Wage and Productivity Di¤erentials in Japan.
The role of Labor Market Mechanisms
D. Gatti, R. Kambayashi and S. Lechevalier
September 2009

Abstract
Two stylized facts characterized Japan during the Lost Decade (19922005): rising wage inequalities and increasing productivity di¤erentials at
the …rm level. Surprisingly, these two facts have never been connected in
the literature. This paper tries to …ll the gap by proposing an explanation,
which focuses on labor market mechanisms. We …rst build an e¢ ciency
wage model with two types of …rms that are distinguished by their job
security scheme and associated incentive mechanisms. We show that asimilar negative productivity shock at the aggregate level leads to di¤erent…rms’reactions: the model predicts an increasing e¤ort from the workers
in the …rms 1, which resort to e¢ ciency wage mechanism. It leads toincreasing productivity and wage di¤erentials, and to a rise of the shareof …rms 1. Then, we test this model on Japanese micro data. For the …rst
time, we match the Basic Survey on Wage Structure and the Employment
Trend Survey for the year 2005. The matched worker-…rm dataset we
get allows us to con…rm the existence of e¢ ciency wages mechanisms on
average. Second, we divide our sample of …rms into two groups by using
the unknown regime switching regression à la Dickens and Lang (1985),
and we then …nd that the primary sector can be characterized by e¢ ciency
wage, whereas the secondary cannot. Then, we simulate the evolution of
the share of the primary sector and …nd that it substantially increased
between 1981 and 2005, in conformity with the predictions of our model.
JEL Classi…cation: L23, J24, J31, J42
Key words: heterogeneity of …rms, e¢ ciency wages, job security, e¤ort,
productivity di¤erentials, wage inequalities.

1

Introduction

For decades, wage inequality has substantially increased in the US, in UK andmany other OECD countries. Japan is no exception. However, until recently,
Respectively: CEPN-Paris 13, PSE, CEPREMAP and IZA; Hitotsubashi University, Institute of Economic Research; EHESS (Paris). Contact: kambayas@ier.hit-u.ac.jp

1there was no consensus regarding whether income inequality widened during
the 1990s and onward (OECD, 2006). For example, Tachibanaki (2005) claimed
that income inequality widened during the 1980s and 1990s. On the contrary,
Ohtake (2005) found that the increase in income inequality was partly due tothe aging population. Focusing on the wage rate, Kambayashi et al. (2008) reconcile these two views; They apply the DiNardo, Fortin, and Lemieux (1996)’s
decomposition technique and they get the following result: “although simple aggregate statistics may give the impression that wage inequality did not change
during the period, the decomposition analysis reveals that the seemingly steadytrend is a product of two opposing trends: 1) declining between-group (de…nedby education, experience, tenure and …rm/establishment size) wage inequality;and 2) increasing within-group inequality among male workers.”
The purpose of Kambayashi et al. (2008) is to assess the reality ofincreasing wage inequality. Moreover, in using DiNardo et al. (1996)’s methodology to decompose the change of the wage distribution into the contributionof the changes in the workers’ skill distribution and the change in the factor
prices, their results can be mobilized to add a piece of evidence to the debate
between SBTC proponents and “revisionists” (Card & DiNardo, 2002). However, there is also another possible interpretation of their result. In the caseof the UK, Faggio et al., (2007) have also found that rising inequalities primarily concern workers with equivalent observable characteristics. To explainthese rising within-group inequalities, they analyze its counterpart, increasing
productivity dispersion across …rms, between and within sectors and show thelink between the two phenomena.
This type of analysis has been conducted by other researchers, like
Mortensen (2003). Although some papers (e.g. Kambayashi et al., 2008, orTachibanaki, 2005) consider the wage di¤erential between …rms of di¤erent sizein Japan, there have been no recent investigation of between-…rms wage dispersion, in connection with productivity di¤erentials. This is all the more surprising since that recent papers have established another stylized fact, increasing
heterogeneity of performances of …rms belonging to the same sectors and size
categories (Fukao & Kwon, 2006; Ito & Lechevalier, 2008). One reason for theabsence of this type of study is that a dominant concern has been the within…rm wage di¤erential between regular and non-regular workers: the rising shareof non-regular workers, which has more than doubled in 20 years to reach almost
1/3 of the workforce, has been a popular explanation of the rising inequalities
in Japan (Ota, 2005). Another possible reason for having neglected the study ofthe link between increasing productivity dispersion and rising wage inequality
is the fact that studies that have taken into account the …rm-size di¤erential
have found that it does not explain the increasing wage gap (e.g. Kambayashi
et al., 2008). The focus on wage di¤erential between …rms of di¤erent size isunderstandable in a country that has been (and still is to a certain extent) characterized by a dual structure according to the size of the …rm. However, the factthat the cleavage along the …rm-size is not a key determinant of increasing wage
di¤erential should not lead to the conclusion that between-…rms wage di¤erential is not important: as the US, Japan is characterized by a decentralized wage2

system (with some important di¤erences between the two systems) that makecrucial the analysis of inter-…rms wage di¤erential. If one aims at connecting
the evolution of productivity di¤erentials and of wage di¤erentials, one has totake into account the fact that the increasing productivity di¤erentials since themid-1990s mainly occurred between …rms of similar size and belonging to samenarrowly de…ned sectors.
Our contribution is threefold. First, we study the evolution of wage
and productivity di¤erentials of …rms by controlling for size and sector. Second,
we propose a theoretical framework that focuses on labor market mechanisms,
without referring to other factors such as the impacts of technical progress
and internationalization. Third, we test our theoretical model by using a rich
employer-employee dataset.
More precisely, in a …rst step, we build an e¢ ciency wage model with one
sector but two types of …rms of similar size. The di¤erence between the two typesof …rms is interpreted in terms of productive models like in Oï (1983), rather
than in terms of monitoring technology like in Bulow and Summers (1986). Moreprecisely, in one type of …rms, the productivity is assumed to be endogenous anddetermined by workers’production e¤ort, while in the other type of …rms, it isexogenous. In solving the model, we determine the values of the employment
in the primary sector (that is the number of …rms), the hiring and separation
rates, the e¤ort and the wage in the primary sector. Finally, we …nd that a
productivity slowdown at the aggregate level leads to rising productivity andwage di¤erentials and increasing share of primary …rms, all these results being
inter-dependent.
In a second step, we test this model on micro data. For the …rst time, wemerge two databases, the Basic Survey on Wage Structure and the Employment
Trend Survey for the year 2005. It allows us to get information on (hourly)
wages, accession and separation rates. We control also for …rms characteristics(size, sector) and for workers characteristics such as age, gender and education.
We …rst con…rm the existence of e¢ ciency wages in the Japanese market on
average. Second, we divide our sample of …rms into two groups by using the
unknown regime switching regression à la Dickens and Lang (1985), and we then…nd that the primary sector can be characterized by e¢ ciency wage, whereas
the secondary cannot. Finally, we run a simulation for the years 1981-2005
and we …nd that the share of the primary sector has substantially increased, inconformity with the predictions of our model.

2

The model

We consider a simple dual labor market model. This dualism corresponds to
two alternative labour organization structures. Firms, that are active on the
primary labour market, need to implement a more productive (but more costly)organizational structure. The other …rms implement a less costly and less productive organizational structure, and hire workers on the secondary labour mar-3

ket. We assume that one …rm equals one job. Hence, employment levels in primary/secondary market stem from …rms distribution across the two productivemodels.
The model’s timeline is the following:t = 0, …rms are matched with a given productive model, employment inprimary and secondary market is derived;t = 1, wages and tenure are determined for primary and secondary jobs;
t = 2, workers e¤ort in primary jobs is determined.
Primary jobs require workers’ implication and e¤ort. An incentive mechanism is at play yielding real wage growth in line with e¤ort. Secondary jobs are
perfectly competitive. No incentive is required, so workers’ utility is equal tounemployed workers’. Unemployment bene…ts depend on taxes raised on wages.
To ensure progressive taxation, only primary market workers are taxed:t w1 L1(1)U
L2 , N being total labour force. Tax rate t is exogenous.
wu =with U = N

2.1

L1

Incentives and e¤ort

We solve the model by backward induction, starting by stage 2.1 We consider
two types of …rms: type1 …rms are active on primary market, while type2 …rms
hire workers only on secondary market. The endogenous number of …rms will
be determined at stage 0 (see section 2.3 below).
Hereafter we display dynamic equations for utilities of shirker (V1S ) and nonshirker workers (V1N S ) employed on primary market jobs, as well as utilities of
unemployed (V U ) and workers employed on secondary market jobs (V2 ):r V1N S
r V1S
r VU
r V2

=
=
=
=

w1 (1 t) e + s1 (V U V1N S )w1 (1 t) + (s1 + q) (V U V1S )wu + a1 (V1N S V U )
w2

(2)
(3)
(4)
(5)

We assume that there is no hiring and …ring on secondary labor market.
From the no-shirking condition (V1N S = V1S ) one gets the standard incentive compatible real wage schedule (e¢ ciency wage) that applies to workers on
primary jobs:
w1e =e [a1 + s1 + r + q wu ]q (1 t)(6)

1 We have to determine values of seven endogenous variables: w , w , e, a , s , L , L :
1
2
1
1
1
2
Seven equations are needed to ensure that all our endogenous variables are determined at
equilibrium.

4Given this condition, type1 …rms endogenously generate an e¤ort’s function
by maximizing job’s value. Values of jobs on primary / secondary markets(respectively J1 and J2 ) are given by the following equations:

J1

=

J2

=

m1 w1
r + s1
m2 w2
r

with:
m1 (e) = A

p

e

(7)
(8)

(9)

Hence, productivity of primary market jobs is endogenous and determinedby workers’ productive e¤ort. The more intense the e¤ort gets, the higher is
productivity (other things being equal). However, there is a drawback to more
intense e¤ort as it also yields higher disutility for workers: the utility cost ofe¤ort increases.
This is a crucial aspect of the model. Unlike standard Shapiro and Stiglitz[1984] kind of models, we consider that e¤ort is endogenous. Firms have aninterest in trying and improve e¤ort. In fact, given equation (9), one can seethat increasing e¤ort allows …rms to increase their productivity. On the other
hand, it is clear from equation (2) that increasing e¤orts yields a higher utility
cost for workers. However, workers get paid back for their e¤ort because real
wages are set according to an incentive compatible e¢ ciency wage mechanism.
Hence, subject to the e¢ ciency wage constraint, workers are indeed willing toincrease their e¤ort. Concerning …rms, equation (6) clearly says that they areforced to pay higher wages, when e¤ort increases, in order to prevent shirking.
Pro…ts’maximization yields an endogenous e¤ort’s function:
dJ1
de

=

e =

2.2

@m1
@w1e
=0
@e
@e
2
A q (1 t)
2 (a1 + s1 + r + q)

0)

(10)

Wages and tenure

We now turn to stage 1 of the model. As in standard labour market models,…rms compete to attract workers. In our framework, …rms can compete on both
wages and work condition. In particular, type1 …rms can o¤er various degrees
of job tenure (measured by s1 ). Higher job security rises workers’ utility ande¤ort, and lowers the incentive wage. Hence, there is a trade-o¤ for …rms: either
proposing workers higher wages or better tenure.
Because of (perfect) competition across …rms, wages and tenure are set so
as to ensure that jobs values are driven down to zero:

5

J2
J1

=
=

0 ) m1 (e) = w1
0 ) m2 = w2

(11)
(12)

This implies that w2 is simply set equal to exogenous productivity m2 . Regarding condition (11), one should recall that m1 is determined according toequation (9). Substituting (9) in condition (11) yields the following zero-pro…twage schedule:
w1zp =

A2 q (1 t)
2 (a1 + s1 + r + q)

(13)

In our model (as in other standard dual labor market models) secondary
market jobs provide no extra-rent for workers. Hence, for workers employed in
type2 …rms, utility is set equal to V U :V U = V2

(14)

U

Substituting equations (4) and (5) for V and V2 in condition (14), one getsan additional relation between w1 and w2 . We will call this a "no-migration"
condition as it prevents ‡ows from (to) secondary market to (from) unemployment:
w2nm =

a1 [(1t) w1 e] + (r + s1 ) wu
a1 + r + s1

(15)

with wu being determined according to equation (1).
Finally, we need to ensure that ‡ows on the labor market are at equilibrium.
Hence, a ‡ow equilibrium condition is considered, ensuring that hiring always
matches …ring:a1 U = s1 L1(16)

Recall that U = N L1 L2 , and that employment is determined by the
number of …rms on primary/secondary market.
At the equilibrium, the e¢ ciency wage and the zero-pro…t wage schedules
should cross. By substituting equations (10), (1) and (16) for e, wu and a1
in equation (6), and then equating (11) and (6), allows us to determine theseparation rate as a function of hiring conditions:
2 t a1(17)
1+t
We now turn to the no-migration condition. At the equilibrium, workers
should be indi¤erent between secondary jobs and unemployment. We substituteequations (10), (1) and (16) for e, wu and a1 in equation (15), and then impose(12). This allows us to determine the equilibrium hiring rate:2s1 (a1 ) =2 We actually have two roots for a . However, we can proove that there is a unique positive
1
root.

6

a1 =

"

2

A2 q (1 t)
4 (q + r) (1 + t) m2 +
p
p
3=2
A
q (1 t)A2 q (1 t) 4 (q + r) (1 + t) m2
4 (1 + t) m2

#

(18)

We can now solve the model recursively. From (17), one easily gets theequilibrium separation rate:s1 =2 t a1
1+t(19)

From equation (10), one has:e =A q (1 t)
2 (a1 + s1 + r + q)

2

(20)

Finally, equation (13) yields:
w1 = m1 =2.3

A2 q (1 t)
2 (a1 + s1 + r + q)

(21)

Productive model and employment

At stage 0, …rms are distributed across the two existing productive models.
We simply assume that adopting type1 productive model is costly. This costdepends on the speci…city of such a productive model (tbc). Moreover, the costis likely to be higher under bad macroeconomic conditions.
Let us take the simple situation where the cost of adopting type1 productivemodel is equal to:c(U ) =

+

U

(22)

Hence, if …rms want to adopt the more productive organizational model,
they will only become indi¤erent between type1 and type2 model when thefollowing condition is satis…ed:
m1c(U ) = m2

(23)

t L1
: Condition above thus
From (16) and (19) we know that U = s1a1L1 = 21+t

yields:

L1 = (1 + t)

m1

m2
2 t

=

1+t
(m1
2 t

m2 )

(24)

This allows us to determine the number of …rms adopting type1 productivemodel. It is important to notice that this number is a linear combination of the
productivity di¤erential between type1 …rms and type2 …rms (or equivalentlyof the wage di¤erential between the two types of …rms). Given the assumption of7

a "one worker-one …rm" match, the number of type1 …rms equals the employment level on primary market (L1 ). We can then easily derive unemployment
2tL
U = 1+t1 :2.4

Consequences of lower A

We can now turn to analyze the consequences of exogenous changes in given
parameters of the model onto the equilibrium values of relevant endogenous
variables. We are particularly interested in assessing the consequences of economic crisis. As the Japanese economy during the Lost Decade (1992-2005) has
been characterized by a slowdown of the productivity growth at the aggregate
level (Yoshikawa, 2008), the relevant parameter in our model is therefore A, theexogenous productivity component of primary market jobs. Crisis in our model
can be thought of as yielding a once and for all drop of A. In this section we
asses the consequences of such a drop on the macroeconomic equilibrium of themodel.
One can show that:
@s1
>0
@A
A decrease in A yields a lower s1 . Hence, one consequence of the crisis ishigher tenure for employed workers, and greater job security.
From equations (20) and (21), one sees that increased tenure (i.e. lowers1 ) yields higher e¤ort and wages for primary market workers. As a directconsequence, the share of primary …rms also increases: this result will be usedin the empirical part. One can show that these results hold in spite of the directo¤setting e¤ect of lower A:
@e
@A
@w1
@A
@L1
@A

< 0
=
=

@m1
<0
@A
1 + t @m1
<0
2 t @AOne should note that overall productivity of …rms proposing primary market
jobs increases following the crisis. This is entirely due to the increase in productive e¤ort, i.e. to the endogenous intensi…cation of work in primary jobs. As
a consequence, productivity di¤erentials across …rms proposing primary versus
secondary market jobs increase due to the crisis.
To summarize, let us assume that crisis brings about a drop of the exogenous
component of productivity (being this due to a drop in technological capabilities and/or to other demand-driven factors). As a consequence, …rms seek anintensi…cation of productive e¤ort to compensate for the drop in productivity.
However, work intensi…cation yields higher utility costs for workers. Hence,…rms have to compensate for that in order to avoid shirking. To ensure higher
8e¤ort …rms act on two distinct grounds. First, real wages w1 associated withprimary market jobs increase to o¤set growing utility cost of e¤ort. This is indeed a standard result. However, in our model job security is also endogenous.
Hence, …rms can provide higher job security to primary market workers in orderto favor an increase in e¤ort, as indicated in equation (20). This is what happens at the equilibrium: primary market workers get higher job security andwages as a consequence of the exogenous productivity drop.
Moreover, as a consequence of increased m1 , the productivity di¤erential
across the two types of …rms increases. This pushes …rms to adopt type1 productivemodel up to the point where condition (23) is again satis…ed. According to (24)this yields a higher proportion of type1 …rms as well as higher unemployment
at the equilibrium.
One should note that all results above can be derived under more generalassumptions concerning productivity of type2 …rms. In particular, one can assume that m2 also depends on A. Hence, a lower A yields a drop in type2 …rms
productivity. Our main results still hold under this assumption, but are more
contigent to speci…c parametric restrictions.

2.5

Comments

The result we get deserve some comments, especially before turning to theempirical part of the paper. The …rst comment concerns the nature of thedi¤erences between the two types of …rms. In our model, thes di¤erence areinterpreted in terms of productive models, like in Oï (1983), rather than in termsof monitoring technology used in di¤erent sectors, like in Bulow and Summers(1986). More precisely, in one type of …rms, the productivity is assumed to beendogenous and determined by workers’ production e¤ort, while in the othertype of …rms, it is exogenous. The main novelty of the model by comparison toprevious formalization is that the type1 …rms endogenously generate an e¤ortfunction. To put it di¤erently, in this case, the adjustment is made through thee¤ort. The di¤erence between these two types of …rms does not concern theability of some of them to restructure or to downsize while the others are rigid.
The key mechanism that we emphasize is the job security and the associatedincentive scheme (e¢ ciency wages): …rms may decide to adopt this organizationor to prefer a competitive scheme.3
The second remark concerns the causes of the evolution of wage and productivity di¤erentials. Layard, Lickell and Jackman (2005) show how the link
between workers wages and employer productivity can be modeled in a variety
of ways (union bargaining, e¢ ciency wage, rent-sharing and search-based). The
fact that e¢ ciency wage applies to Japan is a matter of empirical investigation
and we provide a new test for it. However, the most important for us in the
3 Our interpretative framework should be also distinguished from other explanations focusing on labor market mechanisms, such as the role of labor unions (Freeman & Medo¤, 1983),
the role of size and/or human capital (Haltiwanger et alii, 1999), or the di¤erence in capital/ labor ratio (Leonardo, 2007), for example.

9choice of the model is that we are in a non competitive environment and that difference in the productive organization is the essence of productivity di¤erential.
After having characterized the two types of productive models, we interpret theincreasing wage and productivity di¤erentials as the result of the di¤erenciatedreactions of the two types of …rms to a similar shock at the aggregate level. The
question of the nature of this shock is completely open. The most important isthat we are able to study the evolution of the di¤erential without introducing
any assumption regarding technical progress or internationalization. The originof the growing di¤erential lies in the initial di¤erence of productive models andin the di¤erentiated response to the productive shock. It means that we focus
on labor market mechanisms, without refereeing to any technological story, likein Faggio et al. (2007) for example.4

3

An empirical test on Japanese micro data

3.1

Empirical Strategy

According to the model of the previous section, we should …nd a negative relationship between ‡ow behaviors and wage level in the primary sector (as inequation (21)), whereas there should be no correlation between them in thesecondary sector. The goal of this empirical part is to explain the actual differentials of productivity and wage by applying the above dichotomy to theJapanese economy.
Ideally, testing our model would require a micro panel dataset including data on wages, accession and separation rates. Moreover, the sample period
should correspond more or less to the so-called Lost Decade (1992-2005), during
which the Japanese economy has been characterized by a long stagnation andthe question of increasing di¤erentials has particularly attracted attention. Unfortunately, to our knowledge, this database does not exist publicly in Japan.
However, we could get access to the Basic Survey on Wage Structure (BSWS)
and the Employment Trend Survey (ETS) for the year 2005. Thanks to the…rst survey, we get information on wages and thanks to the second one we getaccession and separation rates data. Then, using an identi…cation key provided
by the Ministry of Health, Labor and Welfare (MHLW), we are able to matchthese two datasets at establishment level. In doing so, we build an employeremployee dataset. The interest of such database to study the type of question weare interested is well-known (Abowd and Kramarz, 2001; Abowd et al., 1999).
By using this one-shot cross-sectional data, we …rst detect the existenceof e¢ ciency wage mechanism through the criterion described above, the existence of a negative correlation between ‡ow structure and wage. To do this, we
4 Faggio et alii (2007) provide a test for the Caselli (1999)’s model, according to which
the increasing dispersion of productivity (and thus of average wages among …rms) can be
explained by the di¤erentiated rate of introduction of new technologies.

10

estimate a Mincerian equation for male regular workers, in which the dependent
variable is the logarithm of scheduled hourly wage rates and the explanatory
variables are workers characteristics such as sex, education, tenure and dummiesof prefectures (Kambayashi et al, 2008). The mean of residual of this equationfor each establishment can be interpreted as establishment speci…c components.
Under the model, when an establishment belongs to the primary sector, themean residual of the establishment should be negatively correlated with themagnitude of out‡ow.
In the next step, we use the unknown regime switching technique (Dickens & Lang, 1985; Ishikawa & Dejima, 1994) to decompose the economy into
two types of establishments, because we do not have any explicit ex-ante criteria
to de…ne which establishment belongs to which sector. After, having done thisdecomposition, we check whether we observe in both sectors a similar relation
between the mean residual and the ‡ow structure or not. Finally, we are able
to simulate the evolution of productivity and wage di¤erentials induced by ourmodel by extending the decomposition into two sectors to the Lost Decade.

3.2

The dataset

In this part, we match two databases, the Basic Survey on Wage Structure(BSWS) and the Employment Trend Survey (ETS) for the year 2005. As the
BSWS is an individual survey and the ETS an establishment survey, we get amatched worker–establishment database. The key issue is the size of the sample
after matching.
3.2.1

The BSWS individual survey and the ETS establishment survey

The BSWS individual survey is a sample survey conducted by MHLW, once a
year, at the end of June. It covers private establishment over 5 employees andpublic establishment over 10 employees. All industries other than agriculture
are surveyed. Workers are re-sampled within an establishment. The sample
size is about 78000 establishments and 1.6 million workers per year. It is a rich
set of information on establishment and individual characteristics. The mostimportant feature for us is rich data on wages.
As for the ETS, it is an establishment survey conducted by MHLW,
twice a year, at the end of June and December. It covers public and private
establishments with more than 5 employees in all industries, except agriculture. Newly separated and newly hired workers (within the sampling period)
are re-sampled within an establishment. The sample size is about 10000 establishments, 80,000 in‡ow workers and 90,000 out‡ow workers per year. It gives
information on the numbers of new entrants and of separations.

113.2.2

Matching the two surveys

Regarding the BSWS, data is collected at the end of June 2005. Sample isrestricted to regular fulltime employees. As for the ETS, in‡ow/out‡ow is thenumber of acquisition/leave for regular fulltime workers between July and December 2005 (during half a year after the BSWS data point). The ratio is basedon the stock number of regular fulltime employee at the beginning of July 2005.
We match these two surveys by using a key provided by the MHLW.
Although the size of matched sample is 2733, we found some possible inconsistencies in data. The data point of BSWS is the end of June 2005, and thatof the second ETS is the beginning of July 2005 (next day of the BSWS data
point). We proceed to a sample restriction as follows:- 4 establishments are excluded due to the negative employment stock at the
beginning of July;- 250 establishments are excluded due to the inconsistency of industry classi…cation between BSWS and the second ETS;
- 435 establishments are excluded due to the inconsistency of …rm size andestablishment size classi…cations between BSWS and the second ETS.
As a result, the …nal size of the match sample is 2044 establishments. For
the BSWS, the matching rate is only 5% but from the point of view of ETS,
it is 30%, which is quite acceptable. Finally, please note that this restriction
is pretty conservative in that there is a possibility for an establishment and/or…rm to move to another classi…cation at the beginning of July.

3.3

Detecting the existence of e¢ ciency wage schemes

The fact that e¢ ciency wage is a satisfying model for the Japanese labor market
is a matter of empirical investigation. However, depending on the exact natureof the e¢ ciency wage model, the empirical strategy may drastically vary. Moreover, the results may be ambiguous, as it is sometimes di¢ cult to empiricallydistinguish between the predictions of di¤erent models (Manning, 2003). For
example, Abe and Ohashi (2004) con…rm the existence of e¢ ciency wage modelin Japan by analyzing the wage pro…les. In our case, in order to detect theexistence of e¢ ciency wage on average, we proceed as follows.
First, according to a conventional procedure in the usage of the BSWS, wecalculate the scheduled hourly wage as the monthly salary (excluding various
allowances) per scheduled working hour (wi;2005 ). Secondly we limit the sample
to regular male in private and over 30 employee …rms except for constructionindustry to keep the comparability to public data. Thirdly we regress the logof scheduled hourly wage on dummies for educational level, age, age squared
divided by 100, tenure, tenure squared divided by 100, and prefecture dummies(Xi;2005 ) according to the standard Mincerian equation (see Kambayashi et al.,2008) as follows:
wi;2005 =+ Xi;2005 + ui;2005

12

(25)

Here ui;2005 is, given Xi;2005 , normally distributed unobservable term withmean zero. By using the estimated coe¢ cients in equation (25), we can produce the residual for each individual. If the human capital markets are perfect, the residual of (25) can be interpreted as unobserved matching rent (orestablishment-individual speci…c components) which a certain worker can enjoy
just because one belongs to a speci…c establishment. The summary statistics ofthe residual is reported in Figure 1.

Figure 1: Summary Statistics for Residual of Mincerian Equation at Individual
Level

ResidualObs
48,681

Mean
0.000

Std. Dev.
0.323

Min.
-3.978Max.
3.819

We take the mean of residual for each establishment to produce the establishment …xed e¤ect. The summary statistics of the mean residual for eachestablishment is reported in Figure 2.
Figure 2: Summary Statistics for Residual of Mincerian Equation at Establishment Level

ResidualObs
31,852Mean-0.039Std. Dev.
0.249

Min.
-1.673Max.
1.934

The next step is to see the nature of wage premium from a view point of ‡ow
structure. According to equation (21), the wage premium should be negatively
correlated with separation rate as well as accession rate in the primary sector.
The next …gure (Figure 3) is the scatter plots for the mean residual and ‡ow
ratios at establishment level. There seems to be a slight negative relationship,
which means that the turnover decreases as the average residual increases as
implied in equation (21).

These negative relationships can be con…rmed in the following simple regression reported in Figure 4. After controlling industries, …rm size and overtime
ratio, we can …nd slightly signi…cant negative correlation between out‡ow ratio
13

0

0

outflow ratio of regular male
.5
1
1.5

inflow ratio of regular male
.5
1
1.5

2

2

Figure 3: The Mean Residual and Flow Structure

-1

-.5

0
mean of residual

.5

1

-1

-.5

0
mean of residual.5

and mean of residuals. Therefore, we …nd empirical evidence to support thepredictions of (21). It implies the existence of e¢ ciency wage mechanism in theJapanese labor market on average.
Although we can con…rm the e¢ ciency wage on average, the negative correlation does not seem to be universal when we look at Figure 3. This leads us
to further investigations to divide the matched sample into the two categories,
primary and secondary sectors.

3.4

Identifying two types of …rms: a switching regression
approach

To empirically test our model, the ideal would be to get simultaneously the two
following results: 1) identi…cation of two types of …rms; 2) detection of e¢ ciency
wages for one type and of competitive wage setting for the other one according
to same criterion used in the former step (negative correlation between the mean
residual and the out‡ows). Unfortunately, to our knowledge, there is not suchprocedure. Therefore, we proceed in two steps, …rst in identifying two types of…rms and, second, in checking whether there is a signi…cant di¤erence between
these two types for …rms regarding the correlation between wages and ‡ows.
To divide the sample of …rms into two tiers, criteria such as …rm size or
industry can o¤er the key of identi…cation. However, in adopting such a priori
classi…cation, the problem is not only to misclassify some …rms. More deeply, ithinders to consider the possibility of within-groups heterogeneity: for example,
two …rms with similar size or belonging to the same sectors may choose di¤erent wage and productive systems. This is why we adopt the unknown regime
switching regression à la Dickens and Lang (1985).5 With this methodology,
5 A well-known limit of this methodology, which has been already applied to the Japanese

141Figure 4: OLS Estimates of the E¤ect of Flow Structure on Mean Residual(Sample: 2005 BSWS and ETS matched sample)
(1)

(2)

Dependent
Variables
Inflow Ratio

(3)

(4)

Mean of Residual
0.010(0.036)-0.067(0.039)*Outflow Ratio-0.016(0.021)Gross Flow Ratio-0.012(0.024)

Excess Flow Ratio
Observations
R-squared

1899
0.28

0.28

0.28

0.28

Note: Standard errors in parentheses, and *** p<0.01, ** p<0.05, * p<0.1. Other explanatory variable includes
average overtime ratio, 4 firm size dummies, 9 industry dummies and constant. Gross flow ratio means inflow ratio
plus outflow ratio. Excess flow ratio means gross flow ratio minus the absolute value of employment growth rate.

sample separation is a priori unknown and the segmental choice between two
sectors is explicitly endogenized (Sousa-Poza, 2004). The system of estimation
is as follows:8< Rj;p =
R =
: j;s
z=

p + Yj;p p + Zj;p p + Vp
s + Yj;s s + Zj;s s + Vs
3 + Vj 3 + Zj 3 + V3

9
=
;

and

Rj = Rj;p
Rj = Rj;s

if
if

z 0
z<0

(26)

where
Rj;k is the mean residual of establishment j of sector k (p: primary, s:
secondary);
Yj is the separation ratio of establishment j;
Zj are control variables;z is a latent variable which splits the sample into two kinds of sectors;
Vj provided the key to identify the division of sectors.
Because Rj;k is the mean residual and can be interpreted as a quasi-rent,
industry and …rm size should matter. Therefore, we include 9 industry dummies
labor market by Ishikawa and Dejima (1994), is that it provides a test for dual labor markets
and does not recognized the possibility of three segments. However, from the point of viewof the question we address in this paper, it is not a problem as we explicitly focus on thecleavage between two types of productive models.

15

and 4 …rm size dummies as controls. Rj;k may also be a¤ected by unobserved
temporary demand shock, causing omitted variable bias. To cope with thepotential bias, we …rstly limit the hourly wage as scheduled wage, which is not
likely to be a¤ected by temporary demand shock. Secondly we introduce theaverage overtime ratio within establishment to directly control the temporary
demand shock.
The next issue, before estimating the system of equations (26) is to de…nethe key to identify the two sectors, Vj . Our strategy is to use the di¤erenceof gross job ‡ow between male and female as the identi…er.6 This idea refers
to a stylized fact characterizing the Japanese labor market, discrimination of
female workers (Wakisaka, 1997). It is well documented that the average wageof female workers is almost 30% below that of male, after having controlled forhuman capital characteristics. Several economists ran the so-called ‘market test’for female discrimination and found empirical evidence, which do not contradict
the existence of discrimination. More deeply, it has been shown that this wage
di¤erential between male and female workers comes from various di¤erences
regarding the job stability: in particular, female workers tend to be adjusted asa bu¤er upon temporary shocks. This fact is con…rmed with our data set: incalculating the turnover rate by gender, we …nd an apparent di¤erence of gross‡ow rates between two regular workers (…gure 5). More precisely, the gross ‡ow
rate is higher and more volatile for female regular workers than for male. As aresult these two turnover rates are not so strongly correlated.7 Therefore, thisis quite consistent if we assume, upon conventional wisdom in this …eld, female
workers are usually treated as more ‡exible inputs in many Japanese …rms.
Figure 5: Summary Statistics for Separation Rate by Gender
Obs

Male
Female

1,933
1,908

Mean

0.249
0.304

Std. Dev.

0.232
0.551

Min.

Median

Max.

0
0

0.19
0.21

3.84
16.00

Our basic hypothesis to di¤erentiate between a primary group of …rms
(characterized by e¢ ciency wage mechanism) and a secondary group of …rms
(where competitive mechanisms apply) is as follows. At …rst, we assume female
workers never join in the primary sector, therefore the exogenous demand shock
directly a¤ect the ‡ow ratio of female workers. If the turnover rate of male
employee is no more than female workers within the same …rm, the male workers
in this establishment are more or less to be shielded from the exogenous demand
shock. We interpret such male workers are likely to belong to the primary sector.
6 Because BSWS individual survey does not contain educational levels for part-time workers, we can not compare full-time workers and part-time workers under the above Mincerian
speci…cation.
7 Actually, the simple correlation coe¢ cient is statistically signi…cant but up to 0.30

16

Therefore, if the turnover rate of male regular workers is smaller than of female,the male workers in such establishments may belong to the e¢ ciency wage sector.
On the other hand, if the turnover rate of male regular workers is bigger thanor equal to that of female, they may belong to the competitive sector. After
having chosen this identifyier, we run the estimation based on equation (26).

Figure 6: Estimated Results of Switching Regression: E¤ect of Flow Structure
on Mean Residual (2005 BSWS and ETS matched sample)
(5)

(6)

(7)

(8)

Full Sample
OLS

Secondary
SectorPrimary
Sector

SwitchMean of Residual

Dependent Variables
Outflow Ratio(latent)-0.0670.030-0.215(0.039)*(0.031)(0.069) ***Gross Flow Ratio
Difference between Maleand Female

Observations
R-squared-0.868(0.015)***

1875
0.28

0.46

0.13

0.94

Note: Standard errors in parentheses, and *** p<0.01, ** p<0.05, * p<0.1. Other explanatory variable includes
average overtime ratio, 4 firm size dummies, 9 industry dummies and constant. Gross flow ratio means inflow ratio
plus outflow ratio. Full estimates are found in the appendix.

The full results are reported in appendix (…gure 9). We focus here on the
key relations between mean of residual and out‡ow ratio, reported in …gure
6. The estimation (5) is the same result as the estimation (2) in …gure 4, in
which we can …nd weakly negative correlation between separation rate and mean
residual. When we divide the sample into two parts, according to the switchingequation (8) (in …gure 6), this negative relation is exhibited more strongly andsigni…cantly in the primary sector (7), whereas it is rather small and statisticallyinsigni…cant in the secondary sector (6). As a whole, these results imply that…rms in the primary sector resort to e¢ ciency wages, whereas …rms in thesecondary sector do not.
Finally, after having decomposed the …rms into two groups and checked that…rms of primary group resort to e¢ ciency wages whereas …rms of the secondary
do not, it may be interesting to check ex post the characteristics of the …rms belonging to each sector, especially in terms of size and industries. The summary
statistics and estimated coe¢ cients of the switching regression are reported inappendix (…gures 8 and 9). A …rst conclusion is that, compared with larger…rms, smaller …rms are not likely to belong to the primary sector signi…cantly;
as for service industries, they are more likely to belong to the primary sector
than manufacturing …rms or electric and utilities. Moreover, as the switching17

regression (8) in …gure 9 is a Probit type, it is necessary to simulate the expost probabilities to evaluate the e¤ect of …rm characteristics. For this purpose,
we divide the sample into three groups by ex post probability to belong to theprimary sector and provide summary statistics for each group in order to compare the …rm characteristics between likely-primary sector …rms (…gure 8). Fourbasic conclusions can be drawn from this exercise. First, the average quasi-rentis larger in the most-likely-primary-sector-…rms. Second, the di¤erence of gross‡ows between male and female workers is much less in the most-likely-primarysector-…rms than the least. Third, the group whose probability to belong to theprimary sector is higher does less contains smaller …rms. For example, in thecase of most-likely-primary-sector …rms the smaller …rms (under 299 employees)
occupies 26.2% whereas it is up to 36.4% in least-likely-primary sector …rms.
Fourth, as for the industries, manufacturing industries are found in the lowest
probability whereas high probability …rms are characterized by high share ofservice industry. This analysis therefore provides another justi…cation to use
the unknown switching regression methodology à la Dickens and Lang.

3.5

Simulation

Ideally, the purpose of this section is to simulate the evolution of productivity
and wage di¤erential on the basis of the previous relationships observed for theyear 2005. In doing so, we are able to check whether our model can replicate thestylized facts that have been emphasized in the introduction. More precisely,from our model, we expect that a negative productivity shock at the aggregate
level (similar to what has been observed in Japan during the Lost Decade)should lead to increasing productivity and wage di¤erentials.
However, because of a lack of data, we are not able to directly con…rm (ornot) the predictions of our model regarding the evolution of wage and productivity di¤erential. Instead, we focus on another relation, given by equation (24),which describes the share of primary …rms as a linear combination of the productivity (respectiv. wage) di¤erential between the two sectors. As seen in thesection 2.4, the rise of the productivity and wage di¤erentials coming from anegative productivity shock at the aggregate level goes hand in hand with a riseof the share of the primary sector. Moreover, we are able to calculate the respective impact of two mechanisms at the origin of the evolution of the primarysector: the e¢ ciency wage mechanism and structural e¤ects regarding industry
and …rm size changes.
As the switching regression is conducted at establishment level, following(12) in …gure 6, we can deduce the probability that establishment j belongs to
the primary sector as:
F^^^
3 + Vj 3 + Zj 3

18

(27)

Here F is the c.d.f. of standard normal distribution, Vj is the di¤erence ofgross worker ‡ow between male and female, and Zj are dummies for industry
and …rm size. Among 1875 establishments in the sample, the mean of imputed
probability of belonging to the primary sector is 0.27.
To evaluate the share of the primary sector in the economy, it is necessary to summarize the probabilities with some weights. The number of maleregular workers may be an available and consistent weight. In this case theweighted average of probabilities will be equivalent to the share of regular maleworkers under the e¢ ciency mechanism; calculated as 0.21. Assuming that female regular workers are under marginal mechanism to keep the consistency
through this article, the share of primary sector is about 0.16 within whole ofregular workers.
In the next step, our concern is to determine of the evolution of theshare of the primary sector. As the published ETS provides the aggregatedworker ‡ows by gender, …rm size, and industry since 1981, we are able to put
in perspective the evolution during the Lost Decade by taking into accountthe evolution in the 1980s. If we assume the switch equation has been stable
over time, we can impute the probability for the average …rm to belong toprimary sector in a certain industry, …rm size and year. By using the imputed
probability, we can deduce the average share of primary sector upon assuming
female workers are always in the secondary sector. Let de…ne St as the shareof primary sector, Et as the number of regular workers and Mkt as the numberof male regular workers of industry-…rm size k in year t. Vkt is the di¤erence of
aggregated gross worker ‡ow between male and female, and Zkt are dummies
for industry and …rm size classi…cation. St should be de…ned as follows:
X

F

^
^
^
3 + Vkt 3 + Zkt 3

:Mkt

k

(28)
Et
In …gure 7 we depict the transition of imputed shares of primary sector
among regular workers between 1981 and 2005.8 We show another computed
shares as if we …x the worker ‡ow in each industry and …rm size as in 1981
(V k1981 ).
St =

X

F

^
^
^
3 + V k1981 3 + Zkt 3

:Mkt

k

St =

(29)
Et
The di¤erence of two lines is produced by the e¤ect of the change of worker‡ow.
As for 2005 in …gure 7, the estimated share of primary sector is 0.23
among whole regular workers. Perhaps due to aggregation, these …gures are
8 Data for 2003 are missing.

19

Figure 7: Transition of Share of Primary Sector
25.0
24.5
24.0
23.5

estimated share of primary sector among
regular workers

22.5

effect of industry and firm size change
22.0
21.5
21.0
20.5

year

20

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

20.0
1981

%

23.0

much higher than micro data-based mean probability, that is 0.16. Therefore
we should be cautious when interpreting the simulated probability based on
aggregated data.
Putting aside the level of shares, a more interesting fact in the …gure
7 is the upward trend of primary sector through decades. We can distinguish2 steps in the rise of this share, between 1981 and 1991 (increase of 0.6) andbetween 1992 and 2005 (increase of 1.9): it is possible to say that this trend has
accelerated from the early 1990s even if it is characterized by ‡uctuations.
Moreover, a second conclusion is that this upward trend largely comesfrom the shift of industry and …rm size. As shown in the full estimates ofswitching regression in the appendix (…gure 9), male regular workers in larger…rms or in service industries are more likely to be in the primary sector thanthose in smaller …rms or manufacturing. Thus, the mean probability will change
when the distribution of industries and …rm size shifts. During decades it is wellknown structural changes in the Japanese economy have been characterized
by a rising share of non-manufacturing industries. It may be the underlying
mechanisms at the roots of the increasing share of primary sector in Japan,
which is depicted in …gure 7. Furthermore, this trend would have been more
rapid if the di¤erence of gross ‡ows between male and female had kept his levelof 1981. In other words the e¤ect of the change of out‡ow –that is, here, a proxy
for the strength of e¢ ciency wage mechanism - has been somewhat negative. Itmeans that, especially between 1990 and 1993 when the trends of two lines areapparently reversed, Japanese …rms have weakened the mechanism of e¢ ciency
wage by relatively using more male out‡ow rather than female out‡ow.
As a whole, the simulated evolution of the share of the primary sector
con…rms a prediction of our model. However, the speci…c and relative impactof the e¢ ciency wage mechanism in the rising share of the primary sector has
declined from the 1990s.

4Conclusion

In this paper, we have proposed a framework aiming at connecting two stylized
facts that characterized the Japanese economy during the Lost Decade (19922005): rising wage inequalities and increasing productivity di¤erentials. First,
we have build an e¢ ciency wage model with two types of …rms: …rms of the
primary sector adopt e¢ ciency wage scheme, whereas the labor market of thesecondary sector is competitive. A key feature of this model is that the …rst
type of …rms endogenously generate an e¤ort function. In this model, a negative
productivity shock at the aggregate level produces increasing productivity andwage di¤erentials, as well as rising share of the primary sectors.
Second, we test this model on Japanese micro data. For the …rst time, wematch the Basic Survey on Wage Structure and the Employment Trend Survey.
The matched worker-…rm cross-section dataset we get allows us to establish that
there is a negative correlation between the mean of the residual and ou‡ows

21

and, thus, to con…rm the existence of e¢ ciency wages mechanisms on average.
Moreover, by using the unknown regime switching regression methodology à la
Dickens and Lang (1985), we are able to distinguish between two sectors and, ina second step, to show that one can be characterized by e¢ ciency wage and theother one not, by using the same criterion. Finally, we study the evolution of theshare of primary sector by simulation. The fact that it substantially increased
between 1981 and 2005 is conformed to the predictions of our model. However,
the relative importance of the e¢ ciency wage mechanism by comparison to
structural evolutions of industry and …rm size changes has to be nuanced.
Important issues are related to this work. First of all, it is con…rmed that rise
wages inequality is related to increasing productivity dispersion among Japanese…rms. Second, the rising share of primary sector and its associated increasing
wages and tenure is rather counter-intuitive if one considers the state of thedebate on the end of the so-called "Japanese employment system". Third, thisfocus on labor market mechanisms and choices regarding the productive modelat the level of the …rm shows that it is possible to generate a similar trend ofrising wage inequalities to the one that has been observed in Japan during theLost Decade, without resorting to hypotheses regarding SBTC or globalization.
At the same time, we are conscious that our work has important limitations
and that we should be about our conclusions. We can think about at least two
extensions of this work. First, it is desirable to study directly the evolutionof inter-…rms wage and productivity di¤erential rather than indirectly throughthe share of primary …rms. Second, it would be necessary in the future todecompose precisely the overall wage di¤erentials into within-…rms and between…rms di¤erentials.

References[1] ABE Masahiro & OHASHI Isao (2004), Inter-Industry and Firm Size Effects on Wage Di¤erentials and E¢ ciency Wages in Japan, Hi-Stat Discussion Paper Series No. 25, Hitotsubashi University.
[2] ABOWD John M & KRAMARZ Francis (2001), Inter-industry Wage Differentials in France and in the United States, Cornell University Working
Paper.
[3] ABOWD John M., FINER Hampton & KRAMARZ Francis (1999), Individual and Firm Heterogeneity in Compensation: An Analysis of MatchedLongitudinal Employer-Employee Data for the State of Washington, in J.
Haltiwanger, J. Lane, J. Spletzer, K. Troske (ed.), The Creation and Analysis of Employer-Employee Matched Data, North-Holland, 3-24.
[4] AMABLE Bruno & GATTI Donatella (2006), Labour and product market reforms: questioning policy complementarity, Industrial and Corporate
Change, 15(1).

22[5] AMABLE Bruno & GATTI Donatella (2004), Product market competition, job security and aggregate employment, Oxford Economic Papers,
56(4):667-686.
[6] AMABLE Bruno & GATTI Donatella (2002), Macroeconomic e¤ects ofproduct market competition in a dynamic e¢ ciency wage model, Economics
Letters, 75: 1, pp. 39-46.
[7] BULOW Jeremy I. & SUMMERS Lawrence H. (1986), A Theory of DualLabor Markets with Application to Industrial Policy, Discrimination andKeynesian Unemployment, Journal of Labor Economics, July.
[8] CARD David and DINARDO John (2002), Skill-biased technologicalchange and rising wage inequality: some problems and puzzles, Journalof Labor Economics 20, pp 733-783.
[9] CASELLI Francesco (1999), Technological Revolutions, American Economic Review, vol. 89, pp. 78-102.
[10] DINARDO John, FORTIN Nicole and LEMIEUX Thomas (1996), Labor
rmarket institutions and the distribution of wages, 1973-1992: a semiparametric approach, Econometrica, 65, pp 1001-1046.
[11] DICKENS, William T. & LANG Kevin (1985), A Test Of Dual Labor
Market Theory, American Economic Review, 75(4), 792-805.
[12] DUNNE Timothy, FOSTER Lucia, HALTIWANGER John & TROSKE
Kenneth (2004), Wage and Productivity Dispersion in the US Manufacturing: the Role of Computer Investment, Journal of Labor Economics, vol.
22, issue 2, pages 397-430.
[13] FAGGIO, Giulia, SALVANES Kjell G. & VAN REENEN John (2007),The Evolution of Inequality in Productivity and Wages: Panel Data Evidence. CEP Discussion Paper 821. Centre for Economic Performance, London School of Economics.
[14] FREEMAN Richard B. & MEDOFF James L. (1983), Trade Unions andProductivity: Some New Evidence on an Old Issue, NBER Working Paper
No 1249.
[15] FUKAO Kyoji & KWON Hyeong Ug (2006), Why did Japan’s TFP Growth
Slow Down in the Lost Decade? An Empirical Analysis Based on FirmLevel Data of Manufacturing Firms, Japanese Economic Review, Volume
57 Page 195 - June.
[16] ISHIKAWA Tsuneo & DEJIMA Takahisa Dejima (1994), The Dual Structure of Labor Markets, in Tsuneo Ishikawa (ed.), The distribution of wealthand income in Japan, The University of Tokyo Press, pp.169-209. (inJapanese)23[17] ITO Keiko & LECHEVALIER Sébastien (2009), The Evolution of Productivity Dispersion of …rms. A Reevaluation of its determinants in the caseof Japan, Review of World Economics, 2009, Volume 145, Number 3.
[18] KAMBAYASHI Ryo, KAWAGUCHI Daiji & YOKOYAMA Izumi,Wage
Distribution in Japan, 1989-2003. Canadian Journal of Economics/Revue
canadienne d’economique, Vol. 41, Issue 4, pp. 1329-1350[19] KRUEGER Alan B. & SUMMERS Lawrence H. (1988), E¢ ciency Wages
and the Inter-Industry Wage Structure, Econometrica 56-2.
[20] LEONARDI Marco (2007), Firm Heterogeneity in Capital/labor Ratios
and Wage Inequality, Economic Journal, vol. 117, issue 518, pages 375-398.
[21] MANNING Allan (2003), Monopsony in Motion, Princeton University
Press.
[22] MORTENSEN Dale T. (2003), Wage Dispersion, MIT Press.
[23] OECD (2006), Economic Survey of Japan.
[24] OI,-Walter-Y. (1983), Heterogeneous Firms and the Organization of Production, Economic-Inquiry; 21(2), April, pages 147-71.
[25] OTA Kiyoshi (2005), Rise in labour income inequality with increasing numbers of freeters, ESRI Discussion Paper Series No 140 (in Japanese)[26] OTAKE Fumio (2005), Inequality in Japan, Nihon Keizai Shimbun (inJapanese).
[27] SAINT-PAUL Gilles (1997), Dual Labor Markets: A Macroeconomic Perspective, MIT Press.
[28] SHAPIRO Carl & STIGLITZ Joseph (1984), Equilibrium Unemployment
as a Worker Discipline Device, American Economic Review, vol. 74, issue
3, pages 433-44.
[29] SOUZA-POSA Alfonso (2004), Is the Swiss Labor Market Segmented? An
Analysis Using Alternative Approaches, Labour 18(1), pp 131-161.
[30] TACHIBANAKI Toshiaki (2005), Confronting Income Inequality in Japan:
A Comparative Analysis of Causes, Consequences, and Reform. MIT Press.
[31] WAKISAKA Akira (1997), Women at Work, in SAKO Mari & SATO Hiroko (ed.), Japanese labour and management in transition: diversity, ‡exibility and participation, Routledge.
[32] YOSHIKAWA Hiroshi (2008), Japan’s Lost Decade, I-House Press.

24

Appendix

Figure 8: Appendix 1 - Summary Statistics of Switching Regression Sample

Full Sample Used in the Regression

Industry Dummies

Firm SizeDummies

Variable
Mean Std. Dev. Min
Mean of Residual
0,043
0,237
-1,015
Outflow Ratio
0,126
0,126
0,000
Gross Flow Ratio Difference between Male and Female-0,0460,384-7,058Overtime Ratio
0,083
0,057
0,000
(over 1000)
0,498
0,500
0
(300-999)
0,196
0,397
0
(100-299)
0,187
0,390
0
(30-99)
0,118
0,323
0
(Mining)
0,014
0,117
0
(Manufacturing)
0,605
0,489
0
(Electric and Utilities)
0,026
0,160
0
(Transportation and Communication)
0,070
0,255
0
(Retail, Wholesales and Restaurants)
0,077
0,266
0
(Finance and Insurance)
0,029
0,169
0
(Real Estates)
0,017
0,130
0
(Service)
0,162
0,368
0
Observation
1874

25

Max
0,928
2,058
2,615
0,298
1
1
1
1
1
1
1
1
1
1
1
1

least

more or lessmost0.1> Prob. ofPrimary Sector0.5> Prob. ofPrimary Sector>0.1

Prob. of Primary
Sector >0.5

Mean Std. Dev.
0,025
0,210
0,111
0,112
0,059
0,178
0,091
0,055
0,486
0,500
0,150
0,358
0,211
0,408
0,153
0,360
0,027
0,163
0,910
0,286
0,046
0,210
0,000
0,000
0,017
0,128
0,000
0,000
0,000
0,000
0,000
0,000
957

Mean Std. Dev.
0,024
0,215
0,132
0,124
-0,183
0,306
0,080
0,057
0,553
0,498
0,220
0,415
0,152
0,359
0,075
0,263
0,000
0,000
0,551
0,498
0,011
0,103
0,006
0,080
0,269
0,444
0,115
0,320
0,047
0,212
0,000
0,000
468

Mean Std. Dev.
0,104
0,297
0,150
0,150
-0,125
0,634
0,068
0,057
0,468
0,500
0,269
0,444
0,171
0,377
0,091
0,288
0,000
0,000
0,011
0,105
0,000
0,000
0,285
0,452
0,004
0,067
0,002
0,047
0,022
0,148
0,675
0,469
449

Figure 9: Appendix 2 - Full Results of Switching Regression: E¤ect of Flow
Structure on Mean Residual (2005 BSWS and ETS matched sample)
(5)

(6)

(7)

(8)

Full Sample OLS

Secondary Sector

Primary Sector

SwitchDependent Variables
Outflow Ratio

Mean of Residual(latent)-0.0670.030-0.215(0.039)*(0.031)(0.069) ***Gross Flow Ratio
Difference between Maleand Female
Overtime Ratio-0.868(0.015)***
0.970

1.253

0.179

(0.090)***(0.070)***(0.240)Firm Size Dummies(over 1000)(300-999)(100-299)(30-99)Industry Dummies(Mining)

BASE-0.070-0.069-0.081

0.037(0.013)***(0.010)***(0.033)**(0.016)**-0.170-0.161-0.206-0.071(0.013)***(0.010)***(0.032)***(0.016)***-0.218-0.215-0.219-0.273(0.016)***(0.011)***(0.0395)***(0.019)***

0.176

0.203

0.421

-1.100(0.039)***(0.027)***(0.094)***(0.051)***BASE(Manufacturing)(Electric and Utilities)

0.260

0.260

0.107

-0.304(0.029)***(0.022)***(0.067)(0.037)***(Transportation andCommunication)

0.073

0.015

0.131

1.870

(0.019)***(0.013)(0.038)***(0.023)***(Retail, Wholesales andRestaurants)-0.051-0.064

0.027

0.314

(0.019)***(0.014)***(0.045)(0.022)***(Finance and Insurance)(Real Estates)(Service)
Constant

0.165

0.166

0.164

1.050

(0.029)***(0.019)***(0.061)***(0.035)***

0.143

0.185

0.083

1.230

(0.037)***(0.025)***(0.077)(0.045)***0.127-0.003

0.165

2.238

(0.014)***(0.010)(0.032)***(0.016)***0.004-0.0330.080-1.362(0.013)***(0.010)***(0.037)**(0.010)***

0.28

0.46

0.13

0.94

Observations
R-squared

1874

Note: Standard errors in parentheses, and *** p<0.01, ** p<0.05, * p<0.1. Gross flow ratio means inflow ratio plus
outflow ratio.

26