no questions regarding the number of days of provided and acquired
annual paid leave and the working hours systems, which are explained variables, until 2007.
Therefore, in these cases, 𝑌𝑖𝑡 indicates 0 only for the year 2008 and the year 2009. The
reason why the year 2004 is excluded is that the control variables were not available, as will
be discussed later. The reason why the year 2010 is excluded, is that the year 2010 was
immediately before the implementation of the revised law, and it was difficult to estimate
the clear impact on the working hours system in this year. 𝐴𝑖𝑡 is a dummy variable for therevision that was applied. In this study, we divided the treatment group and the control groupby type of industry and number of employees and prepared a variable that used 1 for thetreatment group for which the revision was considered to be applied. To be more specific,
the employees of wholesale trades, retail businesses, and service businesses 2 4 ) with 100 or
more employees, the employees of other types of industries with 500 or more employees, 2 5 )and people involved in public affairs 2 6) are represented by a 1, and the employees ofwholesale trades and retail businesses with 5 to 29 employees, the employees of ser vicebusinesses and other types of industries with 5 to 99 employees are represented by a 0. 2 7 ) In
this study, we confirm whether or not the coefficient 𝛽4 of the cross-term was significant
after the implementation of the revised law, and verify whether o r not the revision had an
impact. The coefficient 𝛽4 is defined as a difference between the treatment group and thecontrol group before and after the revision, as shown below:
𝑎𝑓𝑡𝑒𝑟

𝑏𝑒𝑓𝑜𝑟𝑒𝑎𝑓𝑡𝑒𝑟

𝑏𝑒𝑓𝑜𝑟𝑒𝛽4𝑍 = (𝑍𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝑔𝑟𝑜𝑢𝑝 − 𝑍𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝑔𝑟𝑜𝑢𝑝 ) − (𝑍𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑔𝑟𝑜𝑢𝑝 − 𝑍𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑔𝑟𝑜𝑢𝑝 )(10)𝑍 ∈ (𝐻1, 𝐻2, 𝑉1, 𝑉2, 𝑊1, 𝑊2, 𝐶, 𝑁1, 𝑁2)
We introduce a rate of change of the production index 2 8 ) of the relevant industry for the24)
Definition of service businesses according to the KHPS: “information services and research services,”“telecommunication services excluding information services,” “medical and welfare services,” “educationand learning support services,” and “other services.”
25)
Concerning the number of employees, the KHPS asks a question regarding the number of employees in
the overall company for which a respondent works on a routine basis . In the KHPS survey, the definition of“overall company” depends on the respondent. Therefore, there is a possibility that unsuitabl e respondents
are included.
26)
As a result of the r ecommendation by the National Personnel Authority in August 2009, an increase in
the overtime rate and settlement of alternative systems for paid leave were introduced along with therevision. Therefore, we included civil services in the treatment group. However, there is a possibility thatsome local authorities receive slightly different treatment from the recommended content.
27)
Because the KHPS gives options such as “wholesale trade or retail business,” we cannot determine
which one a respondent belongs to.
28)
We use the Indices of Mining and Industrial Output for the mining and manufacturing industry, thevalues of the relevant industry of the Indices of Tertiary Industry Activity for the tertiary industry, and theIndices of Government Services , etc. in the Indices of All Industry Activity for the civil services. However,for “education and learning support services” of the KHPS, we use the values of “learning support” from the
Indices of Tertiary Industry Activity.

18

control variable 𝑋𝑖𝑡 in order to control variation in the demand of products. In addition, we
use dummy variables regarding age, job tenure, job type, existence of a labor union at theworksite, 29) job title status, gender, partner status, academic record, and type of industry.
Note that 𝛽 in each formula including 𝛽4 is a parameter, 𝛼𝑖 is an individual effect, and 𝜀𝑖𝑡is an error term. We excluded the rate of change of the production index i n 2004, as we could
not calculate a value used for analysis in 2004 due changes in the industrial classifications.
We confirmed the impact on working hours with formulas (1) and (2), the impact onprovision and acquisition of annual paid leave with formula s (3) and (4), the impact on the
total amount of wages with formula (5) and the wage rate with formula (6) at the same time,
and the ability to concentrate on the job with formula (7). Companies may increase thenumber of employees who work under the worki ng hours systems allowing no overtime
premium to reduce the labor costs derived from the increase in the overtime rate. Therefore,
we confirmed whether or not the application of the working hours system changed after therevision with formulas (8) and (9). Note that formulas (1), (7), (8), and (9) are estimatedwith a linear probability model (LPM). Concerning which model is theoretically valid, thesimplest assumption is that when formulas (1), (2), (5), and (6) show significantly negative
DID estimation values, the Labour Demand Model is relevant. On the other hand, when
formulas (1) and (2) are not significant, in some cases, not only the Employment Contract
Model, but also the Labour Demand Model are relevant. Those cases, for example, are where
the sample size was too small to be significant, alternating with paid leave or when change inthe working hours system was performed and the optimal working hours before the revisionwere less than 55 hours and did not become negative values. Therefore, we prepar e
sub-samples and analyze the paid leave and the working hours systems for more precise
analysis.
Concerning formulas (1) through (7), we used the data of respondents working under the
typical working hours systems or flexible working hours systems and focused on employees
who received overtime premiums. 30)
Table 3 shows descriptive statistics of the variables introduced above. The mean value of29)
Concerning labor union, the KHPS gives options as follows and allows a respondent to select one : “1.
Our worksite has no labor union; 2. Our worksite has a labor union, but I am not a member; 3. I am a memberof the labor union of our worksite; 4. I am a member of the labor union that is not an union of our worksite;
and 5. I am inapplicable ( self-employed worker, freelance professional).” In this study, when the respondent
selected answer 2 or 3, we set the labor union dummy variable as 1, and when the respondent selected
another answer, we set the dummy variable as 0. In Japan, more than half of the labor unions haveunion-shop contracts (the Ministry of Health, Labor, and Welfare’s General Survey on Labour Relations in2011 (Survey on Collective Agreements ) indicated that 64.3% of the companies had union -shop contracts
and provisions of some kind), and generally, almost all regular employees of companies that have labor
unions, except managers, are members of unions. Therefore, instead asking whether or not a respondent is amember, the question to ask is whether or not a respondent’s worksite has a labor union for a more suitable
analysis of Japanese workers. Ohtake (2001) and Ogura (2003) also pointed this out.
30)
Research on the working hours systems began in 2008. For values prior to 2008 , we used the results of2008 for a follow-up study.

19the revision application dummy variable was 0.504 to 0.6 13, and the ratio of the treatmentgroup to the control group was small; the percentage of employees whose overtime working
hours were over 55 hours was 18.1%.

20

Table 3: Descriptive statistics
0.181
46.39

Std.
dev.
0.386
12.96

6.219
-1.51

0.467
0.593

Mean
“Over 55 weekly working hours” dummy variable
Hours worked per week
Hours exceeding 55 hours per week
Real wage (log)Real wage rate (log)Number of days of provided paid leave (excl. thenumber of carry-over days)
Number of days of acquired paid leave“Ability to concentrate on the job” dummy“Discretionary/de facto work” dummy variable“Flextime working without a set schedule”
dummy variable“Revision application” dummy variable“After-revision (2011 or later)” dummy variable“Revision application” dummy variable
×“After-revision (2011 or later)” dummy variable“20s” dummy variable“30s” dummy variable“40s” dummy variable“50s” dummy variable“5 or less years of job teanure” dummy variable“6 to 10 years of job tenure” dummy variable“11 to 15 years of job tenure” dummy variable“16 to 20 years of job tenure” dummy variable“21 or more years of job tenure” dummy variable“Junior high graduate” dummy variable“High-school graduate” dummy variable“Junior college/technical college graduate”
dummy variable“University graduate/master’s degree” dummy
variable“Other” dummy variable“Clerical job” dummy variable“Sales/service job” dummy variable“Managerial job” dummy variable“Other” dummy variable“Existence of labor union at worksite” dummy
variable“Female” dummy variable“Construction industry” dummy variable“Manufacturing industry” dummy variable“Wholesale/retail/catering/hotel industry”
dummy variable“Finance/insurance” dummy variable“Transportation industry” dummy variable“Medical/welfare” dummy variable“Education/learning support industry” dummy
variable“Other” dummy variable“Holding a managerial post” dummy variable
Production index change rate (%)
Total unemployment rate
“Married” dummy variable
Number of children

0.687

Std.
dev.
0.464

6.26
-1.77

0.438
0.501

Mean

Mean

Std.
dev.

9.221

7.084

Mean

Std.
dev.

14.48

7.296

5.467

5.881

0.606
0.407

0.489
0.491

0.537
0.434

0.499
0.496

0.504
0.349

0.501
0.477

0.545
0.503

0.249

0.433

0.206

0.405

0.158

0.365

0.083
0.299
0.367
0.251
0.163
0.195
0.155
0.155
0.332
0.058
0.419

0.275
0.458
0.482
0.434
0.369
0.396
0.362
0.362
0.471
0.233
0.493

0.068
0.339
0.425
0.168
0.177
0.271
0.171
0.1
0.28
0.062
0.478

0.252
0.474
0.495
0.375
0.382
0.445
0.377
0.301
0.45
0.241
0.5

0.079
0.333
0.405
0.183
0.198
0.237
0.158
0.14
0.267
0.059
0.486

0.27
0.472
0.491
0.387
0.399
0.426
0.365
0.347
0.443
0.235
0.5

0.108

0.31

0.094

0.293

0.104

0.353

0.478

0.354

0.479

0.063
0.241
0.131
0.065
0.563

0.243
0.428
0.337
0.247
0.496

0.012
0.097
0.206
0.029
0.667

0.108
0.297
0.405
0.169
0.472

Mean

Std.
dev.

0.211

0.408

Mean

Std.
dev.

0.04

0.195

Mean

Std.
dev.

0.081

0.274

0.606
0.557

0.489
0.497

0.499
0.502

0.586
0.466

0.493
0.499

0.613
0.559

0.487
0.497

0.261

0.44

0.278

0.448

0.347

0.476

0.34

0.474

0.061
0.303
0.461
0.176
0.139
0.2
0.212
0.109
0.339
0.079
0.394

0.239
0.461
0.5
0.382
0.347
0.401
0.41
0.313
0.475
0.27
0.49

0.076
0.298
0.357
0.27

0.265
0.457
0.479
0.444

0.064
0.295
0.365
0.275
0.138
0.197
0.165
0.143
0.357
0.062
0.4

0.246
0.456
0.482
0.447
0.345
0.398
0.371
0.35
0.479
0.241
0.49

0.061
0.282
0.377
0.28
0.135
0.186
0.163
0.148
0.369
0.062
0.396

0.24
0.45
0.485
0.449
0.341
0.389
0.369
0.355
0.483
0.241
0.489

0.306

0.127

0.334

0.103

0.305

0.107

0.31

0.333

0.472

0.382

0.487

0.372

0.483

0.373

0.484

0.018
0.153
0.178
0.046
0.623

0.132
0.36
0.383
0.209
0.485

0.018
0.176
0.152
0.042
0.63

0.134
0.382
0.36
0.202
0.484

0.062

0.241

0.062

0.241

0.24
0.132
0.064
0.564

0.427
0.339
0.244
0.496
0.484

0.5

0.472

0.499

0.232
0.096
0.248

0.423
0.294
0.432

0.224
0.09
0.243

0.417
0.286
0.429

0.217
0.092
0.242

0.412
0.289
0.428

0.48

0.5

0.481

0.5

0.44

0.497

0.461

0.5

0.221
0.093
0.253

0.415
0.291
0.435

0.041
0.162
0.145

0.199
0.369
0.352

0.066
0.178
0.196

0.249
0.383
0.397

0.067

0.25

0.091

0.287

0.159

0.366

0.142

0.35

0.093

0.29

0.102

0.303

0.099

0.299

0.053
0.061
0.112

0.225
0.239
0.315

0.065
0.139
0.038

0.247
0.346
0.192

0.043
0.127
0.053

0.204
0.334
0.225

0.053
0.06
0.107

0.224
0.237
0.309

0.057
0.057
0.109

0.231
0.231
0.312

0.056
0.057
0.109

0.23
0.232
0.312

0.051

0.219

0.103

0.305

0.064

0.244

0.051

0.221

0.054

0.227

0.055

0.228

0.287
0.364
0.974
4.373

0.452
0.481
4.269
0.408

0.189
0.389
-0.04
4.369

0.392
0.488
4.021
0.405

0.196
0.412
0.355
4.331

0.397
0.493
4.485
0.396

0.292

0.455

0.287
0.508

0.453
4.693

0.29
0.496

0.454
4.75

0.503

0.502

N=2116

N=339

N=393

0.824 0.382
1.539 1.118
N=165

Estimation
formula
（1）A,
（5）A,
（6）A

Estimation
formula
（1）B,
（5）B,
（6）B

Estimation
formula（2）

Estimation
formula（3）,（4）

1.412 1.128
N=2069

N=1691

N=1768Estimation
formula（7）

Estimation
formula（8）

Estimation
formula（9）

Source: Calculations made by the authors using data from the KHPS.
Note: We excluded the respondents that gave no reply or selected “Inapplicable.” We also excluded the values outside of the range between themean +/- standard deviation ×3 as outliers.

21

6.

Estimation results
Table 4 shows the estimation results of formulas (1) through (9). 3 1 ) As a result of theHausman Test, formulas (2), (5)A, and (6)A supported the Fixed -Effects Model, and theother results supported the Random-Effects Model (or Random-Effects LPM). Because
formulas (3) and (4) had no variation in the revision application dummy variable of one
identical person, and the Fixed -Effects Model could not estimate the parameters of 𝛽3𝑉1 and𝛽3𝑉2 , we used only the Random-Effects Model for estimation.
To conserve space in this article, we did not include the results of the model not supportedby the Hausman Test. 32) The values in the top column of the explanatory variables arecross-term coefficients.
In this section, we will review the following five points in this order: (1) working hours,(2) provision and acquisition of paid leave, (3) wages and wage rates, (4) t he ability toconcentrate on the job, and (5) working hours systems.
First, we confirmed the impact on working hours. Formula (1)A showed no significant
cross-term coefficient and did not show that the revision was effective. As confirmed in theprevious section, the working hours of approximately 80% of the regular employees wereless than 55 hours before the revision, and the impact of the change in the overtime rate wasconsidered to be small. However, what was the result of the data of employees who had
already worked long hours on a daily basis before the revision? Formula (1)B showed only
the data of employees who had already worked more than 55 hours per week on average forthe same companies for the period before the revision fr om 2004 to 2009. By using thisformula, we confirmed that the working hours were reduced significantly after the revision(marginal effect -0.200). 33) On the other hand, were employees’ hours that exceeded morethan 55 working hours per week reduced? The r esults of formula (2) showed no significant
cross-term coefficient, and did not show that those employees’ hours exceeding more than 55
31)
The results do not use robust standard errors because the robust standard errors do not allow oper ationof the Hausman Test. Although there are several test methods with the robust standard errors to determine
whether the Fixed-Effects Model or the Random-Effects Model is appropriate, we could not find a test
method that was applicable to a case where the explanation variables of both models did not match.
32)
In the Fixed-Effects Model (or Fixed-Effects LPM), we excluded from the estimates the “ female”
dummy variable, “academic record” dummy variable, and “industry type” dummy variable as shown i n Table
3.
33)
There is concern that a sample selection bias appeared depending on whether or not the average
working hours before the revision were over 55. To confirm this point, we used Heckman’s two-step method ,and found that 𝛽4𝐻1 was negative and significant, and the inverse Mills ’ ratio was not significant. For the
first-step estimation at this time, we used the following variables from the survey in 2009 (when there wasno reply in 2009, we used information from before and after 2009) such as the “age” dummy variable, “ jobtenure” dummy variable, “academic record ” dummy variable, “job type” dummy variable, “industry t ype”
dummy variable, “number of employees” dummy variable, “labor union” dummy variable, “female” dummy
variable, ““ holding a managerial post” dummy variable ” dummy variable, “region” dummy variable, and“municipal size” dummy variable. The dummy variable for “number of employees” was not used in pastestimations, but what we used to evaluate the size of the company within the five stages .

22hours per week were reduced. 34)
Secondly, we confirmed the impact on the provision and acquisition of paid leave.
Formulas (3) and (4) showed results concerning the number of days of provided paid leave,
and also the number of days of acquired paid leave, but the cross -term was not significant.
The employees who were supposedly impacted by the revision were those who worked morethan 55 hours per week. However, it should be noted that the sample size was reduced to 165
when we limited the data in this way.
Thirdly, we confirmed the impact on wages and wage rates. Formula (5)B showed theimpact on wages and formula (6)B showed the impact on wage rates by using the sample
from formula (1)B. Formula (5)B showed a reduction in wages after the revision, as the
cross-term was negative and significant (10% level). 35 ) However, formula (5)A, used toestimate the whole sample, showed no significant cross -term, and therefore, we consider
there was almost no overall impact. On the other hand, formula (6)B showed no significant
cross-term, and did not show an impact on wage rates. 3 6 ) Note that formula (6)A did not havea significant cross-term, similar to (5)A. With the results mentioned above, we consider theemployees’ working hours that exceeded more than 55 hours per week were reduced, andwages were also reduced at the same time, but the wage rate showed no significant reduction,and there is still a possibility that the companies maintained the same hourly wage rates as
before the revision. We suspect that the reasons for the reduction in the amount of wages
were the reduction in the overtime premiums along with reduction in the opportunities forlong overtime hours. However, we cannot verify this further because the KHPS does nothave questions regarding overtime premiums.
Fourthly, we confirmed the impact on the ability to concentrate on the job. In cases of
employees who worked for more than 55 hours per week on average before the revision, we
suspect they had received extra wages in many years. However, it cannot be said that thewage rates decreased while the opportunities for long -term work of over 55 hours per week
decreased. One of the reasons for this was improvement in the productivity of employees. It
was difficult to measure the productivity itself, so we focused on the ability to concentrateon the job in this section, instead. Formu la (7) showed a 10% level, but the cross -term waspositive and significant (marginal effect 0.05). This result was derived from research among34)
We estimated using the Random-Effects Tobit Model that was based on the assumption that the working
hours of 55 hours or less per week were reduced, but found that 𝛽4𝐻2 was not significant.
35)
Similar to formula (1)B, we used Heckman’s two-step method . The inverse Mills ’ ratio was not
significant, and 𝛽4𝑊1 was negative and significant at the 10% level.
36)
Similar to formulas (1)B and (6)B, we used Heckman‘s two-step method. The inverse Mills’ ratio was
not significant. However, 𝛽4𝑊2 was negative and significant at the 10% level. Because the inverse Mills’
ratio was not significant, we decided that the sample selection problem was so small that we did not need toaddress it and that we could interpret according to the panel estimation results. However, for confirmation,
we discussed this point in the section on limitations.

23

whole sample. One of our interpretations regarding this effect is that the companies to which
the revision was applied immediately began efforts to improve labor efficiency.
Finally, we confirmed the impact on the working hours systems. Formulas (8) and (9)analyzed the working hours systems and similarly showed no significant cross -term.
Therefore, the research has not shown that the companies to which the revision was applied
responded to the increase in the overtime rate by adjusting their working hours systems. 3 7 )As mentioned above, according to the results, although the system changed as a result of
the revisions to the Labour Standards Act of 2008, only a small impact could be confirmed
statistically on all employees in the companies to which the revision was applied; however,there were statistically significant results in the reduction of working hours 3 8 ) and in the
reduction in wages 39) for the employees who worked long hours before the revision. It wasalso clarified that all employees in the companies to which the revision was applied had
improved their concentration on their jobs. 4 0 ) However, it should be reiterated that the
number of employees who worked for more than 55 hours before the revision was small, andif the research target was limited to those employees, the sample size for this analysis would
also be very small. In particular, wh ere 𝛽4 was not significant, we cannot determine that it
had no impact of the revision.

37)
On the other hand, we could assume that the workers who worked under the systems that included
flextime working without a set schedule, discretionary work, and de facto work took a toll on the working
hours. On this point, we selected those workers and analyzed their working hours using the Random -Effects
Tobit Model on the assumption that 40 working hours or less per week were reduced. However, the DID
estimation value was not significant.
38)
The workers whose working hours were significantly reduced were supposedly those with many yearsof job tenure. When we estimated the “job tenure” dummy variable, the “after -revision” dummy variable,
and the “revision application” dummy variable as a Triple Difference, the cross-term of these three variables
tended to be significantly negative in cases of longer job tenure. The same trend appeared when we made thejob tenure a continuous variable. The reason for such a difference should be the focus of a future study.
39)
Concerning formulas (1)B and (5)B, we divided the “after -revision (2011 or later)” dummy variable
into three dummies “2011,” “201 2,” and “2013,” and confirmed a cross -term between them and the “revisionapplication company” dummy variable to check the trend of reduction. As a result, the working hours werereduced over time after the revision, and the amount of wages decreased in 2012. It is difficult to discuss
aspects of this difference and the temporal trend between the working hours and the amount of wages
because the sample size was too small.
40)
The estimation samples included respondents of the companies whose size changed after the revision.
To check whether or not it affected the results, we estimated with the additional dummy variables such as (1)transferred to the control group after 2009 and (2) transferred to the treatment group af ter 2009, and madeadditional estimates based on formulas (1) through ( 9) after excluding those respondents. As a result, wefound that the significance of the cross -term did not change and the cross -term did not increase insignificance. Therefore, we fee l that there was no bias that could change the conclusions of this report.

24

Table 4: Results of formulas (1) through (9)Estimation formula name：

Explained variable：

Sample condition (weekly working hours)：

（1）A

（1）B

“Over 55 weekly
working hours”
dummy variable

Whole
sampleOver 55
hours onaveragebeforerevision
Coef.
-0.200**Coef.
-0.028“Revision application” dummy variable
×“After-revision (2011 or later)” dummy(0.029)(0.094)variable 𝛽4-0.020-0.089“Revision application” dummy variable 𝛽3(0.031)(0.085)“After-revision (2011 or later)” dummy-0.019-0.217***variable 𝛽2(0.024)(0.074)Age (ref = 20s)
“30s” dummy variable
0.043
0.251**(0.036)(0.117)“40s” dummy variable
0.059
0.282**(0.041)(0.128)“50s” dummy variable
0.009
0.366***(0.045)(0.140)
Job tenure (ref = 5 or less years)“6–10 years” dummy variable-0.0280.047(0.027)(0.079)“11–15 years” dummy variable-0.059* 0.043(0.033)(0.097)“16–20 years” dummy variable-0.086** 0.062(0.036)(0.107)“21 or more years” dummy variable-0.103*** -0.076(0.039)(0.100)
Job type (ref = Clerical job)
“Sales/service job” dummy variable
0.057
0.017
(0.038)(0.105)“Managerial job” dummy variable-0.0020.245(0.042)(0.182)“Other” dummy variable
0.004-0.017(0.028)(0.106)“Existence of labor union at worksite”
0.002
0.122*
dummy variable(0.026)(0.070)“Holding a managerial post” dummy
0.020-0.020variable(0.021)(0.063)
Production index change rate
0.001
-0.004(0.002)(0.007)
Total unemployment rate
“Female” dummy variable-0.160*** -0.090(0.036)(0.199)“Married” dummy variable
Number of children“Academic record” dummy variable“Industry type” dummy variable
Constant term
Sample size (the number of individuals)
Estimation method
Hausman Test
Prob>chi2

Yes
Yes
Yes
Yes
0.318*** 0.735***(0.071)(0.188)2116（513） 339（84）Random- RandomEffects
Effects
LPM
LPM
19.67
15.82
0.235
0.465

（２）

(3)
（4）
Number ofdays of
Hours
provided Number ofexceedingpaid leave days of
55 weekly(excl. the acquired
working
number of paid leave
hours
carryover days)

（5）A

（5）B

Real wage (log)

Coef.
0.021

Coef.
0.277

Coef.
-0.968

Coef.
0.017

Over 55
hours onaveragebeforerevision
Coef.
-0.076*(2.095)(2.019)(1.627)(0.015)-1.498(4.682)1.400(1.782)4.457***(1.534)3.173**(1.518)1.783(1.387)-0.971(1.226)0.008(0.030)0.122***(0.019)-3.898-3.505(2.698)(2.467)-7.609** -4.607*(3.583)(2.441)-13.235*** -3.693(4.812)(2.804)1.280(2.073)0.691(2.194)1.013(2.518)-2.010(2.304)-4.839(3.245)-3.103(4.674)-1.543(5.930)3.079*(1.851)
5.955***(1.951)4.464**(2.185)
6.618***(1.871)1.460(1.604)3.432*(1.839)1.680(2.019)1.359(1.822)-3.364(2.998)2.658(3.925)0.128(3.005)0.076(2.081)-3.310**(1.573)-0.171(0.127)

Whole
sample（6）A（6）B

Real wage rate(log)

Coef.
0.046

Over 55
hours onaveragebeforerevision
Coef.
-0.110(0.044)(0.040)0.212***(0.069)0.129***(0.047)-0.068(0.078)0.100**(0.050)0.110***(0.022)0.152***(0.029)0.096***(0.037)0.267***(0.061)0.385***(0.079)0.297***(0.096)

0.034**(0.017)0.049*(0.027)0.014(0.036)-0.009(0.045)-2.074-4.939***(1.767)(1.644)0.097-1.483(2.700)(2.344)-3.912*** -1.817(1.412)(1.330)0.460
0.425(1.175)(1.056)-3.538*** -0.878(1.180)(1.059)-0.018(0.025)-0.005(0.026)0.005(0.020)0.015(0.021)0.034**(0.013)0.391(2.023)

Over 55 hours
currently

WholesampleWholesample

（7）

（8）

（9）

“Flextime“Discretio“Ability toworkingnary/deconcentrate
without afacto
on the job”
set
work”
dummy
schedule”
dummy
variabledummy
variablevariableWholesampleWhole sample

Coef.
0.050*

Coef.
0.007

Coef.
0.020

(0.068)(0.030)(0.013)(0.020)0.270***(0.085)0.215***(0.070)-0.051*(0.031)-0.073***(0.023)0.005(0.017)-0.004(0.011)-0.017(0.025)-0.028*(0.016)0.054(0.057)0.109(0.077)0.066(0.096)

0.294***(0.092)0.496***(0.112)0.437***(0.130)-0.024(0.038)-0.078*(0.043)-0.117**(0.047)0.003(0.021)-0.011(0.024)0.002(0.026)-0.021(0.031)0.014(0.035)0.022(0.038)0.049(0.045)0.055(0.061)0.086(0.079)0.122(0.092)0.115**(0.045)0.164**(0.069)0.151(0.093)0.187(0.117)0.040(0.065)0.046(0.086)0.124(0.101)
0.173(0.109)0.006(0.015)0.016(0.019)-0.004(0.021)-0.004(0.022)-0.013(0.023)0.048*(0.028)0.026(0.030)0.027(0.031)-0.004(0.065)0.110(0.127)-0.003(0.075)-0.018(0.045)0.060(0.037)-0.156**(0.067)-0.069(0.069)-0.070(0.053)0.038(0.054)0.031(0.035)-0.055(0.094)-0.059(0.171)-0.067(0.101)-0.032(0.064)0.114**(0.054)0.011(0.014)-0.034*(0.020)0.000(0.001)0.002*(0.001)-0.086*** -0.038(0.014)(0.039)-0.339(0.255)-0.060*(0.036)0.012(0.059)-0.320(0.255)-0.033(0.021)-0.076***(0.028)1.255(1.960)0.638(1.782)-0.528(0.583)
No
Yes
Yes
No
Yes
No
No
No
No
Yes
20.162*** 15.017*** 1.542
6.389*** 5.740***(4.604)(2.649)(2.521)(0.079)(0.242)393（193） 165（111） 165（111） 2116（513） 339（84）FixedRandom- RandomFixedRandomEffects
Effects
Effects
Effects
Effects
Model
Model
Model
Model
Model
26.72
142.04
14.19
0.045
0.000
0.585

-0.045(0.040)0.002(0.045)-0.011(0.030)-0.109***(0.037)-0.032(0.037)0.029**(0.014)
No
Yes
No
No
Yes
Yes
-1.430*** -2.608*** 0.282***(0.207)(0.319)(0.063)2116（513） 339（84） 2069（540）FixedRandomRandomEffects
Effects
Effects
Model
Model
LPM
40.07
6.73
10.00
0.00080.9782
0.531

Yes
Yes
Yes
Yes
0.017
0.080
(0.040)(0.054)1691（515）1768（527）
Random- RandomEffects
Effects
LPM
LPM
10.56
12.42
0.567
0.413

Source: Estimated by the authors using data from the KHPS.
Note: ***, **, and * individually refer to the 1% level, 5% level, and 10% level and that they are significant. The numbers in ( ) show standard
errors.

25

7.

Limitations of this study
In this section, we will discuss the additional limitations of the results of our study.
First, we discussed the possibility that a variation in significance could depend on the

respondents that have large disparities when various personal properties were controlled, if
the sample size was small. Table 5 shows the results after excluding personal characteristics.
The cross-term coefficients in the table show that wages and the ability to conc entrate on thejob, which were significant in the previous section, were not significant. Although regarding
the ability to concentrate on the job, the sample size of 2069 was limiting, the sample size
concerning wage analysis was only 339, and there may b e limitations regarding thesecoefficients. On the other hand, concerning working hours, (1)B’ results shows that it wassignificant even though the level was 10%, and this is considered to be consistent with theresults discussed in the previous section.
Secondly, we discussed how the difference in the properties of the limited respondents
such as formulas (1)B, (5)B, and (6)B affected the results as a whole. Table 6 shows an LMP
estimation of the probability where the average weekly working hours before the revisionwere over 55 hours (84 respondents), among the people belonging to sample 1A, (5)A, and(6)A (513 respondents). 41) We used the explanatory variable for the research in 2009, but a
few of them included missing values due to no -answer responses. In that case, we used pastvalues, and for missing values otherwise, we used values from the next year or from the year
after the next. The results show that the age group of which average weekly working hours
were over 55 hours before the revision consisted primarily of employees in their 30s and 40s,
compared to those in their 50s of which proba bility was lowest. Concerning job tenure,compared to the group with 11 –15 years that had the lowest probability, the group of
employees with 6–10 years had higher probability. Concerning the industry type, comparedto the manufacturing industry, the construction industry, the wholesale/retail/catering/hotelindustry, the finance industry, and the education/learning support industry indicated a higher
tendency. In addition, there were more male respondents tha n female. Other factors such as
the job type and academic record were not significant. Therefore, we should reiterate that theresults regarding working hours and wages as discussed in the previous section include
variations according to age, job tenure, industry type, and gender. However, we consider thatthe sample selection bias was small enough to have no effect on our results (see footnotes 33,
35, and 36).

41)
As a result of the uneven dispersion test of Breusch-Pagan/Cook-Weisberg, there was a possibility ofuneven dispersion. Therefore, we used the White robust standard errors.

26

Table 5: Results excluding personal characteristics
Estimation formula name：

Explained variable：

（1）A'

（1）B'

“Over 55 weekly
working hours”
dummy variable

Whole
Sample condition (weekly working hours)：
sampleOver 55
hours onaveragebeforerevision
Coef.
-0.177*

Coef.
0.007
“Revision application” dummy variable
×“After-revision (2011 or later)” dummy(0.030)(0.094)variable 𝛽4
0.032-0.036“Revision application” dummy variable 𝛽3(0.061)(0.071)“After-revision (2011 or later)” dummy-0.060** -0.197***
variable 𝛽2(0.024)(0.068)Constant term
0.185*** 0.830***(0.038)(0.054)
Sample size (the number of individuals)
2116（513） 339（84）FixedRandomEstimation method
Effects
Effects
LPM
LPM
Hausman Test
10.98
2.92
Prob>chi2
0.012
0.403

（２）'

(3)'
（4）'
Number ofdays of
Hours
provided Number ofexceedingpaid leave days of
55 weekly(excl. the acquired
working
number of paid leave
hours
carryover days)

（5）A'

（5）B'

Real wage (log)

Coef.
-0.314

Coef.
0.721

Coef.
-2.081

Coef.
0.017

Over 55
hours onaveragebeforerevision
Coef.
-0.064(1.482)(2.071)(1.526)(0.016)(0.044)0.518(0.914)-0.544(1.024)9.011***(0.665)393（193）
RandomEffects
Model
2.88
0.411

4.852*** 3.615***(1.476)(1.284)3.621** -0.082(1.531)(1.112)9.829*** 4.197***(1.118)(0.957)165（111） 165（111）
Random- RandomEffects
Effects
Model
Model

Whole
sampleOver 55 hours
currently

-

-

Whole
sample0.010
0.093(0.031)(0.095)0.076*** 0.143***(0.012)(0.032)6.178*** 6.161***(0.019)(0.054)2116（513） 339（84）FixedFixedEffects
Effects
Model
Model
44.96
25.18
0.000
0.000

（6）A'

（6）B'

Real wage rate(log)

Coef.
0.041

Over 55
hours onaveragebeforerevision
Coef.
-0.104(0.039)(0.068)Whole
sample-0.061-0.122(0.078)(0.149)0.101*** 0.295***(0.031)(0.049)-1.524*** -1.814***(0.049)(0.084)2116（513） 339（84）FixedFixedEffects
Effects
Model
Model
33.6
12.34
0.000
0.0063（7）'“Ability toconcentrateon the job”
dummyvariableWholesample（9）'“Flextime“Discretio
workingnary/dewithout a
facto
set
work”
schedule”
dummy
dummy
variablevariable

Whole sample

Coef.
0.048

Coef.
0.005

Coef.
0.015

(0.030)(0.013)(0.019)-0.030(0.029)-0.084***(0.023)0.256***(0.023)2069（540）

0.023-0.005(0.015)(0.022)-0.002-0.013(0.010)(0.015)0.028** 0.093***(0.013)(0.017)1691（515）1768（527）
Random- RandomRandomEffects
Effects
Effects LPM
LPM
LPM
2.06
1.86
2.45
0.560
0.602
0.484

Source: Estimated by the authors using data from the KHPS.
Note: ***, **, and * individually refer to the 1% level, 5% level, and 10% level and they are significant. The numbers in (errors.

27（8）') show standard

Table 6: LPM estimation regarding workers who worked for more than 55 hours per week on average
before the revision

Coef.
(Std. error)
Age (ref = over 50)“20s” dummy variable
0.007(0.071)“30s” dummy variable
0.084*(0.047)“40s” dummy variable
0.119***(0.040)
Job tenure（ref=11–15 years）“5 or less years” dummy variable
0.066(0.057)“6–10 years” dummy variable
0.144**(0.059)“16–20 years” dummy variable
0.000(0.058)“21 or more years” dummy variable
0.011(0.055)
Job type (ref = Clerical job)
“Sales/service job” dummy variable
0.049(0.071)“Managerial job” dummy variable
0.012(0.064)“Other” dummy variable
0.039(0.038)Industry type (ref = Manufacturing industry)“Construction industry” dummy variable
0.127*(0.067)“Wholesale/retail/catering/hotel industry” dummy variable 0.162**(0.081)“Finance/insurance” dummy variable
0.175*(0.093)“Transportation industry” dummy variable
0.128(0.085)“Medical/Welfare” dummy
0.006(0.049)“Education/learning support industry” dummy
0.263***(0.093)“Other industry type” dummy
0.048(0.039)
Academic record (ref = Junior high graduate)“High-school graduate” dummy variable-0.032(0.078)“Junior college/technical college graduate” dummy variable -0.018(0.087)“University graduate/master’s degree” dummy variable-0.075(0.080)“Other” dummy variable-0.124(0.093)“Existence of labor union at worksite” dummy variable-0.006(0.035)“Holding a managerial post” dummy variable
0.007(0.040)“Female” dummy variable-0.182***(0.038)Constant term
0.105
(0.086)
Sample size
513
Source: Estimated by the authors using data from the KHPS.
Note: ***, **, and * individually refer to the 1% level, 5% level, and 10% level and they are
significant. “Std. error” means robust standard error.

8.

Conclusion
In this study, with performed a DID analysis and verified whether the revisions to the

Labour Standards Act of 2008 contributed to a reducti on in long working hours, by using thethe KHPS. As a result, although we could not confirm impacts on all of the employees, we
confirmed it was effective in reducing the number of overtime hours of employees who had
been conventionally working long hours prior to the revision and should be a target of theincreased overtime rate. The wages for those employees were decreased. The ability to28

concentrate on the job was improved in all employees in companies to which the revis ion
was applied. We could not determine if the provisions allowing employees to alternate
overtime premiums with annual paid leave was effective. However, as previously mentioned,
there are limitations to these results.
To show that the Employment Contract Model is established, we must confirm that theworking hours and wages do not change. Although Trejo (1991) referred to the possibility ofannual adjustments, the KHPS asked about “the average” weekly working hours, and the DID
results of this study inclu ded data from three years after the revision. Even with some
limitations, even if we deny the possibility of a reduction in working hours and wages in theworking hours and the wages, we think that we cannot dismiss the use of the Labour Demand
Model.
A significant limitation of this study, is that because the KHPS provides no information
regarding the capital stock of each company, it is difficult to strictly differentiate thetreatment group from the control group. Due to the unavailability of information regarding
production activities and the number of employees, the analysis extracts such information.
Furthermore, concerning this, this puts a limitation on the essential part of the DID analysis.
During the time period of this analysis, there was an economic downturn precipitated by the
Lehman Brothers bankruptcy. The production condition of the companies represen ted by the
sample employees was not known, and we could not establish controls for the production
activities in this analysis. Therefore, even if the treatment group and the control group can
be strictly differentiated, the DID analysis basis itself is uns table when the impact of theeconomy on production activities is uneven in either group. Consequentially, it would be
beneficial to take a structural estimation approach , taking the companies’ production
activities and employment adjust ments into consideration using Employer -Employee data for
conducting a simulation in order to discuss the potential impacts. However, at present, we do
not know of any available or appropriate data. We would like to conduct this analysis in the
future without these limitations.

29

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