Trends in Worker Displacement Penalties in Japan: 1991-2005
Michael Bognanno* and Ryo Kambayashi**Draft: May 2009

ABSTRACT
We examine the period from 1991 to 2005 to document the effects of a changing Japanese labormarket on trends in the cost of job change. During this period, job change penalties and theextent to which they were age related grew. Evidence is also found of a diminishing specificity
in human capital in industry for job changers in the Japanese labor market. While penalties fromchanging industries were diminishing, career change penalties grew.

Theme: Microeconomics of unemployment
Keywords: Displacement
JEL Classification: J31, J41, J63, J6*Temple University – Department of Economics; IZA
869 Ritter Annex (004-00), 1301 Cecil B. Moore Ave., Philadelphia, PA 19122, USA
E-mail: bognanno@temple.edu**Institute of Economic Research, Hitotsubashi University
2-1 Naka, Kunitachi, Tokyo, 186-8603, JAPANE-mail: kambayas@ier.hit-u.ac.jp

1. Introduction
The labor market adjustment to the economic stagnation in Japan during the 1990s included aclear trend towards more involuntary separations in the labor market, erosion in the earnings of
newly hired workers relative to incumbent workers and job change costs that became more
severe with age. This paper documents the trends in the cost of job change in Japan between
1991 and 2005 using data from the Employment Trend Survey micro-survey conducted by the
Ministry of Health, Labor and Welfare. The findings may be insightful for other countries withstrong internal labor markets facing a long period of economic stagnation.
Computed from publically available data, the reasons for involuntary job
separation during the period are provided in figure 1.1 The turnover rate is defined by the annual
number of separations for each reason divided by the number of employees at the beginning of
the year. The calculation is only for regular workers, defined as those who have been on the job
at least a month or have an open-ended employment contract. The involuntary turnover rate, thesum of the reasons for separation in the table, peaked at 6.3% in 2002, nearly double its rate in1991. This peak coincided with the peak in unemployment of 5.5%. The primary source of
involuntary job separation growth was layoffs due to management convenience (from 0.7% in
1991 to 2.0% in 2002). Increasing trends in contract expiration and dismissals as a source of
involuntary separation are also evident.

1

The statistics presented in figure 1 are not tabulated from the inflow supplement of Employment Trend Survey data.
They come from a government website that classifies the reasons for job separation differently. (Source:
http://wwwdbtk.mhlw.go.jp/toukei/kouhyo/data-rou14/jikei/kd-jikeiretu-13.xls)

1

Figure 1: The Turnover Rate by Reasons of Involuntary Separation (1991-2005)
7.0
Death or injury

6.0

Dismissal

turnover rate (%)

5.0

Mandatory retirement

4.0

Layoff due to Management
convenience

3.0

Contract finished2.0
Shukko assignments
1.0

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0.0

The labor market adjustment to economic conditions occurred not only in the quantityof labor demanded but also in the price. Figure 2 displays the mean of wages changes by the year
and reason for job separation, as well as the unemployment rate.2 As expected, involuntary job
separations carried the largest wage cost and voluntary the least. It is also evident that the wage
outcomes for all three sources of job separation were worsening during the 1990s. For
involuntary job separation, the wage loss grew from 3.3% in 1991 to 8.1% in 1999. The wagechange for voluntary separation fell from 3.9% in 1991 to -0.4% in 1999. The two series arehighly correlated (0.91) despite their level difference during this period. It is interesting to notethat the trend towards larger wage losses ended while unemployment was still rising. From 1991

2

Source: Calculated from the Employment Trend Survey Micro Data. In these data, wages are a categorical variable.
In computing tables 2 and 3, we assign the following values to the categories: wage declines of 30% or more areassign -30%, wage declines of 10 to 30% are assigned -15%, wage changes of +10% to -10% are assign 0%, wage
increases of 10 to 30% are assigned +15%, and wage increases over 30% are assigned +30%. This assignment
follows the work of Abe et al.

2

to 1999, both voluntary and involuntary wage changes series were highly correlated with the
unemployment, -0.97 and -0.94 respectively. After 2000, while the correlation remains high, itloses statistical significance.
Figure 2: Mean of Wage Change by Year (1991-2005)
6.0

6.0

5.0

2.0
0.0

4.0

-2.0
-4.0

unemployment rate (%)

mean of wage change (%)

4.0

Voluntary Separation
Shukko Assignment
Involuntary Separation
Unemployment Rate

3.0

-6.0
-8.0

2005

2003

2001

1999

1997

1995

1993

2.0
1991

-10.0

year

Section 2 of the paper introduces the literature on job displacement penalties andSection 3 describes the data and classifies job changes into voluntary and involuntary. Section 4
provides estimates of an ordered logit model to examine multiple factors influencing job change
penalties and more clearly identify trends. Section 5 considers possible explanations forincreasing job change penalties and the diminishing return to age in the labor market for workers
finding new employment. Section 6 provides a comparison between job change penalties inJapan and the U.S. during the 1990s. Conclusions follow in section 7.

2. Studies on Job Displacement Penalties
Numerous studies have investigated job displacement penalties in the US labor market. Surveysof this literature were conducted by Hamermesh (1989), Fallick (1996) and Farber (1997).

3

Estimates of US job displacement penalties are in the range of 15 to 40% (Topel 1993). Larger
penalties are associated with more firm and labor market experience, periods of higher
unemployment and changing industry upon re-employment. Both Ruhm (1991) and Jacobsen,
LaLonde and Sullivan (1993) found evidence of earnings reductions for displaced workers thatpersisted years after job separation. Jacobsen at al. also found that the earning loses from job
displacement for workers leaving distressed firms began prior to job separation and were little
influenced by changes in firm size, industry, local labor market conditions, gender or age.
Though finding that age had little influence on earnings losses, large losses existed for hightenure workers.
Studies of job change penalties in the Japanese labor market are scarce. Since thecomprehensive study by Abe, Higuchi, Nakamura, Kuhn and Sweetman (2002) using
Employment Trend Survey data from 1995, there has been no research with nationally
representative data to establish how changes taking place in the Japanese labor market haveaffected the wage implications of job change. Abe et al. studied the effects of job change on
wages during a period of moderate GDP growth (2.4%) and unemployment (3.2%) and find that,when all sources of job separation both voluntary and involuntary are grouped together, on
average male and female workers benefited slightly from job change. For both genders, theconsequence of job change was an increase in income of about 2.2%. When only involuntary jobchanges are considered and those transferred temporarily to other companies are excluded (thepractice of shukko), the average male lost 4.3%.3 The mean loss for men results from the losses

3

Using data from 2000 through 2003, Bognanno and Delgado (2005) find much larger job displacement penalties inJapan than Abe et al. They also find evidence of job displacement penalties that are strongly age-related, suggesting
severe consequences for older workers losing jobs in the primary sector. However, their data includes only workers
successfully re-employed through the services of a job placement firm. Because of the specialized sample, thegenerality of their results is unknown. A study with nationally representative data is necessary to substantiate

4

of men 45 and over more than offsetting the gains of younger men. Nearly 28% of men 45 andover suffered wage losses of more than 30%. Abe at al. attribute the large losses for men over 55
to the traditional practice of mandatory retirement in Japan, followed by low paid or part-timework after mandatory retirement. Large wage reductions for older female job changers are muchless frequent. This is suggested by Abe et al. to result because females are less often subject tomandatory retirement.
For greater job displacement penalties for older workers theory offers several
explanations. Four potential sources of job displacement penalties include the loss of specifichuman capital, the loss of a superior job match, the loss of possible union or industry wage
premiums, and the loss of seniority (Fallick 1996). If specific human capital, job match quality
and wage premiums are increasing in job tenure, older workers should have greater losses uponjob displacement. Regarding specific human capital, Koike (1988) has stressed the significanceof on the job training in Japan and Rebick (2005) notes that, in contrast to formal education, on
the job training is harder for employees to convey to a new employer, thus making employmentchange more costly.
Another explanation for greater job displacement penalties for older workers follows
Lazear’s (1979) model of delayed payment contracts. It offers both an explanation of theinstitution of mandatory retirement and of why mandatory retirement might be followed by large
wage losses for older workers with greater tenure. Workers in the model are motivated by acontract that pays them below their marginal product early in their firm tenure and more thantheir marginal product later. Worker motivation derives from the incentive to remain with thefirm in order to collect the premium at the end of the contract. Workers separated from theirwhether job displacement penalties have grown larger over time and whether these penalties have grown morestrongly related to age.

5

firms late in their tenure, lose the amount that they earn above their marginal product when they
face the outside labor market. Mandatory retirement in the model is a device to protect the firmfrom employees wishing to collect wages exceeding their marginal products beyond theanticipated retirement date.
Lazear’s model may be particularly applicable in explaining large job displacement
penalties for older workers in the Japanese labor market. First, mandatory retirement is both legal
and prevalent (Clark and Ogawa, 1992). As of 2002, almost all firms had mandatory retirement(JILPT, 2005, p.53, Table 3-27). Furthermore, workers reemployed by their firm after mandatory
retirement typically take wage reductions of 50-70% (Rebick, 2005). That workers accept such
reductions indicates that they have little opportunity to maintain their prior earnings level in theoutside labor market and lends credibility to the notion that they were receiving wages in excessof their marginal products prior to mandatory retirement. Second, relative to the US and theOECD, firm tenure in Japan is longer (Hashimoto and Raisian, 1985), especially for men(Rebick, 2005). Long tenure provides a basis from which delayed payment contracts are feasible.
Third, consistent with delayed payment contracts, Clark and Ogawa (1992) found that earnings
profiles were steeper in firms with earlier ages of mandatory retirement. Last, Japanese firms
provide workers a substantial payment upon retirement. This payment in itself constitutes one
form of delayed compensation. The amount of this payment is heavily reduced should separation
occur for workers with little tenure, for workers who voluntarily quit or for workers who are
dismissed.4

4

On average, a worker with 40 years of firm tenure receives 27.3 times as much as a worker with three years oftenure (Statistics and Information Department, 2003).

6

3. The Employment Trend Survey Micro Data: 1991-2005
The Ministry of Health, Labor and Welfare has been conducting the Employment Trend Survey
since 1964. The purpose of the survey is to observe labor mobility between regions, industries,
establishment sizes and occupations. The survey encompasses 14,000 establishments with five or
more regular employees sampled from almost all industries.5 Following Abe et al., because thelabor mobility in the public sector is so different from that in private sector, public sector
employees have been deleted from the sample analyzed. We use the Employment Trend Survey
micro-data with the permission of the Economic and Social Research Institute.
An average of 83,316 newly hired workers per year were sampled in the establishments
surveyed in the years from 1991 to 2005. We focus on the job changers within all of newly hired
workers, because others do not have information about their previous employment and the wagechanges resulting from job change.6 From the overall sample of newly hired workers from 1991
to 2005, totaling 1,249,735 workers, 661,560 are job changers (52.9%). From this group,562,844 job changers have relatively complete information and are used in this analysis.
Two variables key in the survey are the wage change present upon reemployment andthe cause of job separation. The survey collects the wage change by categorizing them as a lossof 30% or greater, a loss of 10% to 30%, between a 10% loss and a 10% gain, a gain of 10% to
30% and a gain of 30% or more.
Apart from shukko assignments, the cause of job separation in these data is classified
into one of eight categories: (1) job dissatisfaction; (2) bad human relations in the firm; (3)concern over the future of firm; (4) dissatisfaction over compensation; (5) dissatisfaction with
5

ETS excludes agriculture, forestry and fisheries, domestic services, educational services, and services by foreign
governments and international agencies. Since 2003, educational services were included in the Employment Trend
Survey. However, for consistency with prior years, we excluded this industry.
6
Others include school leavers (graduates in the survey year) and unemployed/inexperienced (those having been
unemployed more than one year or those in their first job).

7

working time and/or number of holidays; (6) marriage, maternity or family care; (7) dismissal or
mandatory retirement; (8) other. We divide these categories into either voluntary or involuntary
job separations. Categories 1–6 and 8 denote job separations initiated by the workers and are
deemed “voluntary.” Separations resulting from dismissal or mandatory retirement are coded as“involuntary.” Reasons for dismissal include plant closings, layoffs and worker misconduct.
Our distinction between voluntary and involuntary job change differs somewhat fromthe job displacement literature. This is due to the aggregation of the job separation causes in the
category of “dismissal or mandatory retirement.” The literature classifies involuntary job
separations as being due to mass layoffs and plant closures but not to firings for cause (Kletzer
1998). In our data we are unable to separate out workers dismissed for misconduct from thosedismissed due to plant closings and layoffs. In fact, in actual Japanese labor disputes, these two
reasons are used interchangeably (Kambayashi et al. (2008b)). Mandatory retirement is not a
feature of the U.S. labor market, except for a few specific occupations, and is not considered in
the literature.7 In regards to the inclusion of those subject to mandatory retirement, workers inthese data losing their jobs for this reason fit within the notion of job displacement because theyfound re-employment and, hence, would presumably have remained in their prior job if allowed.
It is worth noting that workers subject to mandatory retirement in Japan included those under theage of 60 at a significant share of firms in the 1990s, some years earlier than what might beconsidered a typical retirement age.8

7

Mandatory retirement in the U.S. prior to the age of 70 was outlawed in 1978 and banned at any age in 1986.
Mandatory retirement is not prohibited for law enforcement officers, corrections officers, firefighters, air traffic
controllers or commercial airline pilots.
8
The age of mandatory retirement varied across industry and time. Despite the legal invalidation of mandatory
retirement prior to the age of sixty in 1994, the decline in firms with a mandatory retirement age below sixty
adjusted gradually. The ratio of firms with a mandatory retirement age less than 60 according to the annual
Employment Administration Survey (Koyo-kanri Chosa) is as follows: 36.1% in 1991; 29.2% in 1992; 23.4% in
1993; 20.0% in 1994; 15.9% in 1995; 14.2% in 1996; 11.7% in 1997; 9.8% in 1998; 6.7% in 1999; 0.8% in 2000;
0.8% in 2001; 1.0% in 2002.

8

In evaluating the cost of job change, we regard the wage change due to shukko(temporary transfer to another company while preserving the employment contract) as a benchmark. The transfer of employees to associated companies or subsidiaries is a common practiceamong Japanese firms. The main purpose of shukko assignments is to give the worker an
opportunity to gain exposure to the business practices at allied companies in the interest of theworker’s long-term career development.
The fraction of turnover due to this source of job change varied little through ourperiod of examination. Figure 1 shows turnover rates induced by shukko to be stable at about
0.4% since 1994. The contrast between the stability in the rate of shukko and the volatility involuntary/involuntary separations is found not only in turnover rate but also in the job change
penalties. Figure 2 indicates that those who were assigned to shukko did not experience large
wages losses, at its bottom in 2000, shukko assignments resulted in only a 1.2% wage loss, nor
was there a strong trend in wage change during the period.
Since workers subject to skukko remain in continuous employment, they provide a
proxy for how wages changed within the firm in general. Considering that the Japanese economy
had experienced deflation and zero growth beginning in 1992, it is not unusually that the average
wage change with shukko assignments was only slightly negative.
As for the other variables in the data, almost every variable is categorical; for example
age is provided in seven five-year increments and including those 19 or less and 65 or more toform nine categories in total. Present firm size is divided into five categories (5-29, 30-99, 100299, 300-999, 1000+). Previous firm size is divided into seven categories (public organization, 14, 5-29, 30-99, 100-299, 300-999, 1000+). Education is divided into four categories by
graduation level (junior high, high school, junior college, university).

9

Appendix 1 provides sample means for the variables used in this analysis. Of jobchangers in the sample, 40% are female, 17% and 12% are subject to involuntary job change andshukko assignment respectively. Defining career change as a change of both industry andoccupation, 26% of workers change careers, while 25% change only in industry and 7% change
only in occupation. In terms of working hours, 12% of workers are part-time both before andafter job change, 7% move from part-time to full-time and 7% move from full-time to part-time.
While 71% of job changers move to a firm in a different size category, only 17% move tosmaller firms; this is partly because ETS excludes small firms under 5 regular employees.

4. Factors Influencing the Cost of Job Change: Empirical Estimates
According to the literature introduced in section 2, one of the most important factors to affect jobchange penalties is age and gender. It is worthwhile to confirm this conventional wisdom in ourdata. In the next two figures, we depict the transition of mean of job change penalties by age andgender. Along with the discussion in the previous section, we show the relative magnitude ofmean of wage change compared with shukko assignments.

-6.0

-6.0

Involuntary
Separation Shukko
Assignment
-19

65+

60-64

55-59

45-54

-12.0
35-44

-12.0
30-34

-10.0
25-29

-8.0

-10.0
20-24

-8.0

Voluntary
Separation Shukko
Assignment

age

age

(A) Male(B) Female

10

65+

-4.0

60-64

-4.0

55-59

-2.0

45-54

0.0

-2.0

35-44

2.0

0.0

30-34

4.0

2.0

25-29

6.0

4.0

20-24

8.0

6.0

-19

mean of wage change (%)

Figure 3: Mean of Wage Change by Gender and Age (under 19 to over 65)
8.0

Panel (A) and (B) of figure 3 show that older workers tend to face larger job change
costs, but more so for males than for females. Large negative wage changes are more likely for
older males than older females, especially in the involuntary job separation sample. The potential
impact of mandatory retirement for older workers appears significant for both genders but more
so for males. Comparing the results over the whole sample period to those of Abe et al. (2002)
with 1995 data, the mean job displacement penalty for men undergoing involuntary job change, awage loss of 9.3%, is double the 1995 estimate, while for women wages fell 3.4% compared to awage gain of 0.3% in 1995.
In order to examine the factors that may influence job change penalties in a richer
framework than is possible with the summary statistics presented in previous section, we
estimate an ordered logit model. The dependent variable in this study, wage change, is provided
by the Employment Trend Survey as one of five ordered outcomes. To cope with this, weestimate an ordered logit model and define wage lossi to represent the observed percentage wagechange for worker i.

(1)

⎧−30
⎪−15
⎪⎪
wage lossi ≡ ⎨0
⎪15
⎪
⎪⎩30

if
if

%Δw ≤ −30%
− 30% < %Δw ≤ −10%ifif

− 10% < %Δw ≤ +10%
+ 10% < %Δw ≤ +30%

if

+ 30% ≤ % Δw

Presented as a latent variable model and defining wage lossi* as a latent variable, the model is
then

(2)

wage lossi∗ = xi β + ε iand11

wage lossi = k if(3)

τ k < wage lossi∗ ≤ τ k +15( k = −30, − 15, 0, + 15, + 30,

and τ −30 = −∞, τ 45 = ∞, )where wage lossi* denotes the unobserved ordinal percentage change in the wage for individual i,

xi is a vector of indicator variables for worker characteristics and the characteristics of theworker’s initial and subsequent firms and ε is a random error term assumed to have a logistic
distribution. By using estimates coefficients and the logistic distribution function denoted as F,
the probability that a person with xi experiences a k% wage change can be calculated as follows:Pr ⎡⎣ wage lossi∗ = k xi ⎤⎦ = Pr ⎡⎣τˆk < xi βˆ + ε i ≤ τˆk +15 ⎤⎦
(4)

(

) (

= F τˆk +15 − xi βˆ − F τˆk − xi βˆ

)( k = −30, − 15, 0, + 15, + 30,

and τˆ−30 = −∞, τˆ45 = ∞, )4.1 Age Effects

We first estimate an ordered logit model year by year (from 1991 to 2005) and by reason for job
separation (voluntary, involuntary, shukko assignments) to examine the effect of age. The
specifications estimate the effects of individual characteristics (age, age squared, gender andeducation level), and job characteristics in the present job (firm size, industry and occupation) on
the cost of job change. To consider whether human capital is industry, occupation or career
specific, we code three variables for workers changing (1) only in industry, (2) only inoccupation and (3) in career. A career change constitutes a change of both industry andoccupation. The next table presents a summary of estimated coefficients in regards to age withjust sign and significance denoted. The full estimates appear in Appendix 2.

12

Table 1a: Summary of Ordered Logit Estimation (Age Effect)
Involuntary Separation Sample
Data
Age
2

Age

Observations

(1)
1991
0

(2)
1992
0

(3)
1993
0

(4)
1994

0
4463

－
4256

**

－
4397

**

－
4010

***

＋

**

(5)
1995
0

(6)
1996
0

(7)
1997
0

(8)
1998
0

(9)
1999
***

＋

*

＋

－
5248

***

－
4928

*

－
4806

***

－
5238

***

－
5616

***

－
7002

***

－
7759

(22)
1997
0

(24)
1999
**
－
0
23151

(25)
2000
*
－
0
26711

(26)
2001
0

－
32418

(23)
1998
***
－
0
26306

(37)
1997
＋***

(38)
1998
＋***

(39)
1999
＋***

(40)
2000
＋***

＋

(10)
2000

(11)
2001

(12)
2002
0

(13)
2003
0

(14)
2004
0

(15)
2005

－
9650

***

－
10064

***

－
8640

***

－
8583

(28)
2003
0

－
25591

(27)
2002
***
－
0
21960

－
24675

(29)
2004
***
－
0
25917

(30)
2005
*
－
0
27556

(41)
2001
＋***

(42)
2002
＋***

(43)
2003
＋***

(44)
2004
＋***

(45)
2005
＋***

***

－
5059

***

－
4110

***

－
4547

***
***

＋

*
***

Voluntary Separation Sample
Data
Age
2

Age

Observations

(16)
1991
***
＋

(17)
1992
**
＋

(18)
1993
0

(19)
1994
0

(20)
1995
0

***

－
31167

***

－
23763

***

－
21279

***

－
23321

(21)
1996
*
－
0
28218

(36)
1996
＋***

－
38614

*

***

**

***

Shukko Assignment Sample
Data
Age

(31)
1991
＋**

(32)
1992
＋***

(33)
1993
＋***

(34)
1994
＋***

(35)
1995
＋***

2

***

***

－
3304

***

－
4327

***

－
4841

Age
－
－
Observations
2483
3145
*** p<0.01, ** p<0.05, * p<0.1
Full estimates are found in Appendix 2

***

***

－
5020

***

－
4828

***

－
4526

***

－
4141

***

－
4629

***

－
5070

－
4446

***

Wage changes are mainly negative across the three panels in Age2 and zero or positivein Age. This provides for concavity in the age/wage change profile. Significant negative
coefficients in the second order term imply a disproportionately larger penalty for older cohorts.
For involuntary/voluntary sample, sometimes the concavity is weakened and coefficients becomemonotonically negative. On the other hand, the concavity of job changers by shukko assignments
is robust through 15 years.
The nonlinearity of logit model makes it difficult to evaluate the magnitude ofcoefficient estimates for age. To gain insight into the magnitudes, we impute the effect of age on
the probability for each wage change category k while equalizing the other explanatory variables
to the sample mean. For example, let yi be the partial set of explanatory variables with effects
that are under consideration, and zi be other explanatory variables ( zi is the mean of pooled
sample). For each estimate of separation reason j in year t, (2) becomes(5)

wage lossijt ∗ = yi β1jt + zi β 2jt + ε i jt

( j = voluntary, involuntary, shukko; t = 1991," , 2005)

The probability to be imputed is13

Pk jt ( yi ) ≡ Pr ⎡⎣ wage lossijt ∗ = k yi , zit ⎤⎦() (= F τˆkjt+15 − yi βˆ1jt − zit βˆ2jt − F τˆkjt − yi βˆ1jt − zit βˆ2jt(6)( k = −30, − 15, 0, + 15, + 30,)

and τˆ−30 = −∞, τˆ45 = ∞, )The next figures depict the change in the probability of being represented in each wage change
category as a function of age in 1993 for the average person in the pooled sample. The figures
present the results for both voluntary and involuntary separations in comparison to those withshukko assignments; that is, Pkj,1993(age) - Pkshukko,1993(age) (j=voluntary, involuntary; age=18,…,
65).

Figure 4: Estimated Relative Probability of Wage Change by Age
1993

14

51

54

57

60

63

54

57

60

63

48

45

42

39

36

33

51

48

45

42

39

36

33

30

27

24

18

63

60

57

0

54

-0.5
51

0.05

48

-0.45

45

0.1

42

-0.4

39

0.15

36

-0.35

33

0.2

30

-0.3

27

0.25

24

-0.25

21

0.3

21

(D) from +10% to +30% (k =15)

-0.2

18

probability(C) from -10% to +10% (k =0)

30

27

24

18

63

60

57

54

51

0
48

0
45

0.05

42

0.05

39

0.1

36

0.1

33

0.15

30

0.15

27

0.2

24

0.2

21

0.25

21

(B) from -30% to -10% (k =-15)

0.25

18

probability(A) under -30% (k =-30)

(E) over +30% (k =30)
0.06

probability

0.05
0.04

voluntary separation
1993
involuntary separation
1993

0.03
0.02
0.01

63

58

53

48

43

38

33

28

23

18

0
age

According to panel A, if voluntary and involuntary job changers are 18 and other attributes areequal to the mean of pooled sample, they experience a 30% wage decline with the probability of
about 0.039 and 0.031 respectively. For involuntary job changers, the probability rises strongly
after age 35 to 0.211 at age 65. As for voluntary separations, the relative likelihood of 30% dropin wages does not grow much with age. It stands at 0.060 at age 65. Considering the previous
discussion of shukko assignment as a bench mark, ageing did not increase the job change cost forvoluntary job changers in panel A. Both involuntary and voluntary job changers increased intheir likelihood of falling into the moderate wage loss category in panel B, though the probability
diminishes in the 60s. In terms of the wage gains in panels D and E, increases in age reduce theprobabilities for both voluntary and involuntary job changers. Taken as a whole, worse wagechange outcomes befall older job changers. As these panels were produced to represent therelationships in 1993, we need further analysis to determine how the patterns changed with time
during the 1990s.
Figures 4 and 5 are similarly constructed. However, figure 5a depicts the results for
only involuntary separations and at five points in time: 1993, 1996, 1999, 2002 and 2005.

15

Figure 5a: Estimated Probability of Wage Change by Age
Relatively Evaluated Involuntary Separation
1993, 1996, 1999, 2002, 2005(A) under -30% (k =-30)(B) from -30% to -10% (k =-15)

0.4

0.25

0.35
0.2

probability

0.3
0.25

0.15

0.2
0.1

0.15
0.1

0.05

0.05
51

54

57

60

63

54

57

60

63

48

45

42

39

36

33

(D) from +10% to +30% (k =15)

0

0.2
0.18

-0.1

0.16
0.14

-0.2

0.12

-0.3

0.1
0.08

-0.4

0.06
0.04

-0.5

0.02
48

45

42

39

36

33

30

27

24

18

63

60

57

54

51

48

45

42

39

36

33

30

27

24

21

0
18

-0.6

21

probability

51(C) from -10% to +10% (k =0)

30

27

24

21

18

63

60

57

54

51

48

45

42

39

36

33

30

27

24

21

0
18

0

(E) over +30% (k =30)
0.045
0.04

probability

0.035
1993
1996
1999
2002
2005

0.03
0.025
0.02
0.015
0.01
0.005
62

58

54

50

46

42

38

34

30

26

22

18

0
age

Figure 5a shows the time series effect of age on the job change cost for the involuntarily
separated. In terms of the extreme wage loss in panel A, the wage penalty for older workers
became more severe after 1996 for older workers. Extreme wage gains for very young workers
increased in likelihood in panel E, even as unemployment was rising. Unemployment peaked in
2002, yet probability of a 30% wage increase in 2002 and in 2005 was higher than in 1999 for16young involuntary job changers. Below we introduce the same analysis with same set of years
but for voluntary job changers.

Figure 5b: Estimated Probability of Wage Change by Age
Relatively Evaluated Voluntary Separation
1993, 1996, 1999, 2002, 2005(A) under -30% (k =-30)(B) from -30% to -10% (k =-15)
0.18

0.1

0.16

0.08

0.14
0.12

0.04

0.1

0.02

0.08
0.06

0

0.04

-0.02

0.02
51

54

57

60

63

54

57

60

63

48

45

42

39

36

33

(D) from +10% to +30% (k =15)
0.3

0
-0.05

0.25

-0.1
-0.15

0.2

-0.2

0.15

-0.25
-0.3

0.1

-0.35
-0.4

0.05

-0.45

0.07
0.06
0.05

1993
1996
1999
2002
2005

0.04
0.03
0.02
0.01

age

17

62

58

54

50

46

42

38

34

30

26

22

18

0

48

45

42

39

36

33

30

27

24

21

18

63

60

57

54

51

48

45

(E) over +30% (k =30)

probability

42

39

36

33

30

27

24

0
21

-0.5
18

probability

51(C) from -10% to +10% (k =0)

30

27

24

18

63

60

57

54

51

48

45

42

39

36

33

30

27

24

21

0
18

-0.04

21

probability

0.06

Figure 5b shows the time series effect of age on job change costs for voluntary separations. For
those over 50 in panel A, the probability of a large wage was greater in 2005. The probability ofwage gains diminished in all years with age in panels D and E.

4.2 Industry, Occupation and Career Effects
When considering whether human capital is industry, occupation or career specific, we
code three variables: for workers changing only their industry, changing only their occupation orchanging their career. A career change constitutes a change of both industry and occupation. Thebase category is workers remaining in the same industry and occupation. Table 1b is thesummary of estimated sign of coefficients.

Table 1b: Summary of Ordered Logit Estimation (Industry, Occupation, and Career Change Effect)
Involuntary Separation Sample
Data
Industry Change
Occupation Change
Career Change
Observations

(1)
1991
0

(2)
1992

(3)
1993

(4)
1994

*

*

**

－***

－***

－***

**

***

－
4256

***

(17)
1992

－

－
4463

(5)
1995
0

(6)
1996

(7)
1997

** *

***

－* **

－* **

－** *

－
4397

* **

－
4010

* **

－
5248

** *

(18)
1993

(19)
1994

(20)
1995

－

－

(8)
1998
0

(9)
1999

(10)
2000

***

***

－***

－***

－***

－
4928

***

－
4806

***

－
5238

***

－
5616

(21)
1996

(22)
1997

(23)
1998

(24)
1999

－

－

－

(11)
2001
0

(12)
2002
0

(13)
2003
0

(14)
2004
0

(15)
2005
0

－***

－***

－***

－***

－***

－***

－
7002

***

－
7759

***

－
9650

***

－
10064

***

－
8640

***

－
8583

(25)
2000
0

(26)
2001
0

(27)
2002
0

(28)
2003
0

(29)
2004
0

(30)
2005
0

－

***

Voluntary Separation Sample
Data
Industry Change

(16)
1991
－

*

－

***

－

***

－

* **

－

* **

－

** *

－

***

－

***

－

***

Occupation Change

－

***

－

***

－

***

－

* **

－

* **

－

** *

－

***

－

***

－

***

－

***

－

***

－

***

－

***

－

***

－

Career Change
Observations

－
38614

***

－
31167

***

－
23763

***

－
21279

* **

－
23321

* **

－
28218

** *

－
32418

***

－
26306

***

－
23151

***

－
26711

***

－
25591

*

－
21960

***

－
24675

***

－
25917

*

－
27556

(32)
1992
0

(34)
1994
0

(35)
1995
0

(36)
1996
0

(37)
1997
－**

(38)
1998
－***

(39)
1999
0

(40)
2000
＋**

(41)
2001
－**

(42)
2002
－*

(43)
2003
0

(44)
2004
0

(45)
2005
0

0

(33)
1993
－**
0

0

*

－
***
－
5070

***

－
0
4446

**

－
4327

＋
0
4629

**

***

－
***
－
4526

**

－
3304

－
***
－
4828

0

0
3145

－
0
5059

－
***
－
4110

***
***

Shukko Assignment Sample
Data
Industry Change

(31)
1991
0

Occupation Change0Career Change0
Observations
2483*** p<0.01, ** p<0.05, * p<0.1
Base = no change
Full estimates are found in Appendix 2

+

0
* **

* **

－
4841

** *

－
5020

***

**

－
4141

**

0
0
4547

As a whole, industry, occupation, and career change negatively affect the wage change
in separations compared with those who changed jobs without changing industry or occupation.

18

Even job changers in the shukko category sometimes had to accept a significant wage reduction.
The statistical significance of the industry change dummy vanished after 2000 for both voluntaryand involuntary job changers.
As in the discussion of age effects, we can evaluate the magnitude of industry,
occupation, and career change by imputing the relative probability in comparison to shukko
assignment; that is Pkinvoluntary,t(change) - Pkshukko,t(change) (change=no change, industry change,
occupation change, and career change; t=1991,…, 2005). For simplicity, the sample here islimited to involuntary separation and two classes of wage change. Panel (A) aggregates theprobabilities and presents wage losses of 10% or more and panel (B) aggregates the probabilities
and presents wage gains of 10% or more. The 15 year average over the sample period for nochange in industry or occupation, industry change, occupation change and career change are inparenthesis in figure 6.

Figure 6: Estimated Probability of Wage Change by Industry, Occupation, and Career Change
Relatively Evaluated Involuntary Separation
0.35

0.20

(0.29)
0.30

0.15

0.25

probability(0.26)
0.20(0.20)

0.15

0.10

no change
industry change
occupation change
career change(0.08)
0.05(0.05)

0.00(0.05)(0.18)
0.10(A) Wage losses of 10% and more

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1991

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

-0.05
1992

probability(0.09)(B) Wage gains of 10% and more

It is clear that workers remaining in the same industry and occupation fair better than workers
changing in one or both dimensions in both panels, for wage losses in panel A and wage gains inpanel B. On average over 15 years, involuntary job changers who changed neither industry nor

19occupation experienced a 10% loss or greater with a probability of 0.18 and a 10% gain or morewith a probability of 0.09. Penalties from changing industry or occupation still exist after
separately accounting for career changes. As for the involuntary job separation with industry
change, the results are only slightly worse than for those without any change. Career changers
fared the worst over the period. This result is partially in accord with those of Munch (2006) who
found support for career specific human capital using Danish data. The penalty for changing
careers in our data is more than the penalty for changing only one’s industry over the sample
period, but it is less than the penalty for changing one’s occupation in some years and the twodiffer only slightly.
While industry change was initially disadvantageous relative to no change, thedifference between the profiles disappears entirely in both panels after 2001. There are twoforces potentially related to the relative fall in the cost of industry change. First, wage
differentials between industries were declining as displayed in figure 7 that shows the coefficientof variation of 2-digit industry dummies in the standard Mincerian equations from the background materials of Kambayashi et al. (2008). This would at least reduce the cost of industry
change for those leaving jobs in higher paid industries. It is also possible that industry-specifichuman capital diminished in importance due to falling training expenditures at the firm level thattook place during the 1990s.99Japanese firms reduced training expenditures during the 1990s. The share of training expenditures in total labor
cost declined from 0.36% in 1991 to 0.28% in 2002. However, the average share during 1980s was approximately
0.30%. Therefore, the decline in 1990s might be interpreted as a return to previous levels. (Ohki (2003) figure 2,
originally from Survey on Working Condition, MHLW.)

20

Figure 7: Wage Differential due to Industry (1991-2002)
0.14

coefficient of variation of industry premium
kambayashi et al. (2008)

0.135
0.13
0.125
0.12
0.115
0.11
0.105

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0.1

4.3 Firm Size Effects
Figure 8 shows the imputed effects of changing firm size. As in figure 6, panel (A) aggregates
the probabilities and presents losses -10% or more, panel (B) aggregates the probabilities andpresents gains of +10% or more. The average over the sample period is in parenthesis.

Figure 8: Estimated Probability of Wage Change by Firm Size Change
Relatively Evaluated Involuntary Separation
0.45

0.25

0.40

0.20
(0.34)(0.09)

0.15
probability

probability

0.35

0.30

0.25

no change
up
down

0.10

0.05

(0.07)(0.21)
0.00

0.20(0.03)(A) Wage losses of 10% and more

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

-0.05
1994

1992

1991

1993

(0.18)

0.15(B) Wage gains of 10% and more

Panel (A) indicates that the sum of the probabilities of losing 10% or more in wages is increased
by 13% by moving to a smaller new firm upon reemployment and decreased by 3% for moving
21

to a new larger firm. Correspondingly in panel (B), the probability of gaining 10% or more inwages is increased 2% by moving to a larger new firm and diminished by 4% for moving to a
new smaller firm.

5. Discussion of Trends in Job Change Penalties
Figure 9 is insightful in depicting the changing consequences of job loss for older workers within
our sample period. The figure plots age-wage profiles for newly hired workers and career
workers who have all of their labor market experience within the same firm. These profiles are
generated from estimations on cross-sectional micro data from 1993 and 2003 for regular
workers in the private sector.10 The age-wage profiles for career workers are virtually identical in1993 and 2003 and diverge with age from the profiles of newly hired workers. The disparity inwages between career workers and the newly hired workers grows strongly with age. This isconsistent with our estimates pointing to job loss penalties that increase with age.

10

Kambayashi et al. (2008a) report these estimations from their analysis of the Basic Survey of Wage Structure. The
estimations are from its back ground materials and included controls for gender, educational level, age, age2, firmsize, industry and prefecture. The effect of age on wages is calculated and then standardized so that an 18-year-old
worker has wages equal to one in each of the profiles. Career workers are those with firm tenure equal to their age
minus the years required to attain their educational level.

22Figure 9: Age-Wage Profile for New Hires and Career Workers without a Job Change

2.2
2Career workers 1993

1.8

Career workers 2003
New Hires 1993

1.6

New Hires 2003

1.4
1.2
1

60

58

56

54

52

50

48

46

44

42

40

38

36

34

32

30

28

26

24

22

20

18

0.8
Age

Notes: The estimation of ln(hourly_wage) = const. + female dummy + educational dummies + age + age2 + Firm size dummies + Indusry dummies+ Prefecture dummies was performed in 1993 and 2003.
Source: The back ground material of Kambayashi et al. (2008a) analysis of the Basic Survey of Wage Structure restricted to private sector, regular
workers. The effect of age on wages is calculated and then standardized so that an 18 year old worker has wages equal to one in each of the profiles.
Career workers are those with firm tenure equal to their age minus the years required to attain their educational level.

While the profiles for career workers in 1993 and 2003 are similar, the slope of thewage profile for newly hired workers in 2003 fell significantly from the wage profile in 1993. As
a result, the penalty for older job changers was increasing. This is also consistent with ourfinding in figure 5 of an increasingly negative outcome for older workers during the sample
period.
To examine potential reasons behind the increase in the cost of job change, we firstconsider the trends in basic labor market conditions. In the period from 1974 to 2005, the gross
turnover rate fluctuated between 25 and 35% with no trend during the period.11 Similarly, the11

The gross turnover rate is the number of workers leaving employment and the number of workers entering
employment divided by the total number of workers in the beginning of the year. Figure 2 in the working paper
displays the gross turnover rate and inflow rate of job changers over time.

23

inflow rate of job changers in the labor market fluctuates between 5 and 10% with no trend.12
The increasing trend in overall job change penalties cannot be related to turnover rates in any
readily apparent way.
Another avenue for investigation concerns the potential loss of specific capital. Did alarger percentage of workers lose specific capital during the sample period due to industry
change? In fact over the sample period there was very little change in the percentage of workers
finding reemployment in the same industry and occupation. The percentage of workers changing
industry fell by 0.3% between 1991 and 2005. In the same period, occupation changes increased
1.1%. These small changes were offset by a fall in career changes of 4.5%. The net effect is that
3.8% more workers undergoing job change remained in the same industry and occupation. Thereis no support for the argument that an increase in the percentage of job changers losing specificcapital explains the increase in job change penalties.
If about same percentage of workers remained in their industry and occupation throughjob change, was it the case that the penalties associated with industry change grew? Figure 7
displays two points. First, industry change penalties actually fell during the sample period.
Second, wage differentials between industries were falling. Hence, increasing job change
penalties do not appear to be driven by increasing penalties for changing industry. This point wasalso evident in figure 6 that showed that the costs of changing industry and occupation
diminished, as did the cost of going to a smaller firm upon reemployment.
The only labor market condition that we can point to in suggesting a reason for theincreasing job change penalties is the rising unemployment rate and the increase in thepercentage of involuntary job changes displayed in figure 1.

12

The inflow rate of job changers is the number of workers entering employment from previous employment with
unemployment duration of less than a year divided by the number of workers at the beginning of the year.

24

As for the older workers, we can only speculate as to why older job changers weretreated increasingly less favorably in the labor market. It may be that prior to Japan’s longrecession, firms could afford to honor the delayed payment contracts entered into by older
workers at their prior firms. A second potential explanation is that technological change induced
a loss of human capital for older workers relative to younger workers between 1993 and 2003.
The effects of a loss in human capital for older workers would be felt by those changing jobs.
Implicit contracts would protect workers remaining in continuous employment. This explanationis consistent with wage profiles remaining the same for career workers but falling in slope for the
newly hired workers in 2003. However, if the technological change was global, we are at a loss
to explain why job change penalties were not also increasing and related in ages in the US. To
illustrate this point, we provide a comparison between Japan and U.S. in the next section.

6. A Japan/U.S. Comparison
Interestingly, a comparison of the US and Japan in table 2 shows that the countries do not sharethe same trends in job loss penalties. We report Farber’s (2003) results using the Displaced
Worker Survey and tailor our sample and estimation to match his as closely as possible.13 Thebase category for the US is white males from 20-24 with 12 years of education and less than one
year of tenure. Given the base category, insignificant results for the age group dummy variableof those 25-34 is not surprising. Those aged 35-44 in the US sample ending in 1993 lose 13% in
earnings relative to the base category. In the later sample years for the US, this age category has
results that are statistically insignificant, though the coefficients become positive and close tosignificant in 2001. Similarly, for US workers aged 45-54, job change penalties are significantly

13

Both samples are restricted to those between 20 and 64 and undergoing full-time to full-time job transitions.
Rather than ordered logit, our estimates in table 1 are OLS to facilitate a comparison to Farber’s estimates.

25

greater in the 1993 and 1995 samples but lose significance thereafter. For US workers aged 5564, significantly negative effects are only evidenced in 1993 and 1995. They become
insignificant and positive by 2001. There is no evidence of increasingly age-related job change
penalties in the US, rather age-related penalties appear to decrease.

Table 2: US/Japan Comparison
Estimation method
Dependent variable
Data
Base category
PeriodOLS
Post-displacement log weekly earnings minus pre-displacement log
Wage Δ= -30%(Δ<-30%), -15%(-30%<Δ<-10%), 0(-10%<Δ
weekly earnings<+10%), 15%(10%<Δ<30%), 30%(Δ>30%)
Farber's U.S. estimates from DWS (weighted by CPS sampling weights)
White males, 20-24, 12 yrs education, less than 1 yr tenure

Japan: Employment Trend Survey Microdata, Involuntary Separtion
Males, 20-24, 12 yrs education(1)
1991-93(6)
1992-93(2)
1993-95(3)
1995-97(4)
1997-99(5)
1999-01(7)
1994-95(8)
1996-97(9)
1998-99(10)
2000-01

0.035
0.053
0.028
0.065
0.075
0.001
-0.014-0.001-0.027-0.006(0.050)(0.042)(0.062)(0.058)(0.051)(0.005)(0.006)**(0.006)(0.005)***(0.005)
0.030
0.015
0.052
-0.019
0.011
0.034
0.040
0.029
0.037
0.024
Female(0.025)(0.024)(0.033)(0.031)(0.029)(0.003)***(0.003)***(0.004)***(0.003)***(0.003)***
0.084
0.028
-0.041
0.038
0.000
0.037
0.042
0.050
0.045
0.025
Education < 12
(0.043)(0.042)(0.056)(0.060)(0.058)(0.003)***(0.004)***(0.004)***(0.004)***(0.004)***0.029-0.006-0.011-0.013-0.003-0.025-0.020-0.010-0.006-0.009
Education 13-15(0.028)(0.028)(0.040)(0.037)(0.035)(0.007)***(0.007)***(0.005)*(0.005)(0.004)**0.067-0.0220.052-0.0180.019-0.018-0.011-0.013-0.003-0.007
Education > 15
(0.032)*(0.030)(0.042)(0.040)(0.038)(0.005)***(0.005)**(0.004)***(0.004)(0.004)*-0.019
0.008
0.016
0.002
-0.095-0.010-0.002-0.007-0.003-0.006
Age 25-34(0.043)(0.043)(0.060)(0.058)(0.051)(0.006)(0.007)(0.006)(0.006)(0.005)-0.128-0.080-0.0510.018
0.081-0.023-0.011-0.021-0.021-0.025
Age 35-44(0.044)**(0.045)(0.061)(0.059)(0.053)(0.006)***(0.007)(0.007)***(0.006)***(0.006)***-0.139-0.095-0.044-0.046-0.101-0.054-0.055-0.070-0.054-0.074
Age 45-54(0.049)**(0.048)*(0.065)(0.063)(0.056)(0.006)***(0.007)***(0.006)***(0.006)***(0.005)***-0.222-0.150-0.1230.021
0.035-0.121-0.116-0.152-0.133-0.155
Age 55-64(0.060)**(0.063)*(0.083)(0.077)(0.072)(0.006)***(0.007)***(0.006)***(0.006)***(0.005)***
N
2032
1663
1558
1492
1804
6972
7049
7163
7858
9510
R-squared
0.057
0.052
0.027
0.015
0.032
0.160
0.155
0.217
0.201
0.221
Note: U.S. estimates come from Farber (2003). Farber also controlled for nonwhite, for tenure of 1-3, 4-10, 11-20, over 20, 2 yrs since job loss, 3 years since job loss but, since we lackthese variables, we do not report them for the U.S.
Both samples are restricted to those between 20 and 64 and undergoing full-time to full-time job transitions.

Constant

The base category for Japan is males from 20-24 with 12 years of education. Just as
with the US, the dummy variable estimates for Japanese males 25-34 are insignificant. For all of
the older age categories in Japan, workers have larger wage losses than those 20-24. Moreover,an increasing trend in the job loss penalty for the oldest workers is evident. The job change
penalty grows from 12.2% for workers 55-64 in 1993 relative to the base category to 15.5% by
2001. This contrasts with the US where workers 55-64 lost an additional 22.2% in earnings in26

1993 relative to the base category but had no significant difference beginning in 1997 andcontinuing to 2001.
If technological change reduced the productivity of older workers in Japan during thesample period, causing increasingly large job change costs, this was not evident in the U.S. The
technological change argument is not supported in this comparison.

7. Conclusion
Few studies of job change in the Japanese labor market appear in the literature. This is largely
due to the difficulty of obtaining worker level data. We study a large national sample of workers
to document basic trends regarding the wage implications of job change. In doing so, both
anticipated and novel results were found.
Because of a worsening in the labor market conditions during the period studied, atleast in terms of the unemployment rate, it is not surprising that workers changing jobs had
increasingly less favorable outcomes between 1991 and 2005. Given the strong returns toseniority in Japan, it was also not surprising that older workers had larger job change penalties
than younger workers.
The extent to which older workers suffered reduced wages from job change grew
during the sample period, a finding not seen in U.S. data. Correspondingly, there was a reductionin the return to age for those finding new employment in the Japanese labor market. We offered
two hypotheses for these findings. First, the reluctance of firms to honor the delayed payment
contracts that workers had entered into with their previous employers may have been increasing.
Second, technological change may have eroded the human capital of older workers. The effectsof this would be felt by the older workers needing to find new employment since continuously27employed workers are shielded by the implicit contracts operating within the firm. Both of thesehypotheses merit further investigation as we can say nothing conclusively about why older
workers are receiving a lower return on their experience.

28

7. References
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"Worker Displacement in Japan and Canada." In Peter Kuhn, ed., Losing Work, Moving On:
International Perspectives on Worker Displacement. Upjohn Institute for Employment Research,
Chapter 3.
Blau, Francine, and Lawrence Kahn. 2000. “Gender Differences in Pay.” The Journal of
Economic Perspectives, Vol. 14, No. 4. (Autumn), pp. 75-99.
Bognanno, Michael, and Lisa Delgado. 2008. “Earnings Losses Following Job Change in Japan:
Evidence from a Job Placement Firm.” Research in Labor Economics, Vol. 28, pp. 225-250.
Clark, Robert, and Naohiro Ogawa. 1992. "The Effect of Mandatory Retirement on Earnings
Profiles in Japan." Industrial and Labor Relations Review, Vol.45, No.2, pp. 258-66.
Fallick, Bruce. 1996. "A Review of the Recent Empirical Literature on Displaced Workers."
Industrial and Labor Relations Review, Vol. 50, No.1, pp. 5-16.
Henry Farber. 1997. "The Changing Face of Job Loss in the United States, 1981-1995."
Brookings Papers on Economic Activity. Microeconomics, pp. 55-142.
Henry Farber. 2003. "Job Loss in the United States, 1981-2001." Princeton University, Industrial
Relations Section, Working Paper #471.
Daniel Hamermesh. 1989. "What Do We Know About Worker Displacement in the UnitedStates?" Industrial Relations, Vol. 28, No.1 (Winter), pp. 51-59.
Masanori Hashimoto, and John Raisian. 1985. "Employment Tenure and Earnings Profiles inJapan and the United States." American Economic Review, Vol. 75, No.4, pp. 721-735.
Japan Institute of Labor, Policy and Training. 2005. “Labor Situation in Japan and Analysis,
2004/2005.” http://www.jil.go.jp/english/laborinfo/library/situation2004.htm.
Kambayashi, Ryo; Daiji Kawaguchi, and Izumi Yokoyama. 2008a. “Wage Distribution in Japan:
1989-2003.” Canadian Journal of Economics, Vol.41, No.4, pp.1329-1350.
Kambayashi, Ryo. (ed.) 2008b. Kaiko-kisei no Hoh to Keizai (Law and Economics in Dismissal
Regulation) Nihon-Hyoron-Sha.
Kato, Takao. 2001. “The End of Lifetime Employment in Japan?: Evidence from National
Surveys and Field Research.” Journal of the Japanese and International Economies, Vol.15, No.
4, pp. 489-514.
Kletzer, Lori. 1998. “Job Displacement.” The Journal of Economics Perspectives, Vol. 12, No. 1(Winter), 115-136.

29

Koike, Kazuo. 1988. Understanding Industrial Relations in Modern Japan. Translated by Mary
Saso. New York: St. Martin’s Press.
Jacobson, Louis S., Robert J. LaLonde and Daniel G. Sullivan. 1993. “Earnings Losses ofDisplaced Workers.” The American Economic Review, Vol. 83, No. 4, pp. 685-709.
Munch, Jakob Roland. 2006. “Career Changes and the Loss of Human Capital.” Unpublishedpaper, University of Copenhagen.
Rebick, Marcus. 2005. The Japanese Employment System: Adapting to a New Economic
Environment. Oxford: Oxford University Press.
Ruhm, Christopher J. 1991. “Are Workers Permanently Scarred by Job Displacements?”
The American Economic Review, Vol. 81, No. 1, pp. 319-324.
Statistics and Information Department. 2003. Ministry of Health, Labour and Welfare. “2003
Survey of Employment Management – Retirement Management,” Table 7.
Tachibanaki, Toshiaki. 1998. ‘Introduction to Wage Differentials: An International Comparison.’In Toshiaki Tachibanaki, ed., Wage Differentials: An International Comparison. New York: St.
Martin’s Press.
Topel, Robert. 1993. "What Have We Learned from Empirical Studies of Unemployment andTurnover?" American Economic Review, Vol. 83, No. 2, Papers and Proceedings of the Hundred
and Fifth Annual Meeting of the American Economic Association, pp. 110-11.

30

Appendix 1: Summary Statistics

Graduation
level

Variable
Wage change (%)
AgeFemaleJunior highHigh school
Junior college
University

Involuntary quit
Shukko assignment

Previous firm size

Firm size dummies

Present firm size

Previous occupation

Occupation dummy variables

Present occupation

Previous industry

Industry dummy variables

Present industry

MiningConstruction
Manufacturing
Electricity/ gas
Communication/ transportation
Wholesale/ retail/ restaurants
Finance/ insurance
Real estate
Service
Agriculture
MiningConstruction
Manufacturing
Communication/ transportation
Wholesale/ retail/ restaurants
Finance/ insurance
Service
Other
Only industry change dummy
Technician
Manager
Administration
Sales
ServiceCommunication/transportation
Production
OthersTechnician
Manager
Administration
Sales
ServiceCommunication/transportation
Production
Other
Only Occupational change dummy
Career change dummy
Working time
Present part time dummy
Change incontinuing part timeworking hours
from part time to full time
dummy
from full time to part time
5 - 2930 - 99
100 - 299
300 - 999
over 1000
Public organization
1-4
5 - 2930 - 99
100 - 299
300 - 999
over 1000

Year

Dummy for firm
size change

up
down
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005

N
562844562844562844562844562844562844562844562844562844562844562844562844562844562844562844562844562844562844562036562036562036562036562036562036562036562036562036562,036562844562844562844562844562844562844562844562844562751562751562751562751562751562751562751562751562036562036562844562844562844562844562844562844562844562844562844560527560527560527560527560527560527560527560527560527560527562844562844562844562844562844562844562844562844562844562844562844562844562844562844562844Mean-0.19937.492
0.395
0.118
0.527
0.155
0.200
0.169
0.115
0.011
0.045
0.475
0.010
0.058
0.061
0.024
0.023
0.293
0.007
0.005
0.069
0.333
0.071
0.106
0.047
0.236
0.126
0.250
0.143
0.054
0.174
0.063
0.120
0.042
0.322
0.083
0.148
0.064
0.158
0.100
0.144
0.043
0.256
0.086
0.074
0.260
0.186
0.120
0.070
0.067
0.062
0.178
0.224
0.206
0.328
0.023
0.026
0.213
0.233
0.187
0.123
0.195
0.709
0.539
0.170
0.081
0.069
0.056
0.053
0.060
0.068
0.075
0.065
0.059
0.068
0.069
0.064
0.071
0.069
0.073

1=female, 0=male
1=junior high, 0=other
1=high school, 0=other
1=junior college, 0=other
1=university, 0=other
1=if reason of quit is mandatory retirement, dismissal or end of contract, 0=other.
1=if reason of quit is shukko assignment, 0=other.

1=mining, 0=other
1=construction, 0=other
1=manufacturing, 0=other
1=electricity/ gas, 0=other
1=communication/ transportation, 0=other
1=wholesale/ retail/ restaurants, 0=other
1=finance/ insurance, 0=other
1=real estate, 0=other
1=service, 0=other
1=agriculture, 0=other
1=mining, 0=other
1=construction, 0=other
1=manufacturing, 0=other
1=communication/ transportation, 0=other
1=wholesale/ retail/ restaurants, 0=other
1=finance/ insurance, 0=other
1=service, 0=otherOther
1=change of industry & same occupation, 0=other
1=technician, 0=other
1=manager, 0=other
1=administration, 0=other
1=sales, 0=other
1=service, 0=other
1=communication/transportation, 0=other
1=production, 0=other
1=other, 0=occupation listed above
1=technician, 0=other
1=manager, 0=other
1=administration, 0=other
1=sales, 0=other
1=service, 0=other
1=communication/transportation, 0=other
1=production, 0=other
1=other, 0=occupation listed above
1=change of occupation & same industry, 0=other
1=change of industry & occupation, 0=other
1=part time, 0=full time
1=changed, 0=others
1=changed, 0=others
1=changed, 0=others
1=employment of 5 - 29, 0=other
1=employment of 30 - 99, 0=other
1=employment of 100 - 299, 0=other
1=employment of 300 - 999, 0=other
1=employment of over 1000, 0=other
1=public organization, 0=other
1=employment of 1 - 4, 0=other
1=employment of 5 - 29, 0=other
1=employment of 30 - 99, 0=other
1=employment of 100 - 299, 0=other
1=employment of 300 - 999, 0=other
1=employment of over 1000, 0=other
1=up or down, 0=invariant
1=up, 0=down or invariant
1=down, 0=up or invariant
1=1991, 0=other
1=1992, 0=other
1=1993, 0=other
1=1994, 0=other
1=1995, 0=other
1=1996, 0=other
1=1997, 0=other
1=1998, 0=other
1=1999, 0=other
1=2000, 0=other
1=2001, 0=other
1=2002, 0=other
1=2003, 0=other
1=2004, 0=other
1=2005, 0=otherAppendix 2a: Results of Ordered Logit (Involuntary Separation Sample)
(1)

(2)

(3)

(4)

0.039

(6)
(7)
(8)
(9)
(10)
(11)
Wage variation (five categories: -30, -15, 0%, 1: +10, +30)
1995
1996
1997
1998
1999
2000
2001
Involuntary Separations
Ordered Logit
prefecture
0.023-0.026
0.023
0.016
0.050
0.027
0.041

1991

1992

1993

1994

-0.0140.002-0.002(0.017)(0.019)(0.018)(0.018)**（0.016)（0.016)0.004-0.050-0.051(0.023)(0.021)**(0.020)**-0.097-0.078-0.034-0.088-0.084-0.123-0.098-0.114-0.073-0.078-0.075-0.095(0.021)***（0.018)***（0.018)*（0.019)***（0.020)***（0.019)***（0.016)***（0.016)***（0.014)***（0.013)***（0.015)***（0.014)***-0.116-0.214(0.077)(0.102)**-0.165-0.1990.000-0.386-0.363-0.103-0.293-0.321-0.096-0.035-0.024-0.0450.001(0.094)*(0.098)**（0.085)（0.080)***（0.080)***（0.085)（0.080)***（0.071)***（0.070)（0.059)（0.057)（0.060)-0.424-0.507（0.059)-0.476-0.820-0.588-0.690-0.769-0.699-0.393-0.740-0.458-0.403-0.461-0.241-0.222（0.084)***

Dependent variable
Data
Estimation method
Control
Age

Base = noBase = no changechangeBase = countiniously
fulltime

Changes in career
statusFirm sizechangeChanges in
employment status

Age2
Industry Change
Occupation Change
Career Change
Larger Firm
Smaller Firm
continuously partime
from partime to
fulltimefrom fulltime toparttime

Base=junior highHigh school
Junior college
University
30 - 99

Base = 5 - 29

Present firm size
dummy

Graduate level
dummy

Female dummy

100 - 299
300 - 999
over 1000
Construction(5)（0.016)（0.018)（0.017)***（0.014)*（0.014)***

(12)

(13)

(14)

(15)

2002

2003

2004

2005

0.007

0.008

-0.0010.021（0.012)（0.012)（0.013)（0.013)*(0.117)***(0.156)***(0.147)***(0.143)***（0.121)***（0.124)***（0.130)***（0.128)***（0.115)***（0.106)***（0.099)***（0.083)***（0.083)***（0.086)***-0.173-0.990-0.790-0.937-0.723-0.886-0.723-0.809-0.841-0.645-0.668-0.461-0.496-0.427-0.363(0.073)**(0.106)***(0.101)***(0.100)***（0.088)***（0.084)***（0.086)***（0.089)***（0.084)***（0.073)***（0.071)***（0.060)***（0.059)***（0.063)***（0.065)***

0.458

0.118

0.107

0.088

0.115

0.166

0.205

-0.002

0.121

0.330

0.276

0.421

0.365

0.245

0.298

(0.069)***(0.080)(0.082)(0.078)（0.069)*（0.070)**（0.070)***（0.074)（0.069)*（0.061)***（0.059)***（0.051)***（0.049)***（0.054)***（0.052)***-0.534-0.649-0.890-0.510-0.708-0.585-0.621-0.428-0.834-0.502-0.564-0.706-0.559-0.750-0.494(0.082)***(0.098)***(0.095)***(0.102)***（0.087)***（0.089)***（0.091)***（0.092)***（0.079)***（0.076)***（0.072)***（0.064)***（0.064)***（0.068)***（0.067)***

0.318

0.170

0.202

0.141

0.131

0.279

0.350

0.470

0.652

0.610

0.433

0.527

0.709

0.520

0.671

(0.116)***(0.148)(0.133)(0.118)（0.108)（0.113)**（0.112)***（0.122)***（0.102)***（0.080)***（0.077)***（0.068)***（0.065)***（0.072)***（0.070)***

1.296

1.584

1.061

1.823

1.357

1.678

1.386

1.890

1.743

1.664

1.738

1.681

1.893

1.712

1.833

(0.134)***(0.178)***(0.169)***(0.164)***（0.158)***（0.150)***（0.137)***（0.151)***（0.144)***（0.114)***（0.112)***（0.092)***（0.089)***（0.090)***（0.091)***-1.533-1.374-1.588-1.556-1.659-1.307-1.271-1.703-1.560-1.319-1.313-1.425-1.382-1.330-1.258(0.127)***(0.160)***(0.150)***(0.147)***（0.122)***（0.112)***（0.112)***（0.114)***（0.102)***（0.087)***（0.080)***（0.071)***（0.067)***（0.075)***（0.078)***

0.137

0.652

0.401

0.644

0.358

0.510

0.607

0.606

0.645

0.317

0.428

0.492

0.316

0.395

0.279

(0.071)*(0.088)***(0.083)***(0.087)***（0.075)***（0.079)***（0.078)***（0.080)***（0.075)***（0.065)***（0.063)***（0.055)***（0.052)***（0.058)***（0.057)***-0.180-0.052-0.262-0.301-0.324-0.261-0.303-0.374-0.377-0.094-0.264-0.343-0.1470.002-0.046(0.095)*(0.077)(0.078)***(0.079)***（0.070)***（0.075)***（0.081)***（0.084)***（0.079)***（0.073)（0.069)***（0.069)***（0.068)**（0.073)（0.071)-0.4800.035-0.544-0.055-0.588-0.230-0.189-0.222-0.082-0.143-0.261-0.218-0.190-0.0410.000(0.128)***(0.163)(0.152)***(0.158)（0.139)***（0.119)*（0.120)（0.128)*（0.117)（0.103)（0.100)***（0.089)**（0.087)**（0.094)（0.092)-0.1660.178-0.441-0.108-0.352-0.106-0.202-0.119-0.1680.131-0.086-0.149-0.073

0.127

0.222
（0.092)**(0.129)(0.147)(0.129)***(0.140)（0.119)***（0.115)（0.119)*（0.122)（0.115)（0.104)（0.099)（0.093)（0.089)（0.094)-0.230-0.4950.013-0.454-0.482-0.293-0.319-0.454-0.110-0.304-0.468-0.484-0.395-0.387-0.308(0.127)*(0.126)***(0.133)(0.133)***（0.117)***（0.129)**（0.127)**（0.137)***（0.126)（0.107)***（0.106)***（0.092)***（0.104)***（0.104)***（0.111)***-0.470-0.546-0.244-0.696-0.578-0.571-0.636-0.500-0.585-0.418-0.777-0.565-0.677-0.776-0.458(0.126)***(0.130)***(0.138)*(0.135)***（0.119)***（0.129)***（0.126)***（0.140)***（0.128)***（0.106)***（0.106)***（0.094)***（0.106)***（0.103)***（0.108)***-0.589-0.649-0.268-0.504-0.945-0.682-0.574-0.598-0.464-0.459-0.655-0.621-0.707-0.641-0.384(0.130)***(0.140)***(0.149)*(0.146)***（0.128)***（0.135)***（0.133)***（0.147)***（0.137)***（0.115)***（0.110)***（0.101)***（0.109)***（0.107)***（0.112)***-0.325-0.484-0.165-0.328-0.568-0.417-0.617-0.316-0.491-0.439-0.763-0.855-0.919-0.728-0.379(0.134)**(0.147)***(0.148)(0.150)**（0.130)***（0.137)***（0.136)***（0.148)**（0.136)***（0.114)***（0.111)***（0.100)***（0.110)***（0.107)***（0.113)***

0.300

0.593

-0.246

0.474

1.107

0.158

-0.108

0.232

0.605

0.196

-0.3920.186-0.0360.848-0.153(0.270)(0.203)***(0.253)(0.271)*（0.339)***（0.240)（0.252)（0.294)（0.304)**（0.263)（0.299)（0.230)（0.250)（0.313)***（0.292)Electricity/gas
Base = mining

Present industry dummy

Manufacturing

Communication/transportation
Wholesale/ retail/restaurants
Finance/ insurance
Real estate
Service

Administration
Base = technician

Present occupation dummy

Manager

Sales
ServiceCommunication/transportation
Production
Otherscutoff point

TAU30
TAU15
TAU0
TAU-15Observations
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1-0.2120.189-0.556

0.121

0.812

-0.354-0.561-0.073-0.039-0.604-0.888-0.289-0.4550.414(0.230)(0.186)(0.239)**(0.252)（0.333)**（0.231)（0.246)**（0.287)（0.290)（0.253)**（0.290)***（0.218)（0.237)*（0.300)（0.274)*0.173-0.786-1.068

0.266

0.054

-1.355-0.851-0.282-1.379-1.279-2.000-0.987-0.993-0.735-1.045（0.357)***(0.382)(0.291)***(0.316)***(0.371)（0.411)（0.347)***（0.463)*（0.447)（0.426)***（0.421)***（0.402)***（0.292)***（0.322)***（0.382)*-0.2080.253-0.575

0.285

0.595

-0.552-0.327-0.194-0.068-0.642-0.445-0.241-0.4750.282-0.578(0.301)(0.322)(0.290)**(0.306)（0.385)（0.282)*（0.284)（0.320)（0.330)（0.287)**（0.337)（0.249)（0.262)*（0.315)（0.281)**-0.317-0.060-0.080-0.4230.728-0.238-0.886-0.183-0.198-0.860-0.941-0.318-0.6200.089-0.562(0.273)(0.290)(0.304)(0.341)（0.381)*（0.288)（0.301)***（0.336)（0.323)（0.283)***（0.317)***（0.241)（0.252)**（0.313)（0.286)**-0.641-0.763-0.820-0.4000.142-0.532-0.827-0.060-0.321-0.320-0.855-0.240-0.3520.499-0.568(0.342)*(0.331)**(0.345)**(0.346)（0.402)（0.301)*（0.293)***（0.322)（0.361)（0.310)（0.326)***（0.259)（0.285)（0.342)（0.321)*-0.369-0.359-0.450

0.225

0.666

-0.564-0.621-0.280-0.412-0.521-1.291-0.324-0.618-0.029-0.349(0.419)(0.265)(0.309)(0.326)（0.379)*（0.279)**（0.286)**（0.325)（0.330)（0.284)*（0.325)***（0.272)（0.290)**（0.337)（0.308)-0.4410.058-0.492-0.2070.748-0.324-0.628-0.071-0.097-0.705-0.836-0.395-0.5190.331-0.366(0.237)*(0.190)(0.243)**(0.260)（0.337)**（0.236)（0.250)**（0.290)（0.290)（0.254)***（0.291)***（0.220)*（0.236)**（0.304)（0.275)0.380-0.358-0.300-0.346-0.555-0.201-0.031-0.386-0.106-0.340-0.349-0.060-0.205

0.000

0.053

(0.208)*(0.192)*(0.164)*(0.178)*（0.152)***（0.145)（0.152)（0.151)**（0.140)（0.128)***（0.128)***（0.112)（0.115)*（0.113)（0.116)-0.335-0.234-0.631-0.532-0.214-0.769-0.573-0.687-0.437-0.286-0.407-0.596-0.400-0.346-0.176(0.099)***(0.169)(0.147)***(0.166)***（0.141)（0.134)***（0.133)***（0.131)***（0.127)***（0.108)***（0.104)***（0.087)***（0.086)***（0.090)***（0.087)**-0.012-0.262-0.255

0.520

0.105

-0.015-0.025-0.321

0.038-0.3160.006-0.207-0.024

0.028

0.136

(0.153)(0.282)(0.236)(0.267)*（0.204)（0.197)（0.217)（0.184)*（0.177)（0.153)**（0.149)（0.125)*（0.118)（0.133)（0.124)

0.082

0.484

-0.032

0.303

0.226

0.014

-0.099

0.127

0.003

0.052

0.120

0.026

0.136

0.403

0.086

(0.251)(0.185)***(0.155)(0.173)*（0.146)（0.146)（0.152)（0.150)（0.136)（0.118)（0.111)（0.094)（0.096)（0.109)***（0.109)-0.613-0.0360.091-0.121

0.040

0.032

0.153

0.141

0.487

0.001

-0.019-0.047

0.315

0.330

0.657
（0.153)***(0.371)*(0.248)(0.213)(0.254)（0.240)（0.198)（0.220)（0.208)（0.214)**（0.189)（0.202)（0.158)（0.163)*（0.137)**

0.279

0.587

0.073

0.240

0.278

0.178

0.346

0.273

0.171

0.197

0.089

0.158

0.006

0.030

0.294

(0.127)**(0.164)***(0.138)(0.156)（0.132)**（0.126)（0.128)***（0.130)**（0.115)（0.100)**（0.095)（0.080)**（0.080)（0.084)（0.079)***-0.0920.164-0.045

0.424

0.071

-0.277-0.257-0.042

0.280

0.310

0.570

-0.084

0.046

0.254

0.135

(0.098)(0.174)(0.149)(0.165)**（0.145)（0.141)*（0.147)*（0.154)（0.151)*（0.136)**（0.125)***（0.113)（0.112)（0.131)*（0.153)-3.393-3.560-4.886-3.019-3.519-5.329-4.283-4.475-3.389-4.052-4.448-4.267-4.725-3.709-3.766（0.396)***(0.431)***(0.475)***(0.481)***(0.505)***（0.506)***（0.442)***（0.446)***（0.501)***（0.489)***（0.410)***（0.430)***（0.354)***（0.363)***（0.426)***-1.727-2.447-3.574-1.641-2.145-3.997-2.954-3.307-2.232-2.911-3.446-3.152-3.581-2.572-2.593(0.428)***(0.473)***(0.479)***(0.502)***（0.505)***（0.439)***（0.444)***（0.500)***（0.488)***（0.409)***（0.429)***（0.353)***（0.362)***（0.425)***（0.395)***

0.339

1.018

-0.255

1.487

1.010

-0.848

0.057

0.871

1.657

0.867

0.422

0.465

0.090

0.934

0.764

(0.428)(0.472)**(0.474)(0.502)***（0.504)**（0.435)*（0.441)（0.496)*（0.486)***（0.405)**（0.425)（0.350)（0.358)（0.422)**（0.392)*

2.574

3.709

2.080

3.525

3.239

1.146

2.239

2.600

3.514

2.317

1.971

2.071

1.643

2.454

2.424

(0.435)***(0.490)***(0.486)***(0.512)***（0.512)***（0.441)***（0.450)***（0.505)***（0.497)***（0.411)***（0.430)***（0.354)***（0.362)***（0.425)***（0.395)***4463.

-0.477

4256
.

4397
.

4010
.

5248
.

4928
.

4806
.

5238
.

5616
.

7002
.

7759
.

9650
.

10064
.

8640
.

8583
.

Appendix 2b: Results of Ordered Logit (Voluntary Separation Sample)
(16)

(17)

(18)

(19)

0.061

0.014

0.011

0.011

(21)
(22)
(23)
(24)
(25)
(26)
Wage variation (five categories: -30, -15, 0%, 1: +10, +30)
1995
1996
1997
1998
1999
2000
2001
Voluntary Separations
Ordered Logit
prefecture-0.005-0.011
0.003-0.027-0.020-0.014-0.006

1991

1992

1993

1994

-0.0350.001-0.022（0.005)***（0.006)**（0.007)（0.007)（0.007)（0.007)*（0.006)（0.007)***（0.008)**（0.008)*（0.008)（0.008)***（0.008)（0.008)***（0.008)*-0.114-0.037-0.035-0.037-0.017-0.011-0.031

0.005-0.010-0.013-0.0220.012-0.029-0.007-0.015（0.007)***（0.008)***（0.009)***（0.009)***（0.009)*（0.008)（0.008)***（0.010)（0.010)（0.010)（0.010)**（0.011)（0.010)***（0.010)（0.010)-0.049-0.164-0.186-0.169-0.115-0.109-0.112-0.106-0.098-0.0440.007-0.046

0.003

0.006

0.009

Dependent variable
Data
Estimation method
Control
Age

Base = noBase = no changechangeBase = countiniously
fulltime

Changes in career
statusFirm sizechangeChanges in
employment status

Age

2

Industry Change
Occupation Change
Career Change
Larger Firm
Smaller Firm
continuously partime
from partime to
fulltimefrom fulltime toparttime

Base=junior highHigh school
Junior college
University
30 - 99

Base = 5 - 29

Present firm size
dummy

Graduate level
dummy

Female dummy

100 - 299
300 - 999
over 1000

(20)

(27)

(28)

(29)

(30)

2002

2003

2004

2005

-0.014（0.026)*（0.030)***（0.034)***（0.036)***（0.035)***（0.031)***（0.029)***（0.035)***（0.036)***（0.033)（0.033)（0.036)（0.034)（0.033)（0.031)-0.203-0.231-0.256-0.217-0.215-0.205-0.235-0.180-0.382-0.191-0.141-0.186-0.117-0.161-0.150（0.042)***（0.044)***（0.051)***（0.055)***（0.051)***（0.046)***（0.043)***（0.051)***（0.053)***（0.050)***（0.050)***（0.052)***（0.049)**（0.048)***（0.048)***-0.096-0.159-0.125-0.155-0.098-0.119-0.143-0.134-0.131-0.101-0.061-0.130-0.129-0.053-0.080（0.025)***（0.027)***（0.030)***（0.032)***（0.031)***（0.028)***（0.026)***（0.032)***（0.034)***（0.031)***（0.032)*（0.035)***（0.033)***（0.031)*（0.031)***

0.424

0.436

0.393

0.237

0.293

0.286

0.340

0.244

0.246

0.298

0.336

0.344

0.299

0.235

0.273

（0.024)***（0.026)***（0.030)***（0.032)***（0.031)***（0.028)***（0.026)***（0.031)***（0.033)***（0.031)***（0.031)***（0.033)***（0.031)***（0.030)***（0.029)***-0.451-0.478-0.444-0.516-0.402-0.405-0.378-0.507-0.501-0.351-0.362-0.382-0.318-0.376-0.388（0.033)***（0.036)***（0.039)***（0.042)***（0.042)***（0.038)***（0.036)***（0.043)***（0.043)***（0.042)***（0.043)***（0.045)***（0.043)***（0.041)***（0.041)***-0.015-0.1060.017-0.057

0.182

0.206

0.253

0.218

0.348

0.377

0.413

0.461

0.363

0.387

0.206

（0.040)（0.042)**（0.046)（0.047)（0.043)***（0.039)***（0.037)***（0.043)***（0.042)***（0.039)***（0.041)***（0.043)***（0.040)***（0.040)***（0.039)***

1.103

1.271

1.266

1.274

1.474

1.455

1.605

1.756

1.720

1.687

1.662

1.936

1.882

1.710

1.611

（0.042)***（0.046)***（0.052)***（0.052)***（0.051)***（0.045)***（0.042)***（0.048)***（0.051)***（0.046)***（0.047)***（0.051)***（0.046)***（0.045)***（0.044)***-1.447-1.619-1.707-1.722-1.691-1.502-1.529-1.753-1.638-1.562-1.685-1.502-1.620-1.633-1.736（0.053)***（0.055)***（0.056)***（0.056)***（0.053)***（0.047)***（0.045)***（0.049)***（0.049)***（0.046)***（0.048)***（0.050)***（0.048)***（0.050)***（0.053)***

0.020

0.001

-0.001

0.015-0.061-0.088-0.125-0.047-0.050-0.151-0.150-0.148-0.029-0.100-0.101（0.024)（0.027)（0.030)（0.032)（0.031)**（0.028)***（0.026)***（0.031)（0.033)（0.030)***（0.031)***（0.033)***（0.032)（0.031)***（0.030)***-0.168-0.095-0.159-0.101-0.116-0.053-0.120-0.1350.011-0.026-0.078-0.006-0.190-0.134-0.118（0.027)***（0.031)***（0.036)***（0.038)***（0.039)***（0.038)（0.036)***（0.046)***（0.049)（0.050)（0.053)（0.061)（0.058)***（0.057)**（0.053)**-0.385-0.362-0.349-0.341-0.386-0.226-0.285-0.332-0.207-0.203-0.303-0.117-0.314-0.260-0.281（0.044)***（0.048)***（0.054)***（0.057)***（0.054)***（0.046)***（0.044)***（0.055)***（0.058)***（0.057)***（0.060)***（0.067)*（0.064)***（0.063)***（0.059)***-0.173-0.282-0.294-0.244-0.281-0.042-0.089-0.176-0.091

0.009-0.181

0.090-0.181-0.177-0.158（0.043)***（0.048)***（0.055)***（0.059)***（0.057)***（0.052)（0.049)*（0.060)***（0.063)（0.061)（0.063)***（0.070)（0.067)***（0.065)***（0.061)***-0.251-0.264-0.212-0.118-0.330-0.115-0.153-0.145-0.230-0.224-0.194-0.208-0.153-0.277-0.243（0.044)***（0.046)***（0.051)***（0.051)**（0.051)***（0.049)**（0.048)***（0.057)**（0.056)***（0.056)***（0.058)***（0.064)***（0.065)**（0.066)***（0.067)***-0.470-0.496-0.434-0.345-0.438-0.311-0.316-0.281-0.507-0.380-0.469-0.247-0.165-0.330-0.204（0.045)***（0.047)***（0.053)***（0.053)***（0.053)***（0.051)***（0.049)***（0.059)***（0.058)***（0.057)***（0.058)***（0.064)***（0.066)**（0.065)***（0.064)***-0.484-0.537-0.409-0.276-0.443-0.329-0.208-0.263-0.324-0.258-0.423-0.387-0.215-0.281-0.191（0.046)***（0.049)***（0.055)***（0.057)***（0.056)***（0.053)***（0.051)***（0.061)***（0.060)***（0.059)***（0.060)***（0.067)***（0.068)***（0.066)***（0.067)***-0.307-0.392-0.277-0.203-0.334-0.279-0.212-0.269-0.411-0.310-0.369-0.346-0.151-0.224-0.178over 1000
Construction

Electricity/gas
Base = mining

Present industry dummy

Manufacturing

Communication/transportation
Wholesale/ retail/restaurants
Finance/ insurance
Real estate
Service

Administration
Base = technician

Present occupation dummy

Manager

Sales
ServiceCommunication/transportation
Production
Otherscutoff point

TAU30
TAU15
TAU0
TAU-15Observations
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1（0.047)***（0.050)***（0.057)***（0.058)***（0.056)***（0.054)***（0.051)***（0.062)***（0.061)***（0.060)***（0.060)***（0.068)***（0.068)**（0.067)***（0.067)***-0.164-0.163-0.513

0.329

0.033

0.014

0.149

-0.237-0.471-0.344-0.226-0.1890.174-0.189-0.083（0.094)*（0.098)*（0.123)***（0.128)**（0.117)（0.126)（0.123)（0.127)*（0.152)***（0.153)**（0.169)（0.198)（0.182)（0.258)（0.232)-0.399-0.401-0.7230.018-0.168-0.180-0.017-0.365-0.490-0.422-0.370-0.200

0.032

0.087

0.176

（0.085)***（0.087)***（0.110)***（0.115)（0.106)（0.116)（0.115)（0.115)***（0.142)***（0.141)***（0.157)**（0.185)（0.168)（0.245)（0.217)-0.630-0.550-1.001-0.099-0.350-0.315-0.462-0.532-0.799-0.914-0.542-0.316-0.581-0.920-0.005（0.151)***（0.158)***（0.191)***（0.204)（0.181)*（0.190)*（0.204)**（0.222)**（0.266)***（0.280)***（0.271)**（0.281)（0.237)**（0.335)***（0.307)-0.053-0.030-0.5690.323-0.077-0.178-0.004-0.099-0.458-0.490-0.448-0.197-0.162-0.043-0.006（0.102)（0.105)（0.123)***（0.131)**（0.121)（0.127)（0.126)（0.130)（0.156)***（0.153)***（0.170)***（0.203)（0.183)（0.249)（0.222)-0.523-0.453-0.943-0.005-0.337-0.484-0.304-0.552-0.800-0.669-0.839-0.320-0.394-0.294-0.030（0.098)***（0.101)***（0.125)***（0.133)（0.122)***（0.127)***（0.126)**（0.127)***（0.151)***（0.149)***（0.165)***（0.193)*（0.175)**（0.249)（0.220)-0.716-0.585-0.628-0.062-0.410-0.023-0.191-0.312-0.430-0.288-0.459-0.067-0.024-0.0630.114（0.112)***（0.125)***（0.146)***（0.147)（0.141)***（0.141)（0.142)（0.137)**（0.164)***（0.162)*（0.176)***（0.206)（0.189)（0.259)（0.231)-0.445-0.150-0.7450.097-0.421-0.432-0.403-0.505-0.593-0.289-0.731-0.236-0.152-0.2760.403（0.110)***（0.131)（0.140)***（0.151)（0.141)***（0.140)***（0.141)***（0.143)***（0.168)***（0.159)*（0.177)***（0.217)（0.208)（0.266)（0.237)*-0.506-0.469-0.6930.011-0.220-0.309-0.265-0.525-0.651-0.597-0.522-0.346-0.266-0.1250.042（0.087)***（0.089)***（0.112)***（0.118)（0.109)**（0.118)***（0.118)**（0.118)***（0.144)***（0.142)***（0.158)***（0.187)*（0.169)（0.246)（0.218)-0.160-0.1690.027-0.214-0.251-0.151-0.145-0.2880.033-0.091-0.197-0.191-0.238-0.080-0.182（0.070)**（0.085)**（0.090)（0.095)**（0.098)**（0.087)*（0.081)*（0.092)***（0.095)（0.084)（0.082)**（0.086)**（0.084)***（0.080)（0.077)**-0.370-0.362-0.320-0.406-0.483-0.392-0.456-0.565-0.388-0.294-0.287-0.385-0.445-0.265-0.248（0.040)***（0.044)***（0.050)***（0.055)***（0.053)***（0.046)***（0.041)***（0.048)***（0.051)***（0.047)***（0.046)***（0.048)***（0.047)***（0.045)***（0.044)***-0.131-0.1350.001-0.133-0.195-0.160-0.214-0.270-0.095-0.170-0.133-0.276-0.190-0.227-0.180（0.055)**（0.058)**（0.066)（0.070)*（0.067)***（0.058)***（0.056)***（0.062)***（0.065)（0.058)***（0.060)**（0.064)***（0.060)***（0.058)***（0.056)***

0.231

0.050

0.108

0.040

-0.174-0.110-0.140-0.146-0.011-0.090-0.107-0.133-0.167-0.048-0.232（0.071)***（0.048)（0.052)**（0.055)（0.053)***（0.048)**（0.046)***（0.051)***（0.054)（0.051)*（0.050)**（0.051)***（0.050)***（0.052)（0.052)***-0.005-0.006

0.286

0.226

0.315

0.394

0.382

0.184

-0.073

0.230

0.245

0.221

0.214

0.234

-0.009（0.088)（0.075)***（0.081)***（0.087)***（0.084)***（0.076)***（0.073)**（0.087)（0.095)**（0.090)***（0.093)**（0.105)**（0.096)**（0.080)（0.082)

0.305

0.029

0.069

0.034

-0.124-0.015-0.080-0.123-0.0670.031-0.137-0.061-0.040-0.0480.011（0.046)***（0.043)（0.050)（0.054)（0.051)**（0.046)（0.041)**（0.049)**（0.051)（0.046)（0.046)***（0.050)（0.047)（0.044)（0.042)0.107-0.139-0.0460.072-0.187-0.116-0.144-0.233-0.1420.005-0.252-0.197-0.365-0.358-0.352（0.038)***（0.055)**（0.060)（0.063)（0.063)***（0.059)**（0.056)***（0.067)***（0.081)*（0.076)（0.075)***（0.079)**（0.075)***（0.097)***（0.107)***-3.445-4.305-4.196-3.484-4.023-4.215-3.958-4.750-4.815-4.415-4.454-4.352-3.408-4.199-3.731（0.144)***（0.159)***（0.185)***（0.193)***（0.188)***（0.184)***（0.178)***（0.200)***（0.224)***（0.216)***（0.235)***（0.262)***（0.246)***（0.302)***（0.276)***-1.986-2.712-2.713-1.953-2.529-2.739-2.439-3.543-3.598-3.247-3.230-3.132-2.253-2.933-2.477（0.142)***（0.157)***（0.183)***（0.191)***（0.186)***（0.182)***（0.176)***（0.199)***（0.222)***（0.215)***（0.233)***（0.261)***（0.245)***（0.301)***（0.275)***0.270-0.491-0.5830.224-0.256-0.529-0.178-0.276-0.467-0.154-0.063-0.022

0.897

0.041

0.521

（0.142)*（0.156)***（0.182)***（0.191)（0.186)（0.182)***（0.176)（0.197)（0.221)**（0.214)（0.232)（0.260)（0.245)***（0.301)（0.274)*

2.664

1.887

1.696

2.405

1.899

1.621

2.056

1.354

1.149

1.508

1.608

1.566

2.516

1.656

2.146

（0.143)***（0.157)***（0.183)***（0.193)***（0.187)***（0.183)***（0.177)***（0.198)***（0.222)***（0.215)***（0.233)***（0.261)***（0.246)***（0.301)***（0.275)***

38614
.

31167
.

23763
.

21279
.

23321
.

28218
.

32418
.

26306
.

23151
.

26711
.

25591
.

21960
.

24675
.

25917
.

27556
.

Appendix 2c: Results of Ordered Logit (Shukko Assignment Sample)
(31)

(32)

(33)

(34)

0.122

0.185

0.158

0.134

(36)
(37)
(38)
(39)
(40)
(41)
Wage variation (five categories: -30, -15, 0%, 1: +10, +30)
1995
1996
1997
1998
1999
2000
2001
Shukko Assignment
Ordered Logit
prefecture
0.186
0.227
0.175
0.244
0.258
0.329
0.351

1991

1992

1993

1994

（0.048)**（0.043)***（0.041)***（0.035)***（0.036)***

Dependent variable
Data
Estimation method
Control
Age

Base = noBase = no changechangeBase = countiniously
fulltime

Changes in career
statusFirm sizechangeChanges in
employment status

Age2
Industry Change
Occupation Change
Career Change
Larger Firm
Smaller Firm
continuously partime
from partime to
fulltimefrom fulltime toparttime

Base=junior highHigh school
Junior college
University
30 - 99

Base = 5 - 29

Present firm size
dummy

Graduate level
dummy

Female dummy

100 - 299
300 - 999
over 1000
Construction(35)（0.035)***（0.038)***（0.046)***（0.050)***（0.044)***（0.044)***

(42)

(43)

(44)

(45)

2002

2003

2004

2005

0.223

0.182

0.316

0.126

（0.047)***（0.049)***（0.052)***（0.051)**-0.193-0.264-0.232-0.216-0.279-0.315-0.263-0.320-0.377-0.488-0.477-0.348-0.296-0.450-0.223（0.060)***（0.053)***（0.049)***（0.043)***（0.044)***（0.043)***（0.045)***（0.055)***（0.058)***（0.053)***（0.052)***（0.056)***（0.056)***（0.060)***（0.058)***0.144-0.085-0.556-0.0810.040-0.013-0.255-0.454-0.0930.391-0.317-0.294-0.200-0.134-0.098（0.178)（0.160)（0.151)***（0.136)（0.138)（0.126)（0.127)**（0.166)***（0.174)（0.153)**（0.150)**（0.156)*（0.163)（0.166)（0.161)-0.3910.231-0.395

0.394

0.000

-0.304-0.361-1.172-0.3980.682-0.595-1.052-0.562-0.545-0.386（0.284)（0.260)（0.248)（0.224)*（0.201)（0.203)（0.217)*（0.239)***（0.270)（0.284)**（0.248)**（0.240)***（0.269)**（0.263)**（0.273)

0.268

0.047

-0.842-0.523-0.902-0.603-0.614-1.154-0.464-0.054-0.775

0.153

0.328

-0.6790.346（0.231)（0.219)（0.209)***（0.190)***（0.189)***（0.178)***（0.183)***（0.213)***（0.231)**（0.220)（0.242)***（0.260)（0.270)（0.240)***（0.272)-0.128-0.193

0.315

0.277

0.086

0.348

0.273

0.421

0.269

0.089

0.199

0.420

0.646

0.356

0.141

（0.178)（0.165)（0.149)**（0.139)**（0.126)（0.122)***（0.123)**（0.155)***（0.173)（0.157)（0.158)（0.160)***（0.167)***（0.168)**（0.156)-0.603-0.315-0.043

0.477

0.221

-0.646-0.160-0.031-0.195-1.066-0.813-0.752-0.070-0.049-0.701（0.228)***（0.227)***（0.213)（0.206)（0.186)**（0.180)（0.175)***（0.174)（0.226)（0.221)（0.234)***（0.218)***（0.227)***（0.223)（0.240)

1.466

2.138

1.041

-1.498-0.381

0.020

1.730

1.556

0.718

4.324

0.608

-1.342-2.464-1.5631.817（0.854)*（1.065)**（0.799)（0.678)**（0.564)（0.823)（0.628)***（0.993)（0.865)（0.831)***（0.789)（0.772)*（0.636)***（0.617)**（0.512)***

3.490

0.481

1.965

-0.1203.171-0.586

1.323

0.222

4.033

2.932

3.039

4.410

2.795

3.325

2.069

（0.602)***（0.942)（0.890)**（1.064)（0.620)***（0.969)（1.043)（1.685)（0.814)***（1.083)***（0.945)***（0.667)***（0.840)***（0.580)***（0.724)***

1.398

1.562

-0.697-3.203-0.662-1.419-3.0290.240-1.985-0.566-0.372-3.322-2.323-4.151-2.638（0.833)*（0.991)（0.989)（0.904)***（0.748)（0.855)*（0.564)***（1.223)（0.704)***（0.895)（1.087)（0.636)***（0.866)***（0.558)***（0.604)***

0.068

0.318

0.065

0.036

0.223

0.317

0.186

0.863

0.670

0.407

-0.073

0.327

0.588

0.519

0.412

（0.305)（0.258)（0.265)（0.208)（0.196)（0.212)（0.223)（0.297)***（0.302)**（0.301)（0.240)（0.236)（0.277)**（0.272)*（0.240)*-0.3590.018-0.094-0.565-0.185-0.371-0.293-0.581-0.214-0.014-0.770-0.575-1.528-0.132-0.086（0.245)（0.212)（0.218)（0.174)***（0.201)（0.206)*（0.220)（0.294)**（0.288)（0.239)（0.274)***（0.320)*（0.454)***（0.375)（0.360)-0.806

0.482

0.416

-0.839-0.749-0.284-0.516-0.520-0.573-0.152-0.997-0.316-1.691-0.3560.344（0.393)**（0.370)（0.334)（0.306)***（0.294)**（0.286)（0.303)*（0.405)（0.396)（0.328)（0.360)***（0.395)（0.507)***（0.442)（0.421)-0.4600.271-0.030-0.495-0.465-0.214-0.428-0.423-0.1910.084-0.725-0.428-1.409

0.073

0.431

（0.297)（0.260)（0.254)（0.215)**（0.233)**（0.234)（0.247)*（0.327)（0.323)（0.279)（0.316)**（0.351)（0.475)***（0.403)（0.387)-1.5180.247-1.372-0.696-0.469

0.549

0.005

0.321

-1.689-0.419-2.009-0.377-0.693

0.162

0.296

（0.406)***（0.512)（0.629)**（0.505)（0.393)（0.413)（0.478)（0.614)（0.526)***（0.607)（0.667)***（0.594)（0.634)（0.657)（0.663)-1.167-0.101-1.599-0.266-0.637

0.272

0.339

-0.183-2.071-0.491-1.411-0.967-0.943

0.085

0.392

（0.393)***（0.487)（0.607)***（0.486)（0.378)*（0.395)（0.464)（0.576)（0.512)***（0.587)（0.652)**（0.565)*（0.628)（0.620)（0.631)-1.5860.326-1.362-0.708-0.558-0.144-0.177-0.811-2.110-0.377-2.242-1.249-0.832-0.4840.501（0.385)***（0.485)（0.607)**（0.482)（0.366)（0.384)（0.460)（0.566)（0.508)***（0.585)（0.650)***（0.558)**（0.620)（0.624)（0.629)-2.033-0.127-1.605-0.893-1.069-0.453-0.499-0.983-2.063-1.670-2.471-1.840-0.827-0.278-0.276（0.409)***（0.503)（0.619)***（0.500)*（0.376)***（0.393)（0.470)（0.577)*（0.511)***（0.602)***（0.658)***（0.569)***（0.634)（0.645)（0.639)0.121-0.637-0.536

0.346

1.154

0.626

-0.4660.108-0.0090.725-0.020

1.147

0.167

0.117

-0.511（0.435)（0.501)（0.517)（0.466)（0.491)**（0.392)（0.434)（0.553)（0.761)（0.562)（0.709)（0.630)*（0.796)（0.682)（0.629)Electricity/gas
Base = mining

Present industry dummy

Manufacturing

Communication/transportation
Wholesale/ retail/restaurants
Finance/ insurance
Real estate
Service

Administration
Base = technician

Present occupation dummy

Manager

Sales
ServiceCommunication/transportation
Production
Otherscutoff point

TAU30
TAU15
TAU0
TAU-15Observations
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1-0.371-0.311-1.525

0.076

0.349

1.036

-0.443

0.611

0.395

1.010

0.237

2.312

0.640

0.181

（0.287)（0.326)（0.433)***（0.399)（0.436)（0.344)***（0.397)（0.502)（0.731)（0.529)*（0.683)（0.596)***（0.745)（0.632)（0.559)-0.122-1.039-1.274

0.141

0.733

1.024

-0.593

1.406

0.825

1.861

0.940

2.867

1.333

0.284

0.600

（0.550)（0.448)**（0.529)**（0.502)（0.503)（0.431)**（0.544)（0.659)**（0.866)（0.691)***（0.824)（0.685)***（0.824)（0.711)（0.625)-0.450-0.466-0.676-0.210

0.574

0.904

-0.317

1.246

0.157

0.886

0.829

1.919

1.794

0.536

0.429

（0.403)（0.413)（0.519)（0.479)（0.520)（0.389)**（0.440)（0.559)**（0.783)（0.591)（0.745)（0.662)***（0.785)**（0.661)（0.590)-0.810-0.784-1.481

0.342

0.277

1.085

-0.923

0.117

0.712

0.401

-0.097

2.459

0.584

0.193

0.345

（0.473)*（0.564)（0.552)***（0.517)（0.527)（0.428)**（0.491)*（0.596)（0.828)（0.634)（0.736)（0.719)***（0.822)（0.712)（0.648)-0.165-0.973-1.332

0.855

0.176

0.266

0.014

1.066

0.548

1.197

0.636

0.602

-0.693-1.0900.229（0.537)（0.530)*（0.561)**（0.525)（0.533)（0.407)（0.444)（0.576)*（0.858)（0.667)*（0.808)（0.640)（0.792)（0.724)（0.715)-0.747-1.185-1.383-0.327-0.0450.884-0.686

0.716

0.892

1.517

0.276

1.192

-0.144-1.059-0.620（0.440)*（0.408)***（0.464)***（0.441)（0.478)（0.409)**（0.452)（0.597)（0.803)（0.607)**（0.740)（0.668)*（0.803)（0.728)（0.672)-0.755-0.304-1.188

0.006

0.204

0.728

-0.733

0.498

0.157

0.824

0.699

2.192

0.182

0.138

-0.256（0.322)**（0.351)（0.428)***（0.401)（0.441)（0.350)**（0.408)*（0.515)（0.740)（0.540)（0.690)（0.606)***（0.757)（0.658)（0.582)

0.249

0.165

-0.462

0.012

0.247

0.033

-0.110-0.165-0.200-0.057

0.009

0.331

-0.135-0.0410.018（0.212)（0.196)（0.180)**（0.163)（0.160)（0.149)（0.155)（0.204)（0.202)（0.179)（0.191)（0.193)*（0.200)（0.204)（0.194)-0.223-0.079-0.921-0.302-0.111-0.086-0.427-0.489-0.284-0.179-0.043-0.074-0.540-0.048-0.136（0.224)（0.198)（0.198)***（0.185)（0.172)（0.163)（0.167)**（0.212)**（0.228)（0.201)（0.211)（0.198)（0.212)**（0.218)（0.204)-0.838-0.032

0.111

0.148

0.295

-0.092-0.057-0.247-0.539-0.349

0.565

0.189

-0.494-0.5550.015（0.479)*（0.461)（0.355)（0.290)（0.251)（0.301)（0.330)（0.372)（0.371)（0.422)（0.420)（0.338)（0.394)（0.390)（0.403)0.521-0.471-0.819

0.456

0.969

-0.1720.658-0.300-1.263-2.020-1.577-0.393-0.527-0.7821.004（0.469)**（0.481)（0.393)（0.338)**（0.272)*（0.288)***（0.367)（0.336)*（0.458)（0.420)***（0.415)***（0.433)***（0.425)（0.537)（0.477)-0.203

0.107

0.135

0.434

0.031

1.543

-0.350-1.505-0.773-1.894-1.1660.892-1.219-0.1391.036（0.768)（0.469)（0.475)（0.508)（0.440)（0.448)***（0.392)（0.507)***（0.554)（0.618)***（0.467)**（0.711)（0.406)***（0.723)（0.486)**-0.9540.254-0.131-0.163

0.093

0.384

-0.403-0.441-0.5900.016-0.713

0.117

0.101

0.256

0.602

（0.403)**（0.218)（0.204)（0.169)（0.169)（0.168)**（0.171)**（0.216)**（0.234)**（0.185)（0.197)***（0.211)（0.232)（0.235)（0.223)***-0.685

0.051

1.105

-0.788-0.537-0.314-0.459-0.7530.767-0.972-0.443

0.539

0.255

0.433

1.782

（0.235)（0.393)***（0.345)**（0.282)*（0.331)（0.295)（0.297)**（0.457)*（0.385)**（0.522)（0.515)（0.576)（0.756)（0.648)***（0.441)-5.630-2.297-6.150-4.266-2.335-0.543-3.606-0.214-3.650-0.650-1.562-2.311-3.910-0.263-4.195（1.116)***（1.078)**（1.135)***（0.929)***（0.954)**-0.939（1.050)***-1.259（1.416)***-1.219-1.391（1.352)*（1.521)**-1.426（1.488)***-4.150-0.811-4.619-2.951-1.1590.564-2.4500.746-2.4560.750-0.568-1.323-3.0100.606-2.877（1.107)***（1.072)（1.130)***（0.925)***（0.951)（0.938)（1.048)**（1.258)（1.415)*（1.219)（1.390)（1.352)（1.520)**（1.427)（1.487)*

2.145

5.476

1.739

3.373

5.391

7.021

4.149

8.777

5.880

9.351

7.726

6.796

5.768

8.794

5.253

（1.103)*（1.081)***（1.120)（0.925)***（0.958)***（0.951)***（1.049)***（1.284)***（1.415)***（1.237)***（1.404)***（1.356)***（1.515)***（1.438)***（1.483)***

4.243

8.162

4.093

6.022

7.546

9.605

6.083

10.257

7.882

11.118

9.615

8.646

7.110

10.1737.247（1.116)***（1.108)***（1.136)***（0.950)***（0.973)***（0.975)***（1.060)***（1.303)***（1.453)***（1.258)***（1.427)***（1.367)***（1.529)***（1.451)***（1.500)***2483.

-0.078

3145
.

3304
.

4327
.

4841
.

5020
.

4828
.

4526
.

4141
.

4629
.

5070
.

4446
.

5059
.

4110
.

4547
.