Discussion Paper No. 511

AN EMPIRICAL EVIDENCE OF
MORAL HAZARD DUE TO
UNEMPLOYMENT BENEFIT

Yasushi Ohkusa

June 2000

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

An Empirical Evidence of Moral Hazard due to
Unemployment Benefits ∗)Yasushi Ohkusa
Osaka University
Abstract
This paper examines how the unemployment beneﬁt (UB) aﬀects the intensity or search
eﬀort of the unemployed, which is measured by the working conditions of a new job resulting
from search behavior, controlling for the endogeneity of the duration of unemployment.
Estimation results show that the hiring probability of UB recipients is signiﬁcantly lower
than that of non-recipients, with the magnitude being approximately halved. Additionally,
the working conditions of new jobs of UB recipients are signiﬁcantly worse than those of
non-recipients with regard to wages, occupation and ﬁrm size. This implies that there isvery strong moral hazard in the UB system.
JEL Classiﬁcations:J63, J64, J65
Keywords:Unemployment Beneﬁt, Duration of Unemployment, Moral Hazard, Search Effort, Working Condidtions
Correspondence:Yasushi Ohkusa, Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka Ibaraki Osaka, Japan
Tel:81-6-6879-8566

Fax:81-6-6878-2766e-mail:ohkusa@iser.osaka-u.ac.jp

1

Introduction

In Japan the unemployment rate rose rapidly in the 1990s and reached 4% in 1998. Theoﬃcial labor white paper of the Ministry of Labor of the Japanese Government in 1999
indicated that the structural and frictional unemployment rate was more than 3%, and that
economic policy to counter recession could not solve this high unemployment problem,at least in the short term. Among the OECD countries, Japan has not had the lowest
unemployment rate. The Netherlands in 1998 and the USA in 1999 caught up with and thensurpassed Japan. In the recent period of high unemployment in Japan, the shortcomings
in the Employment Insurance System1) should be remarkable, as this had been the basedon the long low unemployment rate. This paper focus on the unemployment beneﬁt (UB)
solely among several aspects of the Employment Insurance System, and examine its eﬀects
on search behavior of the unemployed2) .
There are many investigations evaluating the UB eﬀect on the behavior of the unemployed. Even though simple international comparisons without care are dangerous becauseof diﬀerent UB systems and backgrounds3) , the elasticities of UB levels with respect tounemployment duration of about 0.3 in UK (Narendrannathan, Nickell and Stern (1985))
and about 0.4 in the USA are suggestive, and imply that the unemployment period is
extended by about 10 days if the UB level increases by 10% (Moﬃtt (1985))4) . On theother hand, the eﬀect of potential UB duration on unemployment duration is larger. Katz
and Meyer (1990) predicts that this elasticity is twice as large as that of the UB level.
Christoﬁelds and McKenna (1995) also ﬁnds that the hiring rate signiﬁcantly increases
in the last month of the UB period in Canada. Similar patterns are conﬁrmed in Japan(Tcahibanaki (1984)). Moreover, Ehrenberg and Oaxzca (1976) ﬁnd that the replacement
rate eﬀect on wage gains is limited: for female it is signiﬁcant but not for male. Overall,these studies indicate some evidence of moral hazard in the UB recipient’s behavior.
However, even if the UB extends the duration of unemployment, it may not be that1

moral hazard is directly implied. As the UB loosens the budget constraint of its recipientsunambigiously, it should extend the duration of unemployment. The problem is whether
the recipients depress their intensity of search behavior, rather than simple experiencing
increases in their duration of unemployment. Although the unemployed experience a longer
duration of unemployment, if they do not lower the intensity of their search behavior, they
will receive good conditions in their new jobs and thus should not be judged to be in moral
hazard. Therefore, moral hazard of the recipients should be measured by the conditionsof the new jobs in comparison with those of previous job rather than by examining theduration of unemployment or the unemployment rate. This paper examines this aspect ofmoral hazard in the UB.
The paper is organized as follows. The next section explains the data in detail, andSection 3 presents the testable hypotheses. Section 4 describes the estimation model andits results are shown in Section 5. Finally, the results are summarized and suggestions forfurther research are made.

2

Data

The following analysis use micro-data from the Survey of Job Changers (Tenshokusha
Sougou Jittai Chosa in Japanese) of 1998 conducted by the Ministry of Labor. This survey contains detailed information including individuals’ characteristics, the condition ofprevious and current jobs, unemployment beneﬁts, and unemployment duration of 10000
regular workers. The surveyed workers had the following three characteristics:• They were hired in the past 12 months.
• They had worked in another ﬁrm in the past 12 months before they were hired in thecurrent ﬁrm.
• They are regular workers.

2

The feature of this survey is that the conditions (including wages, occupation, rank, industry and ﬁrm size) of both the previous job and the current job are available. Thus any
change in working conditions is easily measured.
As the main purpose of this paper is to test the UB eﬀect, those individuals in the sample
who had not experienced unemployment are excluded in the following analysis. Hence thesample size is 7842.
The summary statistics are shown in Table 1. The index of changing conditions between
the previous and current job is deﬁned as follows. Concerning rank and ﬁrm size, it isset at 3 if the new job has better conditions, 2 if there is no change in conditions and1 if otherwise. Better conditions mean larger ﬁrm scale and higher rank. Concerning
occupation and industry, as there is no clearly deﬁned good or bad occupation (industry),
the index is deﬁned as 1 if the unemployed change their occupation (industry) and 0
otherwise, as their experience in the previous job is more useful in the same occupation(industry) than in another one. Note that ‘better conditions’ refers to a deﬁned lower value
in spite of a larger value in wage, ﬁrm size and rank.

3

The Testable Hypothesis

The testable hypothesis is simply whether the UB reduces intensity of search. The main
problem for this test is that intensity of search is not directly observable for the ecnometrician. If intensity of search can be observed, there is no room for moral hazard. Thus itis evaluated indirectly, in the following manner.
As the UB loosens the budget constraint of the recipients and reduces the disutility fromthe extended duration of unemployment, the recipients’ duration of unemployment should
increase unambigiously. If there is no moral hazard, the recipients can enjoy ﬁnding a job
with better conditions. Conversely, as non-recipients cannot enjoy UB, they may accept
a job with worse conditions so as to shorten the unemployment duration. Therefore, the3

recipients should get better jobs than the non-recipients.
If there is moral hazard, then, as the recipients depress the intensity of search, thediﬀerence of the new job condition for recipients and non-recipients depresses in comparison
with the no moral hazard case. In the extreme case, if there is no diﬀerence between the
new job condition for recipients and non-recipients, the intensity of search would be greatly
reduced to cancel out any eﬀect of extended unemployment duration. In the worst case,if the conditions in the new job for recipients are worse than for non-recipients, there isstrong evidence of deep moral hazard. Therefore, moral hazard is deﬁned as the change
in conditions between a previous and current job, which deﬁnes the outcome of searchbehavior, controlling for unemployment duration.

4

The Estimation Model

A two step strategy is employed to estimate the eﬀect of the UB, because the durationof the unemployed period is endogenous. At ﬁrst, duration analysis, in particular, Cox’s(1972) proportional hazard model for the duration of unemployment is applied. This modelis represented as:h(t) = h0 (t)eXi α(1)where h(t) is the hazard function and h0 (t) is the base hazard function.
Duration is deﬁned as the duration of the unemployed. By this deﬁnition of duration,
workers who do not experience unemployment are excluded because their duration is 0.
Duration of search, which includes on-the-job search time, is a potential alternative candidate, but as this paper’s main focus is to evaluate the eﬀect of the UB and its justiﬁcation
is diﬃcult a priori, on-the-job search time is included to the deﬁnition of duration. Henceon-the-job search is only included as an explanatory variable. Another candidate is theduration of the UB. However, because this excludes UB non-recipients by deﬁnition, it is
not appropriate for evaluating the eﬀect of the UB.

4The explanatory variables Xi are: dummies for UB recipients, gender, a quadratic form
for age and tenure, education level, duration of on-the-job search, number of turnover,
occupation, position, industry, scale of ﬁrm, a dummy for a voluntary quitter, a dummy
for any licenses, and regional dummies. Tenure, occupation, position, industry, and scaleof ﬁrm apply to the previous job.
In principle, if we could use household structure and income, the marginal utility ofincome is easily deﬁned. Unfortunately, such useful information is not available, so genderand age are used as their proxies. Moreover, age and tenure aﬀect unemployment durationin several ways. The most direct way is through the potential duration of the UB, which
is determined by age and tenure in the previous job, and the level of the UB, which isdetermined by the average wage in the last six months. Of course, these eﬀects would extend
the duration of unemployment because of the relaxed budget constraint. Moreover, age
and tenure (and education level) represent the level of human capital and its composition,either general or ﬁrm-speciﬁc. The unemplyed with more general human capital would
more easily ﬁnd a new job. Conversely, if they have more ﬁrm-speciﬁc human capital,
because it is not useful in new job, they would not ﬁnd new jobs easily and thus theirduration of unemployment would be extended. However, as these eﬀects are represented
by a few variables, and it is impossible to separate them.
The duration of on-the-job search implies heterogeneity in the preparation for job search
before unemployment. Moreover, number of turnover can lead to mental damage or predictability due to turnover and unemployment. Workers who have quit frequently would
become used to job search and would experience less damage from turnover. Since occupation, position, industry and scale of ﬁrm are deﬁned for the previous job, these coeﬃcientsmeans their inertia. Namely, the diﬃculty of changing occupation reduces hiring probability.
The dummy for the UB recipients is the most important variable for this research; it
extends the duration of unemployment. For a worker who voluntarily quits the previous
5job, the UB is prolonged for three months, and so they would like to get a new job as soon
as possible or extend their unemployed duration for three months. Moreover, they could
prepare for a long time before quitting. However, as workers who have not experienced
unemployment are excluded, the sample does not contain those who quit voluntarily totake up a better job.
The dummy for licenses represents their speciality. So as to overcome the problem of
its endogeneity, this variable is the one only the case they have licenses before they are
unemployed. The regional dummies may exclude diﬀerences in the regional labor market.
From the estimated coeﬃcient α̂ and h0 (t), the hazard function and survival function
are calculated as:ĥ(t) = ĥ0 (t)eXi α̂Ŝ(t) = 1 −

 t
0

(2)

ĥ(τ )Ŝ(τ )dτ.

Using these relationships, the expected unemployment duration is calculated as:

D̂i =

 t
0

τ ĥ(τ )Ŝ(τ )dτ.

(3)

In the second step, the change in the working conditions for wages, occupation, position,industry and ﬁrm size is regressed on Xi and D̂i . In the case of wages, position and ﬁrm
size, the dependent variable is a trinominal variable which is set to 3 if the conditions
improve in the current job, 2 if the conditions of the current job are the same as in theprevious job, and 1 if the conditions in the current job are worse than in the previous job.
Concerning ﬁrm size, a larger ﬁrm deﬁnes a good condition. In the case of occupationand industry, as it is hard to deﬁne a good or bad occupation (industry), the dependent
variable is a binomial one which is set to 1 if the previous and current jobs are in the same
occupation (industry). If experience in the previous job has some value for the workers,changing occupation (industry) means a bad condition. Hence, if a variable has a positive
coeﬃcient, it is interpreted as improving the working condition in the case of wage level,6

position, and ﬁrm size. Conversely, it is interpreted as worsening the working condition in
the case of occupation and industry.
The estimation method used is the ordered probit method for the case of wages, position,and ﬁrm size, and the probit method for occupation and industry. Both methods emploies
a heteroscadasticity consistent estimator. The estimation equation isjD̂i + εji
∆Cij = β0j + Xi β j + βD

(4)where j indicates estimation for wage, position, ﬁrm size, occupation and industry, and∆Cij means change in these conditions between the previous and the current job. The
explanatory variable Xi is the same as in the ﬁrst step.
The second step adds D̂i to the list of explanatory variables. As D̂i is a highly nonlinear function of Xi , there is no problem of identiﬁcation, even if there are no additional
explanatory variables in the ﬁrst step. The coeﬃcients of the UB recipients’ dummy precisely demonstrate the eﬀect of the UB on the job conditions, controlling for the extended
eﬀect of unemployment duration by the UB. This is the most important factor in thispaper. If these coeﬃcients imply worse conditions than those for UB non-recipients, there
is powerful support for moral hazard from the UB.

5

Estimation Results

The estimation results for the unemployment duration are shown in Table 2, and the change
in conditions, which are deﬁned by wage level, occupation, rank, industry and ﬁrm size,
are shown in Tables 3 to 7, respectively.
As the estimated coeﬃcients in Table 3 are α in eq. (1), if they are positive, the associated variable raises the hiring probability and shortens the unemployment duration.
The table shows that on-the-job search duration, being female, a high school or technicalschool graduate, a clerical, sales, service, or production and construction worker, or a reg-7

ular worker in the previous job are associated with signiﬁcantly lower hiring probabilities.
Conversely, the unemployed who are executives of corporations, and from ﬁrms with 30–99
employees in the previous job, enjoy signiﬁcantly higher hiring probabilities.
The negative eﬀect of on-the-job search on hiring probabilities implies that the intensity in these activities would be very low and it is harmful for early hiring. The lack ofany signiﬁcant age and tenure eﬀect may imply that there is variable contamination, andcanceling out of each other by the eﬀects through the UB system, household structure andhuman capital.
The dummy for UB recipients is signiﬁcantly negative, which is consistent with theoretical prediction. Its magnitude shows that the hiring probability of UB recipients is lower
than by an amount of 45%, which is an exponential transformation of the estimated coefﬁcient of -0.78. As the hiring probability for UB recipients is almost half that of no UB
recipients, the duration of unemployment should be twice as large.
Those workers who quit voluntarily from the previous job suﬀered signiﬁcantly lower
hiring probabilities. This is consistent with the fact that the deﬁnition of voluntary quitting
excludes those who to quit to move to a better job. There is no eﬀect of licenses.
Next, the estimation results for the second step are summarized as follows. As there
are many tables and coeﬃcients, we display only the important coeﬃcients. First, theestimates of unemployment duration are not signiﬁcant in all cases. This means that shortunemployment duration is not a favorable factor and that long unemployment duration is
not an unfavorable factor leading to a better job condition. This result presents negative
evidence for the hypothesis that the search intensity is constant over the unemployment
period.
Secondly, in the wage, occupation and ﬁrm size estimations, the UB recipients signiﬁcantly change to worse job conditions. The eﬀect of this is that the UB recipients experience
an 8% points lower probability of a wage increase in the new job, a 3.8% points higher probability of a change in occupation, and a 2.8% points lower probability of a ﬁrm size decrease8

in comparison with UB non-recipients. Conversely, they enjoy better job conditions; UB
recipients have a 2% points higher probability of promotion in the new job in comparison
with the previous job. However it is noted that meaning of positions vary widely over ﬁrmsize and other conditions. For example, the same positions do not the same responsibility,
tasks, and so on in large ﬁrms and small ﬁrms. Thus we should not pay so much attention
this results of position. In the industry equation, there is no signiﬁcant relation. Since industry classiﬁcation in this estimation is very imprecise, the industrial speciﬁcity in humancapital cannot be deﬁned, which leads to an unclear result. Overall, we can conclude that
UB recipients suﬀer from worse conditions even though they enjoy longer unemployment
durations than UB non-recipients. This is powerful evidence of moral hazard by UB5) .
Voluntarily quitting workers have experienced better conditions with respect to wages
and worse conditions for position, occupation and industry in comparison with the previous
job. These results imply that they gain higher wages by changing voluntarily and eagerly
their position, occupation and industry. Though license holders cannot enjoy good conditions with respect to wages and ﬁrm size, they do not suﬀer from changes in occupationand industry. Moreover, they are signiﬁcantly promoted in their current jobs from theprevious ones. Hence, license-holding enhances a stable working condition.

6

Concluding Remarks

This paper examines how the UB aﬀects intensity or search eﬀect of the unemployed, whichis measured by the working conditions of a new job, being the outcome of search behavior,controlling for the endogeneity of the duration of unemployment. The estimation results
show that the hiring probability of UB recipients is signiﬁcantly lower—about half that of
non-recipients. Thus the duration of unemployment of UB recipients should be twice thatof UB non-recipients, ceteris pulibus.
Additionally, the estimation results in the second step show that unemployment duration,

9

which is estimated in the ﬁrst step, does not signiﬁcantly aﬀect the change in working
conditions of new jobs over previous jobs with respect to wages, occupation, position,industry and ﬁrm size. Moreover, UB recipients are signiﬁcantly worse oﬀ than nonrecipients as far as wages, occupation and ﬁrm size are concerned. This is very strong
evidence of moral hazard stemming from the unemployment beneﬁt system.
Finally, we make some remarks about further research. Firstly, the sample in this paper
excludes those who are non-regular workers and not currently in the labor force. Because
outﬂow to non-regular workers in the labor market and outﬂow to outside labor market arevery important aspects of the unemployment situation and the functioning of the UB, it isnecessary to consider this eﬀect in evaluating the UB scheme. Secondly, further researchwould control for the omitted variables in this analysis, such as household assets, income
from other members of the household, and household structure, which are not available
from this data set. Moreover, so as to more directly evaluate the inertia in required wages,
occupation, and so on, panel analysis of the eﬀect of these variables on unemployment
duration would be very important.
Overall, as this research shows that UB undoubtedly induces moral hazard, more dataneeds to be collected, reﬁned in deﬁnition and improved in measurement, so as to evaluate
the ineﬃciencies in the UB system more rigorously, to suggest ways of moving to a more
eﬃcient UB system.

10Footnotes*) This paper have permission to use and analysisthe micro data of the Survey of Job
Changers (Tenshokusha Sougou Jittai Chosa as a part of the outcome of the study
group, represeted by Prof. Mitani at Kobe Univeristy. I thank him, Prof. Muramatsu
at Nazan Univeristy and Prof. Ohta at Nagoya University for useful support anddiscussions. Thie paper was presented at Labor Study Group Meeting in 2000. I
also thank to the participants of this meeting, especially Prof.Ohtake, my cooleague,
for usefule suggstion and discussions. Finally I thank Ms.Kazuko Matsumoto for her
helpful support to my research. Needless to say, any remaining errors are mine.
1) In Japan, unemployment insurance is called the Employment Insurance System, andincludes not only unemployment beneﬁts, but also subsidies for temporarily declining
industries, training costs for certain occupations, payment for childcare leave, and soon.
2) Yashiro and Futagami (1998) discuss aspects of the Employment Insurance Systemother than UB.
3) Atkinson and Micklewright (1991) is a good survey of this ﬁeld.
4) Ohtake (1987) examines the relationship between UB levels and unemployment rates
using macro data, but is unable to ﬁnd any close relationship.
5) This result holds even in the case where those more than 60 years of age are excluded
from the sample.

11

References[1] Acemoglu and Duron (1997), ”Good Jobs versus Bad Jobs: Theory and Some Evidence,” Woring paper in MIT.
[2] Atkinson and Micklewright (1991), ”Unemployment Compensation and Labor Market
Transitions: A Critical Review,” Journal of Economic Literature, vol.XXIX, pp.16791727.
[3] Christoﬁelds,L.C. and C.J.McKenna (1995), ”Unemployment Insurance and Moral
Hazard in Employment,” Economic Letters, pp.205-210.
[4] Cox,D. (1972), ”Regression Model and Life Tables,” Journal of the Royal StatisticalSociety Series B, pp.187-220.
[5] Ehrenberg and Oaxzca (1976), ”Unemployment Insurance, Duration of Unemployment, and Subsequent Wage Gain,” American Economic Review, pp.754-765.
[6] Katz and Meyer (1990), ”The Impact of the Potential Duration of Unemployment
Beneﬁts on the Duration of Unemployment,” Journal of Public Economics, 41(1),pp.45-72.
[7] Moﬃtt (1985), ”Unemployment Insurance and the Distribution of Unemployment
Spells,” Journal of Econometrics, 28(1), pp.85-101.
[8] Narendranathan, Nickell and Stern (1985), ”Unemployment Beneﬁts Revisited,” Economics Journal , 95(378), pp.307-29.
[9] Ohtake,F.(1987),”Unemployment

and Unemployment Insurance (in Japanese:Situgyou to Koyou-Hoken-Seido),” Economic Studies Quarterly.

12[10] Tachibanaki,T. (1984), ”Measurement of Unemployment Duration and Its International Comparison (in Japanese:Situgyou-Kikan no keisoku to Kokusai-Hikaku9,”,in K.Koike ed. Contemporary Unemployment (in Japanese: Gendai no Sitsugyou),
Dobunkan.
[11] Yashiro, N. and K.Futagami (1998), ”Reform at Unemployment Insurance and Working of Elderly (in Japanese: Koyo-Hoken-Seido-Kaikaku to Koureisha-Shugyou)” in
Hatta and Yashiro ed. Reform at Social Security (in Japanese Shakai-Hoken-Kaikaku),
Nihon-Keizai-Shinbunsha.

13

Table １: Summary Statistics
Mean

Duration of Unemployment
Duration of Job Search
Female Dummy
Age
Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Number of TurnoverOccupation type2(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7(previous)
Occupation type8(previous)
Occupation type9(previous)
Occupation type2(current)
Occupation type3(current)
Occupation type4(current)
Occupation type5(current)
Occupation type6(current)
Occupation type7(current)
Occupation type8(current)
Occupation type9(current)
Position type2(previous)

Standard
Deviation
3.168709
3.555293
5.860248
6.424305.3740526.4839079
34.19846
11.21833
4.64676
4.074059.482188.4997142.1292319.3354777.097524.2966887.2249874.4176004
2.401619
1.639915.0386559.1927856.237241.4254181.1110409.3142027.0976503.29686.0160435.1256505.0535624.2251663.3050783.4604697.0037898.0614484.0509096.2198269.22524.4177667.139338.3463208.1139464.3177663.011622.107184.061521.2402986.2553057.4360605.0072006.0845557.0210992.1437241（ Table 1: continue)

14

Minimum Maximum
.5
.5
0
16
.25
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

12
36
1
69
12.5
1
1
1
1
22
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

（ Table 1: continue)
Position type3(previous).0381554.1915834
Position type4(previous).0207202.1424549
Position type5(previous).0349968.1837834
Position type6(previous).1180038.322633
Position type7(previous).7626027.4255147
Position type2(current).0247725.1554409
Position type3(current).0485339.214905
Position type4(current).0379171.1910078
Position type5(current).0618049.240816
Position type6(current).119186.3240278
Position type7(current).6937563.4609613
Regular Worker(previous).8327438.373228
Mining(previous).0054341.0735204
Construction(previous).0877038.2828815
Manufacturing(previous).2917983.4546187
Gas and Electric Power(previous) .0211045.1437419
Trans. and Comm.(previous).0684949.2526092
Wholesale and Retail(previous).1486162.3557322
Bank and Insurance(previous).0461266.2097725
Real estate(previous).0113737.106046
Service industry(previous).2767598.447425
Government(previous).0353848.1847621
Construction(current).0389085.1933892
Manufacturing(current).3965387.4892095
Gas and Electric Power(current) .0161698.1261362
Trans. and Comm.(current).0409298.19814Wholesale and Retail(current).1848156.3881724
Bank and Insurance(current).0169277.1290089
Real estate(current).0160435.1256505
Service industry(current).2690753.4435072
Firm Size 500∼999(previous).0622789.2416767
Firm Size 300∼499(previous).0649318.246421
Firm Size 100∼299(previous).1759727.380821
Firm Size 30∼99(previous).2299141.420804
Firm Size 5∼29(previous).2488631.4323816(Table 1: continue)

15

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

(Table 1: continue)∼4(previous).04333.2036117
195.0393
609.0668
Firm Size (current)
Dummy for UB Recipient.2610234.4392204
Duration of UB Receive
24.28488
54.32623
Dummy for Voluntary Quitters.8046993.396457
Dummy for License Holder.3988125.489685
Rate of Change in Wage-.0269707.1696776
Dummy for Change in Occupation .3721577.4834106
Dummy for Change in Industry.5687216.4952861
Change in Firm Size
1.869126.8501358
Change in Position
2.079383.4730603

0
5
0
0
0
0
-.3
0
0
1
1

1
14967
1
450
1
1
.3
1
1
3
3

Note: This table omits junior high school graduates from the education background, professional and technical workers (occupation type1) in occupation, executives in position,
agriculture in industry (previous), mining in industry (current) in the industry and thoseﬁrms with over 1000 employees in ﬁrm size. Change in ﬁrm size is 3 if ﬁrm size in thecurrent job is larger than in the previous job, 2 if ﬁrm size in the current job is the sameas in the previous job, and 0 otherwise. Change in position is 3 if position in the current
job is higher than in the previous job, 2 if position in the current job is the same as in theprevious job, and 0 otherwise.
The types of occupation are: 1) professional and technical workers, 2) managers andoﬃcials, 3) clerical and related workers, 4) sales workers, 5) service workers, 6) protective
service workers, 7) workers in transport and communications occupations, 8) craftsmen,mining, production process and construction workers and laborers, and 9) others.
The types of position are: 1) executive of corporations, 2) heads of department, 3) headsof section, 4) subsection chief, 5) manager, 6) specialist, 7) routine worker.
Industry classiﬁcations are Mining, Construction, Manufacturing, Gas and Electric Power,
Transportation and Communication (in short, Trans. and Comm.), Wholesale and Retail,Bank and Insurance, Real Estate, Service industry and Government.

16

Table 2: Duration Estimation for Unemployment

Female Dummy
Age
Age2
Tenure (previous)
Tenure (previous) 2
Age·Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Duration of On-the-Job Search
Number of TurnoverOccupation type2(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7(previous)
Occupation type8(previous)
Occupation type9(previous)
Position type2(previous)
Position type3(previous)
Position type4(previous)
Position type5(previous)
Position type6(previous)
Position type7(previous)
Regular Worker(previous)
Mining(previous)
Construction(previous)
Manufacturing(previous)
Gas and Electric Power(previous)

Estimated Coef.
-.1292168.0120892-.0001624.0175959.0001485-.0005928-.0877143-.136156-.0791194-.0946776-.0370096.009894-.0511267-.1240269-.1049634-.1101175-.3004563-.0629823-.1241396-.2042104-.3155505-.3241059-.3572895-.54853-.5218918-.5684531-.0085236-.1028412.0430046.0537256.0374852(Table 2: continue)

17

z value-4.4691.389-1.3571.231
0.151-1.619-1.766-2.281-1.250-1.635-14.3071.175-0.603-2.750-2.021-2.071-3.013-0.966-2.676-1.089-1.673-1.798-1.874-2.976-2.926-3.246-0.260-0.504
0.308
0.396
0.242

p-value
0.000
0.165
0.175
0.218
0.880
0.105
0.077
0.023
0.211
0.102
0.000
0.240
0.546
0.006
0.043
0.038
0.003
0.334
0.007
0.276
0.094
0.072
0.061
0.003
0.003
0.001
0.795
0.614
0.758
0.692
0.809

(Table 2: continue)
Trans. and Comm.(previous)-.0189335-0.134Wholesale and Retail(previous) .0178834
0.130Bank and Insurance(previous)-.0286312-0.198
Real estate(previous).0213294
0.124
Service industry(previous)-.030959-0.227
Government(previous)-.1711204-1.157
Firm Size 500∼999(previous)-.0555677-1.015
Firm Size 300∼499(previous).0189805
0.350
Firm Size 100∼299(previous).0128645
0.312
Firm Size 30∼99(previous).0697418
1.742
Firm Size 5∼29(previous).0442434
1.097
Firm Size ∼4(previous)-.0154371-0.238Dummy for UB Recipient-.7824282-27.405
Dummy for Voluntary Quitters -.1747492-5.252
Dummy for License Holder.0160505
0.655

0.893
0.896
0.843
0.901
0.820
0.247
0.310
0.726
0.755
0.082
0.273
0.812
0.000
0.000
0.512

Note: This table shows estimation results of duration of unemployment by using Cox’s(1972) proportional hazard model. Prefecture dummies and the ﬁrm size dummy in theprevious job are included as explanatory variables. The log-likelihood is -64538.733. The
null hypothesis that all coeﬃcients are 0 is rejected at the 1% signiﬁcance level.

18

Table 3: Estimation Result for Change in Wage
Estimated z value
Coef.

Female Dummy
Age
Age2
Tenure (previous)
Tenure (previous) 2
Age·Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Duration of On-the-Job Search
Number of TurnoverOccupation type1(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7 (previous)
Occupation type8(previous)
Occupation type9(previous)
Position type2(previous)
Position type3(previous)
Position type4(previous)
Position type5(previous)
Position type6(previous)
Position type7(previous)
Regular Worker(previous)
Mining(previous)
Construction(previous)
Manufacturing(previous).097242.792.018651.810-.00033-2.361.022761.329-.00231-2.008-.00100-2.297-.22885-3.869-.37797-5.330-.33836-4.501-.24609-3.558-.00379-1.177-.03851-3.924-.21340-2.066-.16654-3.112-.08896-1.460-.12686-2.033-.15164-1.253.044520.581-.15879-2.898.006990.030-.14834-0.651-.28952-1.322-.43652-1.878-.38044-1.664-.75091-3.388-.69560-3.177-.46557-12.098.065190.262-.12132-0.719.115320.701(Table 3: continue)

19

p-value marginal marginal
eﬀect
eﬀect
increase nochange0.005.03120.003120.070.00598.000600.018-.00010-.00001
0.184.00730.000730.045-.00074-.000070.022-.00032-.00003
0.000-.07343-.00736
0.000-.12128-.012150.000-.10857-.010880.000-.07897-.007910.239-.00121-.000120.000-.01236-.001230.039-.06848-.00686
0.002-.05344-.00535
0.144-.02854-.00286
0.042-.04070-.004080.210-.04866-.00487
0.562.01428.001430.004-.05095-.005100.976.00224.000220.515-.04760-.004770.186-.09290-.009310.060-.14007-.01404
0.096-.12208-.012230.001-.24096-.024150.001-.22321-.022370.000-.14939-.01497
0.793.02091.002090.472-.03893-.003900.483.03700.00371

(Table 3: continue)
Gas and Electric Power(previous)-.11128-0.596
Trans. and m.(prevs)-.04287-0.251Wholesale and Retail(previous).05206
0.313
Bank and Insurance(previous)-.03083-0.175
Real estate(previous)-.44057-2.121Service industry(previous).084040.510-.17355-0.857
Government(previous)
Firm Size 500∼999(previous).160502.464
Firm Size 300∼499(previous).28227
4.414
Firm Size 100∼299(previous).31029
6.304
Firm Size 30∼99(previous).40696
8.486
Firm Size 5∼29(previous).48036
9.927
Firm Size ∼4(previous).49298
6.515
Dummy for UB Recipient-.27527-5.766
Dummy for Voluntary Quitters.171064.313
Dummy for License Holder-.02036-0.711
Duration of Unemployment(estimated) -.01556-0.277threshold(lower)-1.35650-.59100
threshold(upper)

0.551
0.802
0.754
0.861
0.034
0.610
0.391
0.014
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.477
0.782

-.03571-.01375.01670-.00989-.14137.02697-.05569.05150.09057.09957.13059.15414.15819-.08833.05489-.00653-.00499-.00358-.00137.00167-.00099-.01417.00270-.00558.00516.00908.00998.01309.01545.01585-.00885.00550-.00065-.00050

Note: This table shows estimation results of the change in wages by using the ordered
probit model with heteroscadasticity consistent. The dependent variable of change in wageis 3 if wage in the current job is higher than in the previous job, 2 if wage in the current
job is the same as in the previous job, and 0 otherwise. Prefecture dummies are included
as explanatory variables. The log- likelihood is -7947.0864 and pseudo R2 is 0.0688. The
null hypothesis that all coeﬃcients are 0 is rejected at the 1% signiﬁcance level.

20

Table 4: Estimation Results for Change in Occupation

Female Dummy
Age
Age2
Tenure (previous)
Tenure (previous) 2
Age·Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Duration of On-the-Job Search
Number of TurnoverOccupation type1(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7 (previous)
Occupation type8(previous)
Occupation type9(previous)
Position type2(previous)
Position type3(previous)
Position type4(previous)
Position type5(previous)
Position type6(previous)
Position type7(previous)
Regular Worker(previous)
Mining(previous)
Construction(previous)
Manufacturing(previous)

Estimatedz value
Coef.
-.09603-2.440-.00733-0.626-9.62e-6-0.059-.07020-3.619.001801.379.001182.406.372595.407.252673.080.34482
3.986.464805.858.000260.074-.00539-0.488.006950.061.013380.220.267703.942.384325.523
1.250529.014.27695
3.274.182572.936
1.03406
4.232-.48700-1.945-.13405-0.564-.01911-0.076-.12068-0.484-.21149-0.874-.03001-0.126-.18201-4.238-.40301-1.524-.53994-2.987-.83759-4.767(Table 4: continue)

21

p-value
0.015
0.531
0.953
0.000
0.168
0.016
0.000
0.002
0.000
0.000
0.941
0.626
0.951
0.826
0.000
0.000
0.000
0.001
0.003
0.000
0.052
0.573
0.940
0.628
0.382
0.900
0.000
0.128
0.003
0.000

marginal
eﬀect-.03584-.00275-3.61e-6-.02632.00067.00044.13932.09725.13384.17930.00009-.00202.00261.00502.10328.14948.45522.10732.06919.39020-.16265-.04905-.00714-.04427-.07671-.01128-.06950-.13746-.18116-.28492

(Table 4: continue)
Gas and Electric Power(previous)-.47113
Trans. and m.(prevs)-.19569
Wholesale and Retail(previous)-.26474
Bank and Insurance(previous)-.32365
Real estate(previous)-.18535
Service industry(previous)-.37995-.39582
Government(previous)
Firm Size 500∼999(previous).07688Firm Size 300∼499(previous)-.02279
Firm Size 100∼299(previous)-.06067
Firm Size 30∼99(previous)-.20098
Firm Size 5∼29(previous)-.00368
Firm Size ∼4(previous).09089Dummy for UB Recipient.10257
Dummy for Voluntary Quitters.16188Dummy for License Holder-.16015
Duration of Unemployment(estimated) -.00210
constant.06302-2.349-1.072-1.491-1.720-0.839-2.162-1.8101.077-0.322-1.117-3.760-0.069
1.079
1.956
3.607
-4.963-0.034
0.177

0.019
0.284
0.136
0.085
0.401
0.031
0.070
0.281
0.748
0.264
0.000
0.945
0.281
0.050
0.000
0.000
0.973
0.859

-.15810-.07088-.09544-.11363-.06697-.13711-.13615.02915-.00851-.02258-.07372-.00138.03454.03876.05953-.05965-.00079

Note: This table shows the estimation results of the change in occupation by using the
probit model with heteroscadasticity consistent. The dependent variable, change in occupation, is 1 if occupation in the current job is not the same as in the previous job, and 0
otherwise. Prefecture dummies are included as explanatory variables. The log-likelihood
is -4839.7302 and pseudo R2 is 0.0718. The null hypothesis that all coeﬃcients are 0 is
rejected at the 1% signiﬁcance level.

22

Table 5: Estimation Result for Change in Position
Estimated z value
Coef.

Female Dummy
Age
Age2
Tenure (previous)
Tenure (previous) 2
Age·Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Duration of On-the-Job Search
Number of TurnoverOccupation type1(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7 (previous)
Occupation type8(previous)
Occupation type9(previous)
Position type2(previous)
Position type3(previous)
Position type4(previous)
Position type5(previous)
Position type6(previous)
Position type7(previous)
Regular Worker(previous)
Mining(previous)
Construction(previous)
Manufacturing(previous)-.50233-12.079.016401.367-.00002-0.140.040502.051.004903.776-.00051-1.063.41178
5.961.578696.976.53942
6.097.668608.355.002390.661.037143.283.337373.087-.03890-0.635-.06915-0.993-.08564-1.195-.30632-2.345-.37406-4.211-.22955-3.659.057840.224
1.23045 4.678
1.35701 5.313
1.64743 6.133
1.78870 6.720
2.81205 10.757
3.66406 14.089.153883.361-.85703-3.023-.13251-0.705-.29773-1.628(Table 5: continue)

23

p-value marginal marginal
eﬀect
eﬀect
up
no
change0.000-.10022.046930.171.00327-.00153
0.889-4.60e-6 2.15e-6
0.040.00808-.003780.000.00097-.000450.288-.00010.000040.000.08215-.038470.000.11545-.05406
0.000.10762-.050400.000.13339-.062470.509.00047-.000220.001.00741-.00347
0.002.06730-.031520.526-.00776.003630.321-.01379.006460.232-.01708.008000.019-.06111.028620.000-.07463.034950.000-.04579.021440.823.01153-.005400.000.24548-.114960.000.27073-.12679
0.000.32867-.153920.000.35686-.167120.000.56103-.262740.000.73102-.342340.001.03070-.01437
0.003-.17098.080070.481-.02643.012380.103-.05940.02781

(Table 5: continue)
Gas and Electric Power(previous)-.19314-0.920Trans. and m.(prevs)-.36847-1.930Wholesale and Retail(previous)-.07903-0.427
Bank and Insurance(previous)-.14577-0.743
Real estate(previous)-.13603-0.594Service industry(previous)-.03572-0.195-.19662-0.865Government(previous)
Firm Size 500∼999(previous).11272
1.535
Firm Size 300∼499(previous).05404
0.736
Firm Size 100∼299(previous).057991.035
Firm Size 30∼99(previous).16347
2.995
Firm Size 5∼29(previous).10393
1.894
Firm Size ∼4(previous).061170.704
Dummy for UB Recipient.10213
1.890
Dummy for Voluntary Quitters-.15148-3.371
Dummy for License Holder.087072.630
Duration of Unemployment(estimated) .09815
1.561
threshold(lower)
2.740195.76996threshold(upper)

0.357
0.054
0.669
0.458
0.553
0.845
0.387
0.125
0.462
0.301
0.003
0.058
0.482
0.059
0.001
0.009
0.119

-.03853-.07351-.01576-.02908-.02713-.00712-.03922.02249.01078.01157.03261.02073.01220.02037-.03022.01737.01958.01804.03442.00738.01362.01270.00333.01837-.01053-.00504-.00541-.01527-.00971-.00571-.00954.01415-.00813-.00917

Note: This table shows the estimation results of change in position by using the ordered
probit model with heteroscadasticity consistent. The dependent variable, change in position, is 3 if position in the current job is higher than in the previous job, 2 if position in
the current job is the same as in the previous job, and 0 otherwise. Prefecture dummies
are included as explanatory variables. The log-likelihood is -4323.6349 and pseudo R2 is
0.2008. The null hypothesis that all coeﬃcients are 0 is rejected at the 1% signiﬁcancelevel.

24

Table 6: Estimation Result for Change in Industry

Female Dummy
Age
Age2
Tenure (previous)
Tenure (previous) 2
Age·Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Duration of On-the-Job Search
Number of TurnoverOccupation type1(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7 (previous)
Occupation type8(previous)
Occupation type9(previous)
Position type2(previous)
Position type3(previous)
Position type4(previous)
Position type5(previous)
Position type6(previous)
Position type7(previous)
Regular Worker(previous)
Mining(previous)
Construction(previous)
Manufacturing(previous)

Estimatedz value
Coef.
-.11628-2.812-.01933-1.586.000160.980-.06157-3.064.001671.244.001092.178.184222.668.088101.058.259402.907.232182.870.00542
1.433-.00905-0.799.305112.523.50902
7.947.29245
4.097.197772.730.517783.724.277163.077.20313
3.110.677512.611-.39174-1.441-.35423-1.358-.09519-0.345-.17505-0.647-.26416-1.004-.00559-0.022-.16731-3.615-5.22823-12.932-5.15090-15.323-6.64153-19.824(Table 6: continue)

25

p-value
0.005
0.113
0.327
0.002
0.214
0.029
0.008
0.290
0.004
0.004
0.152
0.424
0.012
0.000
0.000
0.006
0.000
0.002
0.002
0.009
0.149
0.174
0.730
0.518
0.316
0.983
0.000
0.000
0.000
0.000

marginal
eﬀect-.04552-.00754.00006-.02404.00065.00042.07180.03414.09832.08910.00212-.00353.11418.18980.11050.07563.18442.10441.07852.23015-.15526-.14045-.03747-.06920-.10451-.00218-.06439-.59332-.75035-.98670

(Table 6: continue)
Gas and Electric Power(previous)-4.53564
Trans. and m.(prevs)-5.25164
Wholesale and Retail(previous)-5.90307
Bank and Insurance(previous)-5.09331
Real estate(previous)-5.60329
Service industry(previous)-6.0057
Firm Size 500∼999(previous)-.00899
Firm Size 300∼499(previous)-.23616Firm Size 100∼299(previous)-.12891
Firm Size 30∼99(previous)-.27180
Firm Size 5∼29(previous)-.14609
Firm Size ∼4(previous)-.00491
Dummy for UB Recipient.04557
Dummy for Voluntary Quitters.12360
Dummy for License Holder-.14654
Duration of Unemployment(estimated) -.03586
constant
6.45195-12.281-15.354-17.514-14.689-15.249-17.898-0.119-3.198-2.266-4.897-2.603-0.0550.812
2.633-4.330-0.543
2.011

0.000
0.000
0.000
0.000
0.000
0.000
0.905
0.001
0.023
0.000
0.009
0.956
0.417
0.008
0.000
0.587
0.044

-.62017-.71859-.86792-.67334-.60748-.97306-.00351-.09349-.05069-.10712-.05738-.00191.01775.04859-.05735-.01400

Note: This table shows estimation results of change in industry by using the probit modelwith heteroscadasticity consistent. The dependent variable, change in occupation, is 1 if
industry in the current job is not the same as in the previous job, and is 0 otherwise.
Prefecture dummies are included as explanatory variables. The log-likelihood is -4350.9134
and pseudo R2 is 0.1698. The null hypothesis that all coeﬃcients are 0 is rejected at the
1% signiﬁcance level.

26

Table 7: Estimation Result for Change in Firm Size
Estimated z value
Coef.

Female Dummy
Age
Age2
Tenure (previous)
Tenure (previous) 2
Age·Tenure (previous)
High School Graduate
Covation School Graduate
Two Tear College Graduate
University Graduate
Duration of On-the-Job Search
Number of TurnoverOccupation type1(previous)
Occupation type3(previous)
Occupation type4(previous)
Occupation type5(previous)
Occupation type6(previous)
Occupation type7 (previous)
Occupation type8(previous)
Occupation type9(previous)
Position type2(previous)
Position type3(previous)
Position type4(previous)
Position type5(previous)
Position type6(previous)
Position type7(previous)
Regular Worker(previous)
Mining(previous)
Construction(previous)
Manufacturing(previous)-.01156-0.291-.00424-0.365.000030.214.047262.423-.00165-1.271-.00054-1.113.008000.121.071550.894.195982.307.415625.335.004301.180-.03188-2.870-.03902-0.326-.13616-2.240-.24402-3.486-.19756-2.796.009380.069-.15174-1.747-.23453-3.771.043380.177.168070.584.331791.197.585722.022.473521.657.452841.629.42381
1.541.036290.800.216600.768.256551.378.32085
1.769(Table 7: continue)

27

p-value marginal marginal
eﬀect
eﬀect
increase nochange0.771-.00218-.000330.715-.00079-.000120.831
6.44e-6
1.00e-6
0.015.00891.001380.204-.00031-.00004
0.266-.00010-.00001
0.904.00150.000230.371.01348.002090.021.03694.005730.000.07834.012160.238.00081.000120.004-.00600-.000930.744-.00735-.001140.025-.02566-.00398
0.000-.04600-.007140.005-.03724-.005780.945.00176.000270.081-.02860-.004440.000-.04421-.006860.859.00817.001260.559.03168.004920.231.06254.00971
0.043.11041.017140.097.08926.013860.103.08536.013250.123.07989.012400.424.00684.001060.443.04083.006340.168.04836.007510.077.06048.00939(Table 7: continue)
Gas and Electric Power(previous).36846
1.779
Trans. and m.(prevs).36354
1.927
Wholesale and Retail(previous).37677
2.053
Bank and Insurance(previous).41941
2.108
Real estate(previous).26723
1.171
Service industry(previous).344201.898.44702
1.995
Government(previous)
Firm Size 500∼999(previous).45783
5.183
Firm Size 300∼499(previous).766499.176
Firm Size 100∼299(previous)
1.26604 18.972
Firm Size 30∼99(previous)
2.26259 33.994
Firm Size 5∼29(previous)
3.48503 48.737
Firm Size ∼4(previous)
11.4397 0.000
Dummy for UB Recipient-.13148-2.418Dummy for Voluntary Quitters-.00929-0.206
Dummy for License Holder.028470.868
Duration for Unemployment(estimated) .03689
0.585
threshold(lower)
2.131123.37422threshold(upper)

0.075
0.054
0.040
0.035
0.242
0.058
0.046
0.000
0.000
0.000
0.000
0.000
1.000
0.016
0.837
0.386
0.558

.06945.06853.07102.07906.05037.06488.08426.08630.14448.23865.42651.656952.15646-.02478-.00175.00536.00695.01078.01064.01102.01227.00782.01007.01308.01340.02243.03706.06623.10201.33487-.00384-.00027.00083.00108

Note: This table shows estimation results of change in ﬁrm size by using the ordered probit
model with heteroscadasticity consistent. The dependent variable, change in ﬁrm size, is 3
if ﬁrm size in the current job is larger than in the previous job, 2 if ﬁrm size in the current
job is the same as in the previous job, and is 0 otherwise. Prefecture dummies are included
as explanatory variables. The log-likelihood is -5355.7874 and pseudo R2 is 0.3691. The
null hypothesis that all coeﬃcients are 0 is rejected at the 1% signiﬁcance level.

28