KIER DISCUSSION PAPER SERIESKYOTO INSTITUTEOFECONOMIC RESEARCH
Discussion Paper No.957“Polarization and Persistence in the Japanese Labor Market”

Hiroshi Teruyama and Hiroyuki Toda

January 2017

KYOTO UNIVERSITY
KYOTO, JAPANPolarization and Persistencein the Japanese Labor Market ∗Hiroshi Teruyama†and Hiroyuki Toda‡
January, 2017

Abstract
This study analyzes the persistence of regular and non-regular employment status in Japan for workers that change jobs. In particular, we investigate two hypotheses behind this persistence. The first is the dependence of the employment
status in the current job on that in the previous job. The second is the dependenceof the employment status in the current job on that in the initial job, which is calledfirst job eﬀects. While both types of dependence are empirically verified, the former
is shown to be quantitatively more substantial. Therefore, the serially dependent
structure of employment status matters critically to the segmentation of the labormarket in Japan.
JEL Classification: J42, J62, J70, C35.
Keywords: dual labor market, non-regular workers, state dependence, cohort eﬀects,
first job eﬀects, Japanese labor market.

1

Introduction

The sharp increase in the number of non-regular workers has become a major issue
in Japan. The concept of non-regular employment is generally used to denote theopposite of regular employment, which refers to stable, long-term, and full-time jobs.
Because non-regular workers receive limited opportunities for on-the-job training andcareer development, increasing the relative share of such workers has been considered
to represent a collapse of the traditional Japanese employment system.1 Indeed, an∗This is the revised version of Works Discussion Paper No.9 (Recruit Works Institute) with thesame title. This work was supported by JSPS KAKENHI Grant Numbers 24243035 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
For a general view of the rise in non-regular employment in Japan, see Rebick (2005). Asano etal. (2013) also empirically examine the causes of the increases in the number of non-regular workers,
while Hijzen et al. (2015) consider recent changes in employment adjustments in Japanese firms andthe influence of the rising number of non-regular workers.

1

expanding income gap recently observed in Japan is considered as a consequence of thislabor market polarization.
Despite this social concern, however, few attempts have thus far been made to empirically examine the diﬀerentials between these two employment statuses based on
micro data. Among them, Kambayashi and Kato (2012) confirm the general perceptionthat non-regular jobs can be characterized by factors, such as low wages, low job security, and low opportunities for training and development, relative to regular jobs. The
coexistence of both types of jobs is a typical property of dual labor markets.2
Before the 1990s, the dualism in the Japanese labor market was mainly attributed
to diﬀerences in firm size. That is, the large-firm sector formed the primary market
and the small-firm sector formed the secondary market. Thus, the increase in theshare of non-regular employees in recent years suggests the beginning of a new era oflabor market polarization in Japan.3 An essential property of dual labor markets is
the rationing of primary jobs, that is, regular jobs. This property implies that labormobility between the primary (regular job) and secondary (non-regular job) sectors issluggish. In contrast to the scarcity of research on the diﬀerent work conditions in these
two sectors, a considerable number of studies have examined the transition between
these sectors and suggested that mobility is restricted. If this is true, one question
arises: when are workers divided into the segmented sectors?
We investigate this question empirically by using micro survey data on employment
in Tokyo metropolitan area.4 Two main explanation arise when considering the timingof the selection of workers. The first is that selection depends on an individual’s recent
work experience in that holding a non-regular position currently substantially reduces
the possibility of finding a regular position in the next job. This would reduce labormobility between sectors. In this case, selection occurs at each job turnover.
The second explanation is that selection occurs at the point of entry into the labor
force. Under this hypothesis, a worker is assigned a diﬀerent career path according to
the type of job he or she secured at the time of entry into the labor market (i.e., justafter graduation). If a worker starts working in a non-regular job, it becomes diﬃcult toswitch to a regular job even if he or she could obtain one. The permanent diﬀerentiation
caused by the initial states in the labor market is called “first job eﬀects.” Under firstjob eﬀects, the temporary business-cycle conditions at a worker’s time of entry havea permanent influence on his or her lifetime working conditions, such as earnings andemployment stability. These phenomena are called “cohort eﬀects.” The hypothesisof selection at the point of entry is a convincing argument in Japan, as recruitment ishighly concentrated on new school graduates. If such eﬀects exist, the polarization of
the labor market leads to more serious disparities in working conditions among workers.
In this study, we aim to distinguish these two mechanisms empirically. One diﬃculty2

For recent developments in dual labor market theory, see Saint-Paul (1996) and Ishikawa (2002).
In particular, the latter concerns the dualism of the Japanese labor market.
3
Ariga and Okazawa (2011), Kalantzis et al. (2012), and Kitagawa (2014) discuss the possibility
that recent changes in the Japanese economy have induced polarization and segmentation.
4
This area consists of the prefectures of Tokyo, Kanagawa, Chiba, and Saitama.

2

in investigating these mechanisms is that spurious dependence among one’s employment
statuses can be caused by his or her unobserved individual attributes. If those timeinvariant attributes aﬀect the job decisions throughout one’s career, the types of hisor her jobs correlate intertemporally. We construct an econometric model that candistinguish genuine state dependence from such spurious dependence.
The remainder of this paper proceeds as follows. Section 2 briefly reviews several
aspects of non-regular employment and discusses its recent trends in Japan. We survey
the literature on the persistence in the labor market in Section 3 before formulating
issues on the intertemporal dependence of states in Section 4. Section 5 presents ourempirical strategy to distinguish the sources of persistence. In Section 6, we explain ourdataset and examine the property of these data. Then, we specify the equations for theestimation. Section 7 provides the main results, and Section 8 concludes.

2

Non-regular Employment in Japan

Despite the social importance of non-regular employment, a definition of this notion
lacks consensus. Kambayashi (2013) summarizes diﬀerent definitions of non-regular
employees based on the statistics published by the Japanese government.5 He argues
that definitions can be divided into three types. The first type classifies employees basedon contract length. A typical criterion is whether the contract length (including an indefinite duration6 ) exceeds a certain period (typically, 12 months). Another criterion iswhether the contract has a fixed term. Non-regular employment by the latter definitionis broader since it includes workers on long fixed-term contracts.
The second type of definition is based on working hours and is close to the general
notion of part-time employees. Typically, workers whose weekly working hours are below
35 are distinguished as part-time or short-time employees. However, some surveys do
not adopt an absolute standard of working hours and define part-time employees as
those whose scheduled working hours are fewer than the working hours prescribed in
the formal work regulations of the establishment.
The third type is distinguished based on the title or description used by the workplace. Non-regular employees might be called part-time, temporary, contract workers,
and so on. Kambayashi and Kato (2012) find that the distinction based on job title
is more representative of working conditions, such as wages, hours of work, probabilityof quitting, and opportunities for training than contract length. It suggests that classifying workers based on a few dimensions of employment properties, such as working
hours or contract length, is somewhat misleading.
Figure 1 shows the trends of the proportion of non-regular workers according tothese three definitions. This figure indicates that the proportions and trends diﬀer
by definition. The share of workers on short fixed-term contracts, called temporary
5

See also Kambayashi (2010).
We use the phrase “indefinite duration” to mean a contract that guarantees employment until the
mandatory retirement age.
6

3

workers in the survey, increased from 10% in the mid-1980s to 15% in the early 2000s
before stabilizing. On the other hand, the shares of both description-defined non-regular
workers and persons working shorter hours, called part-time workers in the surveys, havebeen increasing at a similar rate, even after the 2000s. The share of description-defined
non-regular workers reached 35% recently, which was 20% more than the share in the
1980s. The part-time worker ratio is roughly 5% less than the non-regular worker ratio.7
This means that some non-regular workers work the same number of hours as full-timeworkers. Further, more than half of non-regular workers now have longer (i.e., spanningmore than a year) contracts, and the increased share discussed above is driven by thistype of non-regular worker.
Non-regular employment in Japan can be said to be close to the atypical or nonstandard employment seen in European countries and the United States. However,the notion of atypical workers is broad even across those countries, and Ogura (2002)
compares and summarizes the notion for Japan, European countries, and the UnitedStates.8 According to Ogura (2002), there exists a unique status of Japanese workers
that can be categorized as atypical employees. So-called “quasi-part-time” workers tend
to work full-time hours; however, they can also include part-time employees whose working hours are shorter than but close to those of full-time employees. A large proportionof these quasi-part-time employees are also thought to work under indefinite-duration orlong fixed-term employment contracts. Kambayashi and Kato (2012) state that the proportion of indefinite-duration contract workers (or those that have a minimum one-year
contract) is increasing in Japan. Nevertheless, they are not termed regular employees
in the workplace because they lack the equivalent opportunities for training or promotion. These workers roughly correspond to the quasi-part-time workers discussed herein.
Throughout this paper, we use the term “non-regular” to refer to the Japanese typeof atypical employment that is close to the description-defined non-regular employment
described above. We adopt this phrasing in order to emphasize the existence of employees with permanent contracts and without the “regular” title/description, which is
unique to Japan. Moreover, the wording “non-regular employment” is also widely usedin Japan.9

3

Literature on the Persistent Initial Conditions in the
Labor Market

An essential property of dual labor markets is the rationing of primary jobs. This
property implies that labor mobility between sectors is inactive. Indeed, studies of
the transition between sectors have suggested that such mobility is actually restricted.
7Note that these ratios are not directly comparable in a strict sense. See the note under Figure 1.
According to Ogura (2002), atypical workers sometimes include the self-employed. As the survey
targets employed persons, we use the word “employee” and “worker” interchangeably.
9
We also use “employment status” to indicate the type of contract, which should be partially implicit(i.e., regular or non-regular employment).
84

For example, Hirata and Yugami (2011) point out that the transition from non-regular
to regular jobs is more sluggish in Japan compared with Germany and the United
Kingdom.10
The majority of recent research on the transition between employment statuses inJapan has focused on the role of one’s early career, especially the first job just after
graduating school. Many researchers find that employment status in the first job isresponsible for an employee’s subsequent employment status for the long-term, basedon the positive correlation between employment status in the first job and that in thecurrent job. A representative study in this field is Kondo (2007), who estimates a probit
model where the probability of regular employment at present depends on employment
status in the first job with other control variables. Kondo’s research is notable since it is
the first attempt to consider the eﬀects of the initial employment status on the current
status by paying attention to the problem of endogeneity, which is typically observed insuch a situation. Unobservable individual heterogeneity regarding employment statusdetermination brings about a correlation between the dummy of the first employment
status and disturbances, as discussed in detail in Section 4. Following Neumark’s (2002)
argument of the need for valid instruments to estimate the eﬀects of early job stability on
current wages, Kondo (2007) estimates a bivariate probit model on employment statusdetermination by using a local labor market condition index in the year of finishing one’s
education as an “instrument” for the first employment status.11 Her results show thestrong persistence of employment status. Based on the results of the basic estimation,
she concludes that an individual who obtained a regular job upon entering the labormarket has about a 50% greater opportunity of working in the regular sector at present
and, moreover, that the eﬀects are permanent.
Hamaaki et al. (2013) examine the degree to which the probability of regular employment is aﬀected by employment status several years after graduation in addition
to the status of the first job for female workers. They estimate the influences of theinitial employment status and/or the employment status k years after graduation on
the probability of current regular employment. A multivariate probit estimation is usedin the spirit of Kondo (2007). They find that the eﬀects of the employment status justafter graduation on the current status decrease gradually and cease about 10 years later.
Furthermore, the impact of the employment status in the first job is dominated by thatin the next job if workers change jobs within a few years of graduation. That is, theessential factor is the employment status experienced during the early stage of one’s
career.
10

Their notion of non-regular employment corresponds to temporary employment; however, the definitions of temporary employment they use diﬀer across countries. Interestingly, the transition rates
they calculate indicate that the transition from regular to non-regular jobs is smaller than the reverse
transition in each country. See also Shikata (2011) for the international comparison of the transition
from non-regular to regular jobs.
11
More specifically, Kondo (2007) uses the job opening ratio, namely the ratio of job vacancies to job
seekers, of the local prefecture. In the context of bivariate probit modeling, her “instrument” can beinterpreted as a variable that satisfies the “exclusion restriction” described in Section 5.

5

Similarly, Esteban-Pretel et al. (2011a) conduct a structural estimation on a job
search model for young male workers in Japan. Their model consists of three states: regular employment, non-regular employment (“contingent employment” in their words),
and unemployment. The transition probabilities to regular employment from each ofthese three initial states are then simulated based on the estimated parameters. Theyshow that the probability of regular employment is higher if one’s initial state is unemployment than if it is non-regular employment. However, the eﬀect of the initial state is
temporary and the transition probabilities to regular employment converge to the samelevel within 15 to 20 years.
In sum, although judgments on the permanency of the eﬀects of the initial employment status diﬀer by study, there is no disagreement on their persistence in Japan.
The notion of persistence, not limited to the eﬀect of initial employment status, hasalso been demonstrated in various aspects of the labor markets outside Japan. It is
usually formulated as a model where the current state depends on past states, which iscalled “state dependence.” Heckman (1981a) defines state dependence as the conditional
probability that an individual’s experience of an event in the future is a function of pastexperiences. An individual’s labor market outcome such as labor force participation,
turnover, and unemployment generally shows strong state dependence.12
Many researchers examine the state dependence of low-paid employment. Thosestudies are close to our topic of interest since secondary employment can be characterized by low-paid and unstable jobs.13 According to Arulampalam et al. (2000),
the possible sources of state dependence of unemployment include one’s unemployment
history as a screening device by employers and the depreciation of human capital during unemployment. The sources of state dependence of low paid employment can besimilarly considered. 14
Although the entire history of states may aﬀect the current state, it is usual to restrict the lag structure to the past few periods, because the eﬀects of previous experience
are thought to depreciate over time in most cases. From this perspective, many studiesof labor market transition assume a small-order Markov process to describe state dependence. For example, for the UK labor market, Arulampalam et al. (2000) consider state
dependence in the unemployment probability by including the one-year lagged unemployment status in the explanatory variables. Similarly, to estimate female labor force
participation in the United States, Hyslop (1999) derives a first-order Markov model byusing a stochastic dynamic programming model of search behavior. Prowse (2012) also
considers the dynamics of female labor force participation in the United Kingdom forfull-time and part-time workers. She assumes that states in the past two years aﬀect the12
Arulampalam and Stewart (2009) survey various empirical studies of economic behavior under state
dependence. Heckman (1981a,b) deal with the “initial condition problem” in the estimation of dynamic
non-linear panel data. This problem arises when unobserved individual heterogeneity exists and theinitial observation coincides with the starting value of the examined stochastic process.
13
See Cai (2014) for the eﬀects in the Australian labor market.
14
For example, Stewart (2007) examines the state dependence of unemployment and low-paid employment by using British household survey data.

6

current state. These models reflects the ideas that state dependency operates strongly
between time points proximate to each other and its influence diminishes with the distance between the time points. In this paper, we refer to this type of state dependence
as “serial state dependence.”
A first-order Markov process plays a central role in the analysis of labor market
dynamics. The research field on gross labor flows has been devoted to the estimation ofa transition matrix in order to describe workers’ mobilities among employment states(e.g., employment, unemployment, and not in the labor force).1516 This method reflects
the view that aggregate flows among states can be characterized suﬃciently by using
first-order Markov chains. It is natural to think that flows, namely the number ofpeople who move between pairs of states, are determined mostly by stocks, that is,
the number of people in each state.17 If we consider the individual behavior behind
the aggregate phenomenon, we see that the incidence of a person being in a certain
state is aﬀected by one’s previous state. A first-order Markov model also has aﬃnities
with stochastic dynamic programming, which is used to analyze labor search models.
The solutions usually have the forms of first-order serial state dependence, and they
are manipulated for numerical simulations to mimic workers’ actual transitions.18 For
example, one empirical research stream investigates how to extend a labor search model
based on Mortensen and Pissarides (1994) in order to explain actual unemployment
dynamics numerically. Although the evaluation of the explanatory power of the labor
search model varies across studies, the idea behind the research is that incorporating
realistic factors into a basic labor search model is promising for exploring labor market
fluctuations.19
The above argument suggests that it is plausible to expect that transitions between
employment statuses can be captured as a first-order Markov process. From this viewpoint, the persistence of the first employment status observed in the Japanese labormarket can be attributed to strong serial state dependence. However, we must beaware that the persistence can also arise through the above-mentioned “cohort eﬀects”
15

Representative early contributions include Abowd and Zellner (1985), Poterba and Summers (1986),
and Blanchard and Diamond (1990) for the United States’ labor market. Esteban-Pretel et al. (2011b)
analyze labor flow dynamics in Japan as well as review the literature on labor flow analysis in theJapanese labor market.
16
The application of the first-order Markov model is not restricted to labor flow analysis, and it is
widely used to analyze social processes in many fields of social sciences. See Bartholomew (1982).
17
Actually, first-order Markov chains are often used to describe labor market flows. For example,
Choi et al. (2014) report that estimated age-specific Markov transition matrices can replicate theactual lifetime profiles of labor force participation and unemployment in the United States quite well.
18
Esteban-Pretel et al. (2011a) mentioned above, are included in this line of research.
19
See, for example, Shimer (2005), Mortensen and Nagypál (2007), Hagedorn and Manovskii (2008),
and Pissarides (2009) for the United States’ labor market. Miyamoto (2011) extends Mortensen–
Pissarides’ model by introducing the training costs of firm-specific skills to explain the Japanese data.
The development of the labor search model also influences studies of labor flows where implications ofthese models on labor flows are examined empirically by using a continuous-time first-order Markov
model. See, for example, Fujita and Ramey (2009) and Shimer (2012). For the Japanese labor market,
Lin and Miyamoto (2012) present a recent contribution in this line.

7

or “first job eﬀects.” Although such terms are often used interchangeably, they should
be diﬀerentiated in the strict sense. We start by focusing on first job eﬀects. Under theirdefinition, the property and quality of the first job upon entering the labor market havelong-term eﬀects on the property and quality of future jobs. Since data containing information on the first job are scarce, only a few studies inspect first job eﬀects directly. In
this field, Oyer (2006) considers the careers of doctoral-level economists who graduatedfrom leading economics departments in the United States. He examines the long-run
eﬀects of the quality of the first job on job quality in the future and finds strong persistence. As possible sources of the persistence, he mentions firm-specific human capital
investment, evolving tastes based on experience and the environment, influence fromco-workers, signaling eﬀects of past states, and costly search. As mentioned above,
Kondo (2007) and Hamaaki et al. (2013) exploit survey data on the employment status
in the first job or on the entire job history of individuals to analyze the first job eﬀects
of employment status.
Cohort eﬀects attract more attention than first job eﬀects. Indeed, the latter isa prerequisite of the former. In the context of labor mobility, cohort eﬀects refer tothe fact that each group of workers who entered the labor market at the same time
experiences a distinct transitional process afterward depending on the labor market
conditions at the time of entry.20 The period just after finishing school has a special
meaning for one’s career. If the labor market conditions aﬀect the employment status
or job quality of new entrants and if first job eﬀects exist, then cohort eﬀects arise.
Since cohort eﬀects can be tested by using the year of graduation and labor market
conditions (e.g., the unemployment rate) in that year, they are less restrictive regarding
the data to be used.21 Thus, a multitude of studies of cohort eﬀects exist. von Wachterand Bender (2008) report that a part of wage diﬀerentials can be attributed to diﬀerences in firm-entry cohorts and that cohort eﬀects are persistent in Germany. Kahn
(2010) examines the eﬀects of labor market conditions, proxied by the national or local
unemployment rate, in the year of entry on the wages, tenure length, and prestige ofoccupations regarding young male college graduates in the United States. The resultshows a long-run negative impact on wages from adverse labor market conditions uponentry. Raaum and Røed (2006) consider the eﬀects of labor market conditions on theprobability of being non-employed in Norway. A notable feature of their study is that
educational choices and the non-employment probability are estimated simultaneously.
They find that the local unemployment rates at the time of entry exhibit a persistenteﬀect on employment prospects, although they find no evidence that such labor market
20

The term “cohort eﬀects” can also be used in the broader sense. Ohtake and Inoki (1997) summarize
three routes by which a diﬀerence in generation can aﬀect an individual’s lifetime outcome, namely
improvement in the labor productivity of the younger generations by technological progress, the numberof workers in a generation, and employment conditions upon entering the labor market. Indeed, Genda(1997), Ohtake and Inoki (1997), and von Wachter and Bender (2008) all consider how the number ofworkers in a generation aﬀect job properties such as wages. In this paper, we concentrate on cohort
eﬀects based on the year of labor market entry unless otherwise noted.
21
Even if a dataset has no information on graduate years, the graduate year of an individual can be
estimated by his or her age and educational background.

8

conditions aﬀect an individual’s educational attainment.22 Oreopoulos et al. (2012) also
discover the persistent eﬀects of the initial local unemployment rates on present wages
for Canadian university graduates. They stress that a significant part of wage losses
due to adverse entry timing recovers through the process of mobility to higher paying
employers; however, this recovery period proceeds gradually. Consistent results are also
found by von Wachter and Bender (2008). For male workers in Austria, Brunner andKuhn (2014) also detect the long-run impact of the initial local unemployment rates oncurrent wages. In addition, they find that the explanatory power of the unemployment
rate upon entry reduces when they include variables for the quality of a worker’s first
employment such as mean compensation, age, or firm size in the explanatory variables.
This result suggests the importance of the quality of one’s first employment as a sourceof cohort eﬀects.
Cohort eﬀects have been examined in the Japanese labor market. Early contributions in this field were Genda (1997) and Ohtake and Inoki (1997). For male regular
workers (defined by contract length), Ohtake and Inoki (1997) extract a part due tocohort eﬀects from individual wages, years of tenure, and firm size. Good economic
conditions at the time of entry are shown to have permanent positive eﬀects on wages
and tenure length. Similarly, Genda (1997) finds that long-term wage increases arehigher among workers who entered in strong market conditions. The Japanese employment practice known as “lifetime employment” focuses on hiring new graduates
and, thus, the job market for displaced workers is underdeveloped. Under such a labormarket institution, finding good employment opportunities at the beginning of one’s
career may display persistent positive eﬀects on a worker’s outcome. Genda (1997) andOhtake and Inoki (1997) both consider the long-term employment practice to be themain source of cohort eﬀects in Japan as the period of search is limited to the time ofentry.23
Genda et al. (2010) compare the eﬀects of the local unemployment rate in the entry
year on current wages, employment probability, and full-time employment probability
for Japanese and American male workers. Their conclusion is that adverse labor market
conditions at the time of entry induce a persistent reduction in wages and employment
probability in Japan, while the eﬀects are less persistent in the United States. In
addition, for the Japanese labor market, they show that a recession at time of entry
reduces the probability of having a full-time job in the long run, which negatively aﬀects
wages. The fact is consistent with the evidence of first job eﬀects found by Kondo (2007)
and Hamaaki et al. (2013).
As shown above, various studies in many developed countries present evidence of
persistent or even permanent entry-time cohort eﬀects on an individual’s labor market
outcomes. The sources of cohort eﬀects include job search, human capital accumulation
22

Raaum and Røed (2006) estimate the probability of non-employment conditional on the one-year
lagged state of non-employment in order to consider state dependence. Their concern on the point isclose to ours, as we explain in Section 4.
23
Genda (1997) and Ohtake and Inoki (1997) show that the long-term employment system also createssignificant cohort eﬀects owing to the generational size in Japan.

9

(e.g., Kahn, 2010; Oreopoulos et al., 2012), and statistical screening (e.g., Genda et al.,2010). Note that these explanations can also be applied to explain the sources of serial
state dependence. A critical diﬀerence is that the early period in one’s career plays
a special role in cohort eﬀects. Oreopoulos et al. (2012), for example, explain thatthe job search cost increases with age and that prolonged search periods in a recession
cause a persistent loss in firm- and industry-specific human capital accumulation by new
entrants. Genda et al. (2010) also state that starting one’s career in a non-regular job
signals low productivity, especially for high school graduates in the prevalent schoolbased hiring system in Japan. This non-regular status makes it diﬃcult for non-regular
workers to switch to regular jobs in the future. The employment status in one’s youth
can also play a special role under the information cascade, which refers to a situationin which an employer follows the preceding employer’s decision independent of his or
her private signal. Thus, if workers were hired in non-regular jobs at the beginningbecause of adverse economic conditions, employers might only oﬀer them opportunities
of non-regular employment afterward.24
Recall that the argument on cohort eﬀects premises the existence of first job eﬀects.
If first job eﬀects exist, the initial state directly influences the current state in a way
diﬀerent from any other past states. Cohort eﬀects are generally identified by theobserved positive correlation between the first and current states or the entry-year
economic conditions and current outcome. However, the correlation can evolve becauseof either serial state dependence or cohort eﬀects. Although these two phenomena arequite diﬀerent, previous studies have paid insuﬃcient attention to distinguishing themand have scarcely considered them simultaneously. We deal with this problem in thenext section.

4Long-term Consequences of Employment Status upon
Entry

As discussed in the previous section, the correlation between the first and the current
employment status can evolve because of either serial state dependence or first job
eﬀects. However, the two phenomena are quite diﬀerent. Suppose that current employment status depends on recent preceding statuses (i.e., serial state dependence). In
addition, suppose that there is no direct eﬀect from the initial to the current status. If
serial state dependency is strong, the initial status can aﬀect the current status in thelong run by repeating the serial-state-dependence sequence. The first job does not playa special role. Even if every worker follows the same transition process irrespective of
the employment status of the first job, the initial and the current employment statuses
will be correlated for a considerable period. On the other hand, in the case of first job
eﬀects, the initial state directly influences the current state in a way diﬀerent from anyother past states.
24

See Kübler and Weizsäcker (2003) for information cascades in the case of the employment decision.

10

To clarify this argument, let us consider a two-state Markov transition model of job
turnover. Our empirical analysis concentrates on the transition between employment
statuses accompanied by a job change. In addition, it excludes the transition into andout of the labor force.25
Thus, each worker belongs to one of the employment statuses represented by{
1 = regular employee
0 = non-regular employee.
Let us assume that a worker’s transition between these employment statuses follows thetransition matrix:)(
p00 p01P =,p10 p11
where pij represents the probability of moving status i to status j, that is, a conditional
probability pij = Pr{Jm = j|Jm−1 = i} (i, j = 0, 1, m = 1, 2, · · · ). Here, Jm denotes theemployment status just after m-th job change, and J0 denotes the initial employment
status. We assume that the transition follows a first-order Markov process, except thepossible eﬀects of first job. As discussed in Section 3, a first-order Markov process canbe justified empirically and theoretically to describe a variety of dynamic activities in
the labor market.
Suppose that each worker moves following the common P . Then, the conditional
probabilities pij among workers are the same irrespective of their values of J0 . That is,
the initial employment status does not aﬀect the current transition probability. Let us
write this situation as pij |J 0 =0 = pij |J 0 =1 (i, j = 0, 1).26 In this case, the probabilityof regular employment at a certain time depends on the initial employment status
only indirectly through the dependence on its previous state in each period: the initial
employment status aﬀects the second employment status, which in turn aﬀects the thirdand so forth. This eﬀect gradually declines over time.
This can be formally stated as follows.27 Let us denote Jm ’s distribution asπm = (Pr{Jm = 0}, Pr{Jm = 1}),m = 0, 1, 2, . . . .

The process that starts from the initial distribution π0 with the transition matrix P
reaches a distribution πm = π0 P m after m job changes. It is known that if P is
irreducible, that is, every state is reachable from the other states, and aperiodic, then25

We adopt these restrictions because our dataset has no information about such transitions. See
Section 6 for the description of our dataset. Furthermore, the determinants of employment status withand without a job change, and those of the transition into and out of the labor force are thought
to be diﬀerent. For example, a firm may employ workers in non-regular positions in order to collectinformation on their ability or aptitude for a probationary period in preparation for a transition into
regular employment. See Genda (2009), Kosugi (2010), Hirata and Yugami (2011), and Shikata (2011)
for the transition from a non-regular to a regular position in the internal labor markets.
26
pij |J0 =k means the transition probability from state i to state j on the condition that J0 = k.
27
For the notions and properties related to Markov processes, see Durret (1999).

11

there exists a unique stationary distribution π (a row vector) that satisfies the condition
πP = π and P m converges to ιπ as m → ∞, where ι is a column vector of ones.
Therefore, for any initial distribution π0 ,lim πm = lim π0 P m = π0 lim P m = π0 ιπ = π,

m→∞

m→∞

m→∞

where π0 ι = 1 since the sum of probabilities equals one. This implies thatlim Pr{Jm = k|J0 = 0} = lim Pr{Jm = k|J0 = 1},

m→∞

m→∞

k = 0, 1.

In sum, in the absence of first job eﬀects, we find a significant correlation between
the current and the initial employment statuses only if the number of job changes since
entry is small and/or the probabilities of staying in a certain state, that is, the diagonal
elements of P , are large. The initial employment status does not play any special role
here. A process starting from state k at entry has the same transitional characteristics
as a process starting from state k in any other period.
This is clearly diﬀerent from the meaning of first job eﬀects, as we have alreadyexamined. In the presence of first job eﬀects, working a non-regular job in one’s youth
reduces the probability of finding a regular job persistently. In the context of theMarkov transition model, this can be interpreted as the situation where workers areconfronted with distinct transition matrices P , depending on their initial status J0 .
More concretely, the probabilities of moving to regular jobs for workers who started
their career with non-regular jobs are lower than the corresponding probabilities for
those who started with regular jobs: p01 |J 0 =0 < p01 |J 0 =1 . The former workers also
lose regular jobs by turnover more frequently than the latter: p10 |J0 =0 > p10 |J0 =1 .
Consequently, the probabilities of having regular jobs diﬀer between these two groups
even long after job market entry, and this distinction does not disappear over time. Thatis, since the transition probabilities, and hence the limiting (stationary) distributions,28
depend on their initial employment statuses, we havelim Pr{Jm = k|J0 = 0} ̸= lim Pr{Jm = k|J0 = 1},

m→∞

m→∞

k = 0, 1.

The employment status upon entry is in turn exposed to the labor market conditions
at that time. Therefore, the common experience among the same generation induces
cohort eﬀects.

5

Empirical Strategy

This section describes the multivariate probit model used herein to inspect first job
eﬀects. Following the argument in the preceding sections, we adopt a first-order Markov
process to describe serial state dependence.
In the 2 × 2 transition matrix case here, the stationary distribution is given by π
(p10 /(p10 + p01 ), p01 /(p10 + p01 )).
28

12

=

Let Ji , Ji′ , and Ji0 denote the current, previous, and initial employment status ofworker i (i = 1, . . . , N ), respectively. Note that we have changed the notation slightly.
Now, suppose that Ji is determined by a binary choice model:()
Ji = 1 α1 Ji0 + β1 Ji′ + γ1⊤ Xi + ui > 0 ,(1)where 1(·) is the indicator function that equals one if the statement in the parenthesis
is true and zero otherwise, and Xi is a vector of the exogenous variables aﬀecting thedetermination of the current status. The disturbance term ui is assumed to be normally
distributed with mean 0, and its variance is normalized to one. Although the normalityof the error is not essential for the argument in this section, it is necessary for the
maximum likelihood estimation of the model.
For the moment, we assume that the past employment statuses Ji0 and Ji′ in addition
to Xi are also exogenous. (We relax this assumption later.) Then, under the normalityof ui , (1) is a standard probit model, and we have
Pr{Ji = 1|Ji′ = ji′ , Ji0 = ji0 , Xi = xi }= Pr{ui > −α1 Ji0 − β1 Ji′ − γ1⊤ Xi |Ji′ = ji′ , Ji0 = ji0 , Xi = xi }= Pr{ui > −α1 ji0 − β1 ji′ − γ1⊤ xi },(2)

where ji0 , ji′ = 0, 1 and xi is a realization of Xi . The last equality follows from theexogeneity of the conditioning variables. Similarly, Pr{Ji = 0|Ji′ = ji′ , Ji0 = ji0 , Xi =xi } = Pr{ui ≤ −α1 ji0 − β1 ji′ − γ1⊤ xi }. Therefore, if and only if α1 = 0, this conditional
probability does not depend on the value of Ji0 , and we have
Pr{Ji = ji |Ji′ = ji′ , Ji0 = 0, Xi = xi } = Pr{Ji = ji |Ji′ = ji′ , Ji0 = 1, Xi = xi },ji , ji′ = 0, 1,(3)given the realized value of Xi . Note that this corresponds to the case pij |J0 =0 = pij |J0 =1(i, j = 0, 1) in the job transition model discussed in the previous section, and can beinterpreted as the serial state dependence case. On the contrary, if and only if α1 ̸= 0,
then the equality in (3) does not hold and first job eﬀects exist, which corresponds tothe case pij |J0 =0 ̸= pij |J0 =1 (i, j = 0, 1).
Thus, by estimating the probit model in (1) and examining whether α1 = 0, we canfind the intrinsic relationship between the initial and the current employment statuses.
If the null hypothesis α1 = 0 is rejected, then (3) is denied, and hence, first job eﬀects
are detected. Otherwise, (3) is verified and the observed correlation between Ji0 and Ji
should be caused by serial state dependence.
The assumption that the past states Ji0 and Ji′ are exogenous may be unsuitablein practice. The typical endogeneity problem may exist, since individual preferences
and abilities related to the employment status included in the disturbance ui generally
influence the choice of employment status throughout one’s life.29 If that is the case,29

The factor in a disturbance corresponds to the time-invariant individual eﬀect in panel data models.

13

ui is correlated with both Ji0 and Ji′ in (1), and therefore, Ji0 and Ji′ are endogenous
although they are predetermined. For example, workers with a preference for flexible
working or a job without transfer tend to find a non-regular job. Heckman (1981a)calls such factors individual heterogeneity, under which spurious state dependence ofthe current state on the past state might be observed. He also points out the needto distinguish genuine state dependency, which he calls structural dependency, fromspurious dependency.
To deal with the possible endogeneity of the past employment statuses, we mustalso consider the equations generating Ji0 and Ji′ , and estimate the whole system of
equations by using the maximum likelihood method. Unfortunately, however, it isdiﬃcult to construct a general and still estimable model for this purpose. (The reason
is explained in footnote 30.) Hence, we restrict our attention to the case in which
individuals change their jobs exactly twice. In this case, we can postulate that Ji0 andJi′ are generated by()
andJi′ = 1 α2 Ji0 + γ2⊤ Yi + vi > 0

(4)

(
)
Ji0 = 1 γ3⊤ Zi + wi > 0 ,(5)where 1(·) is the indicator function as before, and Yi and Zi are the vectors of theexogenous variables aﬀecting the determination of individual i’s previous and first employment statuses, respectively.
Note that since we have assumed that Ji in (1) depends on the previous employment
status Ji′ , Ji′ in (4) should depend on the one before the previous employment status, Ji′′ ,say, as well as the first employment status Ji0 in order for the two equations to be consistent. However, for individuals who changed their jobs precisely twice, Ji′′ coincides with
Ji0 , and therefore, (4) is the appropriate equation for the previous employment status
for such individuals.30 Equation (5) has only exogenous explanatory variables because
when individuals choose their first jobs, they have no past job experiences. Thus, wefind it reasonable to assume that for the individuals who changed their jobs exactly
30

In general (for individuals whose turnover is higher than two), since we have assumed the possibility
that the determination of employment status depends on both the previous and the initial statuses as in(1), the same structure should appear in every determination equation of the employment status except
the first and second ones.
To be concrete, consider the status determination equation for the m-th job (m ≥ 3), on the righthand side of which the (m − 1)-th employment status has to appear as an explanatory variable, justlike Ji′ in (1). We need, in turn, another equation for the determination of the (m − 1)-th employment
status and must have the (m − 2)-th employment status on the right-hand side. In the same manner, weneed to define the status determination equations for the (m − 2)-th, (m − 3)-th, . . . , 3rd employment
statuses. The first and second employment status equations have diﬀerent structures as discussed in
the text.
Consequently, we have to know the entire history of job changes to estimate such a general system.
On the other hand, the dataset we use in this study contains information only on the initial, previous,and current employment statuses. Moreover, even if complete information on the history of job changes
is available for each worker, we need the system consisting of m equations for the respondents whochanged their jobs m − 1 times. It is practically diﬃcult to estimate such a (potentially) large system.

14

twice, the employment status determinations are described by the system of equations(1), (4), and (5). Moreover, the dataset we use in this study contains information only
up to the second job change, as explained in detail in Section 6. Given the feasibilityof estimation and the availability of data, we restrict our analysis to the case where
workers changed jobs exactly twice.
The disturbance vector (ui , vi , wi )⊤ in (1), (4), and (5) is assumed to have a trivariate
normal distribution with mean vector 0 and variance–covariance matrix Σ. The diagonal
elements of Σ are normalized to unity to identify the model. Thus, we have a trivariate
probit model with endogenous binary explanatory variables. The recursive structure of
the model, that is, no endogenous explanatory variable in (5), only one such variablein (4), and two in (1), allows us to compute the likelihood function. The derivationof the likelihood function and its practical implication are given in the Appendix. By
applying the maximum likelihood method, we can consistently estimate the parametersof the system.
Now, consider the implication for first job eﬀects in the trivariate model above.
⊤ ⊤ ⊤Define a random vector Gi = (Xi⊤ , Yi⊤ , Zi⊤ )⊤ and a real vector gi = (x⊤i , yi , zi ) to
simplify the notation. Then, the equality of the conditional probabilities corresponding
to (3) is
Pr{Ji = ji |Ji′ = ji′ , Ji0 = 0, Gi = gi } = Pr{Ji = ji |Ji′ = ji′ , Ji0 = 1, Gi = gi },ji , ji′ = 0, 1.

(6)

As before, this equality does not hold, that is, first job eﬀects exist, if α1 ̸= 0. Hence, it
is still essential to examine whether α1 = 0 in the present setup. However, it should be
noted that α1 = 0 does not necessarily imply (6) if the variance–covariance matrix Σ
is not diagonal.31 To see this, suppose α1 = 0 and write the first equality in (2) (withXi = xi replaced by Gi = gi ) separately for ji0 = 0 and 1:
Pr{Ji = ji |Ji′ = ji′ , Ji0 = 0, Gi = gi } = Pr{ui > −β1 Ji′ − γ1⊤ Xi |Ji′ = ji′ , Ji0 = 0, Gi = gi },Pr{Ji = ji |Ji′ = ji′ , Ji0 = 1, Gi = gi } = Pr{ui > −β1 Ji′ − γ1⊤ Xi |Ji′ = ji′ , Ji0 = 1, Gi = gi },(7)
where ji , ji′ = 0, 1. The second equality in (2) (with Xi = xi replaced by Gi = gi )no longer holds because the conditional distribution of ui is not the standard normal,
the assumed unconditional distribution, if Σ is not diagonal, and hence, Ji′ and Ji0 are
endogenous. The two conditional probabilities above generally diﬀer since the conditional distributions of ui given Ji0 = 0 and that given Ji0 = 1 are diﬀerent if ui iscorrelated with vi and/or wi . Suppose, for example, that ui is positively correlatedwith wi . Then, given Ji′ = ji′ and Gi = gi , ui tends to take a higher value when Ji0 = 1,
that is, wi > −γ3⊤ zi , than when Ji0 = 0, that is, wi ≤ −γ3⊤ zi . Hence, the conditional
probability of Ji = 1, that is, ui > −β1 ji′ − γ1⊤ xi , is higher when Ji0 = 1.
31
If Σ is diagonal, then the disturbances ui , vi , and wi are independent. In this case, the variables′
Ji and Ji0 are exogenous in (1), and the argument in the first half of this section applies.

15

The case in which (6) fails to hold because of the correlations among the disturbancesui , vi , and wi , although α1 = 0, corresponds to spurious state dependence in theterminology of Heckman (1981a), as discussed above. An individual who preferred tofind a non-regular job upon labor market entry, for instance, would prefer to have a
non-regular job at present, too. We can diﬀerentiate this case from the first job eﬀectcase in which (6) does not hold because of α1 ̸= 0, since the variance–covariance matrix
Σ of the disturbances as well as the coeﬃcient parameters in the trivariate probit modelare estimated consistently by using the maximum likelihood method.
In the literature, the following type of bivariate system has often been estimated:
the initial status equation (5) and the current status equation()
Ji = 1 αJi0 + γ ⊤ Xi + ui ,(8)
where the initial regular employment opportunity increases the probability of regular
employment at present if α is positive. In practice, assuming α to be a fixed coeﬃcient is
considered too restrictive since it means that first job eﬀects should be either permanentor non-existent. Therefore, typically, researchers allow α to be a function of τ , namely
the time elapsed since the beginning of a worker’s first job.32 Previous studies haveoften detected α as being a decreasing function of τ ; however, the declining speed ofthe eﬀect of Ji0 is slow. These results have been interpreted as evidence that the firstjob exhibits long-lasting eﬀects. As we have already seen, however, a bivariate model,such as (5) and (8) cannot distinguish serial state dependence from first job eﬀects.

6

Data Description and Empirical Specification

6.1

Working Person Survey (WPS)

The data we use are taken from the Working Person Survey (WPS) conducted every
two years in September by the Recruit Works Institute. The purpose of the WPS is toreveal the status of working individuals and their attitudes towards employment. To thisaim, the survey asks respondents about subjective recognition and objective attributes
related to their present and past jobs. The key questions, for example, position, age,
and working hours, remain unchanged, but various other questions change across thesurvey years.
The data are gathered by an online survey via a dedicated website. The sample size
is about 10,000, and participants are chosen by random sampling from each population
segmented by gender, age, and employment area. Subjects are resampled every survey
year. Thus, the WPS does not have a panel structure.
The coverage of the WPS is as follows. First, respondents must be aged between 18
and 69 years, but students are excluded.33 Second, respondents must have worked at
32
See, for example, Genda et al. (2010), Kahn (2010), and Hamaaki et al. (2013) although theirmodel specifications are not the same as (8).
33
Only the 2014 survey contains individuals aged over 59 years.

16least one day during the past week in the month before the survey. Third, respondents
must be a regular employee, contract or entrusted employee, temporary worker, parttime worker, dispatched worker, outsourced worker, or freelancer; self-employed workers
are excluded. Fourth, respondents must work in the Tokyo metropolitan area.
Although the WPS is not a panel survey, it has many retrospective questions, especially regarding past jobs. In this study, we adopt the survey results from 2012 and2014 since only these surveys have information on the first and previous jobs necessary
for our empirical method. The questionnaire asks about working patterns in the current
job as well as in the initial job, that is, the first job after finishing school. In addition,
the WPS asks whether respondents have any experience of changing jobs. If they have,it asks about working patterns in the previous job, that is, the job just before the current job. We then define a binary index for each employment status, which equals one
if a respondent is a regular employee and zero otherwise.34 The indices for the current,
previous, and initial employment statuses are the explained variables, Ji0 , Ji′ , and Ji , inthe trivariate probit model (1), (4), and (5), respectively.35

6.2

Employment Status Transition

In the WPS dataset, respondents’ educational backgrounds are classified into seven categories: junior high school, senior high school, vocational school, junior college, technicalcollege, college or university, and graduate school. We refer to individuals who belong
to one of the last two categories simply as “university graduates” and the remaining
individuals as “high school graduates.” Table 1 summarizes the WPS respondents’ employment status transitions from the initial or previous job to the current job by genderand by the grouped educational backgrounds stated above. The respondents who havenever changed jobs are excluded, and the initial and previous jobs are the same forthose who have changed their jobs only once. In the two columns corresponding to eachcombination of gender and educational background, each row reports the proportionsof the individuals who moved to non-regular or regular current jobs among those who
were non-regular or regular employees in their initial or previous jobs. The percentages in parentheses indicate employment status transitions for workers who experienced
turnover just twice. (The reason we consider this case separately is given below.)
The upper panel of Table 1 shows the results for male workers. Let us first examine
the male workers’ transitions from the initial job to the current one. Seventy-three
percent of the high school graduates and 81% of the university graduates who started
34

To be more precise, the 2012 survey asks about the working patterns of the initial job in a way
unlike the 2014 survey. The question in the 2012 survey is divided into two parts: working patterns andcontract types. For “working patterns,” the choices are “full-time (weekly working hours more thanor equal to 35)” or “part-time (weekly working hours less than 35).” For “contract types” (actuallycontract length, in this case), the choices are “indefinite-term” and “fixed-term.” These subquestions do
not correspond to the question adopted in the 2014 survey (or to the questions on previous and current
jobs in the 2012 survey). We use these contract types to construct an index for the initial employment
status, which takes one if a respondent has an indefinite-term contract and zero otherwise.
35
The WPS asks for just one employment status for each job taken.

17working in regular jobs hold regular jobs at present, too. On the other hand, both areless likely to have regular current jobs if they engaged in non-regular first jobs. The
proportion of non-regular to regular transitions is 49% for high school graduates and
58% for university graduates. In either group, the decrease is more than 20 percentage
points compared to the regular to regular transitions. It could suggest that the initial
employment status significantly aﬀects the current employment status.
Proceeding to the male workers’ transitions from the previous job to the current one,
we find rather strong persistency in the regular employment status: the proportions of
the regular employees in the current jobs among those who were regular employees
in their previous jobs are 82% and 86% for high school and university graduates, respectively. Observe also that the transitions from non-regular previous jobs to regularcurrent jobs are more likely in percentage terms to occur than the other way round. The
percentages of the non-regular to regular (regular to non-regular) transitions are 33%(18%) for high school graduates and 42% (14%) for university graduates. As a result, if
there are similar numbers of regular and non-regular male workers in the beginning, we
will observe a tendency for them, either high school or university graduates, to moveinto regular employment as the transitions are repeated.
The lower panel of Table 1 shows the results for female workers. Looking at theinitial to current transitions for women, the percentages of the non-regular to regulartransitions are smaller than those of the regular to regular ones as in the male case.
However, we find that the diﬀerences are significantly smaller in magnitude than thosein the male case, that is, 8 percentage points (31% − 23%) for high school graduates and4 percentage points (42%−38%) or university graduates. Hence, the initial employment
status might not aﬀect the current one for women as much as for men.
We also find a distinction between genders in the previous to current transitions,that is, strong persistency of non-regular, rather than regular, employment status isobserved for women. The proportions of female workers who have non-regular current
jobs among those who previously had non-regular jobs are 85% for high school graduatesand 79% for university graduates. Furthermore, the regular to non-regular transitions
occur much more often for women than for men. The percentages of the regular to
non-regular transitions are 51% and 40% for female high school and university graduates, respectively. Thus, if there are similar numbers of regular and non-regular female
workers in the beginning, we will observe a tendency for them to change to non-regularjobs in contrast to the case of male workers.
Note that in the preceding arguments we did not consider the individual heterogeneities of workers, for example, their attributes, or the economic conditions aﬀecting
workers’ and employers’ decisions, such as the unemployment rate in the relevant year.
The employment status determination is naturally considered to depend on such factors,
and hence, the casual observations we made above may be misleading. In the following, we investigate the mechanisms behind the facts shown in Table 1 more closely, bymaking use of the statistical model introduced in the previous section.
We mention some caveats before proceeding. In order to make our statistical analysis

18

possible, we restrict our attention to the subsample in which individuals change theirjobs exactly twice, as mentioned in Section 5. The figures in parentheses in Table 1
indicate the percentages of the transitions for such workers, which are close enough tothe counterparts for the larger sample consisting of workers who experienced turnover at
least once. This could be interpreted as supporting our expectation that restricting thesample would not introduce too a harmful bias. On the contrary, the loss of eﬃciency inthe estimation caused by dropping many respondents from the sample might be rather
serious. Table 2 shows the distribution of the amount of turnover by gender and status
of initial employment. We find that the size of the subsample used for our estimation is
only less than 15% of the whole sample either for male or female workers. It is, however,diﬃcult to construct and estimate a more general model that can allow for any numberof job changes as detailed in footnote 30.36

6.3

Specification of the Estimated Equation

This subsection explains the specification of each equation in the system, namely (1),(4), and (5). Let us start with the initial state equation, (5). Since it is the first choiceof employment status, the equation does not contain past employment status on theright-hand side. Therefore, the explanatory variables are all exogenous.
Our choice of exogenous variables Zi is divided into two groups. The first grouprepresents workers’ abilities or possible signals for them. We include educational background as such a variable. We take junior high school graduates as the base category.37
Another proxy for ability is respondents’ self-assessment of their record in the final
grade of junior high school. They choose an answer from five ranked alternatives: upper, upper-middle, middle, lower-middle, and lower. We then construct four dummy
variables for which the lower rank is the base.
The second group of variables concerns situations or circumstances that are thought
to be exogenous to workers. Age and marital status when a respondent found his or her
first job are included. Marital status is a dummy variable that takes one if respondents
were married at the time of finding their first job and zero otherwise. It is hardly
conceivable that workers adjust these factors in response to the statuses of employment
36
Table 2 shows that individuals whose number of job changes equals zero or one account for the mostof those excluded from the sample used for our estimation. To see how these proportions of individuals
may aﬀect the results, we estimate the first state equation (5) for a sample including workers whochanged their jobs once or less in addition to those who changed their jobs twice. Furthermore, weestimate the bivariate system of equations (4) and (5) for a sample including individuals who changed
their jobs once or twice. The estimated coeﬃcients in either case are generally close to those in thecorresponding equations of the trivariate system presented in Section 7. In particular, all the signsof the significantly estimated coeﬃcients in both cases are the same as the corresponding ones in thetrivariate system. Thus, excluding the individuals who experienced turnover once at most does notseem to cause a serious problem.
37
The 2014 survey asks about education levels in two ways. One is the school from which a respondent
graduated before starting his or her first job. The other is the final academic background (at the timeof the survey). These questions consider the possibility of recurrent education. We use the former for
the initial state equation and the latter for others.

19they can engage in. In addition, the unemployment rate at the time of entry is included.
This represents the business-cycle conditions reflected in the labor market.38 Workersof the same generation are aﬀected by the same business-cycle conditions when they
search for their first jobs. Thus, the unemployment rate in the initial state equation isa key variable to detect cohort eﬀects.
Next, let us examine the explanatory variables in the previous state equation (4).
These contain the initial state Ji0 , which is thought to be endogenous. The otherexplanatory variables Yi are exogenous and include educational background and selfassessment of junior high school record as proxies for ability. Age, marital status, andunemployment rate at the time of finding the previous job represent uncontrollable
events for respondents. Hence, these variables have the same meaning as Zi in theinitial state equation. In addition, the number of months from the end of the initial job
to the beginning of the previous job is considered as an explanatory variable.39 Thisvariable measures the eﬀect of the duration unemployed or out of the labor force.
Finally, let us turn to the current state equation (1). Both the previous Ji′ and theinitial Ji0 appear on the right-hand side. As explained in Section 5, if the initial state
influences the current state only because it depends on the previous state, the initial
status Ji0 should lose its explanatory power when the previous status Ji′ is included. If
Ji0 still shows a significant eﬀect, it means that the early sate of one’s career aﬀects the
future transition directly. The other variables correspond to those in the previous state
equation.
To sum up, for the exogenous explanatory variables Xi , Yi , and Zi , we include aworker’s attributes and characteristics that may influence the determination of employment status as well as a variable to represent labor market conditions, that is, the annual
unemployment rate. Some of the exogenous variables such as self-assessment of of junior high school record are common in all equations, while the other exogenous variables
appear only in one. For instance, the unemployment rate in the year of transition to
the current job appears as an explanatory variable only in the current state equation.
Thus, it may be regarded that some exclusion restrictions are imposed on each equationin the system. Unlike linear simultaneous equation models, these exclusion restrictions
are not needed to identify the multivariate probit model. Nevertheless, the existence of
these restrictions might help obtain good estimates in our non-linear model.
The initial state Ji0 has both direct and indirect eﬀects. In the context of the jobchange model presented in Section 4, the indirect eﬀect arises through Ji′ and decreases
at a rate of convergence of P t . On the contrary, the direct eﬀect alters the transition
matrix P . If the initial condition diﬀerentiates the transition matrix P permanently,
the direct eﬀect does not diminish. More realistically, the direct eﬀect itself may also38
We use the annual nationwide unemployment rate taken from the Labour Force Survey issued by
the Ministry of Internal Aﬀairs and Communications. We do not use the local unemployment rate since
it is available only from the 1980s. Our sample contains those who finished their academic careers before
the 1980s. Indeed, the oldest graduated in the 1950s.
39
This represents the period between the first and second jobs, since we restrict subjects to workers
who experienced just three jobs.

20reduce its influences on the current state. To take this possibility into account, weestimate the current and previous state equations including the cross terms between
the initial employment status dummy Ji0 and external labor market experience. Here,external labor market experience is defined as the number of years from the starting
year of the first job to that of the current or previous job. A negative coeﬃcient of thiscross term means that the initial job becomes less influential in the job determination
at a later period since entry. Such a decline in first job eﬀects may occur because
employers attach less importance to one’s early career as information of ability whenrecruiting regular employees or because human capital accumulated within the initial
regular job depreciates over time. In addition, we allow for diﬀerent responses to theinitial employment status according to educational background. Several previous studies
suggest that the size and persistence of cohort eﬀects, or first job eﬀects, diﬀer according
to workers’ educational backgrounds.40 Finally, we should consider the diﬀerences inthe determination of employment status between genders. Female workers are thought
to select non-regular jobs more voluntarily than male workers since female workers
often participate in the labor market secondarily in Japan. Therefore, we estimate theequations separately by genders.
The above consideration leads to the following modifications to (1) and (4), respectively:[{]}
Ji = 1 (α11 + α12 Di ) + (δ11 + δ12 Di )τi Ji0 + (β11 + β12 Di )Ji′ + γ1⊤ Xi + ui > 0 (9)and[{Ji′ = 1(α21 + α22 Di ) + (δ21 + δ22 Di )τi′]}0⊤
Ji + γ2 Yi + vi > 0 ,(10)where τi is defined as years from the beginning of the first job to that of the current
job, τi′ as years from the beginning of the first job to that of the previous job, and Di as
an education dummy that takes one if a respondent is a “university graduate” definedin the previous subsection and zero otherwise. As a result, our estimated system of
equations consists of (9), (10), and (5).
Our main concern is equation (9). (We cannot extract first job eﬀects by examining
the coeﬃcients of J 0 in (10), as discussed in Section 5.) If the coeﬃcient of Ji0 , thatis, α11 + α12 Di , is positive in (9), first job eﬀects exist as explained in Section 5. The
probability of regular employment is higher if one’s initial employment status is regular.
On the contrary, if the coeﬃcient of the cross term τi ·Ji0 , that is, δ11 +δ12 Di , is negative,
this indicates a decreasing influence of first job eﬀects. The eﬀect of the first employment
status on the current employment status declines as one’s years of experience increase.
The total amount of survived first job eﬀects for a worker with external experience τi
is given by (α11 + α12 Di ) + (δ11 + δ12 Di )τi .

40

See, for example, Genda et al. (2010).

21

7

Empirical Results

7.1

Probit Estimation

Table 3 shows the results of the estimation described in Section 6. The results for the
male and female respondents are reported in Columns 1–3 and Columns 4–6, respectively. Note that the current job corresponds to one’s third job since we restrict subjects
to workers who changed jobs exactly twice.
Columns 1 and 4 show the coeﬃcient estimates of the current state equation (9). We
describe only the results for high school graduates, that is, Di = 0. As we see shortly,
however, all the coeﬃcients of the interactions with Di are statistically insignificant for
both men and women.41 Hence, the following statements apply, regardless of workers’
educational background.
The coeﬃcient β11 of Ji′ , a dummy for the previous employment status, is significantly estimated and positive for both genders, suggesting that the current employment
status depends on the previous status. That is, serial state dependence is observed.
The most notable finding here is that the coeﬃcient α11 of Ji0 , a dummy for the initial
employment status in (9), is also significantly estimated and positive for both maleand female respondents despite introducing the previous status Ji′ as an explanatory
variable. As explained in Section 5, this fact means that the initial employment status directly aﬀects the current employment status, not merely through a succession of
turnovers. This finding implies that the initial status alters the subsequent transition
process. The estimated coeﬃcient δ11 of the cross term between Ji0 and external experience τi is significantly negative for men. That is, first job eﬀects decrease over time.
However, the estimated coeﬃcient is relatively small in absolute value compared withthat of Ji0 . This finding suggests that first job eﬀects may be long lasting. For women,
the estimated coeﬃcient is not significantly diﬀerent from zero, and thus, first job eﬀects
do not attenuate.
As mentioned above, none of the coeﬃcients of the cross terms with the dummy
for university graduates, that is, Di · Ji0 , Di · τi · Ji0 , or Di · Ji′ in (9), is significantly
estimated, irrespective of gender. Genda et al. (2010) find a large diﬀerence in the eﬀects
of entry conditions according to workers’ educational backgrounds. Our results thus
contrast with their findings, perhaps because the subjects in our dataset are restricted
to residents in the metropolitan area, in which the population of university graduatesis larger than that in other areas. Thus, the market for workers with a university
degree is more competitive in the metropolitan area, suggesting that this group may
not enjoy an educational advantage. In terms of the coeﬃcients of the variables reflecting
personal conditions at the time of taking the current job, we find that the estimatedcoeﬃcient of age has a negative sign but is insignificant. Further, the coeﬃcient of
the interval between the two jobs is significantly estimated with a negative sign andsuggests that the probability of being a regular worker declines as the period of non41

In this study, we judge the significance of estimated parameters based on the 5% level unlessotherwise noted.

22

employment lengthens. The estimated coeﬃcients of the educational dummies display
no significant relation to the current employment status. Educational level is thought
to represent a worker’s ability, or may work as a signal of ability, and thus aﬀect thedetermination of employment status. However, the results show no evidence of such ahypothesis. A junior high school record generally does not seem to signal a worker’s
ability since employers cannot observe it. Therefore, if the variable is eﬀective, it reflects
a worker’s innate or acquired general ability. However, we cannot find such evidence.
These findings are common for both genders. The estimated coeﬃcient of marital status
displays a significant positive sign for men and a negative sign for women. This finding
suggests that a male (female) worker seeks a regular job more (less) often if he (she)is married. The reverse signs of marital status seem natural in Japan since a sizable
proportion of married women are homemakers and thus when they enter the labor force,
they do so only in a secondary capacity. The unemployment rate at the time of finding
the current job does not influence the determination of employment status for either
gender. Overall, the estimation results in Columns 1 and 4 suggest that the initial andprevious employment statuses are more influential than the labor market conditions on
the probability of finding a regular job.
Next, we present the results of the other two equations. Although they are estimatedsimultaneously to deal with the endogeneity problem in the current state equation,
it is useful to examine the appropriateness of the system by checking the estimatedcoeﬃcients in these two equations. More importantly, cohort eﬀects can be confirmedby examining the initial state equation as described in Section 6.
The results regarding the previous state equation (10) for men and women are shownin Columns 2 and 5, respectively. The previous job is the second job for the subjects inour sample. The estimated coeﬃcient of the initial employment status, Ji0 , is positive
and that of the cross term with the external experience, Ji0 ·τi′ , is negative. The estimatedcoeﬃcient of Ji0 is significant for each gender. That of Ji0 · τi′ is significant only for men.
The estimated coeﬃcient of Ji0 · τi′ · Di is not significant, however, and thus, the eﬀects
do not diﬀer by educational background. These features of the initial status eﬀect areconsistent with those in the current state equation. Moreover, educational backgroundand a junior high school score are not significant. These results are also similar to thosefrom the current state equation.
Among the variables relating to the conditions at the time of finding the previous
job, the coeﬃcients of age and marital status present results similar to those in thecase of the current state equation. On the contrary, the duration of non-employmentshows the negative eﬀect only for women. For men, an absence of work in one’s career
does not work disadvantageously for obtaining regular employment. The unemployment
rate at the time of finding a job shows the negative eﬀect on the probability of finding
regular employment. This finding diﬀers from the result in the current state equation,suggesting that if a worker repeats job changes, the labor market condition might become less important to the determination of his or her employment status, and the past
employment status becomes more influential.

23

Let us turn to the results of the initial state equation (5). The estimates for male andfemale respondents are presented in Columns 3 and 6, respectively. Here, the estimatedcoeﬃcients of the proxy variables for workers’ abilities are significant for both genders
in contrast to those in the other two equations. First, new graduates with a higher
level of education have more opportunities of being employed as regular workers intheir first jobs.42 Second, the junior high school score is also an eﬀective determinantof employment status. Although the estimated coeﬃcients do not necessarily increase
along with score levels and are not necessarily significant, we find a tendency thata new entrant with a middle or higher junior high school score can find a regular job
more frequently.43 This finding suggests that educational background or the junior highschool score is eﬀective only for the determination of the first employment status. While
employers regard applicants’ abilities as important when they select new graduates, theyemphasize workers’ past employment statuses rather than abilities when they recruit
mid-career workers.
Age at the time of entry to the labor market reduces the probability of finding
regular employment. The positive eﬀect of education mitigates the negative eﬀect ofage. Marital status does not aﬀect the probability of finding regular employment at the
beginning of one’s career for men, but it does reduce the probability for women.
Importantly, the unemployment rate at entry shows a negative impact. If workers
happened to have entered the labor market in recessionary conditions and failed toengage in regular employment regardless of their abilities or preferences, this in turnassigns them a low probability of transiting from a non-regular to a regular position,
as indicated by the result of the current state equation. Judging from the estimates in
the current state equation, the eﬀect of labor market conditions at entry decreases asexternal labor market experience rises for men, but the pace is slow.
Combining the results of the three equations highlights that the dependence of thecurrent employment status on the initial employment status does not arise merely because the transition probability between non-regular and regular employment is low.
Instead, the initial state diﬀerentiates the future transition probability even among homogeneous workers. In addition, at the time of entry, the possibility of finding a regular
job is aﬀected by the aggregate labor market conditions. Therefore, temporary businesscycle conditions may aﬀect a certain generation’s lifetime employment prospects, thatis, cohort eﬀects are present.
However, the conclusions drawn thus far have been stated qualitatively only. The
estimated coeﬃcients cannot reveal the quantitative impact, namely the size of the eﬀect
on the probability of finding regular employment. Moreover, the eﬀect of a change in an
explanatory variable on the transition probability diﬀers among workers according to
the values of the other variables. Thus, the size of those eﬀects on the overall economy
depends on the distribution of the values of the exogenous variables across workers. We
42

Male technical college graduates are an exception. The estimated coeﬃcient of the dummy isinsignificant with a large p-value. The large standard error may be due to the small number of relevant
respondents.
43
Most of the estimated coeﬃcients of these dummies are significant at the 10% level.

24

take the average of the individual eﬀects over the sample to evaluate an economy-wide
first job eﬀect quantitatively, as discussed in the next subsection.
Finally, the estimated correlation coeﬃcients among the disturbances of the threeequations are statistically insignificant, as shown in the bottom panel of Table 3. The
correlation coeﬃcients are denoted by ρij , i, j = 1, 2, 3, where the numbers 1, 2, and3 correspond to the initial, previous, and current state equations, respectively. The
reported p-values and results of the likelihood ratio test show that the disturbances inthe system are independent. If unobserved individual characteristics, which are thought
to be the main source of the endogeneity of past employment statuses, are present,
the disturbances should indicate positive correlations. However, we do not find such
evidence in the results, perhaps because the explanatory variables related to individual
abilities and conditions could have eﬀectively absorbed the individual heterogeneity.
If Ji0 and Ji′ are both exogenous, as suggested by the above estimation results, then(9) can be estimated independently. Therefore, we also estimate a univariate probit
model. (This corresponds to estimating the trivariate system under the constraints
that the correlations among the disturbances are equal to zero.) Table 4 presents theestimation results for the univariate model (9), showing that the estimated coeﬃcientsand standard errors in the univariate probit case do not diﬀer conspicuously from thosein the trivariate case (Table 3). The most notable diﬀerence is that the coeﬃcient of Ji0
for men and women is smaller by a sizable amount in the univariate case than in thetrivariate case. Nevertheless, the above arguments based on the trivariate estimation
results are qualitatively unaltered. Moreover, the mean sizes of marginal eﬀects, which
are more important for interpreting the results, diﬀer little between the trivariate andunivariate cases, as shown in the next subsection.

7.2

Quantitative Impact of First Job Eﬀects

In the previous subsection, we found evidence of cohort eﬀects. However, the coeﬃcients of the probit model do not represent the marginal eﬀects of the variables on theprobabilities. Thus, the coeﬃcient estimates in Tables 3 and 4 do not oﬀer quantitative
implications about the transition probabilities. To evaluate the impact of cohort eﬀects(equivalently first job eﬀects in this case), we must thus compute the sample average ofindividual marginal eﬀects as follows.
Consider for each worker i (i = 1, . . . , N ) the conditional probability of the current
employment status being regular given the previous and initial employment statuses (aswell as the values of the exogenous variables):Pr(Ji = 1 | Ji′ = j, Ji0 = k, Gi = gi ),k, j = 0, 1.

(11)

We can compute this conditional probability for each combination of k and j, irrespective of his or her actual current, previous, and initial employment statuses. The
marginal eﬀect of a change in the initial employment status on the conditional probability of finding a regular current job is then given by
Pr(Ji = 1 | Ji′ = j, Ji0 = 1, Gi = gi ) − Pr(Ji = 1 | Ji′ = j, Ji0 = 0, Gi = gi ),25

j = 0, 1.

This diﬀerence can also be regarded as the marginal eﬀect of the initial employment
status on the transition probability from the previous employment status j to the current
employment status 1 for worker i with the values of the exogenous variables equal togi . Similarly, the marginal eﬀect of the previous employment status on the conditional
probability of finding a regular current job is given by
Pr(Ji = 1 | Ji′ = 1, Ji0 = k, Gi = gi ) − Pr(Ji = 1 | Ji′ = 0, Ji0 = k, Gi = gi ),k = 0, 1.

If k = 1 (k = 0), this is the diﬀerence between the regular to regular transition probability and the non-regular to regular one for a worker whose initial employment statusis regular (non-regular).
Note that the values gi of the exogenous variables are diﬀerent among workers as
are the magnitudes of the individual marginal eﬀects defined above. Therefore, we takethe average of the individual marginal eﬀects over the whole sample, which we callthe average marginal eﬀect (AME hereafter) of the initial employment status and theprevious employment status. For example, the AME of the initial employment statuson the conditional probability of the current job being regular is equal to}1 ∑{Pr(Ji = 1 | Ji′ = j, Ji0 = 1, Gi = gi ) − Pr(Ji = 1 | Ji′ = j, Ji0 = 0, Gi = gi )
N
N

i=1

= Pr(J = 1 | J ′ = j, J 0 = 1) − Pr(J = 1 | J ′ = j, J 0 = 0)

j = 0, 1,(12)

where Pr(J = 1 | J ′ = j, J 0 = k), k, j = 0, 1 denotes the sample average of theindividual conditional probabilities (11). In the following discussions, we also refer to(12) as the AME of the initial employment status on the transition probability from a
regular/non-regular previous job to a regular current job.
When the disturbances in the system (9), (10), and (5) are correlated, that is, Ji′
and Ji0 are endogenous, the conditional probability (11) depends on the correlations of
the disturbances as well as the coeﬃcients of the latent equations. (See the argument
following equation (7).) We use the maximum likelihood estimates of these parameters
to compute the conditional probabilities and hence the AMEs.
The estimated average conditional probabilities and AMEs are shown in Panel (A)of Table 5. The first column shows the results for men. Line 1 indicates that theprobability of non-regular to regular transition is 0.460 on average for workers whostarted working as non-regular workers. Line 2 shows that this figure rises to 0.553 if
they started as regular workers. Hence, the AME of the initial employment status on
the non-regular to regular transition probability is 0.093 as shown in Line 5. Thus, theaverage probability for male workers who started their careers as regular employees toreturn to regular jobs even after becoming non-regular is higher by 0.093 than that forworkers whose initial jobs were non-regular.
On the other hand, Lines 3 and 4 indicate that the average probability of regular
to regular transition is 0.820 for male workers whose first employment status was nonregular and 0.854 for those who started working as regular employees. Thus, as shown26

in Line 6, the AME of the initial employment status on the probability of staying in a
regular job is 0.034. Male regular workers find other regular jobs at a probability greaterthan 0.8, irrespective of their initial employment statuses. We can thus conclude fromLines 5 and 6 that first job eﬀects are small.
The figures in Lines 1–4 also suggest that serial state dependence is a dominantsource of the persistence of employment status. Suppose that an individual entered thelabor market as a non-regular worker. The probability that he or she finds anotherregular job is 0.820 (0.460) if his or her previous job was regular (non-regular). Thus,
the AME of the previous employment status is 0.360 when the initial employment status
was non-regular, as shown in Line 7. In the case where the initial employment status
was regular, the probability of finding a regular current job is 0.854 (0.553) if he or she
had a regular (non-regular) status in his or her previous job. Therefore, the AME of theprevious job in this case is 0.301, as shown in Line 8. Thus, we can conclude that theprobabilities of the regular to regular transition are larger than those of the non-regular
to regular transition. This diﬀerence exceeds 0.3, regardless of the initial employment
status. This is evidence of the dominance of serial state dependence.
The second column in Panel (A) displays the results for women. The transition
probabilities from a non-regular job to a regular jobs reported in Lines 1 and 2 aremuch smaller than those for men, irrespective of the initial employment status. Line 2
suggests that even if female workers had regular jobs at first, a later transition from nonregular to regular jobs occurs at a probability of only 0.220. If they started their careers
in non-regular employment, the probability falls to 0.166, as shown in Line 1. Thesefigures suggest that female workers find it diﬃcult to seek regular jobs despite repeated
job changes once they have non-regular jobs. The AME of the initial employment statuson the non-regular to regular transition probability is 0.054 (see Line 5). Lines 3 and 4
show the average probabilities of keeping a regular position during turnovers for female
workers. These probabilities are also much lower, by over 0.3, than those for their malecounterparts. The divergence of probabilities between Lines 3 and 4 is small, meaningthat the AME of the initial employment status on the regular to regular transition
probability is 0.055, as shown in Line 6.
The comparison of the AMEs of the previous employment status between men andwomen in Lines 7 and 8 shows that these figures are close. The dominance of serial
state dependency is also found in the case of female workers. On the other hand, thecomparison of figures in Lines 1–4 between genders reveals that all the probabilities ofmoving to a regular job are lower for women than for men. Even the regular to regulartransition probabilities are around 0.5 in the case of female workers. We find it unusual
for women to change to a regular job.
To summarize the results for both men and women, the AMEs of the initial employment status are not substantial in comparison with the average conditional probabilitiesof having a regular job. Instead, the diﬀerence in the previous status is more influential
on the current status. We consequently conclude that first job eﬀects are not quantitatively important and that serial state dependence plays a prior role in the persistence27

of employment status.
As stated above, the correlations among the disturbances in (9), (10), and (5), thatis, individual heterogeneity, were taken into account to compute the average conditional
probabilities and AMEs reported in Panel (A) of Table 5. However, the estimated correlation coeﬃcients are statistically insignificant for both men and women. These results
imply that there is no individual heterogeneity and thus no spurious state dependence.
Moreover, the estimated signs of correlation are negative in all cases except one case as
described in Subsection 7.1. Individual heterogeneity stemming from abilities or preferences should induce positive correlations. Indeed, it is hard to find a specific example
that can account for the negative correlations. To avoid the undesirable influence of theinsignificantly estimated negative correlations, we also compute the conditional probabilities and AMEs based on the univariate probit model, as displayed in Table 4. This
corresponds to the case in which the correlations among the disturbances are restricted
to zero in the trivariate probit model. In other words, serial state dependence in theunivariate case does not include spurious state dependence.
Since Ji′ and Ji0 in (1) are exogenous in this case, the individual conditional probabilities (11) of the regular current employment are simply given by (2), from which theaverage conditional probabilities and AMEs are calculated in the same way as before.
Lines 13–18 in Panel (B) of Table 5 report the results. Each probability is close to thecorresponding one in the trivariate case. Among them, the largest diﬀerence is observed
for the AME of the initial employment status on the non-regular to regular transition
for male workers. Line 17 indicates that first job eﬀects cause a probability diﬀerence of0.154 in the univariate case, while the diﬀerence is 0.093 in the trivariate case. However,they are smaller than the eﬀects of serial state dependence. The AMEs in Lines 19 and20 suggest that the probability of finding a regular job is about 0.3 to 0.4 higher when
the previous job is regular than when it is non-regular.
Now, we should realize that the eﬀects of the initial employment status on the current
one may decline along with years of external experience since the probit estimations inboth the trivariate and the univariate cases indicate negative coeﬃcients of the crossterm between the initial employment status and external experience (see Subsection 7.1
and Tables 3 and 4). To quantify the rate of this decrease, we calculate the influence of
external experience on first job eﬀects as follows.
Consider a counterfactual situation in which each respondent’s years of external
experience increase from τi to τi + t, where τi is his or her actual external experience,and t represents a certain period of additional years. Recall that τi is defined as elapsed
years from the year of entry to the year of obtaining the current job. Therefore, inreality τi does not change independent of the other explanatory variables related to theyear of hiring or separation. However, we ignore such interactions in order to extract
the pure eﬀects of external experience. Since the AME of the initial employment status(12) depends on workers’ external experience, we compare the AME with the actual τiand τi + t, keeping the other variables unchanged. The diﬀerence represents the eﬀect of
an increase t in external experience on the AME of the initial employment status. That28

is, the change in the AME due to additional t years of external experience is defined by
[
N
1 ∑ {
Pr(Ji = 1 | Ji′ = j, Ji0 = 1, Ti = τi + t, Hi = hi )N
i=1}
− Pr(Ji = 1 | Ji′ = j, Ji0 = 0, Ti = τi + t, Hi = hi ){− Pr(Ji = 1 | Ji′ = j, Ji0 = 1, Ti = τi , Hi = hi )]}′0
− Pr(Ji = 1 | Ji = j, Ji = 0, Ti = τi , Hi = hi ) , j = 0, 1,
where Ti indicates the years of external experience, Hi is a vector of the exogenous
explanatory variables other than Ti , and hi is its realized value.
Note that this should not be interpreted as a decrease in the first job eﬀects t years
after entry, since each respondent’s years of experience are set to τi + t rather than t.
This is a comparison of the AMEs of the initial employment status between workers
with τi years of external experience and those with identical characteristics except thatthey have τi + t years of external experience.
The results are shown in Table 5 in Lines 9–12 for the trivariate case and Lines 21–24
for the univariate case. Since there is little diﬀerence in the estimated AMEs between
the two cases, let us examine the univariate case. The changes in the AMEs of the initial
employment status in the case of t = 1 are shown in Lines 21 and 22. The advantage ofbeing a regular worker upon entry with respect to the non-regular to regular transition
probability would decrease by 0.016 for men and by 0.006 for women if all workers were
employed for an additional year in the labor market before obtaining their current job.
The AME on the regular to regular transition probability would decline by 0.009 formen and by 0.007 for women.
The changes in the AMEs of the initial employment status in the case of t = 10 are
shown in Lines 23 and 24. Although it may not be plausible to assume that workers withthe same characteristics, except a nine-year gap in external experiences exist, we see thateach of the changes in the AMEs at t = 10 is close to 10 times the size of its counterpart
at t = 1. This finding suggests that the AMEs of the initial employment status decline
almost linearly in t, which in turn implies that they would also decline linearly in τi .
Therefore, the first job advantages with respect to the non-regular (regular) to regulartransition probability decrease year-by-year by the amounts indicated in Line 21 (22).
Thus, the changes in the AMEs of the initial employment status are judged to be small
and thus the rate of decrease is low, as expected from the estimated coeﬃcients in the
probit model. However, the AMEs of the initial employment status are small per se,suggesting that most of the existing first job eﬀects cease within 10 years.
Recall that the diminishing first job eﬀects are not detected with statistical significance for women in both the trivariate and the univariate cases in the previous
subsection. However, even if first job eﬀects are permanently eﬀective for women, thesize of the eﬀects is small from the beginning.
29

To sum up, although first job eﬀects, or cohort eﬀects, exist, the quantitative influences on employment status are not that substantial, at least when we evaluate themunder the actual distributions of the other characteristics across workers. On the other
hand, serial state dependency matters more critically for labor market segmentation.
Because the low transition probabilities from non-regular jobs bring about the dual
structure in the labor market, we should examine the mechanism of the intertemporaldependence of employment status to investigate the source of polarization in the labormarket.
Further, cohort eﬀects are potentially at play and they may appear modestly under
the distribution of individual workers’ characteristics or circumstances at the present
moment. If that is the case, the cohort eﬀects might become tangible when situations
change.

8Conclusion

In this study, we examined the persistency of employment status over time against
the background of the recent increase in the share of non-regular workers. The sourceof persistency can be attributed to two phenomena: first job eﬀects and serial state
dependence. As per the term “first job eﬀects,” a worker’s employment status in a job at
the time of entry into the labor market influences his/her future employment statuses fora long time, even permanently. On the other hand, serial state dependence means thatthe determination of a worker’s employment statuses depends on employment statusesin his/her previous job.
The first job eﬀects are confirmed empirically by observing the positive correlation
between the first and the current employment status. However, such correlation canbe found when only strong positive serial state dependence exists, since the initial
status can aﬀect the current one for a long time by repeating intertemporal dependence
between two consecutive employment statuses. However, the two phenomena are quite
diﬀerent. The first job eﬀect hypothesis emphasizes the special role of experiences orenvironments early on in one’s career. On the other hand, serial state dependence occurs
evenly at any stage of one’s career, with no special role of the initial employment status.
We proposed an empirical method to distinguish these two phenomena and to evaluate the significance of each. Methodologically, we estimated a probit model where
the current employment status is an explained variable, and both the initial and previous employment statuses are included in explanatory variables. We considered theendogenous bias arising from individual heterogeneities, which is called spurious state
dependence. To manage endogenous nature, a recursive trivariate probit model thatconsisted of equations for the employment status in the current, previous, and firstjobs, were constructed. This model was estimated by using the data of workers whochanged their jobs just twice, taken from a micro survey on employment in Tokyo
metropolitan area.
Consequently, we verified the presence of both serial state dependence and first job

30

eﬀects on the persistence of employment status. Additionally, we found insignificant
eﬀects of individual heterogeneity on it. Moreover, by exploiting the multivariate structure, we also examined and detected cohort eﬀects, which means that the temporary
economic conditions at a worker’s time of entry have a long-lasting influence on his or
her lifetime working conditions.
To quantify the impact of first job eﬀects and serial state dependence, we computed
the average marginal eﬀects. The results revealed that the quantitative impact of cohort eﬀects on employment status is not that substantial. Furthermore, most existingcohort eﬀects cease within 10 years. On the other hand, serial state dependence has
greater influence on the persistency of employment statuses, and seems responsible forthe rising labor market segmentation. These assertions apply to each gender, although
we found that female workers tend to move into the non-regular sector more frequently
than male workers. However, there is no diﬀerence by educational level. The findingspresented in this paper suggest that we should pursue the mechanism of the intertemporal dependence of employment status to investigate the source of the polarization of
the labor market.

31

Table 1: Employment Status Transition

Men
High school graduate
Sample size = 2,070 (474)
Non-Regular

University graduate
Sample size = 3,529 (819)

Current employment status
RegularNon-regular

RegularInitial employment status
Non-regular

0.51(0.48)

0.49(0.52)

0.42(0.40)

0.58(0.60)

Regular0.27(0.24)

0.73(0.76)

0.19(0.18)

0.81(0.82)

Non-Regular

0.67(0.60)

0.33(0.40)

0.58(0.53)

0.42(0.47)

Regular

0.18(0.16)

0.82(0.84)

0.14(0.13)

0.86(0.87)

Previous employment status

Women
High school graduate
University graduate
Sample size = 3,988 (926)Sample size = 2,213 (553)
Non-Regular

Current employment status
RegularNon-regular

RegularInitial employment status
Non-regular

0.77(0.75)

0.23(0.25)

0.62(0.61)

0.38(0.39)

Regular0.69(0.69)

0.31(0.31)

0.58(0.60)

0.42(0.40)

Non-regular

0.85(0.84)

0.15(0.16)

0.79(0.78)

0.21(0.22)

Regular

0.51(0.48)

0.49(0.52)

0.40(0.37)

0.60(0.63)

Previous employment status

Notes:
Individuals who experienced no turnover are excluded from the sample. The initial
job and the previous job are the same for those who changed jobs once. The numbers
in parentheses are the proportions of transitions among individuals who changed jobs
twice.

32

Table 2: Distribution of the Number of Job Changes

Ratio
Men
Non-Regular
Sample size

Women

Initial employment status
RegularNon-regular

Regular

1,431

9,675

1,667

6,689

0

0.35

0.52

0.31

0.19

1

0.24

0.21

0.18

0.16

2

0.15

0.12

0.15

0.13

3

0.10

0.08

0.13

0.11

4

0.05

0.04

0.07

0.06

5

0.05

0.03

0.08

0.05

6 or more

0.06

0.03

0.07

0.07

Turnover number

Notes:The proportion of individuals who experience turnover at the indicated number oftimes relative to the total of each category.

33

34

Age when finding the initial job

Age when finding the previous job-0.01494(-1.37) [0.172]-0.15881(-0.77) [0.438]

× University graduate (D)Age when finding the current job

0.9677(1.92) [0.055]

Previous employment: Regular (J ′ )0.006101(.0.49) [0.621]-0.008076(-0.84) [0.399]

× University graduate (D) × External experience up to the previous job (τ ′ )-0.004335(-0.45) [0.656]

× University graduate (D) × External experience up to the current job (τ )-0.03377(-2.45) [0.014]-0.04716(-3.60) [0.000]

× External experience up to the current job (τ )0.1452(0.61) [0.541]

1.504(3.70) [0.000]

Previous state
equation(Second job)

Men

2

× External experience up to the previous job (τ ′ )0.3033(1.07) [0.286]

1.609(3.40) [0.001]

× University graduate (D)Initial employment: Regular (J 0 )Current state
equation(Third job)

1

3

-0.09269(-3.90) [0.000]

Initial state
equation(First job)

Table 3: Results of the Trivariate Probit Model-0.004872(-0.49) [0.621]-0.002556(-0.02) [0.987]

1.048(3.51) [0.000]-0.01474(-1.44) [0.150]-0.01696(-1.51) [0.131]-0.08559(-0.35) [0.729]

1.125(3.12) [0.002]

Current state
equation(Third job)

40.01062(0.82) [0.414]-0.002901(-0.25) [0.799]-0.01616(-1.14) [0.253]-0.1861(-0.88) [0.381]

0.7176(1.99) [0.047]

Previous state
equation(Second job)

Women

5

-0.07529(-3.48) [0.001]

Initial state
equation(First job)6

35
-0.4780(-0.70) [0.484]-0.4905(-0.83) [0.409]-0.1597(-0.25) [0.801]

Technical college

University

Graduate school

Upper-middle

0.02830

0.2560(1.05) [0.295]-0.1201(-0.17) [0.862]

Junior college

Junior high school score
Upper-0.5173(-0.90) [0.367]-0.5510(1.00) [0.316]-0.04999(-0.71) [0.475]-0.004331(-2.45) [0.014]

0.4403(3.90) [0.000]

Vocational school

Educational background
High school

Unemployment rate when finding the initial job

Unemployment rate when finding the previous job

Unemployment rate when finding the current job

Interval between the initial job and the previous job

Interval between the previous job and the current job

Marital status when finding the initial job

Marital status when finding the previous job

Marital status when finding the current job

0.10880.2380(1.00) [0.316]

0.7135(1.18) [0.239]

0.3904(0.67) [0.503]

0.1892(0.29) [0.773]

0.1552(0.24) [0.809]

0.2620(0.47) [0.637]

0.1667(0.32) [0.749]-0.2119(-4.34) [0.000]-0.0000225(-0.02) [0.983]

0.4328(3.70) [0.000]

0.40170.4267(1.81) [0.071]

2.549(4.02) [0.000]

2.522(4.34) [0.000]

6.387(0.05) [0.962]

2.117(2.98) [0.003]

2.285(3.93) [0.000]

1.299(2.29) [0.022]-0.3104(-7.69) [0.000]

0.2207(0.70) [0.484]

0.10850.3586(1.40) [0.162]-0.3840(-0.70) [0.486]-0.2813(-0.54) [0.592]-0.1592(-0.25) [0.801]-0.6629(-1.28) [0.201]-0.5392(-1.05) [0.293]-0.4939(-0.98) [0.329]-0.07863(-1.42) [0.155]-0.004780(-5.20) [0.000]-0.7100(-7.54) [0.000]

0.13970.2633(1.14) [0.255]

0.2763(0.49) [0.625]-0.1801(-0.33) [0.739]

0.1478(0.23) [0.817]-0.2992(-0.56) [0.576]-0.2796(-0.53) [0.597]-0.2015(-0.39) [0.698]-0.1445(-3.69) [0.00]-0.002717(-2.87) [0.004]-0.6037(-7.10) [0.00]

0.45830.3378(1.40) [0.161]

2.024(3.15) [0.002]

2.464(4.41) [0.000]

1.651(2.30) [0.021]

2.487(4.49) [0.000]

2.182(3.95) [0.000]

1.912(3.50) [0.000]-0.3354(-9.38) [0.000]-0.7284(-2.49) [0.013]

36-0.2478(-1.02)[0.308]-0.3596(-1.62)[0.106]
0.1626(0.59)[0.555]
4.426[0.2189]
1,256-0.2073(-0.34) [0.732]

0.2303(0.95) [0.343]

0.2495(1.08) [0.281](0.46) [0.642]

1.507(2.09) [0.036]

0.2051(.0.84) [0.403]

0.5251(2.31) [0.021](1.74) [0.083]-0.2742(-0.44) [0.662]

0.1417(0.53) [0.598]

0.1239(0.49) [0.624](0.43) [0.671]-0.02050(-0.11) [0.912]-0.3214(-1.67)[0.095]-0.01658(-0.09)[0.929]
2.51073[0.4734]
1,454

0.02866(0.05) [0.962]-0.1050(-0.43) [0.667]

0.05664(0.25) [0.804](0.61) [0.544]

Notes:
1. Numbers in parentheses are t-values. Numbers in square brackets are p-values.
2. The trivariate probit models were estimated by using the maximum simulated likelihood method. We used the mvprobit[Cappellari and Jenkins (2003)] command in Stata, where the number of pseudo-random variates drawn for simulating the
likelihood was set to 500.

Likelihood-ratio test ofρ21 = ρ21 = ρ32 = 0
Number of observations

the current and the previous equations: ρ32

the current and the initial equations: ρ31

Correlation coeﬃcient between the residuals of
the previous and the initial equations: ρ21

0.2669(0.41) [0.683]

0.1102(0.44) [0.661]

Lower-middle

Constant

0.2955(1.24) [0.217](0.12) [0.906]

Middle1.045(1.53) [0.126]

0.2200(0.87) [0.383]

0.4656(1.99) [0.047](1.93) [0.054]

Table 4: Results of the Univariate Probit Model

1
Men

2
Women

Current state equation(Third job)
Initial employment: Regular (J 0 )0.9737(3.99) [0.000]

0.6282(3.09) [0.002]

× University graduate (D)0.2207(0.76) [0.446]-0.1124(-0.44) [0.675]

× External experience up to the current job (τ )-0.04638(-3.52) [0.000]-0.01722(-1.52) [0.128]

× University graduate (D) × External experience up to the current job (τ )-0.004165(-0.42) [0.676]-0.01405(-1.35) [0.176]

Previous employment: Regular (J ′ )1.217(7.68) [0.000]

1.039(10.27) [0.000]

× University graduate (D)-0.1516(-0.72) [-0.470]

0.007530(0.05) [0.963]

Age whenthe job started-0.01362(-1.23) [0.219]-0.001675(-0.17) [0.865]

Marital status when
the job started

0.4325(3.90) [0.000]-0.7296(-8.77) [0.000]

Interval between the job
and the former job-0.004715(-2.70) [0.007]-0.004693(-5.64) [0.000]

Unemployment rate when
the job started-0.05750(-0.81) [0.416]-0.09155(-1.65) [0.098]-0.3620(-0.66) [0.508]-0.2249(-0.46) [0.645]

Vocational school-0.2393(-0.43) [0.667]-0.2648(-0.54) [0.592]

Junior college

0.1654(0.24) [0.862]-0.3573(-0.72) [0.470]

Technical college-0.1550(-0.23) [0.815]

0.05525(0.09) [0.930]

University-0.1612(-0.29) [0.774]

0.007839(0.02) [0.988]

Graduate school

0.1172(0.19) [0.848]-0.1832(-0.33) [0.738]

0.3301(1.38) [0.168]

0.4200(1.64) [0.100]

0.1155(0.49) [0.623]

0.1929(0.76) [0.445]

Educational background
High school

Junior high school score
Upper
Upper-middle

37

Middle0.3845(1.66) [0.097]

0.2074(0.83) [0.408]

Lower-middle

0.1506(0.60) [0.548]

0.1928(0.71) [0.475]

Constant

0.2851(0.43) [0.666]-0.2681(-0.43) [0.666]

1,256

1,454

Number of observations

Notes:
1. Numbers in parentheses are t-values. Numbers in square brackets
are p-values.
2. The univariate probit models were estimated by Stata’s probitcommand.

38

Table 5: Conditional Probabilities and AMEs

Panel (A)

Men

Women

Trivariate probit
Conditional probability
Non-regular to regular transition
1. Pr(J = 1 | J ′ = 0, J 0 = 0)0.46010.16580.55280.2201

3. Pr(J = 1 | J ′ = 1, J 0 = 0)0.81970.4787

4. Pr(J = 1 | J ′ = 1, J 0 = 1)

0.85360.5341

0.09273

0.054310.033890.05541

Initial employment status: non-regular
7. AME (J 0 = 0): (3) − (1)0.35960.3129Initial employment status: regular
8. AME (J 0 = 1): (4) − (2)0.30070.3140Change in the AME of the initial employment status
9. After 1 year (J ′ = 0)-0.01661-.005820

10. After 1 year (J ′ = 1)

-0.009243-.007390

11. After 10 years (J = 0)-0.1678-.05420

12. After 10 years (J ′ = 1)

-0.1108-.07461

Panel (B)

Men

Women

′

2. Pr(J = 1 | J = 0, J = 1)
0Regular to regular transition

AME of the initial employment status: J = 0 → J = 1
00

Non-regular to regular transition
5. AME (J ′ = 0): (2) − (1)Regular to regular transition
6. AME (J ′ = 1): (4) − (3)′′AME of the previous employment status: J = 0 → J = 1′

Univariate probit
Conditional probability
Non-regular to regular transition
13. Pr(J = 1 | J ′ = 0, J 0 = 0)0.40870.15310.56300.221015. Pr(J = 1 | J ′ = 1, J 0 = 0)0.79970.4543

16. Pr(J = 1 | J ′ = 1, J 0 = 1)

0.85390.53780.1543(3.41) [0.001]

0.06787(2.90) [0.004]

0.05420(1.63) [0.103]

0.08333(2.18) [0.029]′

14. Pr(J = 1 | J = 0, J = 1)
0Regular to regular transition

AME of the initial employment status: J = 0 → J = 1
00

Non-regular to regular transition
17. AME (J ′ = 0): (14) − (13)Regular to regular transition
18. AME (J ′ = 1): (16) − (15)39AME of the previous employment status: J ′ = 0 → J ′ = 1
Initial employment status: non-regular
19. AME (J 0 = 0): (15) − (13)0.3910(10.83) [0.000]

0.3012(10.63) [0.000]

0.2909(9.20) [0.000]

0.3166(13.45) [0.000]-0.01585(-4.43) [0.000]-.005707(-2.15) [0.032]

22. After 1 year (J ′ = 1)

-0.008853(-4.25) [0.000]-.007265(-2.10) [0.036]

23. After 10 years (J ′ = 0)-0.1609(-4.46) [0.000]-.05324(-2.30) [0.022]

24. After 10 years (J ′ = 1)

-0.1054(-3.73) [0.000]-.07334(-2.11) [0.035]

Initial employment status: regular
20. AME (J 0 = 1): (16) − (14)Change in the AME of the initial employment status
21. After 1 year (J ′ = 0)

Notes:
1. Numbers in parentheses are t-values. Numbers in square brackets are pvalues.
2. In Panel (A), we used Stata programs mdraws and mvnp [Cappellari andJenkins (2006)] to simulate the multivariate normal probabilities required forthe computation of the conditional probabilities and marginal eﬀects. The
number of draws option given to the mdraws command was 500. See also the
Appendix to this paper. In Panel (B), Stata built-in commands and functions
were used for the computation of the conditional probabilities and marginal
eﬀects.
We do not provide t-values for the estimated AMEs in Panel (A) because it
is diﬃcult to calculate the standard errors by using the delta method, which
was used to calculate the t-values in Panel (B).

40

Figure 1: Increases in non-regular employment in Japan40

35

non-regular worker ratio (description based definition)
temporary worker ratio (contract-length based definition)
part-time worker ratio (working-hours based definition)

30

%

25

20

15

10

5

0
1985

1990

1995

2000

2005

2010

2015

year

The non-regular worker ratio represents the proportion of non-regular employees to total employees.
The figures are taken from the Labour Force Survey (Ministry of Internal Aﬀairs and Communications).
Non-regular employees are classified according to how workers are called in their workplaces. Six categories, namely “part-time worker,” “temporary worker,” “dispatched worker from temporary labour
agency,” “contract employee,” “entrusted employee,” and “other” are classified into non-regular employees. The remaining categories are “regular employee” and “executive of the company or corporation.”
The temporary worker ratio represents the proportion of temporary and daily employees to total
employees taken from the Labour Force Survey. Temporary employees are defined as “persons who
work on a contract of a month or more but not more than a year” and daily employees are definedas “persons who work on a daily basis or on a contract of less than a month.” Others are classified
as long-term employees. The sharp decline in the temporary worker ratio in 2013 was driven by thechange in the classification of long-term employees in the survey. The survey has diﬀerentiated between
indefinite-duration contracts and limited-duration contracts since 2013. People who selected “temporary
employee” as their employment status are thought to select long-term employees with limited-duration
contracts.
The part-time worker ratio represents the proportion of part-time workers to total regular employees in establishments with more than four regular employees, where regular employees are defined asworkers hired for an indefinite period or for longer than one month, or hired by the day or for less than
one month and who were hired for 18 days or more in each of the two preceding months. (This definitionof regular employees diﬀers from the definition used in the text.) Figures are based on the Monthly

41

Labour Survey (Ministry of Health, Labour and Welfare). Part-time workers have shorter scheduledworking hours per day/working hours per week than ordinary workers.

References
Abowd J.M. and Zellner A. (1985), “Estimating gross labor-force flows,” Journal ofBusiness and Economic Statistics, 3(3), pp.254–283.
Ariga, K. and Okazawa, R. (2011), “Labor immobility in Japan: Its causes and consequences,” in (K. Hamada, A.K. Kashyap and D.E. Weinstein eds.) Japan’s Bubble,
Deflation, and Long-term Stagnation, pp.263–308, Cambridge: MIT Press.
Arulampalam, W., Booth, A.L. and Taylor M.P. (2000), “Unemployment persistence,”
Oxford Economic Papers 52(1), pp.24–50.
Arulampalam, W. and Stewart, M.B. (2009), “Simplified implementation of the Heckman estimator of the dynamic probit model and a comparison with alternative estimators,” Oxford Bulletin of Economics and Statistics 71(5), pp.659–681.
Asano, H., Ito, T. and Kawaguchi, D. (2013), “Why has the fraction of contingent
workers increased? A case study of Japan,” Scottish Journal of Political Economy
60(4), pp.360–389.
Bartholomew, D.J. (1982), Stochastic Models for Social Processes (3rd edition), Chichester: John Wiley & Sons.
Blanchard, O.J. and Diamond, P. (1990), “The cyclical behavior of the gross flows of
U.S. workers,” Brookings Papers on Economic Activity 2:1990, pp.85–143.
Brunner, B. and Kuhn, A. (2014), “The impact of labor market entry conditions on
initial job assignment and wages,” Journal of Population Economics 27(3), pp.705–
738.
Cai, L. (2014), “State-dependence and stepping-stone eﬀects of low-pay employment inAustralia,” Economic Record 90 (291), pp.486–506.
Cappellari, L. and Jenkins, S.P. (2006), “Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation,”
The Stata Journal 6(2), pp.156–189.
Choi, S., Janiak, A. and Villena-Roldán, B. (2014), “Unemployment, participation andworker flows over the life-cycle,” Economic Journal 125 (589), pp.1705–1733.
Durret, R. (1999), Essentials of Stochastic Processes, New York: Springer-Verlag.

42Esteban-Pretel, J., Nakajima, R. and Tanaka, R. (2011a), “Are contingent jobs deadends or stepping stones to regular jobs?: Evidence from a structural estimation,”Labour Economics 18(4), pp.513–526.
Esteban-Pretel, J., Nakajima, R. and Tanaka, R. (2011b), “Changes in Japan’s labormarket flows due to the Lost Decade,” RIETI Discussion Paper Series 11-E-039, The
Research Institute of Economy, Trade and Industry.
Fujita, S. and Ramey, G. (2009), “The cyclicality of separation and job finding rates,”
International Economic Review 50(2), pp.415–430.
Genda, Y. (1997), “Chansu ha ichido: Sedai to chingin kakusa (One chance: Generations
and wage diﬀerentials),” (in Japanese), The Japanese Journal of Labour Studies 449,
pp.2–12.
Genda, Y. (2009), “Seishain ni natta hiseishain: Naibuka to tenshoku no sakini (How
could non-regular employees become regular employees?: Transitions through internalization and separation),” (in Japanese), The Japanese Journal of Labour Studies
586, pp.34–48.
Genda, Y., Kondo, A. and Ohta, S. (2010), “Long-term eﬀects of a recession at labormarket entry in Japan and the United States,” Journal of Human Resources 45(1),pp.157–196.
Greene, W.H. (2011), Econometric Analysis (7th edition), Essex: Pearson.
Hagedorn, M. and Manovskii, I. (2008), “The cyclical behavior of equilibrium unemployment and vacancies revisited,” American Economic Review 98(4), pp.1692–1706.
Hamaaki, J., Hori, M., Maeda, S. and Murata, K. (2013), “How does the first job matterfor an individual’s career life in Japan?” Journal of the Japanese and International
Economies 29, pp.154–169.
Heckman, J. (1981a), “Heterogeneity and state dependence,” in (S. Rosen ed.), Studies
in Labor Market, pp.91–140, Chicago: University of Chicago Press.
Heckman, J. (1981b), “The incidental parameters problem and the problem of initialconditions in estimating a discrete time-discrete data stochastic process,” in (C.F.
Manski and D. McFadden eds.), Structural Analysis of Discrete Data with Econometric Applications, pp.114–178, Cambridge: MIT Press.
Hijzen, A., Kambayashi, R., Teruyama, H. and Genda, Y. (2015), “The Japanese labour
market during the global financial crisis and the role of non-standard work: A micro
perspective,” Journal of the Japanese and International Economies 38, pp.260–281.
Hirata, S. and K. Yugami. (2011), “Shoki kyaria ni okeru naibutoyo to tenshoku: Hiseiki
koyosha no ikou ni kansuru kokusai hikaku (Internal promotions and job changes in43early careers: International comparison on the mobility of non-regular employees),”(in Japanese), JILPT Discussion Paper 11-02, Japan Institute for Labour Policy andTraining.
Hyslop, D. R. (1999), “State dependence, serial correlation and heterogeneity inintertemporal labor force participation of married women,” Econometrica 67(6),pp.1255–1294.
Ishikawa, T. (2002), Income and Wealth, Oxford: Oxford University Press.
Kahn, L.B. (2010), “The long-term labor market consequences of graduating from college in a bad economy,” Labour Economics 17(2), pp.303–316.
Kalantzis, Y., Kambayashi, Y. and Lechevalier, S. (2012), “Wage and productivity
diﬀerentials in Japan: The role of labor market mechanisms,” Labour 26(4), pp.514–
541.
Kambayashi, R. (2010), “Joyo hiseiki roudousha no shoso (Aspects of full-time, nonregular employees),” (in Japanese), Global COE Hi-Stat Discussion Paper Series 120,
Hitotsubashi University.
Kambayashi, R. (2013), “Diﬀerences in definitions of non-regular employee in government statistics,” Japan Labor Review 10(4), pp.55–66.
Kambayashi, R. and Kato, T. (2012), “Good jobs, bad jobs, and the great recession:
Lessons from Japan’s lost decade,” IZA Discussion Paper Series 6666.
Kitagawa, A. (2014), “Wage profiles and income inequality among identical workers: A
simple formalization,” TERC Discussion Paper 314, Graduate School of Economics
and Management, Tohoku University.
Kondo, A. (2007), “Does the first job really matter?: State dependency in employment
status in Japan,” Journal of the Japanese and International Economies 21(3), pp.379–
402.
Kosugi, R. (2010). ‘Career development through non-regular employment: An analysis
of prescribed factors in the transition to regular employee inclusive of promotion(Hiseiki koyou karano kyaria keisei: touyou wo fukumeta seishain heno ikou no kitei
youin bunseki kara’, (in Japanese), The Japanese Journal of Labour Studies 602,
pp.50–59.
Kübler, D. and Weizsäcker G. (2003), “Information cascades in the labor market,”
Journal of Economics 80(3), pp.211–229.
Lin, C-Y. and Miyamoto, H. (2012), “Gross worker flows and unemployment dynamics
in Japan,” Journal of the Japanese and International Economies 26(1), pp.44–61.

44

Miyamoto, H. (2011), “Cyclical behavior of unemployment and job vacancies in Japan,”Japan and the World Economy 23(3), pp.214–225.
Mortensen, D.T. and Nagypál, É. (2007), “More on unemployment and vacancy fluctuations,” Review of Economic Dynamics 10(3), pp.327–347.
Mortensen, D.T. and Pissarides, C.A. (1994), “Job creation and job destruction in thetheory of unemployment,” Review of Economic Studies 61(3), pp.397–415.
Neumark, D. (2002), “Youth labor markets in the United States: Shopping around vs.
staying put,” Review of Economics and Statistics 84(3), pp.462–482.
Ogura, K. (2002), “Hitenkeikoyo no kokusai hikaku: Nippon, amerika, oshu shokoku no
gainen to genjo (Comparison of atypical employment: Concepts and current situationin Japan, U.S. and European countries),” (in Japanese), The Japanese Journal ofLabour Studies 505, pp.3–17.
Ohtake, F. and Inoki, T. (1997), “Roudou shijyou ni okeru sedai kouka (Cohort eﬀects
in the labor market),” in (K. Asako, S. Fukuda and N. Yoshino eds.), Gendai Makuro
Keizai Bunseki (Modern Macroeconomic Analysis), pp., 297–320, Tokyo: Universityof Tokyo Press.
Oreopoulos, P., von Wachter, T. and Heisz, A. (2012), “The short- and long-term careereﬀects of graduating in a recession,” American Economic Journal: Applied Economics
4(1), pp.1–29.
Oyer, P. (2006), “Initial labor market conditions and long-term outcomes for
economists,” Journal of Economic Perspectives 20(3), pp.143–160.
Pissarides, C.A. (2009), “The unemployment volatility puzzle: Is wage stickiness theanswer?” Econometrica 77(5), pp.1339–1369.
Poterba, J.M. and Summers, L.H. (1986), “Reporting errors and labor market dynamics,” Econometrica 54(6), pp.1319–1338.
Prowse, V. (2012), “Modeling employment dynamics with state dependence and unobserved heterogeneity,” Journal of Business and Economic Statistics 30(3), pp.411–
431.
Raaum, O. and Røed, K. (2006), “Do business cycle conditions at the time of labor market entry aﬀect future employment prospects?” Review of Economics and Statistics
88(2), pp.193–210.
Rebick, M. (2005), The Japanese Employment System: Adapting to a New Economic
Environment, Oxford: Oxford University Press.
Saint-Paul, G. (1996), Dual Labor Markets: A Macroeconomic Perspective, Cambridge:
MIT Press.
45

Shikata, M. (2011), “Hiseiki koyou ha ‘ikidomari’ ka?: Roudoushijo no kisei to seiki
koyou heno ikou (Is non-standard employment a ‘dead end?’: Labor market regulationand transition to standard employment)”, (in Japanese), The Japanese Journal ofLabour Studies 607, 88–102.
Shimer, R. (2005), “The cyclical behavior of equilibrium unemployment and vacancies,”
American Economic Review 95(1), pp.25–49.
Shimer, R. (2012), “Reassessing the ins and outs of unemployment,” Review of Economic Dynamics 15(2), pp.127–148.
Stewart, M. (2007), “The interrelated dynamics of unemployment and low-wage employment,” Journal of Applied Econometrics 22(3), pp.511–531.
von Wachter, T. and Bender, S. (2008), “Do initial conditions persist between firms?: Ananalysis of firm-entry cohort eﬀects and job losers using matched employer-employee
data,” in (S. Bender, J. Lane, K. L. Shaw, F. Andersson and T. von Wachter eds.), The
Analysis of Firms and Employees. Quantitative and Qualitative Approaches, pp.135–
162, Cambridge: National Bureau of Economic Research.
Wooldridge, J.M. (2010), Econometric Analysis of Cross Section and Panel Data (2ndedition), Cambridge: MIT Press.

Appendix
Let Gi = (Xi⊤ , Yi⊤ , Zi⊤ )⊤ and gi be its realized value. To compute the likelihood functionof the system (1), (4), and (5), we need the probabilities (conditional on the exogenous
variables):Pr{Ji = ji , Ji′ = ji′ , Ji0 = ji0 | Gi = gi } i = 1, . . . , N,
(A.1)
where ji = 0, 1, ji′ = 0, 1, ji0 = 0, 1.
Suppose, for instance, that ji = 1, ji′ = 1, and ji0 = 1. By using the basic property
of conditional probabilities, we have
Pr{Ji = 1, Ji′ = 1, Ji0 = 1 | Gi = gi }=Pr{Ji = 1 | Ji′ = 1, Ji0 = 1, Gi = gi }Pr{Ji′ = 1, Ji0 = 1 | Gi = gi }.

(A.2)

From (1), the first conditional probability on the right-hand side can be rewritten as
Pr{Ji = 1 | Ji′ = 1, Ji0 = 1, Gi = gi } =Pr{ui > −α1 Ji0 − β1 Ji′ − γ1⊤ Xi | Ji′ = 1, Ji0 = 1, Gi = gi }=Pr{ui > −α1 − β1 − γ1⊤ xi | Ji′ = 1, Ji0 = 1, Gi = gi }.
Hence, (A.2) equals
Pr{ui > −α1 − β1 − γ1⊤ xi | Ji′ = 1, Ji0 = 1, Gi = gi }Pr{Ji′ = 1, Ji0 = 1 | Gi = gi }=Pr{ui > −α1 − β1 − γ1⊤ xi , Ji′ = 1, Ji0 = 1 | Gi = gi }.
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(A.3)

Applying the same conditioning argument to the last probability gives(A.3) =Pr{ui > −α1 − β1 − γ1⊤ xi , Ji′ = 1 | Ji0 = 1, Gi = gi }Pr{Ji0 = 1 | Gi = gi }=Pr{ui > −α1 − β1 − γ1⊤ xi , vi > −α2 − γ2⊤ yi | Ji0 = 1, Gi = gi }Pr{Ji0 = 1 | Gi = gi }=Pr{ui > −α1 − β1 − γ1⊤ xi , vi > −α2 − γ2⊤ yi , Ji0 = 1 | Gi = gi }=Pr{ui > −α1 − β1 − γ1⊤ xi , vi > −α2 − γ2⊤ yi , wi > −γ3⊤ zi | Gi = gi },where the second and fourth equalities follow from (4) and (5), respectively. Thus, since
Gi is assumed to be exogenous, we have under the joint normality of the disturbances(ui , vi , wi )Pr{Ji = 1, Ji′ = 1, Ji0 = 1 | Gi = gi }=Pr{ui > −α1 − β1 − γ1⊤ xi , vi > −α2 − γ2⊤ yi , wi > −γ3⊤ zi | Gi = gi }
∫ ∞
∫ ∞
∫ ∞
=
ϕ3 (u, v, w) dudvdw,−α1 −β1 −γ1⊤ xi−α2 −γ2⊤ yi−γ3⊤ zi(A.4)

where ϕ3 is the trivariate normal density function with mean (0, 0, 0) and covariancematrix Σ. (Due to normalization, the diagonal elements of Σ are assumed to be unity.)
The probabilities (A.1) in which ji , ji′ , and ji0 take other values can be computed ina similar manner. Summing the log of these probabilities for all individuals gives the(conditional) log-likelihood function required for the maximum likelihood estimation of
our model. Observe that (A.4) [or (A.1) in general] is the probability we obtain if all
the explanatory variables in (1), (4), and (5) are treated as exogenous. This fact implies
that we may compute the likelihood function ignoring the fact that the right-hand sidesof (1), (4), and (5) involve endogenous variables. Therefore, we can utilize any computer
program written for standard multivariate probit models (i.e., probit models with only
exogenous explanatory variables) to estimate the system (1), (4), and (5).
For bivariate probit models, authors of some econometrics textbooks, such as Greene(2011) and Wooldridge (2010), point out the fact mentioned in the last paragraph. This
result stems from the recursive structure of the model, and as seen from our expositionin this Appendix, the argument can be extended to recursive models with three or moreendogenous variables. (Note also that the normality of the disturbances does not play
an important role.)
In Section 7.2, we need to compute the conditional probability
Pr{Ji = ji | Ji′ = ji′ , Ji0 = ji0 Gi = gi } =Pr{Ji = ji , Ji′ = ji′ , Ji0 = ji0 | Gi = gi }.
Pr{Ji′ = ji′ , Ji0 = ji0 | Gi = gi }The denominator on the right-hand side can be calculated in a way analogous to (A.4).
For example, if ji′ = 0 and ji0 = 0, we have
∫ −α2 −γ ⊤ yi ∫ −γ ⊤ zi
2
3′0
Pr{Ji = 0, Ji = 0 | Gi = gi } =ϕ2 (v, w) dvdw,−∞−∞

where ϕ2 is the bivariate normal density function with mean (0, 0) and a covariancematrix that is the 2 × 2 lower-right submatrix of Σ given above.
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