Determinants of Understanding of Labor Laws:
Evidence from Japanese University Students

Tomotaka HIRAO

広島大学高等教育研究開発センター
Advancement of Higher Education Research:

No.9

Determinants of Understanding of Labor Laws:
Evidence from Japanese University Students†
Tomotaka Hirao
Department of Economics, Setsunan University, Japan
August 2022
Abstract
This study attempts to investigate Japanese university students’ knowledge of laborlaws using latent rank theory and also attempts to analyze the determinants ofunderstanding of labor laws. Because the hierarchy between employer and employee exist
automatically in capitalist societies, labor laws have to be enacted in order to protect
workers. Understanding of labor laws, therefore, builds core safety nets for workers in
the labor market.
At the same time, the college enrollment rate in recent years in Japan is over 50% of
secondary education graduates, and as a result, college graduates are no longer consideredelite employees, rather, almost all graduates are merely ordinary workers. For that reason,
students need to learn about their employment rights before obtaining initial employment
in order to protect themselves and their future careers. However, there are few studies
regarding Japanese university students’ knowledge of labor laws. This study, therefore,
investigates the understanding of labor laws based on latent rank theory and analyzes thedeterminants by exploratory data analysis.
Empirical results show that the average of correctly answered questions regarding
Japanese labor laws is about 50%. We can view this record as less than positive because
subjects start their job hunting process with less than an ideal amount of legal knowledge.
This research also confirms that there are positive correlations between some dependent
variables (for example, gender, higher GPA in high school, the number of credits acquired
regarding career education, and experience with exploitive labor) and the rank of laborlaw knowledge. This implies that education and experience regarding career development
while enrolled in university correlates with better understanding of labor laws.
Keywords: Labor Law, Latent Rank Theory, Career Education, Safety Net†

This study was supported by JSPS KAKENHI Grant Numbers JP19H00619. I would like to thank
Mynabi Corporation and Research Institute for Higher Education (RIHE), Hiroshima University, for
permission to use the data. I am grateful to Osamu Umezaki, Masataka Murasawa, Hirokazu Miyata,
Shinji Matsumiya for their helpful comments. All the remaining errors are mine.
11. Introduction
This study attempts to investigate Japanese university students’ knowledge of laborlaws using latent rank theory (LRT) and also attempts to analyze the determinants ofunderstanding of labor laws. To what degree do career education, internships andinteractions with adults play a role as determinants?
In the Japanese labor market, the number of temporary workers has increased since
the collapse of the bubble economy in the early 1990s, the ratio of these non-regular
workers in the labor force in 2020 are 54.5% for females and 22.2% for males(Gender Equality Bureau 2021, p.102). In addition, there is significant stagnation ofreal wage growth in Japan over the last few decades (Chun et al., 2021). Realisticallyspeaking, the Japanese employment system (for example, long-term employment,
seniority based wage system and enterprise-based unions) can no longer be expected
to support employees’ career development. Since the 1970s, moreover, trade union
density rate (the share of employees who are union members) has decreased, dropping
to under 20% in recent years (Kato, 2006). Individualization of industrial relations
continue to develop. Because almost all enterprise-based unions consist of regular
workers, the relationship between management and labor is not adversarial under theJapanese employment system. Thus, unions have little power to resolve disputes overworking conditions for workers in Japan.
Understanding of labor laws builds core safety nets for vulnerable workers in the2

labor market under these circumstances. Because the hierarchy between employer andemployee exist automatically in capitalist societies, labor laws have to be enacted inorder to protect workers. In order to protect themselves and their future careers,
students need to learn employment rights before obtaining initial employment. Since
college enrollment rate in recent years in Japan is over 50% for secondary school
graduates, college graduates are no longer thought of as elite employees, and almost
all graduates are considered merely ordinary workers.
On the other hand, the promotion of individual employment rights continues todevelop. Since the 2008 financial crisis (“Lehman shock”), exploitative companies
that flout labor standards have become a significant social problem in Japan.
Exploitative companies called “Burakku Kigyo” in Japan force harsh working
conditions with long working hours, low wages, no overtime pay and other undue
negative pressures on employees. Therefore, promoting individual employment rights
creates momentum to protect workers from such exploitative companies, andimproved labor law education is important as one solution to this issue. In response tothis gain of momentum, the Labour Lawyers Association of Japan (LLAJ) issued astatement to promote legal education with regard to employment rights in 2013, andlater the Japanese government has a formal obligation to propagate basic knowledgeof labor laws in high school and college education by Act Partially Amending theWorking Youth Welfare Act in 2015. The Japan Federation of Bar Associations
3

(JFBA), moreover, submitted a written statement concerning labor laws education to
the Ministry of Education, Ministry of Labor and Ministry of Justice in 2017.
Although this kind of promoting individual employment rights creates momentum forchange, there is little evidence regarding the understanding of labor laws and its
determinants in Japan.
The rest of this paper is organized as follows: the next section reviews recent
relevant empirical studies regarding the understanding of labor laws in Japan; thefollowing section introduces data and variables; the fourth section explains empiricalmethods; the fifth section introduces empirical results; and the final section presents
conclusions.

2. Review of LiteratureThis section summarizes relevant literature that deals with the understanding oflabor laws in Japan. Takahashi (2008) analyzes the effects of legal knowledge
regarding paid leave. This author reveals that employees of small companies have lesslegal knowledge regarding paid leave than employees of large companies, and they
imply that vulnerable workers in the labor market are not thoroughly educated. Thisauthor also points out that if working environments are good, legal knowledge
regarding paid leave facilitates the taking of paid leave. Takahashi’s study, however,
only analyzes the legal knowledge of paid leave and does not investigate other aspects4

of labor laws.
Uenishi et al. (2014) and Umezaki et al. (2015) analyze the effects of Japanese
university students’ understanding of labor laws on obtaining initial employment.
These studies reveal that knowledge of labor laws correlates with likability for labor
unions, and that understanding of labor laws takes a facilitating role for obtaining
employment at a company that has unions. The authors, however, could not elucidate
a causal relationship between the knowledge of labor laws and likability for labor
unions because of data limitations.
Uenishi et al. (2016) and Nagumo et al. (2019) investigate the changing ofindividual’s labor law knowledge using longitudinal data in order to elucidate theproblem of causal inference. The first survey was conducted when subjects were
university students (third or fourth year students), and the second survey was
conducted two years after the first survey. These studies reveal that on average
subjects’ knowledge of labor laws did not improve during the time between university
and workplace. While some knowledge did improve, other knowledge worsened
rather than maintain previous amounts. The authors point out that one possible reason
for this result may be a changing of required knowledge according to their individual
work experience.
Hayashi (2014) also analyzes the effects of Japanese university students’
understanding of labor laws on obtaining initial employment. This author points out
5that although knowledge of labor laws does not increase the probability of obtaining
initial employment directly, understanding of labor laws affects obtaining initial
employment through the number of applications submitted during the job search
process.
In summary, although direct effects of understanding labor laws on labor market
outcomes are not necessarily clearly perceived, there is at least a mediating effect thatknowledge of labor laws affects labor market outcomes through the likability forlabor unions or the number of applications submitted. There is, however, little
literature regarding the determinants of understanding of labor laws in Japan. This
study hopes to be unique and valuable research on the understanding of labor laws inJapan.

3. Data
The data set used in this study was collected in December of 2021 by the Mynavi
Corporation, which is a well-known Japanese college student recruitment agency thatgenerously cooperated with this research. Mynavi collected original data regarding
postgraduate and undergraduate students’ lifestyles. Subjects of this survey wereJapanese university students (third year students) and master’s degree students (firstyear students). The number of returned surveys was 3756.
In this survey, data regarding knowledge of labor laws were collected. There were6

six questions in total, regarding job offers, working hours, overtime pay, paid leave,
workplace accidents, and labor unions (see Table 1). There were three answer options
for each question, namely: 1) Correct; 2) Incorrect; and 3) I don’t know. Although
there were exceptions, as a general rule the correct answer to each question was “2)Incorrect.” There is currently a categorical variable which can take on three different
values. Both “correct” and “I do not know” are considered not correct answers. If thebaseline value is not the correct answer, then the following dummy variable may beconstructed:
Correct answer=1 if individuals choose “incorrect” and 0 otherwise.
The question with the highest percentage of correct answers is Q2 (regarding
working hours) with a result of 64.6%, whereas the question with the lowest
percentage of correct answers is Q5 (regarding industrial accident) with a result of
38.2% (see Table 2). One of the possible reasons for these results may be that whilestudents are familiar with and have practical knowledge about working hours throughpart-time employment, students are unfamiliar with workplace accidents because of
the generally low incidence of workplace injuries. The average of correctly answeredquestions is about 50%.
This study only uses the data of undergraduate students in order to analyze
understanding of labor laws using a relatively homogeneous subject group. The
dependent variable in the estimation is the ordinal scale of knowledge regarding labor7laws based on the information obtained from LRT. The independent variables are as
follows: 1) the number of credit regarding career education; 2) internship experience;3) experience with exploitive labor; and 4) the number of friends who are older than agiven subject. The control variables are gender, college major, university rank, socioeconomic status, and GPA in high school as a proxy for innate ability. The descriptive
statistics are summarized in Table 3.

4. Estimation Methods
4.1. Latent Rank Theory
LRT is a test theory for grading academic achievement not on a continuous scale
but on an ordinal measurement scale1. Shojima (2007) published important researchon origin of LRT. This author doubts that the ability of student A is higher than that ofstudent B if the test score of student A is 80 and that of student B is 792. In fact,
although there is small difference in the academic achievement between the two
students, this difference is a strong possibility within the margin of error. Therefore,
Shojima (2008, p.1) may call the validity of a continuous scale as an evaluating tool
for academic achievements into question and point out that scores on a continuous

1

LRT is also a statistical model based on the mechanism of a self-organizing map or generative
topographic map (Shojima, 2008, p.1).
2
An introduction to LRT is included in Shojima’s website(http://shojima.starfree.jp/ntt/index.htm). This website was referred to in order to explain LRT in
this section. See Shojima (2022) for more detail.
8scale are an undesirable measure for evaluating academic achievements. This author
said that a test is at best capable of classifying academic ability into 5–20 levels. In
fact, professors give students their grades as follows: A+ (excellent); A (good); B(average); C (passing); and F (failure).
Empirical analysis of LRT can be easily accomplished using the Exametrika
software developed by the afore mentioned Kojiro Shojima3. This study analyzes theunderstanding of labor laws using Exametrika in order to sort subject groups’ legal
knowledge.

4.2. Ordered Probit Regression
Because the dependent variable is an ordinal measurement scale created by LRT,
this study requires an ordered probit regression model. I assume 𝑦𝑗 1, 2, 3, 4, 5

for (poor, average, good, very good, excellent). The numbers from 1 to 5 do not havemeaning in terms of these values, rather are just ordered from lowest to highest in thestatistics. These categories are not equally spaced. For example, the difference
between categories “poor” and “average” may not be the same as the difference
between categories “average” and “good”.
Define 𝑦 ∗ as a latent continuous variable. The latent variable model is considered

3

Exametrika can be downloaded from Shojima’s website(http://shojima.starfree.jp/exmk/index.htm).
9as follows:
𝑦∗

𝛼

𝑦∗

𝑗 if 𝑢

𝛽𝑥

𝑒

(1)
𝑦∗

𝑢

(2)

where 𝛼 is an intercept term, 𝑥 stands for the independent variables, 𝑒 is the error
term, and 𝑢 is the cut-off point. As explained above, 𝑦 ∗ is an unobserved variable (thelatent continuous variable 𝑦 ∗ can not be observed), we can, however, observe 𝑦 whenit crosses thresholds.
Although the latent continuous variable 𝑦 ∗ can not be observed, we can observe 𝑦when 𝑦 ∗ crosses thresholds. The choice rule, therefore, is as follows:𝑦

1 if 𝑦 ∗𝑢𝑦2 if 𝑢𝑦∗𝑢𝑦

3 if 𝑢𝑦∗𝑢𝑦

4 if 𝑢𝑦∗𝑢𝑦

5 if 𝑦 ∗𝑢Incidentally, a rearrangement of the equations (1) and (2) is as follows:
𝑢

𝛼

𝛽𝑥

𝑒

𝑢

𝛼

𝛽𝑥

The probability that observation i will select alternative j is:
𝑃𝑟 𝑦

𝑗

𝑃𝑟 𝑢

𝑦∗

𝑢

𝐹 𝑢

𝛼

𝛽𝑥

𝐹 𝑢

𝛼

𝛽𝑥

where F( ) is the standard normal cumulative distribution function.
Using generic representation, the respective probabilities for each category are10

described as follows:
𝑃𝑟 𝑦

1

𝐹 𝑢

𝛼

𝛽𝑥

𝑃𝑟 𝑦

2

𝐹 𝑢

𝛼

𝛽𝑥

𝐹 𝑢

𝛼

𝛽𝑥

𝑃𝑟 𝑦

3

𝐹 𝑢

𝛼

𝛽𝑥

𝐹 𝑢

𝛼

𝛽𝑥

𝑃𝑟 𝑦

4

𝐹 𝑢

𝛼

𝛽𝑥

𝐹 𝑢

𝛼

𝛽𝑥

𝑃𝑟 𝑦

5

𝐹 𝛼

𝛽𝑥

𝑢

This study attempts to analyze the determinants of the understanding of labor laws
using ordered probit regression4.

5. Estimation Results
The number of latent ranks needs to be decided. There are six questions in total, thehighest score value is 6, and the lowest score value is zero. In short, there are sevenpossible scores from zero to six in the understanding of labor laws in this study.
Therefore, there are a few candidate options for the number of latent ranks, namely 2,
3, 4, or 5. The number of the latent ranks was determined using fit indices, such as
root mean square error of approximation (RMSEA), conditional Akaike information
criterion (CAIC) and Bayesian information criterion (BIC).
CAIC and BIC provide a basis for model selection. Information criterion scorescan be used to determine which model is more likely to be appropriate for the same4

For more detail on ordered probit regression, for example, see Greene (2017, chap.18).
11

given data set. CAIC and BIC evaluate models relatively. As is well known, a lower
CAIC and BIC score is better (Akaike, 1973; Bozdogan, 1987; Schwarz, 1978). As
per the results, students were divided into five ordinal groups (see Table 4). Although
weakly ordinal alignment condition (WOAC) is a prerequisite for LRT, the WOAC of
all models was satisfied.
Item reference profile (IRP) calculated by Exametrika is used for interpreting the
behaviors of the correct answer rate. IRP is a plot of the probability that a test item is
answered correctly (see Table 5 and Figure 1). Rank 1 consists of students who would
likely not choose the correct answer for all questions. Rank 2 is composed of students
who would likely not choose the correct answer for all questions, excepting Q2(regarding working hours). Rank 3 is made up of students who choose the correct
answer for Q2 and chose the correct answer for other questions on average, excepting
Q5 (regarding workplace accidents). Rank 4 is comprised of students who were likely
choose the correct answer for all questions, excepting Q5. Rank 5 consists of students
who were likely to choose the correct answer for all questions.
Estimation results of ordered probit regression are presented in Table 6. Empiricalresults show that there are positive correlations between some dependent variables(gender, the number of credits regarding career education, experience with exploitive
labor, and the number of friends who are older than a given subject) and the rank oflabor laws knowledge. Higher GPA in high school also correlates with students
12having an advantage in having a higher ranking. In contrast, university rank, college
major, socio-economic status and internship experience are not statisticallysignificant.

6. Conclusions
First, this study attempts to investigate Japanese university students’ knowledge oflabor laws using LRT. In order for this, students are divided into five ordinal groups.
The average of correctly answered questions is about 50%. We can view this record asless than positive because subjects start their job hunting process with less than an
ideal amount of legal knowledge. The question regarding working hours was easily
answerable for students, whereas the question regarding industrial accidents proved tobe significantly more difficult for the test subjects. Part of the reason for thisdifference seems to stem from variance among subjects’ prior workplace experiences.
Second, this study also attempts to analyze the determinants of understanding oflabor laws. Exploratory data analysis shows that variables such as gender, GPA inhigh school, the number of credits regarding career education, experience with
exploitive labor, and number of friends who are older than a given subject are
statistically significant. In contrast, university rank, college major, socio-economic
status and internship experience are not statistically significant. This implies that
education and experience regarding career development while enrolled in university
13

correlates with better understanding of labor laws.
We do not able at this time to reach anything beyond a tentative conclusion. Further
research is needed to inspect the unsolved issues of where and when should students
learn about Japanese labor laws and how could they more effectively learn about
those laws. There are limitations to this study, namely that the results of this research
are not interpreted as a causal relationship because of data limitations. The findings ofthis study, therefore, need to be carefully interpreted with this limitation in mind.

14

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Bozdogan, H. (1987). Model Selection and Akaike’s Information Criterion (AIC): The
General Theory and Its Analytical Extensions. Psychometrika, 52(3), 345–370.
Chun, H., Fukao, K., Kwon, H. U., & Park, J. (2021). Why Do Real Wages Stagnate
in Japan and Korea? RIETI Discussion Paper Series, 21-E-010, 1–20.
Gender Equality Bureau (2021). The White Paper on Gender Equality 2021. Gender
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Greene, W. H. (2012). Econometric analysis (7th, international ed.). Pearson
Education.
Hayashi, Y. (2014). Empirical Analysis of the Effects on University Students’ Job
Hunting Behavior Based on Their Understanding of Workers’ Rights: Execution,
Criteria and Success of Job Hunting. University Evaluation Review, 13, 135–
145. [Japanese]
Kato, T. (2016). Productivity, Wages and Unions in Japan. ILO Conditions of Workand Employment Series, 63, 1–31.
Nagumo, C., Umezaki, O., Uenishi, M., & Goto, K. (2019). Acquiring and Losing
Knowledge Pertaining to Work Rules: A Study on Educational Practices toCounter Loss of Knowledge. Social Policy and Labor Studies, 10(3), 95–106.
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[Japanese]
Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics,
6(2), 461–464.
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Shojima, K. (2008). Neural Test Theory: A Latent Rank Theory for Analyzing Test
Data. DNC Research Note, 08–01, 1–35.
Shojima, K. (2007). Neural Test Theory. International Meeting of the PsychometricSociety 2007 (Tokyo, Japan), 160.
Takahashi, K. (2008). The Distribution and Function of Legal Knowledge about Paid
Leave. The Journal of Ohara Institute for Social Research, 597, 50–66.
[Japanese]
Uenishi, M., Umezaki, O., Nagumo, C., & Goto, K. (2016). Changes in University
Graduates’ Knowledge of Work Rules and Awareness of Labor Unions: Resultsof the Follow-up Survey. Lifelong Learning and Career Studies, 13(2), 67–81.
[Japanese]
Uenishi, M., Umezaki, O., Nagumo, C., & Goto, K. (2014). Development of College
Students’ Perceptions of Work and Labor Unions, and the Impact of These
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Umezaki, O., Uenishi, M., & Nagumo, C. (2015). The Impact of University Students’
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17Figure 1. Item Reference Profile Graphs

18

Table 1. Questions Regarding Japanese Labor Laws
No.

QuestionQ1“If your job offer is rescinded before
you start working, you are beholden
to the decision of that employer.”

Q2“Working hours a day can be
unlimitedly increased if total
working hours per week do not
exceed 40 hours.”

Q3“An employer can establish theovertime pay rate for theiremployees.”

Q4“If an employer does not approve
workers’ requests for paid leave,
employees cannot take paid leave.”Q5“If an employer does not approve
workers’ requests for paid leave,
employees cannot take paid leave.”Q6“If employees form a labor union to
improve their current work
environment, workers need to gain
permission from the employer.”

Legal Evidence
Labor Contracts Act, Article 16“If a dismissal lacks objectively reasonablegrounds and is not considered to beappropriate in general societal terms, it istreated as an abuse of rights and is invalid.”
Labor Standards Act, Article 32(2)“An employer must not have workers work
more than 8 hours per day for each day of theweek, excluding break periods.”
Labor Standards Act, Article 37(1)“If an employer extends the working hours or
has a worker work on a day off pursuant to theprovisions of Article 33 or paragraph (1) of
the preceding Article, it must pay premium
wages for work during those hours or on thosedays at a rate of at least the rate prescribed by
Cabinet Order within the range of not lessthan 25 percent and not more than 50 percent
over the normal wage per working hour or
working day.”Labor Standards Act, Article 39(5)“An employer must grant paid leave under theprovisions of each of the preceding
paragraphs at the worker's requested timing.”Industrial Accident Compensation Insurance
Act, Article 1“The purposes of the industrial accident
compensation insurance are to pay necessary
insurance proceeds to protect workers who are
injured, fall ill or are disabled, or die, in a
prompt and fair manner, which are caused in
the course of their duties or while commuting
to or from work…”
Labor Union Act, Article 1“The purpose of this Act is to elevate thestatus of workers by promoting their being on
equal standing with their employer in theirnegotiations with the employer; to defend theexercise by workers of voluntary organization
and association in labor unions so that they
may carry out collective action, including thedesignation of representatives of their own
choosing to negotiate working conditions; andto promote the practice of collective
bargaining, and procedures therefore, for the
purpose of concluding collective agreements
regulating relations between employers andworkers.”
19

Table 2. Percentage of Questions Answered Correctly

Q1
Q2
Q3
Q4
Q5
Q6
ALL

Obs.

Mean

3167
3167
3167
3167
3167
3167
3167

0.482
0.646
0.532
0.507
0.382
0.450
2.999

Std.
Dev.
0.500
0.478
0.499
0.500
0.486
0.498
1.833

20

Min

Max

0
0
0
0
0
0
0

1
1
1
1
1
1
6

Table3. Descriptive Statistics
Variable
LRT Rank
FemaleNatural Science
University Rank
Career Education
Internship
Exploitive Labor
Friends
SES
Very Low
Below Average
AverageAbove Average
Very High
Don't Know
GPA in high School
Very Low
Below Average
AverageAbove Average
Excellent
Don't Know

Obs
3167
3167
3167
3167
3167
3167
3167
3167

Mean
Std. Dev.
3.009
1.488
0.678
0.467
0.304
0.460
49.294
7.537
2.382
2.769
0.836
0.371
0.400
0.490
3.069
4.754

Min

Max
1
0
0
35
0
0
0
0

5
1
1
68
10
1
1
50

3167
3167
3167
3167
3167
3167

0.044
0.135
0.153
0.313
0.296
0.059

0.206
0.342
0.360
0.464
0.457
0.236

0
0
0
0
0
0

1
1
1
1
1
1

3167
3167
3167
3167
3167
3167

0.057
0.186
0.313
0.284
0.058
0.102

0.232
0.389
0.464
0.451
0.233
0.303

0
0
0
0
0
0

1
1
1
1
1
1

21

Table 4. RMP-Based Test Fit Index
Rank
2
3
4
5

NFI
0.693
0.773
0.790
0.792

RFI
0.693
0.773
0.790
0.792

IFI
0.720
0.806
0.829
0.836

TLI
0.708
0.790
0.808
0.810

CFI
RMSEA
0.720
0.064
0.806
0.054
0.828
0.052
0.835
0.052

AIC
1657.211111.80970.14930.09CAIC
680.68171.11
60.94
47.99

BIC
818.99304.35189.71172.93

Note: Exametrika was used to calculates fit indices to measure the degree of overall fit of a model todata. See Shojima’s website (http://shojima.starfree.jp/exmk/index.htm) for more detail. NFI (normed
fit index), RFI (relative fit index), IFI (incremental fit index), TLI (Tucker-Lewis index), and CFI(comparative fit index) have a value running from 0 to 1. The higher these numbers are, the more accurate
the fit measurement is. RMSEA (root mean square error of approximation) has a value running from 0
to ∞. The lower these numbers are, the more accurate the fit measurement is. AIC (Akaike information
criterion), CAIC (consistent AIC), and BIC (Bayesian information criterion) are the statistical tools used
for model selection, as per usual. The lower these numbers are, the more accurate the fit measurementis.

22Table 5. Item Reference Profile
Item

Rank 1

Rank 2

Rank 3

Rank 4

Rank 5

Q2

0.335

0.575

0.711

0.770

0.845

Q3

0.140

0.354

0.570

0.736

0.861

Q4

0.106

0.294

0.525

0.732

0.876

Q1

0.182

0.359

0.498

0.624

0.746

Q6

0.086

0.247

0.458

0.656

0.801

Q5

0.072

0.194

0.350

0.551

0.738

23

Table 6. Estimation Results
FemaleNatural Science
University Rank
Career Education
Internship
Exploitive Labor
Friends(1)-0.135 **(0.042)-0.063(0.042)0.008 **
(0.003)0.019 *
(0.008)0.054(0.052)0.200 ***
(0.039)0.009 *
(0.004)

SES
Very Low
Below Average(2)-0.142 **(0.042)-0.062(0.042)0.007 *
(0.003)0.019 *
(0.008)0.043(0.052)0.197 ***
(0.039)0.009 *
(0.004)ref.
0.084(0.112)0.025(0.111)
0.098(0.107)0.055(0.107)-0.180(0.133)

AverageAbove Average
Very High
Don't Know
GPA in High School
Very Low
Below Average
AverageAbove Average
Excellent
Don't Know
/cut1
/cut2
/cut3
/cut4
Observations
Wald Chi2
Log pseudolikelihood(3)-0.141 **(0.042)-0.071(0.042)0.004(0.003)0.018 *
(0.008)0.051(0.052)0.197 ***
(0.039)0.009 *
(0.004)-0.253(0.148)0.210(0.147)0.736(0.148)1.198(0.148)3167
61.010 ***-5021.5-0.271(0.178)0.193(0.178)0.719(0.178)1.183(0.179)3167
70.580 ***-5016.0(4)-0.148 ***(0.042)-0.069(0.042)0.003(0.003)0.019 *
(0.008)0.041(0.052)
0.195 ***
(0.039)0.008 *
(0.004)ref.
0.078(0.113)0.016(0.111)0.087(0.107)0.051(0.108)-0.173(0.133)ref.
0.115(0.095)0.169(0.093)0.218 *
(0.096)
0.288 *
(0.127)0.026(0.107)-0.292(0.171)
0.172(0.171)
0.698(0.171)
1.162(0.171)3167
73.610 ***-5015.0ref.
0.112(0.095)0.160(0.093)0.212 *
(0.096)0.287 *
(0.127)0.035(0.107)-0.315(0.198)0.150(0.198)0.678(0.198)1.142(0.198)3167
82.550 ***-5010.1

Note: Standard errors are in parentheses. *significant at 5%; **significant at 1%; *** significant at 0.1%.

24

RIHE Monograph Series について『Advancement of Higher Education Research: RIHE Monograph Series（旧ディスカッションペーパー
シリーズ）』は、速報性を重視し暫定的にまとめて発信することを目的として企画されたシリーズ（オープン・ジャーナル）です。これまでに、「ディスカッションペーパーシリーズ」として、国際共同研究、公募型
研究、戦略的プロジェクト研究、客員研究員による研究等、成果として取りまとめられたものが発信されました。（執筆者の所属は刊行時のものです）

本研究を進めるにあたり，以下の資金提供を受けた。記して感謝したい。
JSPS 科研費・JP19H00619（研究代表者：松繁寿和，「社会的能力の特定化とその育成適正期および教育効果の検証」

No.9
2022（令和 4）年 8 月 24 日

Determinants of Understanding of Labor Laws:
Evidence from Japanese University Students

執筆者：Tomotaka HIRAO（Setsunan University）〒739-8512

広島県東広島市鏡山 1-2-2 電話 (082)424-6240
https://rihe.hiroshima-u.ac.jp/

発行