PDPRIETI Policy Discussion Paper Series 22-P-025

Compliance Costs of Regulations and Productivity

MORIKAWA, Masayuki
RIETI

The Research Institute of Economy, Trade and Industry

https://www.rieti.go.jp/en/

RIETI Policy Discussion Paper Series 22-P-025
September 2022

Compliance Costs of Regulations and Productivity ∗Masayuki Morikawa (RIETI and Hitotsubashi University)

Abstract
This study proposes a new approach of measuring the compliance costs of rules and regulations
by focusing on labor input and estimating compliance costs based on a survey of workers in Japan.
According to the results, the working hours required to comply with the rules and regulations
account for over 20% of the total labor input. This cost is higher in the finance and insurance
industry, followed by health and welfare; moreover, it is higher in large firms. A large proportionof the working hours of high-wage workers are devoted to compliance tasks. If these costs were
halved, overall economic productivity would increase by approximately 8%. This suggests theimportance of reducing costs through deregulation and digitalization.
Keywords: regulation, rule, compliance cost, productivity, labor input
JEL Classifications: D24, J22, L51, O47

The RIETI Policy Discussion Paper Series was created as part of RIETI research and aims tocontribute to policy discussions in a timely fashion. The views expressed in these papers aresolely those of the author(s), and neither represent those of the organization(s) to which theauthor(s) belong(s) nor the Research Institute of Economy, Trade and Industry.

I would like to thank Masahito Ambashi, Kyoji Fukao, Takeo Hoshi, Kaoru Hosono, Tomohiko
Inui, Sagiri Kitao, Naomi Kodama, Keisuke Kondo, Yoko Konishi, Masato Mizuno, Daigo
Nakata, Toshihiro Okubo, Kazuo Yamaguchi, and Makoto Yano for their helpful comments andsuggestions. This study is supported by the JSPS Grants-in-Aid for Scientific Research(20H00071, 21H00720).
1∗

Compliance Costs of Regulations and Productivity

1. Introduction
Improving productivity growth is a major policy issue in advanced economies. Many studies
have shown that government regulation of businesses, such as entry restrictions and price control,
negatively impacts productivity, whereas deregulation increases it. In line with such studies,deregulation has been promoted in advanced countries since the 1970s. However, in the UnitedStates, it has been indicated that economic deregulation in sectors such as telecommunications,transportation, and energy, has been more than offset in areas such as the environment andoccupational safety, with the total amount of regulations increasing at an average rate of 3.5%
annually (Dawson and Seater, 2013).
Similarly, in Japan, industry-specific economic regulations, such as those applied totelecommunications, electricity and gas, and retail trade have been gradually eased for more than
30 years, since the 1980s. Conversely, labor market, environmental, and consumer protection
regulations (hereinafter referred to as “social regulations”) have been on the rise. According to
the Current Status of Licenses and Permits (Ministry of Internal Affairs and Communications),
the number of licenses and permits increased by 47% (2.5% per year) between 2002 and 2017,with ministries such as the Financial Services Agency, Ministry of Health, Labour and Welfare,and Ministry of the Environment contributing significantly to this trend (see Table 1).
Table 1. The number of licenses and permits by ministries in Japan
Cabinet OfficeFinancial Services Agency
Ministry of Internal Affairs and Communications
Ministry of Justice
Ministry of Foreign Affairs
Ministry of Finance
Ministry of Education, Culture, Sports, Science and Technology
Ministry of Health, Labour and Welfare
Ministry of Agriculture, Forestry and Fisheries
Ministry of Economy, Trade and Industry
Ministry of Land, Infrastructure, Transport and Tourism
Ministry of the Environment
Ministry of Defence
計

2002
219
1,421
575
237
47
727
566
1,543
1,114
1,866
2,042
229
35
10,621

2017
Annal rate Contribution
294
2.0%
1.5%
2,353
3.4%
19.2%
718
1.5%
2.9%
360
2.8%
2.5%
43
-0.6%
-0.1%
842
1.0%
2.4%
473
-1.2%
-1.9%
2,451
3.1%
18.7%
1,770
3.1%
13.5%
2,261
1.3%
8.1%
2,805
2.1%
15.7%
1,075
10.9%
17.4%
30
-1.0%
-0.1%
15,475
2.5%

Notes: Calculated from the Current Status of Licenses and Permits (Ministry of Internal Affairs
and Communications). The figures of the Cabinet Office include those of the Cabinet Secretariat,
Fair Trade Commission, and National Public Safety Commission.
2However, the number of legal licenses and permits does not necessarily indicate the strength of
regulations or their impact. To cope with regulations, firms are forced to perform many
complicated tasks, such as preparing legal documents, creating and storing inspection data, andnegotiating with government agencies. The quantity of tasks varies significantly depending on thespecific regulations.
In addition to legal rules and regulations, various types of compliance are required byadministrative guidance, voluntary rules within the industry, and firms’ internal rules. During the
COVID-19 pandemic, for example, various administrative guidance was provided to businessactivities by central and local governments. Legal regulations, guidance, and rules have a negative
impact on productivity through increased compliance costs that do not directly contribute toproduction. Moreover, they may suppress productivity growth in the medium- to long-term
through their negative impact on firms’ risk-taking and innovation.
According to a survey of Japanese firms (Morikawa, 2019), social regulations applied to allindustries, such as labor market regulations (66.6%) and environmental regulations (33.7%), werecited by many firms as policies with the largest compliance costs, far exceeding industry-specificbusiness licensing and approval (16.7%). However, it is difficult to obtain a comprehensive andaccurate picture of the number and strength of regulations, including those that do not target
specific industries.
The OECD developed and published indicators of product market regulation (PMR) based onquestions posed to national governments regarding regulation-related laws, and, more recently,
indicators of the impact of regulation on downstream industries (REGIMPACT). However, theindicators were created from the perspective of barriers to new entry and competition, which do
not capture the overall compliance costs. Furthermore, industry-specific indicators in the nonmanufacturing sector are limited to energy, transportation, telecommunications, and professional
services.
In the United States, there have been new attempts to quantify federal government regulations,such as a page count of the Code of Federal Regulations (CFR; Dawson and Seater, 2013), andindustry-specific regulatory indicators (RegData) based on textual analysis of the CFR (AlUbaydli and McLaughlin, 2017; McLaughlin and Sherouse, 2019). 1 However, it is highly
debatable whether the quantity of regulation itself captures impacts and costs to industries andfirms. To overcome this limitation, Calomiris et al. (2020) applied natural language processing to

The detailed explanation of RegData is documented in Al-Ubaydli and McLaughlin (2017) and
McLaughlin and Sherouse (2019).
3
1

data from the corporate earnings calls of listed firms in the United States to create a firm-level
cost of regulation index and demonstrate that regulation increases the cost of capital for firms.
This is a unique approach to measuring the cost of regulation for individual firms; however, due
to the nature of the data, it does not cover non-listed firms.
This study proposes a novel approach that focuses on the amount of labor input required tocomply with rules and regulations, and provides an estimate of the actual compliance costs inJapan using data collected through an original survey of workers. According to the results, theworking hours required to comply with rules and regulations account for more than 20% of totallabor input. By industry, this cost is greater in the finance and insurance industry followed by thehealth and welfare industry, and by firm size, it is greater in large firms. A large portion of working
hours of high-wage earners is devoted to these tasks, and if these labor costs could be halved, itwould increase the productivity of the economy by about 8%.
The remainder of this study is structured as follows. Section 2 provides a brief overview of therelated literature. Section 3 explains the survey data and tabulation method. Section 4 reports theresults. Section 5 concludes the paper and discusses future directions for research.

2. Literature Review
Previous studies have shown that economic regulations that restrict competition, including
entry and price regulations, negatively impact productivity and economic growth, and eliminatingor relaxing these regulations positively impact productivity (see, for example, Winston, 1993;
Crafts, 2006; Holmes and Schmitz, 2010, for surveys). Nicoletti and Scarpetta (2003) and Haidar(2012) are examples of studies using cross-country data to indicate that regulatory reforms that
promote competition positively affect economic growth. OECD regulatory indicators (PMR and
REGIMPACT) are frequently used in empirical analyses using cross-country data. Andrews andCingano (2014) combined firm data from several countries with OECD regulatory indicators andfound that regulation impedes resource allocation efficiency and negatively impacts productivity.
This study is unique because it covers product market regulations and labor market regulations.
Studies that comprehensively cover economic and social regulations in the United States
include Dawson and Seater (2013) and Coffey et al. (2020). Dawson and Seater (2013) conducteda unique study that used the number of CFR pages as a measure of regulation quantity. They
demonstrated that the total amount of federal government regulation had been trending upward,
mainly through an increase in environmental and safety regulations. They estimated that theincrease in regulation had significantly reduced output and total factor productivity (TFP), with amagnitude that would reduce the postwar U.S. economic growth rate by two percentage points
4per year. Coffey et al. (2020) estimated the impact on GDP using RegData developed based on a
textual analysis of the CFR. They estimated that regulation reduced the US economic growth rateby approximately 0.8 percentage points per year since 1980. McLaughlin and Mulligan (2020)
summarized the current state of regulation in the United States based on RegData and stated thatthe costs of regulation were extremely large.
Many studies have addressed the impact of social regulations that are not specific to an industry.
In the following, we focus on studies dealing with the effects of such regulations on productivity
and innovation. Empirical studies on labor market regulations showing negative impacts on
productivity include Cingano et al. (2010), Andrews and Cingano (2014), Cette et al. (2016),
Égert (2016), and Amoroso and Martino (2020). In the area of labor market regulations, several
countries adopt “size-dependent regulations” that impose strict labor market regulations on
publicly listed or large firms, while exempting or reducing the regulations on unlisted or small
firms. Studies on French policy, where stringent labor regulations are imposed on firms with over
50 employees, have found that the policy negatively impacts productivity at the aggregate level(Gourio and Roys, 2014; Garicano et al., 2016). Aghion et al. (2021) reported that this sizedependent regulation reduces innovation. 2
Regarding land-use regulations, Cheshire et al. (2014) in the United Kingdom and Herkenhoff
et al. (2018) in the United States indicated that land-use regulations result in reduced productivity.
Studies on environmental regulations include Greenstone et al. (2012), Albrizio et al. (2017), andFeng et al. (2021), although conclusions about the impacts of environmental regulations on
productivity are mixed. A recent study analyzing privacy regulations is that of Janßen et al. (2022)
on the EU General Data Protection Regulation (GDPR), and demonstrated its negative impacts
on innovation.
To summarize, many studies have analyzed the impact of rules and regulations on productivity,and each has its own strengths and weaknesses. In particular, the lack of quantitative data oncompliance costs, covering all rules and regulations, has been an important limitation. This study
attempts to capture, quantitatively, compliance costs in Japan by employing a novel approach thatmeasures the labor input needed to comply with rules and regulations through a survey ofworkers. 3 Although it is a straightforward method, this study is unique because it presents broad
compliance costs by industry and worker characteristics, covering all rules and regulations,
including cross-industry social regulations.

Although not targeting labor market regulation, Hosono et al. (2017) and Hosono et al. (2019)
in Japan show that size-dependent policies, which treat large and small firms differently, may
have a negative impact on firm growth and productivity.
3
Ishizaki (2019), Morikawa (2019), and Takagi and Nakajima (2021) are examples to measurecompliance costs of regulations based on surveys of Japanese firms.
52

3. Outline of the Survey
The survey data used in this study were retrieved from the “Survey of Life and Consumption
under the Changing Economic Structure” designed by the author and conducted by Rakuten
Insight, Inc. The survey was implemented in July 2021 on monitors aged 20 years or older,registered with Rakuten Insight, Inc. The total number of respondents was 8,909; however, only
data from 5,707 of those who worked were used in this study. 4 The composition of the sample
by gender and age is shown in Appendix Table A1.
The key question used in this study was “How much share of your total working hours is spent
to comply with rules and regulations, including voluntary rules formulated at the industry or firmlevel?” The seven response choices were “100%,” “50-99%,” “25-49%,” “10-24%,” “5-9%,” “14%,” and “I do not perform such work.” While we would prefer specific figures, it is difficult forindividual workers to provide exact percentages. For this reason, we set up multiple choices thatwould allow respondents to answer in rough categories, such as whether it is all, more than half,or less than half but more than a quarter of their working hours. In surveys for individual
participants, wordings such as “most,” “quite a bit,” or “somewhat” are often used; however, insuch a design the answer is affected by respondents’ subjective senses/interpretations of thewordings.
The ratio of working hours devoted to compliance (hereinafter referred to as “compliancerelated working hours”) was tabulated based on worker characteristics. Specifically, we calculate
the means and standard deviations by gender, age (10-year intervals), educational background,type of work, industry, occupation, firm size, weekly working hours, and wage level (annual
income from work). In doing so, the median value for each choice was used, and the choice “I donot perform such work” was treated as zero. However, as part-time workers, for example, work
fewer hours per week, we also measure the absolute amount of labor input rather than just theshare of hours worked. As the survey asks about weekly working hours, the compliance-related
working hours per week can be calculated as the weekly working hours for each worker multipliedby the ratio of compliance-related working hours. 5
The number of monitors registered with the Rakuten Insight, Inc. was more than two million.
The survey was conducted to obtain more than 8,000 responses, including approximately 4,985
who responded to the 2020 survey, plus additional respondents. The survey was designed toensure that the composition of respondents by gender and age was consistent with the compositionof Japan as a whole.
5
In the survey, weekly working hours are categorized into twelve choices (from “less than 15hours” to “75 hours or more”), which are the same with the categories in the Employment Status
64

Furthermore, when discussing the impact of compliance costs on productivity, it is preferable
to consider workers’ wage levels. As the survey examines annual earnings from work, figures
weighted by each worker’s hourly wages can also be calculated. 6 These are then aggregated to
calculate the share of compliance-related working hours to the total labor input, weighted by
wages, for the whole economy, as well as by industry and firm size.

4. Results
Table 2 presents the results of tabulating the share of time spent complying with rules andregulations of the total hours worked. Nearly half of the respondents (46.1%) reported that they
did not perform such tasks (expressed as “0%”). Excluding these responses, a large number of
respondents chose “10-24%,” “25-49%,” and “50-99%.”
Table 2. The share of compliance-related working hours
Share of compliance-related hours
100%
50～99％
25～49％
10～24％
5～9％
1～4％
0%

Percentages
3.5%
12.2%
13.6%
14.8%
6.4%
3.3%
46.1%

Notes: N=5,707. The choice “I do not perform such work” was treated as 0%.

The detailed results of compliance-related working hours by worker characteristics arepresented in Appendix Table A2, where means and standard deviations are reported. For all
workers, the average share of compliance-related working hours is 20.7% and the average numberof hours per week is 7.93. This means that, on average, approximately one working day per week
is devoted to such work. However, the standard deviations are large, at 28.9% and 11.98 hours,
respectively, indicating that there is large dispersion of regulatory burden among workers.

Survey (Ministry of Internal Affairs and Communications). The central values of each category
are used in the analysis, where working hours of “75 hours or more” is treated as 80.5 hours.
6
In the survey, annual income from work is classified into 18 categories (from “less than 0.5
million” to “20 million or more”), which is finer than the 16 categories in the Employment Status
Survey. The central values of each category are used in the analysis, where annual earnings of“20 million yen or more” is treated as 21.25 million yen.
7

When weighted by wages, the mean share of compliance-related working hours is somewhat
larger at 23.3% because low-wage workers tend to spend less time complying with rules andregulations. To assess the impact on productivity, it is appropriate to examine this weighted figure.
As total employee compensation out of GDP is about 289 trillion yen (FY2021) in Japan, amechanical calculation of macroeconomic compliance costs yields a huge figure of about 67
trillion yen (289 × 0.233).
Worker characteristics related to relatively high compliance-related working hours are males,
young adults, highly educated, executives, standard (full-time regular) employees, the finance andinsurance industry, public service, management, clerical jobs, security jobs, and large firms (seeFigure 1). The relationship with wages (annual earnings) is an inverted U-shaped relationship,
increasing until around JPY 8 million, and then decreasing gradually. The relationship withworking hours increased until approximately 45 hours per week, after which the relationshipremained almost unchanged. However, there is a large variation (standard deviation) even within
the same category (see Appendix Table 2).
Figure 1. The means of compliance-related working hours by worker characteristics

Note: The categories with high compliance-related working hours are extracted from Appendix
Table A2 and are depicted in the figure. Large firms are those with 1,000 or more employees.

Table 3 presents the results of ordinary least squares (OLS) estimations that explain
compliance-related working hours (share in column (1) and absolute hours in column (2)) byworker characteristics. Most of the coefficients confirm the simple cross-tabulation results
8explained above. Age, industry, occupation, and firm size are significantly associated withcompliance-related working hours. However, unlike the cross-tabulations for education, thecoefficients for university and graduate degrees are significantly negative. In other words, thecross-tabulation results are affected by the fact that employment status, industry, and occupationdiffer substantially according to educational background. The coefficient for female is marginallysignificant (at the 10% level) but quantitatively small for the share of compliance-related working
hours, and is not significantly related to the absolute amount of compliance-related hours. As witheducation, gender differences in the composition of employment status, industry, and occupation
affect simple cross-tabulations. Annual earnings (expressed in log) are positively related to theshare of compliance-related working hours (Column (1)), but not significantly related to theabsolute amount of compliance-related hours (Column (2)).
Table 3. Worker characteristics and compliance-related working hours
FemaleAge 20s
Age 30s
Age 50s
Age 60s
Age 70 or higher
Vocational school
Junior (2-year) college
4-year university
Graduate school
Non-standard worker
Finance & insurance industry
Health & welfare industry
Education industry
Public services
Professional & engineering occupation
Services occupation
Agricultural occupation
Production occupation
Construction occupation
Cleaning, packaging, etc.
Other occupations
100-299 employees
300-499 employees
500-999 employees
1,000 or more employees
Government
ln working hours
ln annual earnings
Nobs.
R-squared(1) Share-1.836(0.943) *6.475(1.485) ***2.633(1.206) **-1.050(1.068)-2.201(1.144) *-2.645(1.602) *-2.912(1.357) *-1.080(1.466)-2.847(1.012) ***-5.874(1.678) ***-4.997(1.053) ***5.612(2.427) **4.779(1.621) ***2.734(1.966)14.273(2.736) ***-3.634(1.320) ***-4.575(1.523) ***-14.982(6.530) **-5.974(2.259) ***-10.576(3.364) ***-9.600(2.395) ***-4.463(1.463) ***2.818(1.191) **4.555(1.661) ***4.463(1.599) ***7.004(1.082) ***4.738(2.985)-0.157(0.923)
1.216(0.517) **5,700
0.07979(2) Absolute hours-0.600(0.374)2.409(0.606) ***1.020(0.498) **-0.332(0.441)-0.886(0.432) **-0.473(0.538)-1.082(0.543) **-0.454(0.522)-0.991(0.398) **-2.388(0.715) ***-1.659(0.379) ***2.533(1.000) **2.214(0.659) ***2.133(0.832) **5.833(1.108) ***-1.285(0.534) **-1.902(0.571) ***-6.348(2.836) **-2.045(0.935) **-4.026(1.353) ***-2.992(0.970) ***-1.598(0.532) ***1.119(0.480) **2.054(0.676) ***1.806(0.652) ***2.747(0.432) ***1.618(1.270)5.920(0.335) ***0.165(0.196)5,700
0.1505

Notes: OLS estimations with robust standard errors are in parentheses. ***: p<0.01, **: p<0.05,*: p<0.10. The reference categories for age, education, industry, occupation, and firm size are age
40s, high school, manufacturing, clerical occupation, and less than 100 employees, respectively.
To reduce the size of the table, only statistically significant categories are presented for industries
and occupations.

Table 4 presents the mean share of total compliance-related working hours to total hours byindustry and firm size. By industry, as might be expected, public service is particularly large.
Additionally, finance and insurance, followed by medical care and welfare, have large percentages,
probably reflecting the large number of industry-specific regulations. Conversely, the percentages
for agriculture, forestry, and fisheries, wholesale and retail trade, restaurants and accommodations,and other services are relatively small. The differences by firm size are clear, suggesting theinfluence of size-dependent regulations, or that larger firms tend to have stricter internal rules.
Whether weights are used does not substantially affect the relative ranking by industry and firm
size.
Table 4. The percentages of compliance-related working hours by industry and firm size
Agriculture
Construction
Manufacturing
Information & communictions
Transport
Wholesale & retail
Finnce & insurance
Real estate
Restaurants and accommodations
Health & welfare
Education
Other services
Public services
Other industries
99 employees or less100-299 employees
300-499 employees
500-999 employees
1,000 or more employees
GovernmentAll(1) Unweighted (2) Weighted
10.1%
10.8%
20.6%
23.6%
22.2%
23.1%
20.3%
21.0%
22.8%
25.4%
17.7%
19.2%
31.4%
30.0%
19.6%
18.7%
17.1%
20.5%
25.3%
25.7%
22.4%
22.4%
17.5%
19.0%
38.4%
38.9%
15.4%
17.7%
16.9%
18.2%
22.3%
23.2%
24.7%
24.7%
25.0%
25.1%
26.2%
27.0%
35.5%
36.4%
21.7%
23.3%

N
59
324
938
255
233
592
219
163
162
639
322
915
346
540
2,807
686
329
394
1,233
258
5,707

Note: Column (2) shows the percentages weighted by each worker’s hourly wage.
10

Finally, we discuss the productivity implications of our results. As presented in the last row ofTable 4, the mean share of compliance-related working hours, weighted by wages, was 23.3%.
As we are measuring labor input, the relationship with TFP depends on labor share (contribution)in production. Assuming a macroeconomic labor share of 2/3, if the working hours for compliancewith rules and regulations were halved, which would lead to a reduction in labor input, the impact
would be an increase in TFP of approximately 7.8% (23.3% × 0.5 × 2/3). 7 As Japan’s current
TFP growth rate is approximately 0.4-0.5% per year, the impact is very large: equivalent to 1520 years of cumulative TFP growth. This is only a mechanical estimate, but it suggests thepotential importance of deregulation and increased efficiency of enforcement of regulations as
growth policies.
This estimate deals only with the effect of labor cost reduction. Some firms may invest in
equipment and software to comply with rules and regulations, which cannot be captured by labor
input. In addition, the possibility that rules and regulations may inhibit firms’ risk-taking andinnovation, and their negative impact on productivity through misallocation of resources, areoutside the scope of this calculation. If such pathways are included, the impact on productivity islikely to be far greater.

5. Conclusion
This study proposes a novel approach to measure compliance costs of regulations by focusing
on working hours devoted to comply with rules and regulations. It quantitatively estimatescompliance costs in Japan based on survey data of workers.
According to the results, the cost of compliance with rules and regulations, including social
regulations covering all sectors of the economy, is very high. A large proportion of the working
hours of high-wage earners are devoted to these tasks, and it is estimated that productivity would
increase by approximately 8% if these costs could be halved. This result suggests the importanceof reducing compliance costs using strategies such as streamlining social regulations anddigitization. Many social regulations intend to ensure safety, security, and other values that differ
from economic efficiency. Therefore, we cannot generally state that they should be reduced oreliminated. However, it is necessary to consider the desirable level of regulations under trade-offs,
considering the quantitative magnitude of compliance costs.

This estimate is almost perfectly consistent with the productivity effect of halving compliancecosts (about 8%) based on a survey of Japanese firms (Morikawa, 2019).
11

7

The analysis in this study depends on a multiple-choice survey, which has limitations in termsof measurement accuracy. In addition, the industries and occupations of workers were categorized
only at a one-digit classification level. A large-scale survey that collects more disaggregatedinformation may enable a more detailed analysis of the relationship with productivity.
Furthermore, the survey is limited to a cross-sectional design. It would be beneficial to conduct
such a survey periodically to observe regulatory changes over time. Finally, similar surveys could
be conducted in other countries to assess the extent of the compliance costs of Japanese rules andregulations from an international comparative perspective.

12

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14

Appendix Tables

Table A1. Gender and age composition of the sample
N
Male
FemaleAge 20s
Age 30s
Age 40s
Age 50s
Age 60s
Age 70 or older
Total

3,496
2,211
568
951
1,420
1,359
1,060
349
5,707

Percentages
61.3%
38.7%
10.0%
16.7%
24.9%
23.8%
18.6%
6.1%
100.0%

Note: The figures are the worker subsamples of the respondents to the “Survey of Life andConsumption under the Changing Economic Structure” conducted in July 2021.

15

Table A2. Compliance-related working hours by worker characteristics(1) Share (%)
MeanStd. Dev.
20.7
28.9
22.2
28.9
18.3
28.8
27.5
31.2
24.0
30.0
21.0
28.9
20.5
28.7
16.4
26.9
13.5
24.3
19.6
30.1
18.8
28.3
18.8
29.1
22.1
28.6
21.3
26.4
23.3
26.3
13.8
25.5
12.5
24.5
25.6
30.0
12.9
26.0
13.8
26.3
16.1
28.6
20.2
29.7
14.1
23.5
9.1
21.3
21.0
26.0
21.9
27.9
20.5
25.5
22.8
31.2
16.8
26.7
30.0
32.9
19.3
28.3
16.5
27.4
23.2
29.9
19.1
28.9
17.3
26.8
37.5
36.0
14.3
26.0

All workers
Gender

Male
Femle
Age
20s
30s
40s
50s
60s
70 or older
Education
High school
Vocational school
Junior (2-year) college
4-year university
Graduate school
Type of work Executives
Self-employed
Family workers
Standard employee
Part-time workers
Temporary workers
Dispatched workers
Contract employees
Entrusted employees
Industry
Agriculture
Construction
Manufacturing
Information & communications
Transport
Wholesale & retail
Finance & insurance
Real estate
Accommodations & restaurants
Health care & welfare
Education
Other services
Public services
Other industries

16(2) Absolute hours
Mean
Std. Dev.
7.93
11.98
9.04
12.55
6.18
10.79
10.46
13.41
9.53
12.89
8.32
12.16
8.24
12.37
5.63
9.87
3.70
7.62
7.25
12.07
7.18
11.87
6.27
10.58
8.77
12.18
8.72
12.04
9.60
12.38
5.21
11.27
3.93
7.93
10.56
13.11
3.24
7.26
3.71
8.04
5.42
10.42
7.34
11.30
4.04
6.54
3.69
8.45
8.17
10.87
8.96
11.94
8.14
10.16
9.14
14.13
6.33
10.74
12.05
14.20
6.15
9.91
5.64
10.49
8.77
12.75
7.38
13.40
6.26
10.27
14.68
15.18
5.01
10.08

N
5,707
3,496
2,211
568
951
1,420
1,359
1,060
349
1,461
658
579
2,582
420
333
590
87
2,964
918
288
136
298
93
59
324
938
255
233
592
219
163
162
639
322
915
346
540

(Continued)(1) Share (%)
MeanStd. Dev.
24.6
26.8
21.7
28.2
25.0
31.5
16.5
28.1
24.2
27.9
16.5
26.9
30.5
34.2
7.5
18.5
17.6
29.0
24.2
32.6
14.2
24.0
11.3
25.0
15.6
28.2
16.3
26.7
21.2
28.5
23.0
28.7
24.3
29.3
25.7
29.9
35.2
36.6
17.1
28.8
15.2
27.9
13.5
24.3
14.9
26.7
19.4
28.3
23.3
29.8
23.8
29.6
23.6
29.4
22.1
27.8
23.1
28.5
15.4
24.4
22.3
28.2
12.4
26.3
13.4
26.6
14.0
26.1
18.7
28.2
19.0
29.5
20.1
29.0
23.6
30.6
24.7
29.6
24.2
29.7
26.5
30.1
25.4
28.4
27.7
30.3
19.7
25.3
23.1
23.8
22.8
25.3
22.4
28.4
25.6
20.7
22.1
27.3

Occupation

Managerial
Professional & engineering
Clerical
Sales
Trade-related
Service
SafetyAgricultural
Production
Transportation & Machinery operation
Construction
Cleaning, packaging, etc.
OthersFirm size
99 or smaller
100-299
300-499500-999
1,000 or larger
Government
Working hours Shorter than 15 hours(weekly)
15-19
20-21
22-29
30-34
35-42
43-45
46-48
49-59
60-64
65-74
75 hours or longer
AnnualLess than 0.5 million
earnings
0.5-0.99
1-1.491.5-1.99
200-2.49
2.5-2.99
3-3.994-4.995-5.996-6.997-7.998-8.999-9.9910-10.249
12.5-14.99
15-17.49
17.5-19.99
20 million or more

17(2) Absolute hours
Mean
Std. Dev.
10.35
12.13
8.74
12.51
9.49
12.89
5.71
10.52
9.98
12.22
5.38
9.65
12.03
15.25
2.88
6.36
7.46
12.52
9.75
14.74
5.81
9.80
4.37
11.16
4.99
10.00
5.85
10.66
8.33
12.00
9.24
12.32
9.85
12.29
10.24
12.71
13.92
15.64
2.23
3.74
2.59
4.74
2.77
4.98
3.80
6.82
6.20
9.05
8.96
11.45
10.46
13.02
11.09
13.81
11.94
14.99
14.32
17.67
10.72
16.99
17.95
22.67
2.96
7.27
3.20
7.13
4.04
8.50
6.20
10.32
7.18
11.95
7.76
11.93
9.32
13.03
10.45
13.55
9.80
12.80
11.61
13.85
10.87
12.93
11.50
12.64
9.02
11.96
9.94
11.27
8.37
10.42
8.22
9.39
9.80
9.13
11.39
17.45

N
638
1,176
1,188
290
361
682
78
54
227
80
61
140
732
2,807
686
329
394
1,233
258
769
325
223
354
323
1,732
628
489
521
161
78
104
396
539
469
308
418
374
737
675
504
352
287
208
122
180
54
21
15
48