KIER DISCUSSION PAPER SERIESKYOTO INSTITUTEOFECONOMIC RESEARCH
Discussion Paper No.961“Wage Profiles in the Japanese Dual Labor Market”

Hiroshi Teruyama and Hiroyuki Toda

January 2017

KYOTO UNIVERSITY
KYOTO, JAPANWage Profiles in the Japanese Dual Labor Market∗
Hiroshi Teruyama†and Hiroyuki Toda‡
January, 2017

Abstract
This study examines the dual structure of wage formation in regular and nonregular employment sectors in Japan. By using data from a series of surveys
conducted in the Tokyo metropolitan area during 2002–2014, two sectoral wage
functions and an employment status determination function are estimated simultaneously. The estimated results reveal several facts regarding the new era of theJapanese dual labor market. While the wages of regular workers rise with yearsof tenure and external experience, those of non-regular workers increase only withthe latter. The wage increases owing to experience are of a similar magnitude between employment statuses (except for female regular workers), and the firm-sizeand educational-background premiums exist only in the regular employees’ wages.
The study also shows that the slopes of regular workers’ wage-tenure profiles havebeen rather stable for more than 10 years since the early 2000s.
JEL Classification: J31, J42, J70, C34.
Keywords: dual labor market, wage profiles, non-regular workers, Japanese labormarket, switching regression model.

1

Introduction

The share of non-regular employees in Japan has been increasing since the 1990s andexceeded 30% by around 2010. It is argued that this increase induces the dual structurein the Japanese labor market and expands income inequality.1 Indeed, several studies
have indicated the persistence of a non-regular employment status, suggesting the polarization in the labor market2 Non-regular employees are considered to receive limited∗
This is the revised version of Works Discussion Paper No.10 (Recruit Works Institute) with thesame title. This work was supported by JSPS KAKENHI Grant Numbers 24243035, 24330074, and
16H03631, the Joint Usage/Research Program of the Institute of Economic Research, Kyoto University,
and JSPS Core-to-Core Program, A. Advanced Research Networks We thank Masahiro Abe, Daiji
Kawaguchi, and Soichi Ohta for their helpful comments.
†
Kyoto University‡
Kyoto University and Osaka University
1
Recently, researchers have started searching for the causes of the increase in the number of nonregular workers. See Ariga and Okazawa (2011), Kalantzis et al. (2012), Asano et al. (2013), Kitagawa(2014), and Miyamoto (2016).
2
See Teruyama and Toda (2016).

1

opportunities for long-term contracts and human capital accumulation. Consequently,
wages of such workers do not increase with tenure and remain at low levels compared tothose of regular workers, who benefit from the Japanese seniority-wage system. However, few empirical attempts have thus far been conducted to examine the diﬀerentials
of wage profiles between these two employment statuses based on micro data.3
Meanwhile, the collapse of the traditional Japanese employment system has been
demonstrated in relation to the changes in the seniority-wage system among regular
employees. Hamaaki et al. (2012) show that the age-wage profiles had been gradually
flattening during the 1990s and 2000s, among workers who continued to work for thesame employer since their graduation. Therefore, we focus on determining whether or
not the reduction in the slopes of wage profiles has been progressing in the 2010s.4
This study focuses on two empirical arguments: (i) the contrasting properties ofwage profiles between regular and non-regular workers, and (ii) the flattening of thewage profiles of regular workers. To this aim, we simultaneously estimate the wage
functions of the regular and non-regular employment, and the allocation mechanism ofworkers into these two employment statuses by using an endogenous switching regression
model.
An econometric test on the dual labor market hypothesis in Japan was pioneeredby Ishikawa and Dejima (1994). Adopting a switching regression approach by Dickens
and Lang (1985), they estimated the wage functions for the primary and secondary
sectors while determining the sector to which a worker belongs. They found diﬀerent
characteristics among the wage functions in the primary and secondary sectors, and also
found involuntary rationing in the former. Apart from the examined period, neither thenotion of dual labor market nor the type of switching regression model used in thisstudy are the same as those of Ishikawa and Dejima (1994). In the Dickens-Lang or the
Ishikawa-Dejima model, the two sectors are not observable in advance. The sector towhich a worker belongs must be determined in the estimation process. In our model,
the classification of workers into the two sectors (whether a regular or non-regular
employee) is known. We find similarities and dissimilarities between our results andtheirs regarding the dual structure of wage profiles, and discuss these points in Section
5.
The remainder of this paper proceeds as follows. Section 2 presents the method used
to estimate the wage functions in the segmented labor markets. Subsequently, our data
source and the construction of variables are explained. Section 3 provides the estimatedresults and Section 4 discusses them in comparison with some related studies. Section
5 concludes.

3

An exception is Yanagida and Miyoshi (2006).
See Rebick (2005) for the long-run transition of wage-age or wage-tenure profiles in Japan since the1960s.
42

2

Estimation of Wage Functions in Segmented Labor Markets

To estimate the wage functions in segmented labor markets, we employ the endogenous
switching regression model, which is described as follows. There are two types of wage
function according to the type of employment status (regular and non-regular). Let us
denote the wage function of regular (non-regular) employees with subscript 1 (subscript2). The wage functions are represented by:
w1i = β1⊤ Xi + u1i

if Ii = 1,

(1)

w2i = β2⊤ Xi + u2i

if Ii = 2,(2)where wji is the log of wage rate, that is, the log of hourly wages, for worker i when
he or she works in a regular (non-regular) job if j = 1 (j = 2), Xi is the vector ofdeterminants for worker i’s wage rate, and uji is the disturbance when the employment
status is j. Ii is an index for the employment status and is determined by()
Ii = 1 Ii∗ = δ ⊤ Xi + γ ⊤ Zi + ui > 0(3)where 1(·) denotes an indicator function with 1(s) = 1 if s is true and 1(s) = 2 otherwise.
Note that Ii is observable, while Ii∗ is not, and if Ii = 1 (Ii = 2), then worker i is in a
regular (non-regular) employment status voluntarily or involuntarily.
Let us refer to (3) as the switching equation. All the determinants of wage rates,that is, Xi , are also included in the switching equation. This is based on the ideathat the expected present value of lifetime benefits from a regular job relative to thatof a non-regular job, is the function of the present wage diﬀerentials between the two
employment statuses, that is, w1i − w2i . In addition, the relative expected present valueof the lifetime costs of working in a regular job to that of a non-regular job, and the
involuntary factors that prohibit a worker from having a regular job, are assumed todepend on the determinants of wage rates Xi and other variables Zi .
Disturbance in the switching equation, ui , is allowed to correlate with those in
the wage functions, u1i and u2i . Assuming that Xi and Zi are exogenous and the
disturbances ui , u1i , and u2i have a joint normal distribution with zero means, themodel can be estimated by the full-information maximum likelihood.

3

Data and Variables

3.1

Working Person Survey

We use the results of the Working Person Survey (WPS) carried out by the Recruit
Works Institute. It has been conducted biennially every September since 2000. The
purpose of the WPS is to reveal the status of working individuals and their attitude
toward employment. To this aim, the survey asks respondents about subjective recognition and the objective attributes related to their present and past jobs. The key
3

questions, for example, those regarding their position, age, and working hours, are kept
unchanged although various questions change across the survey years.
The data are gathered by an online survey via a dedicated website. The sample
size varies from about 6,000 to 17,000, and participants are chosen by random samplingfrom each population segmented by gender, age, and employment area. Subjects are
re-sampled every survey year. Thus, the WPS does not have a panel structure.
The coverage of the WPS is as follows. First, the respondents must be aged between 18 and 59 years; students are excluded.5 Second, respondents must have worked
at least one day during the past week in the month before the survey. Third, respondents must be “regular employees,” “contract or entrusted employees,” “temporary
workers,” “part-time workers,” “dispatched workers,” or “outsourced workers or freelancers”6 ; self-employed workers are excluded. Fourth, respondents must work in theTokyo metropolitan area.7
In this study, we adopt the survey results from 2002 through 2014, as some essential
variables used in the analysis can be constructed from respondents’ answers to thequestions only in these survey years.

3.2

Employment Status

As explained in Section 2, in this study, workers are divided into regular and non-regular
workers. In Japan, the so-called non-regular employment has several definitions. Fromthe viewpoint of diﬀerent statistical surveys published by the Japanese government,
the definition of non-regular employment could be based on three criteria: contract
length, working hours, and title/description of the workplace.8 The definition available
throughout the survey years is based on the title/description. The respondents are asked
about the “type of employment” (i.e., title/description), which consists of “regular
employment” and six other categories as stated in Section 3.1.9 Depending on theiranswers, we divide the respondents into regular and non-regular employees. Those
who choose alternatives other than “regular employment” are classified as non-regular
employees.

3.3

Wage Rates

The WPS asks questions on revenues, including taxes in the previous fiscal year, thatis, from April in the previous year to March in the survey year. Temporary and side5
Only the 2014 survey includes individuals aged from 60 to 69 years. We exclude respondents aged
above 59 years in this study.
6
The description “freelancer” appears only in the 2014 survey.
7This area consists of the Tokyo, Kanagawa, Chiba, and Saitama prefectures. The surveys before
2004 include respondents who work in the other large city areas (Kansai and Tokai). The respondents
who work in areas other than the Tokyo metropolitan area, as included in these surveys, are excludedin this study.
8
Kambayashi (2013) summarizes diﬀerent definitions of non-regular employees based on the representative Japanese government statistical surveys.
9“Type of employment” is a direct translation of the Japanese term used in the survey question.

4

revenues are excluded from the revenues. Regarding working hours, the survey enquires
about the average hours currently worked in a week. The hours worked include hoursof overtime work and exclude hours that involve commuting, mealtime, and breaks.
When a respondent works at more than two workplaces, he or she must report the
total hours worked across them. Based on the answers to these questions, we define
the wage rates, that is, hourly wages, by dividing the annual revenue by the annual
working hours, which is estimated at 365/7 times the average hours worked in a week.
Note that a period in which revenues are earned does not correspond to that in which
the working hours are estimated. In addition, these answers are personal statementswith no documentary evidence. Hence, we expect considerable estimation and reporting
errors in our wage rate data.
In estimating the model, we exclude individuals who report that the annual revenue
is zero. From the remaining sample, those with annual revenues under the first percentile and over the 99th percentile are dropped. Evidently, these sample treatments tosolve the data precision problem are limited. Nevertheless, the estimated wage profiles
reported in Section 4 reveal that the estimated parameters are stable over the years,
and their signs and sizes are plausible.

3.4

Explanatory Variables

The explanatory variables, Xi , in wage functions (1) and (2) are selected as follows.
Following the Mincer-type wage equations, we include years of tenure, years of external
experience, and their squares.10 The years of external experience are defined as thenumber of years from the year a worker obtains the first job to the year of finding
the current job. Although the WPS is not a panel survey, it has many retrospective
questions. The questionnaire asks about the year when the respondent obtained the firstjob after finishing school, and the age at which the respondent found the current job.11
Based on this information, the years of tenure and external experience are calculated.
Years of tenure and years of external experience represent the firm-specific and general human capital, respectively.12 Workers in the primary sector have an opportunityof human capital investments on the job, and their wages reflect the proper rewards tothem. This property of wage determination critically distinguishes the primary market.
If we cannot find such a property in one of the segmented labor markets, then the market is interpreted as secondary. Another explanation is possible for the positive eﬀect
10

Kawaguchi (2011) examines an estimation of the Mincer-type wage equation using Japanese data.
The WPS also reports the year when the respondent found the current job. There exist respondents
whose reported age and year of entering the current work place are inconsistent. As we expect that the
memory based on age is generally more accurate, we measure years of tenure as the number of years
from the age at which he or she found the current job to the age as of the survey.
12
Both general as well as firm-specific human capital may be accumulated under the current employer.
Thus, a part of the wage increases with tenure may belong to the general human capital. Nevertheless,we use the length of external experience instead of the total labor market experience, which include thelength of the current tenure, as years of tenure and years of total market experience are usually highly
correlated.
115

of tenure on wages. That is, a seniority-wage system works as an incentive device inthe primary market, facing competitive wages in the secondary market. Note that thisincentive view could be consistent with the human capital view.
In addition, firm size is considered a determinant of wages. The WPS asks about the
total number of employees in the firm where the respondent is working. A respondentchooses one of 13 firm-size categories, so we construct the firm-size dummy variables
by setting the size of five to nine employees as the base category. It is often stressedthat large-sized firms generally pay higher compensation than small-sized firms do, evenafter the discrepancy of individual employees’ productivity is adjusted. The source of
the “firm-size premium” can be explained in several ways. For example, it may be
because a large firm has more eﬃcient technology or economies of scale to raise each
employee’s productivity regardless of the worker’s innate ability.
Furthermore, the educational background is also considered. The WPS has sevencategories of educational backgrounds: junior high school, senior high school, vocational
school, junior college, technical college, college or university, and graduate school. We
construct six education dummies for which the junior high school graduate is the basecategory. An academic career is regarded as a process of accumulating general humancapital. Higher or longer education might correspond to higher productivity, which isconverted into the wage rate. It may also function as a signal for a worker’s innate
ability. Another proxy for abilities is a respondent’s self-assessment of his or her recordin the final junior high school grade. A respondent chooses an answer from five ranked
alternatives: upper, upper-middle, middle, lower-middle, and lower. We then constructfour dummy variables for which the lower rank is the base.13
The diﬀerences by gender in the evaluation of tenure (firm-specific human capital)and external experience (general human capital) are considered in the estimation. The
female dummy variable, and the cross-terms between the female dummy and years oftenure or years of external experience are included. Thus, a constant wage gap and thediﬀerent slope of wage profile are allowed between genders.
Other variables, that is, Zi , added to the determinants of wages in the switchingequation (3), are the marital status dummy (which takes 1 if married and 0 otherwise),the cross-term between the female dummy and the marital status dummy, and theunemployment rate in the year of the respondent’s entry into the labor market. The
unemployment rate at entry is included to examine “cohort eﬀects,” which is explainedin the next section.

4

Empirical Results

Table 1 shows the estimation results of equations (1), (2), and (3) for each survey year.
Let us start with wage-tenure profiles. Regarding regular workers’ wage functions,shown in Panel (1), the estimated coeﬃcients of years of tenure and their squares are
significant at the 1% level for all the years. Their signs suggest that wage-tenure profiles
13This question appears only in the 2012 and 2014 surveys.

6

have positive and decreasing slopes. However, in non-regular workers’ wage functions,shown in Panel (2), the estimated coeﬃcients of years of tenure and their squares are
not significant even at the 10 % level in most years.14 Therefore, we can conclude thatthe wages of non-regular workers do not increase with tenure length. This feature is in
sharp contrast to the case of regular workers.
Contrary to the wage-tenure profiles, the estimated wage-experience profiles show asignificantly positive and decreasing slope for both regular and non-regular workers inmost years.1516 The values of the estimated coeﬃcients of these terms are stable among
the survey years and are close between the employment statuses. That is, external
experience (general human capital) is reflected in the wage increases equally among
both regular and non-regular workers. This result suggests that firms oﬀer non-regularjobs that require general skills, and that opportunities exist for non-regular workers toaccumulate such human capital.
The diﬀerences in the slopes of wage-tenure profiles between men and women cannot
be observed for regular and non-regular workers in most years. The estimated coeﬃcients of the female dummy are also not significant in most cases. Therefore, we findlittle evidence on wage disparities regarding tenure between the genders. As for regular
workers’ wage-experience profiles, female workers’ profiles lie below male workers’ profiles since the estimated coeﬃcients of the cross-terms between the female dummy andeither external experience or its square are significantly negative in most years. Thisfeature is not found in non-regular workers’ wage-experience profiles.
We draw wage profiles in two dimensions: tenure and external experience. Figure 1
indicates the two wage profiles by using the estimates for male workers in each survey
year. (The profiles can also be applied to female workers except for the regular workers’
wage-experience profiles.) Wage profiles of regular employees have been quite stablefor over 10 years since the early 2000s. Tenure length raises wages more than external
experience does. If a worker continues to work under the same employer for 30 years, hisor her wage rate increases up to about 2.5 to 2.7 times as large as that at the beginning(the log diﬀerence in the wage rates between 0 and 30 years’ tenure is around 0.9). If aworker has a 30-year work experience in the labor market, his or her wage rate becomes
about 1.6 to 1.7 times as large as that at the beginning (the log diﬀerence in the wage
rates between 0 and 30 years’ tenure is around 0.5).17 For non-regular workers, Figure 1
shows that the wage-tenure profiles are unstable, although not statistically significant.
However, the wage-experience profiles are stable among years and the profiles almost
14
An exception is the estimates of the wage function in 2010 where the coeﬃcient of the tenure issignificantly negative, and that of its square is significantly positive, both at the 5% levels.
15
In non-regular workers’ wage functions, the significance levels of the estimated coeﬃcients of squared
external experience are around 10%.
16
Exceptions are non-regular workers’ wage functions in 2010 and 2012. The estimated coeﬃcientsof the years of external experience and their squares in the former are both insignificant. That of thesquared years of experience in the latter is insignificant with large p-values.
17
For female regular workers, increases in wage rates owing to external experience are smaller thanthose for male regular workers, as suggested by the estimated coeﬃcients of cross-terms between thefemale dummy and years of external experience or their squares.

7coincide with the regular workers’ wage-experience profiles.
Regarding the eﬀects of the firm size and educational background, the regular andnon-regular wage functions also show sharp contrasts. These factors increase the wagesof regular workers only. Large-sized firms pay more wages than small- or medium-sized
firms do. Overall, the wage rates in firms with more than 299 employees are 10–30%
higher than those in firms with less than 20 employees. Workers with a university degree
or higher receive wage rates 30–50% higher than the junior high school graduates. In
addition, good junior high school records increase the wage rates. However, there is no
influence of such firm-size or educational factors in non-regular wages.
Let us turn to the switching equation (3), shown in Panel (3). The estimatedcoeﬃcients of tenure length are significant at the 1% level with positive signs, andthe estimated values are rather stable over the survey years. This result implies that aworker with a long tenure has a high probability of being a regular employee, Pr(Ii = 1).
It might simply imply that the employment status is fixed at the beginning of thecurrent employment and that a regular worker changes jobs less frequently. However,
the estimated coeﬃcients of the squares of the tenure length are negative and significant
at the 1% level. That is, the marginal eﬀect of tenure on the probability is diminishing,although the coeﬃcients of square terms are not so large relative to those of tenure
length. This property may suggest that non-regular workers have chances of switching
to regular positions within the same firm during the early years of their employment,
and the chances diminish after a certain tenure length. In addition, this tendency might
be weakened for women as suggested by the estimated coeﬃcients of the cross-term of
tenure and female dummy, which are negative in all survey years and significant at the
1% or 5% level in most years.
On the contrary, external experience tends to reduce the possibility of obtaining a
regular job. That is, either the estimated coeﬃcient of years of external experience or
their squares show a significantly negative sign at the 5% level in most years. This
negative external-experience eﬀect possibly reflects the disadvantage of increasing age,which dominates the human capital eﬀect.18 This eﬀect may appear more strongly forfemales, as the estimated coeﬃcients of the cross-terms between the female dummy
and years of external experience are significantly negative at the 1% level. The paceof increase in the negative eﬀect of years of external experience along with age become
moderate for females, as indicated by the significant positive sign of the estimatedcoeﬃcients of their squares.19
18
The current age is not included in the explanatory variables since years of tenure, external experience, and age are almost collinear.
19
Related to these points, the estimated coeﬃcients of female dummies are consistently positive acrossthe survey years, although the estimates are not always significant. However, note that the significantly
positive coeﬃcient of that variable itself does not necessarily mean that women have a higher probabilityof regular employment, as it represents only a constant wage gap between male and female workers. In
our formulation of the switching equation, the diﬀerences in I ∗ in (3) between the genders should be
evaluated with the cross-terms with tenure, external experience, and their square terms as well as thefemale-dummy term. To assess the diﬀerence between men’s and women’s probabilities of being regular
employees, we calculate the predicted explained variable Ii∗ in the switching equation (3) for various

8The eﬀects of firm size on the regular-employment probability change their tendencies after 2008. Until 2008, workers in large-sized firms generally have greater opportunities for regular employment. However, this tendency is observed to disappear after
2008. There is no clear relation between the possibilities of regular employment and firm
sizes, and in some cases, the possibilities of regular employment are lower in large-sized
firms. For example, the estimated coeﬃcients of firm-size dummies for both the smallest and largest categories show significantly negative signs at the 1% level in the 2012or 2014 survey. An educational background aﬀects the determination of employment
status throughout the survey years. It is more possible for workers with a university or
higher degree to work in a regular employment status.
The probability of regular employment increases for married male workers and reduces for married female workers. The reverse signs of marital status dummies seemplausible in Japan since a sizable proportion of married women are homemakers, and are
not the primary providers of household income even when they work. The unemployment rates at the time of entry into the labor market show significant negative eﬀects
in several years. That is, a worker can have a regular position more easily at present if
the labor market conditions during his or her time of graduation are better. This represents “cohort eﬀects,” which means that the temporary business-cycle conditions at aworker’s time of entry into the labor market have a long-lasting influence on his or her
lifetime working conditions, such as earnings and employment stability. This finding is
consistent with Teruyama and Toda’s (2017) results. They find the existence of cohort
eﬀects by using the WPS of 2012 and 2014, although they conclude that cohort eﬀects
are not so influential quantitatively.20
The bottom of Table 1 shows the estimated correlation coeﬃcients between the disturbances of the switching equation (ui ) and each wage function (uji ). The correlationcoeﬃcients are denoted by ρj ; j = 1, 2, where the number 1 (2) corresponds to the wage
function of regular (non-regular) workers. The estimates of both ρ1 and ρ2 are negative
and the t-values show that they are significant at the 5% level in each survey year. If weconsider that the disturbances in the wage functions represent workers’ abilities, then
the negative correlations might be interpreted as that a worker who has the ability toearn more wages in both regular and non-regular jobs is more likely to take up the latter, ceteris paribus. However, it seems implausible to consider that a more able worker
combinations of years of tenure and external experience with the corresponding estimated coeﬃcients(including the female-dummy coeﬃcient) omitting other variables’ eﬀects. As a result, we find that theprobability of regular work for men dominates that for women in most of the realistic combinations ofyears of tenure and external experience. (Here, “realistic” means that the tenure length is not so longfor non-regular workers.)
20
We cannot determine the reason for the significant and positive estimated coeﬃcient of the unemployment rate at entry in the 2010 survey. As we have seen, the estimation results for the 2010 survey
show several diﬀerent characteristics from those in other survey years, and are overall contrary to theintuition. The results for 2010 might reflect the “unusual” adjustment process of non-regular employment in response to the global financial crisis, although we cannot point out a concrete reason for thediﬀerence in each estimate by simply relating to the unusual employment adjustment. See Hijzen et al.
(2015) for the employment adjustments in Japan during the global financial crisis.

9selects a low-paying job more often either voluntarily or involuntarily. Therefore, weshould consider another possibility. One is that the disturbances in the wage functions
might reflect the unobserved characteristics of a subdivided labor market (such as area,
occupation, or industry type) to which a worker belongs. If the non-regular labor market
is more competitive than the regular labor market since the latter is rationed, workers in
the former will be more sensitive to the wage levels. Consequently, a subdivided market
that pays more wages to its workers for reasons, such as higher productivity or greater
demand, could expand the opportunities for non-regular jobs whose wage levels exceed
the individuals’ reservation levels, and attract more workers. For the workers belonging
to such a market, the probability of working in non-regular jobs will be higher.

5Discussion

This section compares our results with previous related studies. Ishikawa and Dejima’s(1994) work is closely related to our study. They estimate the wage functions in segmented labor markets in the 1980s. They use micro data from the Basic Survey on
Wage Structure (BSWS) conducted by the Ministry of Health, Labour and Welfare in1980 and 1990. Before the 1990s, the share of non-regular workers was small and the
dualism in the Japanese labor market was mainly attributed to diﬀerences in firm size.
That is, large-sized firms formed the primary market and small-sized firms formed thesecondary market. However, Ishikawa and Dejima do not regard firm size as the only
factor for market segmentation. They do not specify in advance a factor causing themarket duality. Instead, following Dickens and Lang (1985), they adopt a switching regression model that defines the sectors in the estimation process. Their results confirm
the coexistence of diﬀerent types of wage functions. Educational background, age, andfirm size are identified as major factors for dividing workers into diﬀerent sectors.
A switching regression model adopted in our study specifies the ex-ante factor toclassify workers. This is because we consider that employment status is evidently themost critical factor to polarize the current Japanese labor market, where the shareof non-regular employees is increasing markedly. That is, we are interested in the
dual structure formed by expanding the non-regular status of employment, a labormarket situation distinct from that before the 1990s.21 Nevertheless, the estimatedwage functions in our study reveal some similarities to those by Ishikawa and Dejima.
First, we find the existence of distinct wage functions between the two sectors. Second,
wage-tenure profiles have steeper slopes than wage-experience profiles in the primarysector do (regular employment sector in our study). Third, the so-called “firm-size
premium” and “educational-background premium” are found in the wage functions of
the primary sector. That is, the properties of the primary-sector wage functions are
similar between these two studies despite the divergences in the estimation periods anddata sources.
21

Indeed, part-time workers are dropped from the sample in Ishikawa and Dejima’s (1994) estimation,and they report that there is no large diﬀerence in their results by this treatment.

10

Conversely, our results display contrasting properties to Ishikawa-Dejima’s regarding
the secondary-sector wage functions. In Ishikawa-Dejima’s secondary-sector wage function, positive slopes of both the wage-tenure and wage-experience profiles are observed,
although these slopes are much flatter than those in the primary-sector wage function.
However, in our estimation, no stable relation is found between the wage rate and tenure
for non-regular workers, although we find positive slopes of wage-experience profiles in
the non-regular wage functions, as did Ishikawa and Dejima. A contrasting finding in
our results is that the slopes of non-regular workers’ wage-experience profiles are close
to those of (male) regular workers’ wage-experience profiles. Furthermore, we could not
find clear evidence on both the firm-size and the educational-background premiums in
non-regular wages, in contrast to Ishikawa-Dejima’s finding of these premiums in thesecondary sector (with smaller values than those in the primary sector).
The above comparison reveals the possibility of transfiguration of the dual structure
after the 1990s along with the marked expansion of the non-regular employment sector.
Based on their findings that the wage rate in the secondary sector increases moderately
both with tenure and experience, Ishikawa and Dejima (1994) demonstrate that thesecondary sector they detect may correspond to a set of the lower-tier of primary sector
and the proper secondary sector in the sense of Piore.22 However, the modern secondary
sector found in our study is closer to the more traditional notion of the dual labormarket. To explain the diﬀerent characteristics of the duality between the periods
examined by Ishikawa and Dejima and by us, it seems plausible to consider that thesegmented labor market comprises more than two sectors, and that the relative sizes ofthe sectors are changing by reacting to the economic circumstances. In such a situation,
the characteristics of two major sectors are detected if the exact two sectors are assumed
in a switching regression model. For further investigation, it might be useful to extend
the switching regression model to allow more than two sectors, as Ishikawa and Dejima
suggest.23
Regarding the diﬀerentials between the genders, Ishikawa and Dejima (1994) findthat female workers’ tenure and external experience are less valued in wage determination. However, we find no such evidence except for regular workers’ wage-external
profiles. Rather, in our estimation, female workers have disadvantages in the possibilityof obtaining regular jobs, which worsens with tenure and external experience.
Another related study is by Yanagida and Miyoshi (2006). They estimate the wage
functions of regular and non-regular employees separately by using micro data froma panel survey conducted by Keio University (Keio Household Panel Survey). Their
22

For the lower-tier primary sector, see, for example, Piore (1975).
Genda (2008) finds a positive relation between annual revenue and tenure length among non-regular
workers even in the 2000s by using data from an originally conducted web survey in 2008, and also fromthe Employment Status Survey (Statistics Bureau, Ministry of Internal Aﬀairs and Communications)in 2002. The above discussion suggests that the discrepancy in his finding as compared to ours mightarise from the diﬀerence in the composition of the types of non-regular workers in the samples. Thatis, the properties of the wage function might diﬀer among the types of non-regular workers, such as
part-time, dispatched, temporary workers, and so on.
23

11estimation period is the mid-2000s (i.e., 2004 and 2005). They show that the wage
rates of non-regular workers do not reflect the tenure and the (total) labor market
experience, and that the wage rates of regular workers grow with tenure and regular
employment experience. Their results on the wage-tenure profiles are similar to ours.
Their interesting findings pertain to the eﬀects of past experiences, which this study
does not examine. They report that only experience in regular work increases thewages of regular workers. This suggests the lack of opportunity of general humancapital accumulation in non-regular jobs. Note that this implication contradicts our
results, which observe a significant positive evaluation of external experience in nonregular workers’ wages. On the contrary, Genda (2008) finds that past work experiencesin non-regular and regular jobs have significant positive eﬀects on the annual incomeof non-regular workers. His finding seems consistent with ours. To summarize, theeﬀects of past work experiences on non-regular (secondary-sector) wages have been
diﬀerently defined and estimated in the literature. Thus, it is fair to say that we have
not yet obtained conclusive evidence on the influence of past experiences on the wage
determination of non-regular workers.
Next, let us consider the transition of the slope of wage profiles in the regular(or primary) sector. Hamaaki et al. (2012) estimate age-wage profiles of “lifetime”
employees. These are the workers hired immediately upon graduation and have beenworking for the same employer ever since. Thus, the implication of ages on wages forlifetime employees corresponds to that of tenure. They use micro data from the BSWS,
which is the same data source used by Ishikawa and Dejima, for the period from 1989
to 2008. They show a clear result that a flattening of the profile (especially in later
career stages) gradually progresses in the 1990s. On the other hand, we do not findthe evidence for the continuation of the flattening of wage-tenure profiles between the
2000s and the former half of the 2010s. More precisely, Figure 1 shows that wage-tenure
profiles in 2002 and 2004 might be slightly steeper than those after 2006. However, thegaps among the slopes of wage-tenure profiles from 2006 to 2014 are rather small. This
comparison suggests that the flattening of wage-tenure profiles had progressed until the
middle of the 2000s, and then stabilized.2425

24
The estimated coeﬃcient of tenure (the square of tenure) in the primary-sector wage equation in the
1980s by Ishikawa and Dejima (1994) is 0.0768 (-0.000698). Our corresponding estimates for the regular
employment sector from 2002 to 2014 are between 0.0401 and 0.05402 (-0.000667 and -0.000368). This
comparison supports the assertion that a flattening of the wage-tenure profiles of regular workers was
progressing in the 1990s although the data sources and methods are diﬀerent between these studies.
25
Yamada and Kawaguchi (2015) estimate male full-time workers’ wage-tenure profile by quantiles
in some years between the 1990s to the 2000s by using the BSWS. The tendency to decrease foundin their estimated slopes of wage-tenure profiles is not as evident as that in Hamaaki et al. (2012).
Interestingly, they show that wage-experience profiles had been clearly flattened, which we do not findin our estimation. To discuss the reason for the diﬀerence is beyond the scope of this study.

12

6Concluding Remarks

In this study, we examine the existence of the dual structure of wage formation in thecurrent labor market in Japan regarding regular and non-regular employment status
as a determinant of the sector. By using data from a series of surveys conducted in
the Tokyo metropolitan area during 2002–2014, we estimate an endogenous switching
regression model where two sectoral wage functions and a sector switching equation are
estimated simultaneously. In particular, we focus on the patterns of wage-tenure andwage-experience profiles between employment statuses across years.
The results are close to the dual labor market view. Several properties of wage
determination in the dual structure are revealed. The regular workers’ wages increase
with years of tenure and external experience and the former shows larger eﬀects. In
contrast, non-regular workers’ wages increase only with external experience. The sizeof wage increase due to experience for non-regular workers is found to be almost similar
to that for male regular workers. Moreover, the slopes of regular workers’ wage-tenure
profiles are stable over more than 10 years since the early 2000s. In addition, thefirm-size and the educational-background premiums exist only in the wages of regular
employees.
The length of the tenure, firm size, and educational level are the determinantsof employment status. Among them, the eﬀects of firm size have recently changed
their pattern and do not show a monotonic relation with the probability of regular
employment. There is little diﬀerence in the eﬀects of tenure on the wages between
male and female workers. Rather, wage diﬀerentials between the genders arise from thediﬀerences in the probability of obtaining a regular job, where weaker eﬀects of tenureand external experience on the probability are found for female workers.
An essential property of the dual labor market is the rationing of primary jobs.
This property implies that labor mobility between the primary and secondary sectors is
involuntarily inactive. Following Dickens and Lang (1985), Ishikawa and Dejima (1994)
examined the rationing of the primary jobs by testing coeﬃcient restrictions inducedby the voluntary choice condition in the model and obtained evidence in support ofrationing. Our study does not pursue that direction, since the test seems to depend on
restrictive assumptions on the structure of individual workers’ decision making. Instead,
we examine the persistency of employment status by controlling for individual attributes
and preferences in a companion paper, where we find the evidence for the involuntary
nature of non-regular employment (see Teruyama and Toda, 2017).
Finally, some issues remain for future research. Note that there is a long history of
debates on the possibility of endogeneity of tenure length in the wage equations.26 In addition, the form of switching equation depends on the idea of static decision making on
26

See, for example, Abraham and Faber (1987), Altonji and Shakotko (1987), Topel (1991), andBuchinsky et al. (2010). See also Toda (2008) for wage profiles in Japan. In the literature, endogeneity
is considered to be caused by unobservable individual workers’ abilities or quality of job match. However,
our results suggest the necessity to consider the heterogeneity in the demand side of labor, as mentioned
in Section 4.

13

the employment status and may be inadequate to capture a realistic dynamic decision.
A critical diﬀerence between our switching equation and that induced by the individual
dynamic maximization is the existence of the state dependence, that is, the previous period’s employment status in explanatory variables, which again increases the possibilityof an endogeneity problem. Extending the model by accounting for a dynamic structureof forward-looking decision making and individual heterogeneity, and estimating it byusing a panel data is a promising direction for future research.27 Finally, the sample ofrespondents in the WPS is restricted to workers in the Tokyo metropolitan area, whichmight introduce bias into the results. It would be desirable to extend this study to usea sample that covers all areas in Japan.

27

Buchinsky et al. (2010) and Fernández-Kranz et al. (2015) are examples of this line of research.

14

15

500–999

300–499

100-299

50–99

30–49

20–29

10–19

Firm size (Number of employees)–4

Women ×External experience2

Women ×External experience

Women ×Tenure2

Women ×Tenure

External experience2

External experience

Tenure2

Tenure-.1247(-2.22) [0.020].06482(1.65) [0.099].1346(3.18) [0.001].09263(2.51) [0.012].1260(3.49) [0.000].1514(4.57) [0.000].1986(5.55) [0.000].226.04780(18.19) [0.000]-.000540(-7.65) [0.000].02913(10.55) [0.000]-.000396(-3.79) [0.000]-.01646(-3.22) [0.001].000312(1.94) [0.052].003407(0.70) [0.482]-.000382(-2.22) [0.027]

2014 survey-.0865(-1.88) [0.060].03416(0.90) [0.366].05265(1.34) [0.180].1070(2.72) [0.007].08875(2.49) [0.013].1283(3.88) [0.000].1921(5.05) [0.000].2232.04421(15.69) [0.000]-.000462(-5.84) [0.000].02419(8.20) [0.000]-.000222(-1.80) [0.072]-.003902(-0.78) [0.434]-.00004(-0.27) [0.787].00891(1.68) [0.092]-.000540(-2.63) [0.008]

2012 survey-.1137(-2.15) [0.031]-.001871(-0.05) [0.963].03283(0.82) [0.412].01533(0.41) [0.685].04131(1.11) [0.265].05146(1.59) [0.112].1208(3.20) [0.001].1165.04459(16.26) [0.000]-.00049(-6.56) [0.000].02714(9.34) [0.000]-.000309(-3.00) [0.003]-.009892(-1.88) [0.060].00012(0.72) [0.469]-.001047(-0.21) [0.830]-.000262(-1.58) [0.114]

2010 survey-.07845(-1.87) [0.062]-.0429(-1.43) [0.152].000453(0.01) [0.990].02242(0.71) [0.481].009685(0.32) [0.753].06284(2.23) [0.025].09476(3.09) [0.002].1251.04343(18.00) [0.000]-.000454(-7.22) [0.000].02503(9.94) [0.000]-.000276(-2.95) [0.003].005349(1.02) [0.306]-.000319(-1.93) [0.054]-.01754(-3.90) [0.000].000123(0.79) [0.427]

2008 survey-.05246(-1.29) [0.196].04093(1.39) [0.165].06109(1.70) [0.090].08987(3.03) [0.002].1231(4.00) [0.000].1146(4.18) [0.000].1481(4.67) [0.000]
.1254

.0401
(13.63) [0.000]-.000368(-5.23) [0.000].02975(11.09) [0.000]-.000492(-5.08) [0.000].002939(0.58) [0.562]-.000229(-1.54) [0.125]-.02333(-4.82) [0.000].000341(2.14) [0.032]

2006 survey

Panel (1): Regular workers

Table 1: Results of Endogenous Switching Regression Models for Regular and Non-regular Wage Determination.009577(0.21) [0.836].1645(4.54) [0.000].1274(3.35) [0.001].1387(3.80) [0.000].1439(4.28) [0.000].1731(5.40) [0.000].1631(4.09) [0.000].2063.05402(19.68) [0.000]-.000667(-9.07) [0.000].02916(10.42) [0.000]-.000365(-3.51) [0.000].000525(0.08) [0.937]-.000138(-0.56) [0.572]-.01629(-2.71) [0.007].000197(0.88) [0.381]

2004 survey.01086(0.35) [0.730]-.02861(-1.22) [0.221].000306(0.01) [0.991].03451(1.50) [0.134].06419(2.80) [0.005].06015(2.85) [0.004].09129(3.68) [0.000].1228.04821(26.48) [0.000]-.000497(-10.56) [0.000].02654(12.96) [0.000]-.000276(-3.57) [0.000]-.000299(-0.07) [0.944]-.000031(-0.19) [0.850]-.01354(-3.47) [0.001]-.000044(-0.28) [0.779]

2002 survey

16

Constant

Women

Graduate school

University

Technical college

Junior college

Vocational school

High school

Educational background

Lower middle

MiddleUpper middle

Upper

Junior high school record

Public agencies

5000–

2000–4999

1000–1999.04425(1.20) [0.229]-2.766(-19.53) [0.000].2146(1.61) [0.108].2896(2.16) [0.031].2713(2.01) [0.045].3428(2.47) [0.014].4057(3.05) [0.002].5687(4.25) [0.000].1897(4.81) [0.000].1227(3.12) [0.002].1216(3.06) [0.002].1193(2.79) [0.005](6.52) [0.000].2463(6.92) [0.000].2534(6.82) [0.000].3058(9.21) [0.000].2767(7.66) [0.000]-.09493(-2.63) [0.008]-2.52(-17.40) [0.000].04465(0.34) [0.733].1320(1.01) [0.312].1714(1.29) [0.198].1580(1.16) [0.245].2293(1.77) [0.077].4089(3.12) [0.002].1843(5.37) [0.000].122(3.53) [0.000].1133(3.28) [0.001].1012(2.55) [0.011](6.20) [0.000].2901(7.28) [0.000].2671(7.32) [0.000].3111(9.13) [0.000].2526(7.09) [0.000]-.03151(-0.78) [0.435]-2.348(-20.54) [0.000].06584(0.61) [0.540].1546(1.43) [0.153].1452(1.32) [0.186].1828(1.59) [0.113].3036(2.84) [0.005].422(3.86) [0.000](3.22) [0.001].1891(5.05) [0.000].1396(3.92) [0.000].2960(8.94) [0.000].2561(6.66) [0.000]-.05614(-1.58) [0.115]-2.479(-53.27) [0.000].1565(4.31) [0.000].2131(5.59) [0.000].2055(4.47) [0.000].1714(3.38) [0.001].3658(9.93) [0.000].5088(9.67) [0.000](3.83) [0.000].1574(4.73) [0.000].1466(4.41) [0.000].1930(6.59) [0.000].2329(7.43) [0.000]-.04062(-1.15) [0.250]-2.494(-46.85) [0.000].1117(3.48) [0.001].1357(3.96) [0.000].2039(4.90) [0.000].2697(5.71) [0.000].3442(10.35) [0.000].5097(9.92) [0.000](3.57) [0.000].1846(6.10) [0.000].2261(7.34) [0.000].2780(9.47) [0.000].3068(9.63) [0.000]-.06815(-1.76) [0.078]-2.64(-55.75) [0.000].1251(3.61) [0.000].1948(5.18) [0.000].2001(4.51) [0.000].2414(4.80) [0.000].3256(9.02) [0.000].5392(10.65) [0.000](5.70) [0.000].2448(6.63) [0.000].2393(6.47) [0.000].3351(9.77) [0.000].3778(10.23) [0.000]-.1103(-4.15) [0.000]-2.458(-72.59) [0.000].1435(5.44) [0.000].219(7.72) [0.000].2457(7.94) [0.000].2292(6.00) [0.000].3663(13.60) [0.000].5218(14.48) [0.000](5.31) [0.000].1265(5.61) [0.000].172(7.42) [0.000].2016(9.54) [0.000].2311(10.43) [0.000]17

500–999

300–499

100-299

50–99

30–49

20–29

10–19

–4

Firm size (Number of employees)

Women ×External experience2

Women ×External experience

Women ×Tenure2

Women ×Tenure

External experience2

External experience

Tenure2

Tenure.2776(3.15) [0.002].1320(1.91) [0.057].02386(0.32) [0.752].04729(0.72) [0.471]-.06047(-1.02) [0.307].01345(0.24) [0.810].08890(1.21) [0.228].03602(0.56) [0.578]-.01592(-0.89) [0.374].000165(0.34) [0.737].02727(3.37) [0.001]-.000404(-1.62) [0.106].02077(1.30) [0.193]-.000515(-0.79) [0.430].01032(0.87) [0.382]-.000359(-1.10) [0.271]

2014 survey.000428(0.01) [0.995]-.1276(-2.12) [0.034]-.0515(-0.80) [0.430]-.1146(-1.94) [0.052]-.09561(-1.68) [0.094].01143(0.21) [0.836]-.03311(-0.57) [0.568]-.07199(-1.13) [0.259].01025(0.64) [0.525]-.000629(-1.15) [0.249].02007(3.07) [0.002]-.000172(-0.85) [0.397]-.008340(-0.57) [0.565].000617(0.99) [0.321].01601(1.30) [0.192]-.000571(-1.73) [0.084]

2012 survey.002529(0.03) [0.974]-.01665(-0.28) [0.780].01401(0.20) [0.840].02462(0.38) [0.704]-.007303(-0.12) [0.903].1081(1.96) [0.050]-.02354(-0.34) [0.731].01515(0.24) ([0.807]-.04269(-2.09) [0.037].001171(2.10) [0.036].003715(0.44) [0.659].000322(1.32) [0.188].03565(2.10) [0.036]-.000994(-1.67) [0.095].02935(2.06) [0.039]-.000940(-2.59) [0.010]

2010 survey-.02736(-0.45) [0.650]-.025(-0.53) [0.599]-.05155(-0.94) [0.346]-.009267(-0.17) [0.865].01416(0.28) [0.778].04011(0.74) [0.459]-.05709(-0.69) [0.487]-.03153(-0.40) [0.688]-.001857(-0.09) [0.926].000183(0.34) [0.734].0265(3.73) [0.000]-.000355(-1.65) [0.099]-.000187(-0.01) [0.992]-.00039(-0.59) [0.552]-.005588(-0.46) [0.644]
0.000000(0.02) [0.987]

2008 survey-.07561(-1.28) [0.200]-.05237(-1.05) [0.293]-.05238(-0.85) [0.396]-.0179(-0.32) [0.748]-.1117(-1.91) [0.056]-.06787(-1.16) [0.246]-.1329(-1.31) [0.189]-.09849(-1.16) [0.248].001602(0.10) [0.923].000254(0.65) [0.517].03047(3.49) [0.000]-.000408(-1.61) [0.108].007512(0.48) [0.631]-.000619(-1.13) [0.258]-.01297(-0.86) [0.391].000187(0.51) [0.611]

2006 survey

Panel (2): Non-regular workers-.06662(-0.76) [0.448].02656(0.35) [0.727].000341(0.00) [0.997]-.03312(-0.40) [0.692]-.009995(-0.12) [0.901]-.01427(-0.18) [0.860].1141(1.07) [0.286]-.003093(-0.03) [0.977].01567(0.42) [0.674]-.001176(-1.02) [0.308].03616(2.96) [0.003]-.00079(-2.18) [0.029].007785(0.20) [0.839]-.001016(-0.64) [0.523]-.02232(-0.89) [0.374].000761(1.32) [0.188]

2004 survey.04627(0.68) [0.497].005979(0.10) [0.916].06288(0.87) [0.385].01865(0.33) [0.744].003721(0.06) [0.956]-.08507(-1.13) [0.259].03829(0.40) [0.688]-.01727(-0.19) [0.850]-.02108(-0.69) [0.492].000502(0.76) [0.447].0324(4.25) [0.000]-.000424(-1.69) [0.090]-.01398(-0.66) [0.510].000605(0.80) [0.426].007853(0.33) [0.738]-.000145(-0.29) [0.773]

2002 survey

18

Constant

Women

Graduate school

University

Technical college

Junior college

Vocational school

High school

Educational background

Lower middle

MiddleUpper middle

Upper

Junior high school record

Public agencies

5000–

2000–4999

1000–1999.02462(0.37) [0.715]-2.828(-21.50) [0.000]-.09624(-0.96) [0.340]-.1227(-1.14) [0.254]-.1042(-0.96) [0.339]-.1561(-0.78) [0.473]-.08650(-0.72) [0.473].03086(0.18) [0.860]-.04615(-0.67) [0.504]-.07369(-1.10) [0.270]-.05604(-0.86) [0.390]-.007678(-0.12) [0.905].07531(1.21) [0.228).0677(1.01) [0.315).1333(2.23) [0.026].02110(0.26) [0.793].07665(1.24) [0.215]-2.729(-19.44) [0.000]-.1958(-1.84) [0.066]-.1325(-1.21) [0.228]-.1880(-1.70) [0.089]-.2868(-1.62) [0.106]-.1238(-1.06) [0.288].05036(0.33) [0.742].06579(1.16) [0.247]-.00499(-0.06) [0.950].01290(0.25) [0.805].02144(0.36) [0.719].03295(0.51) [0.609].01368(0.23) [0.821].07837(1.36) [0.174]-.1669(-2.04) [0.041]-.08248(-1.14) [0.253]-2.562(-18.33) [0.000]-.2268(-2.10) [0.036]-.2209(-1.95) [0.051]-.2207(-1.90) [0.058]-.3591(-1.83) [0.067]-.2185(-1.73) [0.084]-.03578(-0.19) [0.848].06664(0.94) [0.346].06805(1.01) [0.311].07634(1.26) [0.206]-.0254(-0.32) [0.746]-.1038(-1.47) [0.140]-2.706(-29.50) [0.000]-.03825(-0.79) [0.429]-.0305(-0.46) [0.644]-.05712(-0.95) [0.342]-.0575(-0.51) [0.610].006228(0.07) [0.943]-.09025(-0.17) [0.866].02871(0.44) [0.663].07595(0.93) [0.352].005979(0.08) [0.939]-.2656(-2.24) [0.025]-.03473(-0.49) [0.623]-2.837(-28.93) [0.000]-.01235(-0.24) [0.812].02303(0.34) [0.731].004633(0.07) [0.948].2266(1.54) [0.123].03617(0.45) [0.653].2271(0.79) [0.428]-.05744(-0.63) [0.530]-.1478(-2.01) [0.044]-.0264(-0.35) [0.726]-.1631(-1.66) [0.098].02986(0.21) [0.832]-3.149(-18.97) [0.000].0765(0.91) [0.364].1163(1.31) [0.189].1075(1.00) [0.316].2544(1.56) [0.119].1137(1.03) [0.301].05857(0.20) [0.841].000772(0.01) [0.994].04308(0.38) [0.702].08388(0.91) [0.363]-.07181(-0.46) [0.647]-.2417(-1.98) [0.048]-3.124(-20.06) [0.000].1102(1.87) [0.062].1124(1.45) [0.148].009057(0.10) [0.922].027(0.18) [0.857].07286(0.64) [0.524].3411(1.57) [0.117].03444(0.37) [0.711]-.004315(-0.04) [0.969].05289(0.52) [0.602]-.1263(-0.70) [0.483]

19

300–499

100-299

50–99

30–49

20–29

10–19

–4

Firm size (Number of employees)

Women ×External experience2

Women ×External experience

Women ×Tenure2

Women ×Tenure

External experience2

External experience

Tenure2

Tenure-.6597(-6.08) [0.000]-.3340(-3.54) [0.000]-.2011(-1.94) [0.051]-.01457(-0.16) [0.877]-.02024(-0.23) [0.817]-.05627(-0.67) [0.500]-.1014(-1.05) [0.296].1207(10.71) [0.000]-.002230(-6.34) [0.000]-.01855(-1.98) [0.047]-.000317(-1.00) [0.316]-.01991(-1.33) [0.184].000332(0.69) [0.491]-.05618(-4.96) [0.000].001734(4.67) [0.000]

2014 survey-.2239(-2.08) [0.037]-.08251(-0.91) [0.363]-.2598(-2.65) [0.008]-.1335(-1.39) [0.165]-.02642(-0.30) [0.761]-.2447(-2.99) [0.003]-.04483(-0.48) [0.629].1318(11.28) [0.000]-.002295(-6.04) [0.000]-.004722(-0.54) [0.589]-.03562(-2.50) [0.013]-.03584(-2.51) [0.012].000714(1.49) [0.136]-.07988(-7.47) [0.000].002547(7.60) [0.000]

2012 survey-.2105(-1.99) [0.047]-.08066(-0.99) [0.324]-.05826(-0.63) [0.530]-.01406(-0.17) [0.869]-.119(-1.47) [0.140]-.02442(-0.32) [0.747].05968(0.69) [0.491].1675(15.20) [0.000]-.003498(-11.12) [0.000].01543(1.70) [0.089]-.00088(-3.49) [0.000]-.05324(-3.85) [0.000].001406(3.30) [0.001]-.07601(-7.20) [0.000].002178(6.76) [0.000]

2010 survey-.1942(-1.74) [0.081]-.07749(-0.90) [0.370].1047(1.03) [0.303].1827(1.99) [0.047].24(2.60) [0.009].2264(2.57) [0.010].2889(2.37) [0.018].1060(7.82) [0.000]-.002099(-5.46) [0.000]-.00764(-0.63) [0.529]-.000646(-1.97) [0.049]-.04092(-2.33) [0.020].001425(2.39) [0.017]-.03627(-2.57) [0.010].001236(2.94) [0.003]

2008 survey-.1556(-1.46) [0.143].04766(0.55) [0.582].08042(0.79) [0.427].1485(1.66) [0.098].245(2.65) [0.008].3547(4.23) [0.000].5148(4.11) [0.000].1000(5.76) [0.000]-.001876(-4.20) [0.000]-.005687(-0.36) [0.720]-.000846(-2.27) [0.023]-.02931(-1.54) [0.123].000574(0.93) [0.353]-.039(-2.63) [0.008].001184(2.67) [0.008]

2006 survey

Panel (3): Switching equation.08516(0.64) [0.525].03836(0.35) [0.724].3011(2.44) [0.015].2163(1.89) [0.059].3509(2.95) [0.003].3142(2.82) [0.005].1853(1.29) [0.197].1315(6.76) [0.000]-.002579(-5.57) [0.000]-.01389(-0.85) [0.393]-.000243(-0.59) [0.553]-.1136(-4.19) [0.000].004634(4.06) [0.000]-.06376(-3.59) [0.000].001212(2.25) [0.025]

2004 survey-.1069(-1.26) [0.209].03605(0.51) [0.612]-.01989(-0.24) [0.811].05085(0.68) [0.494].1405(1.86) [0.063].2195(3.04) [0.002].2881(3.16) [0.002].1011(8.53) [0.000]-.002494(-10.22) [0.000]-.04419(-3.76) [0.000].00013(0.48) [0.629]-.02193(-1.48) [0.140].000171(0.37) [0.708]-.05517(-4.59) [0.000].001013(2.56) [0.011]

2002 survey

20

Constant

FemaleGraduate school

University

Technical college

Junior college

Vocational school

High school

Educational background

Lower middle

MiddleUpper middle

Upper

Junior high school record

Public agencies

5000–

2000–4999

1000–1999

500–999.008616(0.11) [0.915]-.3604(-1.74)[0.081].4232(2.39) [0.017].5182(2.88) [0.004].5071(2.77) [0.006].9918(4.05) [0.000].7581(4.25) [0.000].9602(4.87) [0.000].3079(3.54) [0.000].3052(3.59) [0.000].2798(3.37) [0.001].09467(1.04) [0.298].000668(0.01) [0.994]-.1812(-1.84) [0.066]-.06885(-0.66) [0.509]-.2532(-2.84) [0.005].009975(0.09) [0.927].1302(1.63) [0.103]-.3770(-1.56) [0.120].3347(1.88) [0.060].4026(2.23) [0.026].4278(2.35) [0.019].7627(3.00) [0.003].6623(3.71) [0.000].9878(5.03) [0.000].2025(2.27) [0.023].2131(2.42) [0.015].1160(1.36) [0.174].000034(0.00) [1.000]-.1709(-1.86) [0.063]-.2308(-2.49) [0.013]-.2399(-2.48) [0.013]-.3665(-4.43) [0.000]-.07127(-0.67) [0.505].2088(2.75) [0.006]-.9104(-4.29) [0.000].4647(3.16) [0.002].6252(4.20) [0.000].6352(4.16) [0.000]
1.044(5.04) [0.000].8798(6.01) [0.000]
1.201(7.12) [0.000]-.04184(-0.49) [0.626]-.05489(-0.62) [0.537].06596(0.73) [0.468]-.111(-1.40) [0.162].04016(0.40) [0.689].1589(1.57) [0.117]-.2977(-1.34) [0.180].1723(1.72) [0.086].44(4.03) [0.000].3681(3.14) [0.002].4573(2.18) [0.029].6912(6.27) [0.000]
1.158(4.07) [0.000].3531(3.05) [0.002].2859(2.46) [0.014].4647(3.75) [0.000].4264(4.04) [0.000].6084(4.27) [0.000].1162(1.15) [0.249].1816(0.63) [0.529].245(2.76) [0.006].4106(4.11) [0.000].4522(4.12) [0.000].1825(0.90) [0.368].5464(5.46) [0.000].6535(2.65) [0.008].3743(3.35) [0.001].4095(3.39) [0.001].3569(3.06) [0.002].3241(3.10) [0.002].3082(2.03) [0.042].5184(4.37) [0.000].3637(1.30) [0.193]-.02683(-0.24) [0.809].1069(0.89) [0.376].2127(1.55) [0.122]-.07129(-0.32) [0.751].4147(3.41) [0.001].9642(3.10) [0.002].5609(3.97) [0.000].1736(1.22) [0.222].5283(3.62) [0.000].1679(1.29) [0.198].5129(2.50) [0.012].4838(6.45) [0.000].9893(4.69) [0.000].1322(1.84) [0.066].2196(2.74) [0.006].331(3.68) (0.000].1506(1.08) (0.281].452(5.70) (0.000].3612(2.26) [0.024].2375(2.62) [0.009].2319(2.37) [0.018].2536(2.52) [0.012].3224(3.83) [0.000].4686(4.16) [0.000]

21

8,463

-.1950(-4.09) [0.000]-1.135(-3.21) [0.001]-.1925-.8129.8134(8.05) [0.000]-1.252(-7.90) [0.000]-.04778(-2.33) [0.020]

9,236-.2428(-4.61) [0.000]-.8960(-3.28) [0.001]-.2382-.7143.9278(12.35) [0.000]-1.428(-12.50) [0.000].01233(0.45) [0.652]

9.428-.1074(-4.08) [0.000]-.9851(-3.37) [0.001]-.1070-.7553.7727(10.09) [0.000]-1.334(-10.99) [0.000].05353(2.10) [0.036]

5,435-.1754(-2.97) [0.003]-1(-2.61) [0.009]-.1736-.7616.9899(9.01) [0.000]-1.455(-9.44) [0.000]-.03117(-0.91) [0.364]

5,397-.3248(-2.40) [0.016]-1.065(-2.49) [0.013]-.3138-.7875.7528(7.03) [0.000]-1.347(-7.91) [0.000]-.1343(-2.59) [0.010]

4,232-.2207(-2.28) [0.023]-1.088(-1.95) [0.051]-.2172-.7961.73(5.28) [0.000]-1.183(-6.68) [0.000]-.1305(-2.89) [0.004]

Notes:
1. Numbers in parentheses are t-values. Numbers in square brackets are p-values.
2. The endogenous switching regression models were estimated by using the movestay command in Stata (see Lokshin and Sajaia, 2004).
3. ρj , (j = 1, 2) indicates the correlation coeﬃcient between uji and ui . The movestay command maximizes the likelihood function withrespect to the Fisher transformations rj of ρj (and other parameters), not the correlation coeﬃcients ρj themselves, and reports rj ’s p-values
only (j = 1, 2). Since the transformation is monotonic, the significance of ρj can be judged from rj ’s p-values.

Number of observations

ρ1
ρ2

r2

r1

Unemployment rate at entry

Female×Married

Married

10,376-.1186(-3.99) [0.000]-1.294(-2.28) [0.022]-.1180-.8603.5692(5.61) [0.000]-.937(-6.01) [0.000]-.2432(-5.42) [0.000]

2002: Wage-Experience Profile
1.5

1

1
log(wage)log(wage)

2002: Wage-Tenure Profile
1.5

0.5
0

0.5
0

-0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

5

1.5

1

1

0.5
0

25

30

35

40

35

40

35

40

35

40

0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

5

2006: Wage-Tenure Profile

10

15

20
years

25

30

2006: Wage-Experience Profile

1.5

1.5

1

1
log(wage)log(wage)

20
years

0

-0.5

0.5
0

0.5
0

-0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

5

2008: Wage-Tenure Profile

10

15

20
years

25

30

2008: Wage-Experience Profile

1.5

1.5

1

1
log(wage)log(wage)15

2004: Wage-Experience Profile

1.5

log(wage)log(wage)

2004: Wage-Tenure Profile

10

0.5
0

0.5
0

-0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

5

10

15

20
years

25

30

Figure 1: Wage-Tenure and Wage-Experience Profiles of Male Regular and Non-regular
Workers

22

2010: Wage-Experience Profile
1.5

1

1
log(wage)log(wage)

2010: Wage-Tenure Profile
1.5

0.5
0

0.5
0

-0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

5

1.5

1

1

0.5
0

20
years

25

30

35

40

35

40

35

40

0.5
0

-0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

5

2014: Wage-Tenure Profile

10

15

20
years

25

30

2014: Wage-Experience Profile

1.5

1.5

1

1
log(wage)log(wage)15

2012: Wage-Experience Profile

1.5

log(wage)log(wage)

2012: Wage-Tenure Profile

10

0.5
0

0.5
0

-0.5

-0.5
0

5

10

15

20
years

25

30

35

40

0

Wage-Tenuare Profile
Regular Employment
Experience = 0
Experience = 10
Experience = 20
Non-regular Employment
Experience = 0
Experience = 10
Experience = 20

5

10

15

20
years

25

30

Wage-Experience Profile
Regular Employment
Tenure = 0
Tenure = 10
Tenure = 20
Non-regular Employment
Tenure = 0
Tenure = 10
Tenure = 20

Figure 1 (continued)

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