Wage and Productivity Differentials in Japan: The Roleof Labor Market Mechanisms
labr_555

514..541

Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier
Abstract. This paper aims at explaining two stylized facts of the Lost Decade in Japan: rising wage
inequalities and increasing firm-level productivity differentials. We build a model where firms can choose
between efficiency wages with endogenous effort and competitive wages, and show that it can replicate
those facts. Using Japanese microeconomic data, we find support for the existence of efficiency wages in
one group of firms and competitive wages in the other group. Based on those results, a simulation showsthat the share of firms using efficiency wages has declined, within sectors, during the Lost Decade, aspredicted by the model.

1. Introduction
Although the Japanese economy is famous for the deflation it has experienced in the last 10
years, less attention has been given to the real side of the economy. Two major stylized facts
characterized Japan during the Lost Decade (1992–2004): rising wage inequalities and increasing productivity differentials at the firm level. Both evolutions occurred against the backdrop
of a slowdown in aggregate productivity. This paper proposes an explanation for those realdevelopments. We argue that wage inequalities are the flip side of productivity differentials,
which themselves originated from choosing different models of work organization as a reaction to the productivity slowdown.
Our contribution is twofold. First, we build a simple efficiency wage model with two typesof firms differing by their compensation scheme and associated incentive mechanisms. We
show that a negative aggregate productivity shock leads to different reactions of both types offirms and, consequently, to increasing productivity and wage differentials. Second, we conduct
an empirical investigation with a Japanese matched employer–employee data set. We showthat firms can be divided into two groups: one group with efficiency wages, and anotherpaying competitive wages, consistent with the model.
Substantial increases in wage inequalities have been observed for more than two decades ina wide range of countries, including the USA, the UK, and many other OECD countries. Thishas given birth to a sizable literature, which first reached the consensus that skill-biased
technological change was the main factor driving inequalities in the late 1990s and the early
Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier (author for correspondence),
EHESS, 105 bd Raspail, 75006 Paris, France. E-mail: sebastien.lechevalier@ehess.fr.
The views presented in this paper are those of the authors and do not necessarily reflect those of theBanque de France or the Eurosystem. We would like to thank Donatella Gatti for her contribution on
an earlier version of this paper. We are also very grateful to two anonymous referees for helpfulcomments, to Professor Naoki Mitani for his invitation to a seminar in Kobe University in May 2012,
and to Arsène Rieber for his advices and suggestions. Usual caveats apply.
LABOUR 26 (4) 514–541 (2012)

DOI: 10.1111/j.1467-9914.2012.00555.x
JEL L23, J24, J31, J42© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd,
9600 Garsington Rd., Oxford OX4 2DQ, UK and 350 Main St., Malden, MA 02148, USA.

Wage and Productivity Differentials in Japan

515

2000s. As emphasized by Machin (2008), the topic has seen a recent renewal of interest as aresult of several developments. Among them, the fact that some countries, previously characterized by relatively stable wage structures, have started to experience rising wage inequalities certainly deserves a new generation of research.
Together with Germany, Japan is one of these countries. Whether income inequalities havereally widened in Japan during the 1990s and onward has first been the subject of a debate but
recent evidence shows that wage inequalities across male workers with similar characteristics
have increased (Kambayashi et al., 2008).
To explain these rising ‘within-group’ inequalities, it is natural to turn to firms’ characteristics. A well-established stylized fact in Japan is indeed the increased dispersion of productivity at the firm level (Fukao and Kwon, 2006; Ito and Lechevalier, 2009). Surprisingly, these
two facts have never been connected in the literature. This paper tries to fill the gap. We adopt
a perspective similar to Faggio et al. (2010) or Mortensen (2003) and analyse the link between
rising wage inequalities and increasing productivity dispersion at the firm level.
Our explanation for rising wage inequalities focuses on labor market mechanisms and firms’
heterogeneity with regard to the choice of their organizational structure. By doing so, weabstract from other factors such as the impact of technical progress or the internationalizationof the economy. In the model, firms can choose between two types of work organizations, acomplex structure with workers’ involvement and job security where the productivity ofworkers depend on their effort (type-I firms), and a simple competitive structure where
workers have an exogenous productivity (type-II firms).1 A key ingredient of the model is that
type-I firms endogenously generate a continuous effort function, contrary to the standard
efficiency wage model where effort is a discrete variable. With this assumption, adjustment to
the productivity slowdown in type-I firms can take place through increased effort, consistent
with the intensification of work that we document for Japan. The core mechanism we emphasize is the interaction between this endogenous effort and the free choice of the organizational
structure. A decrease in aggregate productivity leads to a smaller share of type-I firms, whose
employees provide a higher effort, get a higher wage premium and make these firms overall
more productive in relative terms.
In the empirical part of the paper, we match for the first time the Basic Survey on Wage
Structure (BSWS) and the Employment Trend Survey (ETS) for the years 2005–9, in order tobuild a rich employer–employee data set that allows us to disentangle individual and firm
determinants of wage inequalities.2 We test for the presence of efficiency wages by looking fora negative correlation across firms between job flows and the firm-specific wage premium, aspredicted by the model. We find that the existence of such a correlation on average is notcontradicted by our data. Next, we divide our sample of establishments into two groups byusing the unknown regime switching regression à la Dickens and Lang. We find that a groupof establishments can be characterized by efficiency wage mechanisms, whereas the othergroup cannot. This confirms the key mechanism underlying our explanation for the rising
wage and productivity differential. Our identification assumption in the regime switchingrelies on differences between gross flows of male and female workers, which we argue is a good
indicator of the extent to which (male) workers enjoy job security in Japanese firms. On theJapanese labor market, female workers are usually not part of the regular workforce but areused by firms as a buffer, making them a natural benchmark for the flexible labor input that
we associate with the competitive work organization. Our results are robust to an alternative
specification based on the share of part-timers. We also do robustness checks to rule out
alternative explanations of the correlation between wage premia and job flows, such as
on-the-job search. Finally, we use the regime switching equation to simulate the evolution of© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier

the share of type-I firms and find that it decreased, within sector, at the beginning of the Lost
Decade, consistent with the prediction of the model. These results suggest that our explanation based on efficiency wages and the heterogeneity of firms’ organizational structure is a
plausible candidate to account for the recent joint rise in productivity and wage differentials
in Japan.
The rest of the paper is built as follows. In the next section, we describe some stylized facts
that characterized the Japanese economy from the early 1990s on, focusing on the real side ofthe economy, in particular the rising wage and productivity differentials. Section 3 thendiscusses the recent literature that aimed at connecting wage and productivity differentials.
Section 4 builds an efficiency wage model with the endogenous choice of organizational
structure and shows that it accounts well for the stylized facts. Section 5 tests whether theefficiency wage mechanism embedded in the model is present in the data and identifies the two
types of firms with an unknown regime switching regression. The final section concludes.
2. Stylized facts: the Japanese Lost Decade viewed from the real side of the economy
The Lost Decade in Japan (1992–2004) has been infamous for the long-lasting effect of the
burst of the financial and real estate bubbles and the inability of successive governments todeal with a crisis that has turned into a deflation (Mikitani and Posen, 2000). But the Lost
Decade has also witnessed several important developments in the real side of the economy.
This section reviews the major stylized facts (SF) that motivate our analysis.
SF1: Aggregate productivity has slowed down during the Lost Decade
Although it is less known outside Japan than the deflationist episode, a major feature of the
Lost Decade is the productivity slowdown at the aggregate level, which has been the focus ofa lively academic debate in Japan. Between 1995 and 2004, the annual average gross valueadded growth has been 0.7 per cent in Japan against 3.7 per cent in the USA, with acontribution of total factor productivity (TFP) growth of 0.4 and 1.7 respectively, whereas
during the period 1980–95 gross value-added growth was 3.6 per cent in Japan and 2.9 per centin the USA with a contribution of TFP growth of 1.2 and 0.5 respectively (Fukao andMiyagawa, 2007).3
Hayashi and Prescott (2002) also conducts a growth accounting. Using their data, we candecompose the loss of 3.1 points of growth in per capita income between the periods 1983–91
and 1991–2000 into a loss of 2.2 points in TFP growth and a loss in 0.9 points in number ofhours worked per capita, whereas the ratio of capital to hours worked slightly decreased (by0.1 point).
SF2: Productivity differentials across firms have increased, even after controlling for firm sizeand sector
Recent studies on Japan, using different data sets and different methodologies, have foundan increasing productivity dispersion among Japanese firms during the ‘Lost Decade’ (Fukao
and Kwon, 2006 or Ito and Lechevalier 2009, 2010, among others). Moreover, whereas Japan
has been characterized by size and sector productivity differentials that were relatively higher
than in other developed countries (Yoshikawa, 2008), these recent studies have emphasized
productivity differentials for firms of similar size and belonging to the same narrowly defined
sectors, which have also observed in other countries (Dosi et al., 2010).
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Wage and Productivity Differentials in Japan

Ito and Lechevalier (2009) test whether the introduction of information and communication
technologies had an effect on the evolution of productivity differentials at the sectoral level.
They find no statistically significant effect, which casts doubt on the popular technology-based
explanation according to which innovation explains productivity differentials,4 thus leavingroom for other possible explanations.
SF3: Wage inequalities have substantially increased for workers with similar characteristics
and between firms of the same size
Japan has experienced a significant increase of inequalities to become one of the mostunequal countries of the OECD, when inequalities are measured through the Gini coefficient(OECD, 2006). This statement, now widely accepted, has first been the subject of an intense
academic debate: until recently, there was no consensus regarding whether income inequality
really widened during the 1990s and onward. For example, Tachibanaki (2005) claimed that
income inequalities substantially rose during the 1980s and 1990s, with a Gini coefficientincreasing from 0.278 in the mid-1980s (OECD average: 0.286) to 0.314 in early 2000s (OECDaverage: 0.307). On the contrary, Ohtake (2005) argued that this observed increase in income
inequalities was a statistical artifact largely driven by the aging population. Focusing on labor
income, Kambayashi et al. (2008) reconciled both sides of the debate. Using micro data, they
showed that the distribution of wages remained apparently stable as a result of two opposing
trends: (i) declining between-group (defined by education, experience, tenure, and establishment size) wage inequality; but (ii) increasing within-group inequality among male workers.
The first candidate to explain this increasing wage gap is the introduction of individual
performance-based systems, but it does not seem to have played an important role in Japaneven though such systems have been experimented with.5 The second candidate is related to
the wage differential between regular and non-regular workers. The rising share of nonregular workers, which has more than doubled in 20 years to reach more than a third of theworkforce, has been indeed a popular explanation of rising inequalities in Japan, especiallywithin firms (Ota, 2005). A peculiarity of the Japanese labor market is that non-regular
workers are mostly female whereas regular workers are mostly male, a fact that we will use
later in our identification strategy.6
However, the higher share of non-regular workers does not explain why wage inequalities
have also increased between firms (Ito and Lechevalier, 2009; Tachibanaki, 2005). This has
first gone unnoticed since the literature has first focused on firms of different size and foundthat firm-size differential does not explain the increasing wage gap (e.g. Kambayashi et al.,2008).7
SF4: Work intensity has increased, especially in firms that adopted Toyota-style flexible
production systems
Although labor productivity growth has tended to slowdown, there is some evidence thatwork intensity has increased. Signs of rising work intensity can be captured through indicatorssuch as accidents, depressions, and, in the most extreme cases, suicides on the work place.8 For
example, according to a Governmental Report on Workers’ Accident Compensation (2011),
the number of claims of industrial accidents per 10,000 workers has increased from 0.149 in2001 to 0.317 in 2010. Moreover, since 2007, the number of claims related to psychologicaldiseases corresponds to more than 50 per cent of the total number of claims, whereas it was
only one-third of the total number of cases in 2001. Finally, although it has not been
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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevaliersystematically investigated yet, the anecdotal evidence suggests that these problems concern
more particularly firms who adopted new human resource practices and work organizationalmodels, such as the Toyota-style flexible production system (Lechevalier, 2005).
Given those stylized facts, it is tempting to explain the rising wage inequalities (SF3),
especially between firms, by the increased between-firm productivity differentials (SF2), as it
has been done by the existing literature in the case of other countries (see next section). The
present paper aims at studying this link in a way that is consistent with the two other stylized
facts. Surprisingly, there has been no recent investigation of between-firms wage dispersion inconnection with productivity differentials. One reason for the absence of this type of study
might be the assumption of friction-less labor markets, which does not allow for any connection between wage differential and productivity differential other than human capital.
Another possible reason is the concern for within-firm wage differential between regular andnon-regular workers described above, or the fact that studies that have looked at between-firm
inequality have focused on firms with different sizes, which failed to explain the increasing
wage gap as mentioned above.

3. Review of the literature on productivity and wage differentials
In addition to the stylized facts described above, this paper is motivated by a large body ofliterature, both empirical and theoretical, that investigated the relationship between productivity and wage differentials.
On the empirical side, several studies have shown that variations in wages can be explainedby cross-firms differences in wage policy and productivity, using Danish, US, or UK data
(Dunne et al., 2004; Faggio et al., 2010; Mortensen, 2003). The correlation between wage andproductivity differentials in different countries is now a well-established stylized fact.
Several theoretical mechanisms have been suggested to generate this link between productivity and wage differentials. A first and obvious explanation relies on differences in humancapital (Haltiwanger et al., 1999).
A sizable literature focuses instead on the role of technology. Caselli (1999) explains thegrowing wage differential by different rates of technological adoption. He builds a model
where the adoption of new technologies together with different learning abilities of workers
results in both different wages and different capital–labor ratios. In Leonardi (2007), thefalling price of equipment results in a more dispersed distribution of capital–labor acrossfirms. This, together with a search model of the labor market, generates an increased dispersion of wages for ex ante identical workers. Empirical work by Dunne et al. (2004) for the
USA and Faggio et al. (2010) for the UK have confirmed the link between wage and productivity differentials on the one hand and the rate of technology adoption on the other hand.
Leamer (1999) builds a two-sector Hecksher-Ohlin model with variable effort from workers
and shows that effort and wages are higher in the more capital-intensive sector.
Finally, many papers explain this link by labor market mechanisms. Layard et al. (2005)
review a range of models of imperfect labor markets that are able to generate wage andproductivity differentials — union bargaining, efficiency wage, rent-sharing, or search-based.
Although the mechanisms described above are relevant for many OECD countries, they do
not fit well the Japanese stylized facts described in Section 2. According to SF3, wage
inequalities are found across individuals with similar characteristics and are therefore unlikely
to be explained by differences in human capital or in learning abilities as in Haltiwanger et al.
(1999) or Caselli (1999). According to SF2, productivity differentials have been observed for© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Wage and Productivity Differentials in Japanfirms of similar size and within narrowly defined sectors. Thus, explanations relying on
sectoral differences (e.g. different capital–labor ratio as in Leamer, 1999) are not sufficient toaccount for them. The mechanism proposed by Leonardi (2007) for the USA is able togenerate an increased dispersion of wages and productivity within sectors but implies anincrease in the capital–labor ratio of firms. As documented in SF1, the capital–labor ratio
slightly slowed down in Japan during the Lost Decade. The adoption of better technologies
should also be associated with a higher aggregate productivity whereas productivity growth
has slowed down in Japan (SF1). More generally, in the case of Japan, wage inequalities are
better explained by the characteristics of the labor market (Tachibanaki, 2005) than bydifferences in technology (Ito and Lechevalier, 2009). Finally, few of the models mentioned
above, included those on labor market mechanisms, are able to generate the intensification ofeffort suggested by SF4.
In the next section, we build an efficiency wage model where productivity and wage
differentials stem from the endogenous choice of work organization by otherwise similarfirms. This model is consistent with the four stylized facts described in Section 2 and therefore
overcomes the limits of the existing theoretical literature when applied to Japan.
The increased diversity of human resources and management practices and its impact oneffort and productivity differentials has been documented by Bloom and Van Reenen (2007,2011) for the USA and Europe, and Valeyre (2004) for France. Green (2004) shows that, in thecase of the UK, technological and organizational changes on the one hand, the use ofhigh-commitment human resources policies on the other hand, explain work intensification
better than the reduction of union power or rising job insecurity.
A prediction of the model that will play an important role in the empirical part of the paper
is the correlation between wages and employment flows. This correlation has also been
documented in other OECD countries. Using data from the State of Washington, Abowd
et al. (2006) show that idiosyncratic wage policies of firms are closely related to observed
patterns of worker and job flows at the firm level. Lazear and Shaw (2008) show that anegative correlation between workers’ flows and wage levels is a widespread stylized fact
among 10 OECD countries. On-the-job search offers an alternative theoretical explanation forthis negative correlation, as in Mortensen and Pissarides (1994): workers in more productive
establishments do not have any incentive to search for another job, because they receive
enough match-specific benefits. Therefore, voluntary quits in such establishments are lower,
resulting in a negative correlation between gross flows and productivity. Although this mechanism is relevant for voluntary quits, our efficiency wage mechanisms concerns involuntary
quits. In Japan, the share of voluntary quits has drastically dropped since the mid-1990s. In
the empirical section, we show that our results are robust even when controlling for voluntary
quits.
4. An efficiency wage model with endogenous choice of organizational structureThis section describes a plausible mechanism to account for the stylized facts described in
Section 2. It introduces a simple efficiency wage model with endogenous effort and theendogenous choice of organizational structure. The model is able to reproduce all the stylized
facts described above. Unfortunately, the lack of data makes it impossible to directly test allits predictions. However, the empirical part of the paper (Section 5) will provide some support
to the model by testing its main feature, the existence of efficiency wages in one group of firms.
The framework we use is a simple extension of the classical model proposed by Shapiro
and Stiglitz (1984), with two types of firms as in Bulow and Summers (1986).9 This dualism© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevaliercorresponds to two alternative organizational structures. Some firms (type-I firms)
implement a complex organizational structure with job security and where productivity
depends on specific efforts from workers, whereas other firms (type-II firms) behave competitively on the labor market and implement a simple organizational structure where
workers have constant marginal productivity regardless of their effort. We assume that
one firm equals one job.10 Hence, the levels of employment in the two types of firms, L1 andL2, stem from the distribution of firms across the two productive models, which will be
endogenous.
Jobs in type-I firms require workers’ implication and efforts. Productivity in type-I firms,
m1, is a concave function of the effort e. In order to get closed-form solutions, we assume aconstant elasticity:

m1 ( e ) =

1 η
Ae
η

η < 1,[1]

where A is the economy-wide aggregate productivity. Type-I firms choose both the wage andthe effort required to maximize profits, taking into account the possibility that workers canchoose to shirk and not provide any effort. Shirkers are detected and fired with probability q.
Non-shirkers enjoy job security: they only lose their job when the firm is hit by an exogenous
separation shock, which happens with probability s. Then, the worker becomes unemployed
and the firm exits the economy. The wage is used as an incentive mechanism to ensure thatworkers do not shirk.
In type-II firms, no incentive is required: workers always have an exogenous productivity
m2 = Am. Contrary to type-I firms that offer job security, type-II firms are perfectly competitive. Workers freely choose between supplying their labor to type-II firms on the competitive
spot labor market or being unemployed. Only unemployed workers can look for a job in a
type-I firm; they succeed with probability a, which has to satisfy:11

aU = sL1,[2]

where U is the number of unemployed.
Unemployment benefits wu are financed by a tax raised on wages, with the following budget
constraint:

wuU = t(w1L1 + w2 L2 ).

[3]

The tax rate t is exogenous. The total labor force is N, with:

N = L1 + L2 + U .

[4]

Firms are free to choose their productive model, type I or type II. We also assume free entryof firms in the economy.
The model is solved in three steps:• Step 1: type-I firms choose the wage and the effort that maximize the value of the firm.
• Step 2: workers not employed in type-I firms freely choose between unemployment and a job
in type-II firms, which determines wages in type-II firms.
• Step 3: firms freely enter the economy and choose their productive model (type I or type II),
which determines employments in type-I and type-II firms, as well as the hiring rate a.
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Wage and Productivity Differentials in Japan

Table 1. Variables of the model
Variable

Definition

w1, w2
L1, L2
N, U
wu
e
h
a, s
V1S, V1NS,V U, V2

Wage in type-I and type-II firms
Number of type-I firms and of type-II firms
Total labor force, total unemployment
Unemployment benefits
Effort of workers in type-I firms
Elasticity of a worker’s productivity with respect to its effort
Hiring and firing rates in type-I firms
Utilities of shirker in type-I firms, of non-shirker in type-I firms,
of unemployed, and of worker in type-II firms
Values of a type-I and a type-II firmTax rate raised on wages
Productivity in type-I and type-II firms
Probability of detecting shirker
Interest rate
Idiosyncratic productivity in type-II firms
Aggregate productivity

J1, J2tm1, m2qr
m
A

As the efficiency wage block of the model (step 1) is standard, we put some of the derivationsof the results in Appendix A and keep here only what is necessary to our demonstration. The
definition of all variables is summarized in Table 1.
Step 1: incentives and effort in type-I firms
Type-I firms choose an efficiency wage to make sure that workers will not shirk (seeAppendix A):

w1 =e( a + s + r + q ) wu+.
q(1 − t )
1− t

[5]

Then, they choose the level of effort that maximizes the value of the firm subject to thisefficiency wage schedule. The value of a type-I firm J1 is given by:

J1 =m1 − w1.
r+s[6]

Profits’ maximization yields an endogenous effort function:

dJ1 ∂m1 ∂w1
=
−
= 0,
∂e
∂e
de

[7]

1

 (1 − t )Aq  1−η
e=
.
 a + s + r + q 

[8]

At the optimum, the positive effect on profits of a marginal increase in effort, and therefore
productivity, would be exactly offset by the higher efficiency wage needed to get that higher© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevaliereffort. The fact that effort and productivity are endogenously determined by firms is a crucial
aspect of the model. In contrast, standard models like Shapiro and Stiglitz (1984) assume thateffort is exogenous.
Taken together, the efficiency wage [5] and the optimal level of effort [8] imply a wageschedule given by:η1 (1 − t )q  1−η 1−η wu.
w1 = 
A +1− t
 a + s + r + q 

[9]

Step 2: equilibrium wage in type-II firms
Because type-II firms are perfectly competitive, workers can freely choose to either supply
their labor to type-II firms or be unemployed. In equilibrium, the wage paid by type-II firms
adjusts to make workers indifferent between unemployment and a job in a type-II firm (seeAppendix A):

w2 =

ae
w
+ u .
q(1 − t ) 1 − t[10]From [5] and [10], we can derive the expression of the wage differential:

w1 − w2 =e( s + r + q ).
q(1 − t )[11]

The wage premium paid by type-I firms over type-II firms increases with the effort required
from workers in type-I firms.
Step 3: productive model and employment
Finally, firms choose their productive model. In equilibrium, they should be indifferent
between both models so that the value of a type-I firm should equal that of a type-II firm:
J1 = J2, where:J2 =m2 − w2.
r[12]

As there is free entry, this value is driven down to 0. We have J1 = J2 = 0, which gives:

m1 ( e ) = w1,[13]m2 = w2.

[14]

We can now define an equilibrium of the model.
Definition: An equilibrium is an allocation vector (L1, L2, U, e), a price vector (w1, w2, wu), anda hiring rate a, satisfying the flow equilibrium condition [2], the budget constraint [3], the labor
resource constraint [4], the free entry conditions [13] and [14], the efficiency wage schedule© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Wage and Productivity Differentials in Japan[5], such that type-I firms choose optimally the level of effort [8] and workers are indifferent
between unemployment and jobs in type-II firms [10].
The following proposition describes the equilibrium of the model.
Proposition 1 (Equilibrium): There is a unique A > 1, such that for all A satisfying

 ηµ 
1< 
 1 − η 

−

1−η
η

equilibrium,(1 − t )q
A < A there exists a unique equilibrium with L1 > 0 and L2 > 0. In this
s+r+q

∂a1
∂e
∂( m1 − m2 )∂(w1 − w2 )∂( L1 L2 )
> 0,
< 0,
< 0,
< 0, and
> 0.
∂A
∂A
∂A
∂A
∂A

Proof: See Appendix A.
The lower (upper) bound on A is necessary to get strictly positive employment in type-I(type-II) firms L1 (L2).
Discussion of the results
In our framework, the productivity slowdown of the Lost Decade that we documented in SF1
can be modeled as a decrease in the aggregate productivity A. The proposition shows that thisleads to lower hiring flows into type-I firms, a higher effort in type-I firms, and larger wage andproductivity differentials between type-I and type-II firms, consistent with SF2, SF3, and SF4.
The intuition for this result is the following. In a partial equilibrium framework, i.e. for agiven hiring flow a, the direct effect of a lower aggregate productivity A is to lower the effort
at the firm level (see equation [8]), which would depress the firm-level productivity m1(e) in
type-I firms even further than in type-II firms. However, there is a general equilibrium effectgoing in the opposite direction. Lower profits in type-I firms indeed lead to a reallocation offirms from a type-I to a type-II productive model. With a lower share of type-I firms, the hiring
rate a then decreases (equation [2]). This in turns lowers the value of unemployment as it is
now harder to find a job in type-I firm, making it profitable for type-I firms to increase theeffort required. Indeed, equation [8] shows that the optimal effort is a decreasing function of
the hiring rate. What proposition 1 shows is that this general equilibrium effect dominates sothat the effort e increases when A decreases. This is consistent with SF4, which documents anincrease in the intensification of work.
From equation [11], a higher level of effort then means a larger wage differential, consistent
with SF3, as well as a larger productivity differential between type-I and type-II firms,
consistent with SF2.
To sum it up, a decrease in aggregate productivity leads to a smaller number of type-I firms,
providing a higher effort, getting a higher wage premium and making these firms overall moreproductive in relative terms. The fact that the relative productivity of type-I firms increases
following the crisis is due to the interaction between the general equilibrium effect and theendogenous intensification of work in these firms.
Although this main result is consistent with the stylized facts presented in Section 2, whether
our efficiency wage mechanism is a satisfying model of the Japanese labor market is a matterof empirical investigation.12 The empirical section of this article focuses on detecting efficiency
wages in type-I firms and competitive wages in type-II firms. According to equation [9],conditional on macro variables, efficiency wages in type-I firms are a decreasing function of
the separation rate s. Although the rate of inflow a is a macroeconomic variable (the probability for an unemployed to find a job in any type-I firm), the rate of outflow s is an© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalieridiosyncratic variable specific to each type-I firm.13 Therefore, if the separation rate varies
across firms, we expect to find a decreasing relationship between wages and separation rates
in the cross-sectional dimension of micro data for type-I firms. On the contrary, type-II firms
are perfectly competitive: their wage is pinned down by their exogenous productivity, which isunrelated to employment flows.14 Therefore, we do not expect any cross-sectional relationshipbetween wages and employment flows for type-II firms.
Finally, firms in the model all have the same size and belong to the same sector. Therefore, thepredictions of the model should be understood as holding within groups of sector and size andnot for the entire economy. For example, the share of type-I firms could decrease within sectors
and for firms of the same size but increase overall in the economy. This would be the case if a
sector with a large share of type-I firms becomes larger relative to the rest of the economy.
5. An empirical investigation with Japanese micro data
5.1 Empirical strategy and data set
According to the model of the previous section, we should find a negative relationshipbetween flows and wages in type-I firms (as in equation [9]), whereas there should be no suchcorrelation in type-II firms. The goal of this empirical part is to try to explain the stylized facts
mentioned in Section 2, in particular productivity and wage differentials, by applying thisdichotomy to the Japanese economy.
Ideally, we would like to test the model by using a micro panel data set including data on
wages and employment flows. Unfortunately, to our knowledge, such a database is not
publicly available in Japan. However, we had access to the BSWS and the ETS between theyears 2005 and 2009. The first survey provides information on wages and the second on
employment flows. By matching those two data sets at the establishment level for each year,
we get an employer–employee data set with 9,007 establishments, a well-known type ofdata to address the question we are interested in (Abowd et al., 1999; Abowd and Kramarz,2001). The appendixes describe the two initial databases, explains the matching process(Appendix B), and provides summary statistics (Appendix C).
Using this repeated cross-sectional data, we detect efficiency wages by showing the existenceof a negative correlation between flows and wages. To do this, we first estimate a Mincerian
wage equation for male regular workers with fixed effects on establishment. The dependent
variable is the logarithm of hourly ‘base’ wage rate of each worker (see below) and the
explanatory variables are individual characteristics such as education, tenure, as well as
dummies for prefectures (Kambayashi et al., 2008). The establishment fixed effect can beinterpreted as the establishment-specific wage premium. According to the model, this fixed
effect should be negatively correlated with the magnitude of outflows in type-I establishments.
In a second step, we use the unknown regime switching technique (Dickens and Lang, 1985;
Ishikawa and Dejima, 1994) to decompose the economy into two types of establishments. Thismethodology allows us not to set any explicit a priori criterion to define which establishment
belongs to which type of firm. This is a crucial point, as productivity dispersion has increased
within groups of firms sharing similar characteristics such as size and industry (SF2). After
having identified these two groups of firms, we can check whether there is a negative correlation between wage premia and flows in type-I firms, and no such correlation in type-II firms.
In a third step, we use the result of this estimation and public data with a longer time span
to simulate the evolution of the share of type-I firms in the economy. This will allow us toconfirm an additional prediction of the model.
© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Wage and Productivity Differentials in Japan

5.2 Detecting the existence of efficiency wage schemes
We start by checking whether there is a negative correlation between wage premia andemployment flows in the data, as predicted by the model when firms adopt efficiency wage
schemes.
First, according to a conventional procedure in the usage of the BSWS, we define an hourly‘base’ wage wijt as the wage, excluding various allowances, paid per scheduled hour worked byworker i, in establishment j at time t. The base wage thus excludes bonus payments andovertime work, which avoids a potential bias coming from unobservable temporary labordemand shocks. If the wage were computed using the total wage bill instead, such shocks
would affect both the estimated wage premium and the labor flows.15
Second, we limit the sample to regular male workers in private firms with more than 30
employees, and exclude the construction industry, to keep the comparability to public data.16
Third, we regress year by year the log of the base wage on individual characteristics ofhuman capital (such as educational level, age, tenure), and prefecture dummies (Xijt), in
addition to an establishment fixed effect (ujt). Following the standard Mincerian equation, thespecification is:

wijt = α t + X ijt βt + u jt + eijt(t = 2005, . . . , 2009 ).

[15]

Here eijt is the normally distributed error term given Xijt and ujt. If the human capital market
is perfect, the establishment fixed effects ujt of [15] can be interpreted as the unobserved wage
premium that a worker can enjoy just because he/she belongs to a specific establishment.
The next step is to look at the relationship between the predicted wage premium u!jt andemployment flows. Table 2 shows the negative statistical relationship between the predicted
establishment effects and several kinds of flow ratios at the establishment level, controlling forindustries, firm size, and overtime ratio. This supports the predictions of [9] for the wholesample of firms.
Table 2. OLS estimates of the effect of flow structure on establishment fixed effects (1)sample: 2005–9 BSWS and ETS matched sample(2a)
Dependent variables
Inflow ratio
Outflow ratio

(2b)

(2c)

Predicted establishment fixed effect-0.052(0.013)***Gross flow ratio-0.074(0.017)***-0.038(0.008)***

Excess flow ratio
Observations
R-squared

(2d)

0.27

0.27

9,007

0.27

-0.025(0.010)***0.27

Notes: Standard errors in parentheses, and *** p < 0.01, ** p < 0.05, * p < 0.1. Other explanatory variables include
the average overtime ratio, four firm size dummies, nine industry dummies, and a constant. The inflow(outflow) ratio is the number of employees hired (fired) during a year divided by total employment at the
beginning of the year. The gross flow ratio is the sum of the inflow and the outflow ratios. The excess flow ratiois the difference between the gross flow ratio and the absolute value of the employment growth rate.
© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

526

Yannick Kalantzis — Ryo Kambayashi — Sébastien LechevalierAlthough these results confirm the existence of an efficiency wage mechanism on average,
the negative correlation may not be universal. This leads us to further investigate this issue by
dividing the sample into two categories, type-I and type-II firms.
5.3 Identifying two types of firms: a switching regression approach
To divide the sample of firms into two tiers, a possibility would be to set up an a priori
criterion such as firm size or industry. However, adopting such an a priori classification mightlead to misclassify some firms. More importantly, it rules out by assumption the possibility ofwithin-group heterogeneity, e.g. the possibility that two firms with similar size or belonging to
the same sector may choose different wage schemes and organizational structure. Allowing forwithin-group heterogeneity is important if one considers that previous studies, which have
tried to link increasing productivity differentials to strategic choices of firms (such as investment in R&D or export behavior) have shown its importance (Ito and Lechevalier, 2010).
This is why we adopt the unknown regime switching regression à la Dickens and Lang(1985),17 With this methodology, sample separation is a priori unknown and the choice
between two sectors becomes endogenous (Sousa-Poza, 2004). The system of estimation is,suppressing time dimension, as follows:u!
j ,I = θ I + γ I s j ,I + Z j ,Iδ I + ε j ,I"
u j ,II = θ II + γ II s j ,II + Z j ,IIδ II + ε j ,IIµ j = θ3 + γ 3 K j + Z jδ 3 + ε j 3[16]

and . . . .

 u!
j ,I
uj = 
"u j ,IIif µ j ≥ 0
if µ j < 0

,

where
u!
j ,k is the predicted fixed effect of establishment j of type k (I: type I, II: type II);sj is the separation rate of establishment j;
Zj are control variables;
mj is a latent variable that splits the sample into two types of firms;
Kj provides the key to identify the two types of firms.
Because u!
j ,k is the predicted fixed effect of establishments and can be interpreted as a wage
premium, industry and firm size should matter. Therefore, we include nine industry dummies,
four firm size dummies, and year dummies as controls. As previously discussed, u!
j ,k may also
be affected by unobserved temporary demand shocks, causing omitted variable bias. Thispotential bias is already limited by the fact that we estimate the wage premium using the basewage, not the total wage bill (see above). In addition, we introduce the average overtime ratio
within establishments to directly control for temporary demand shocks.
The main issue, before estimating the system of equations [16], is to define the key to identify
the two sectors, Kj. We propose to use the difference between gross flows of male and female
employees. The reason for this identification strategy is the following. First, a distinctive
feature of the Japanese labor market is that female workers are usually not part of the regular
workforce but are used by firms as a buffer (Abraham and Houseman, 1989; Houseman and
Abraham, 1993). Accordingly, we identify female employees as a benchmark for the most© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

Wage and Productivity Differentials in Japan

527

flexible type of labor input. Then, if the gross flow of male employees is large compared withthat of female employees belonging to the same firm, male workers are considered as a flexible
labor input as well and the firm is classified as a type-II firm with a competitive workorganization. If, on the contrary, the gross flow of male employees is low compared with thatof female employees, male workers are considered as enjoying job security and the firm isclassified as a type-I firm.
This identification strategy relies on the specificity of the Japanese labor market withregards to the dichotomy between male and female employees. Discrimination against female
workers in Japan has been documented by the existing literature (Tachibanaki, 2005;
Wakisaka, 1997). The gender gap in the labor market in Japan is one of the highest among
OECD countries (OECD, 2010, table 1.1, p. 37). The fact that female workers are used as abuffer and thus displays larger gross flows is confirmed in our data set: the gross flow rate ishigher on average and more volatile for female than for male regular workers (Appendix D).
Another option for the identification strategy may be to use the difference between grossflows of part-timers and full-timers within the same establishment. In this case, we would
assume that an establishment that uses part-timers more flexibly than its full-timers is more
likely to treat their full-timers as quasi-fixed input and could be considered as a type-I firm.
However, using part-timer flows drastically limits the sample size and creates biases in theestimated results, because the use of part-timer is heavily concentrated on some establishments(small establishment and/or belonging to the service industry). The share of establishmentsthat do not use female regular worker is at most 12 per cent, whereas the share without any
part-timers is 44 per cent. This makes it easier to econometrically identify the two types offirms that we theoretically distinguished using the dichotomy between male and female
workers than the dichotomy between part-time and full-time workers. In addition, the category of full-time workers does not necessarily coincide with that of regular workers, as
full-time workers can also be used as a buffer to protect the core workforce. Indeed, manyfemale workers who work fulltime are treated as non-regular workers (so-called ‘quasi-parttimers’; OECD, 1998). That being said, we will use this alternative identification strategy as arobustness check (Section 5.5.1).
After having chosen our identification strategy, we run the estimation based on equation[16]. The estimated results are shown in Table 3. Column (2a) is the same as in Table 2, in
which we can find a weakly negative correlation between separation rates and the predicted
establishment fixed effect on average. When we divide the sample into two parts, following theswitching equation reported in column (3c), this negative relation becomes stronger and gains
statistical significance in type-I firms (3b), whereas it becomes rather small and statisticallyinsignificant in type-II firms (3a). These results imply that we cannot reject the hypothesis that
type-I firms resort to efficiency wages, whereas type-II firms do not.
The estimation is also consistent with the identification strategy: the difference between
male and female gross flows affects the establishments’ probability to belong to the type-I
firms negatively as well as significantly in the switching equation (column (3c) in Table 3), as
expected. The estimation is also consistent with the existing literature on wage premium andfirm size: male workers enjoy larger wage premium in larger firms regardless of their organizational structure, type I or type II (columns (3a) and (3b)). Interestingly, the estimatedswitching equation (column (3c)) implies that smaller firms are less likely to belong to type-I
firms (compared with the largest firms), and that firms in the service industry are more likely
to be of type I (compared with those in the manufacturing industry).
As the switching equation (column (3c) in Table 3) is a type of Probit model, we cannot
distinguish the magnitude of the marginal effect of variables directly. Instead, Table 4© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier

Table 3. Estimated results of switching regression: effect of flow structure on predicted
establishment fixed effect 2005 BSWS and ETS matched sample

Dependent variables
Out flow ratio
Gross flow ratio difference
between male and female
Overtime ratio
Firm size dummies(Over 1000)(300–999)(100–299)(30–99)Industry dummies(Mining)(Manufacturing)(Electric and utilities)(Transportation andcommunication)(Retail, wholesales,
and restaurants)(Finance andinsurance)(Real estates)(Service)

Constant
Observations
R-squared

(2a)

(3a)

(3b)

(3c)

Full sample
OLS

Type-II
firms

Type-I
firms

SwitchPredicted establishment fixed effect(latent)-0.074(0.017)***-0.016(0.012)-0.584(0.046)***-0.226-1.204(0.036)***(0.117)***Base-0.134-0.2(0.005)***(0.015)***-0.275-0.26(0.005)***(0.019)***-0.342-0.329(0.007)***(0.021)***
0.139
0.363
(0.014)***(0.050)***
Base
0.204
0.117
(0.007)***(0.036)***
0.245
0.07
(0.007)***(0.020)***-0.0660.053(0.006)***(0.022)**
0.156
0.216
(0.008)***(0.025)***
0.255
0.009
(0.012)*** -0.03
0.064
0.124
(0.005)***(0.017)***
0.12
0.275
(0.006)***(0.021)***
9,007
0.47
0.13

-0.161(0.007)***-0.278(0.007)***-0.346(0.009)***0.161(0.023)***0.203(0.013)***0.122(0.009)***-0.03(0.010)***0.179(0.012)***0.095(0.016)***0.109(0.007)***0.171(0.008)***0.27-0.156(0.043)***-0.048(0.006)***0.091(0.007)***-0.839(0.008)***-0.79(0.010)***0.53(0.025)***-1.945(0.015)***2.062(0.010)***0.96(0.010)***1.127(0.014)***2.377(0.018)***2.139(0.008)***-1.131(0.007)***0.94

Notes: Standard errors in parentheses, and *** p < 0.01, ** p < 0.05, * p < 0.1. Estimation includes year dummies.
Gross flow ratio means inflow ratio plus outflow ratio.

decomposes the sample according to the ex post estimated probability to be type I, andconfirms the difference of firm characteristics between more-likely-to-be-type-I firms andless-likely-to-be-type-I firms. As expected, the average wage premium is much larger in the
more-likely-to-be-type-I firms. The difference between gross flows of male and female workers
is also smaller in the more-likely-to-be-type-I firms. It is also apparent that there are less small
firms among the more-likely-to-be-type-I firms. For example, among the firms whose probability to belong to type I is more than median, the smaller firms (under 299 employees)
represent 22.7 per cent against 26.3 per cent in less likely firms. The table also suggests that therelation between firm size and type of firm is non-monotonous, implying that an a priori© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Wage and Productivity Differentials in Japan

Table 4. Average attributes between more-likely-to-be-type-II firms andmore-likely-to-be-type-I firms
Less than median ofProb. of type-I

More than median ofProb. of type-I

Variable

Mean

Std. Dev.

Mean

Std. Dev.

Predicted establishment fixed effect
Outflow ratio
Gross flow ratio difference between maleand female
Overtime ratio
Firm size dummies(Over 1000)(300–999)(100–299)(30–99)Industry dummies(Mining)(Manufacturing)(Electric and utilities)(Transportation andcommunication)(Retail, wholesales, andrestaurants)(Finance and insurance)(Real estates)(Service)Year dummies
2005
2006
2007
2008
2009
Observation

0.030
0.116
-0.027

0.232
0.104
0.243

0.088
0.158
-0.081

0.307
0.185
0.654

0.090
0.610
0.130
0.163
0.100
0.020
0.876
0.076
0.000

0.056

0.070
0.511
0.262
0.147
0.080
0.006
0.140
0.000
0.172

0.058

0.027

0.142

0.002
0.000
0.000
0.154
0.272
0.204
0.185
0.186

0.092
0.052
0.397
0.285
0.145
0.202
0.150
0.217

4,504

4,503

classification of type of firm would not have been appropriate. As for the industries, manufacturing industries have a low probability of being type I whereas high probability firms arecharacterized by a high share of service industry.
Overall, the estimation of the system of equations supports the existence of efficiency wages
in type-I firms and competitive wages in type-II firms. This result shows the usefulness of theunknown switching regression methodology à la Dickens and Lang.

5.4 Implication for the evolution of the share of type-I firms
A key mechanism of the theoretical model is the reallocation of firms from a type-I to a
type-II organization when the economy is hit by a negative aggregate productivity shock (seeProposition 1). This section uses the result of the estimation to simulate the evolution of theshare of type-I firms.18 To do this, we feed the switching regression estimate (column (3c) in
Table 3) with public data from 1981 to 2005 to compute the probability to be a type-I firm.
The probability of being a type-I firm is calculated as:()

F θ#3 + γ#3 K j + Z j δ#3 ,[17]© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

530

Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier

where F is the cumulative distribution function of the standard normal distribution, Kj is thedifference between gross flows of male and female workers, and Zj are dummies for industry
and firm size. In the sample used for the estimation, the mean (median) over the 9,007
establishments of this probability is 31 per cent (13 per cent). By using the number of maleregular workers as weights, we can compute the share of type-I firms in the economy,
measured by the number of male regular workers. In our sample, it is equal to 19 per cent.
To determine the evolution of the share of the type-I firms over time, we feed [17] withpublished ETS data that provide semi-aggregated worker flows by gender, firm size, andindustry since 1981. If we assume that the switching equation has been stable over time, we candeduce the probability for the average firm to belong to type I in a certain industry, for acertain size class and in a certain year. As the model predicts a shrinking share of type-I firms
within a homogenous sector and for a given size (normalized to 1), the simulation has toabstract from changes in the composition of sector and size. To do this, we fix the compositionof industry and firm-size as it was in 1981. More precisely, denote St1981 the average share of
type-I firms at year t, based on the number of male regular workers of industry-firm size k in
the year 1981, which we denote Mk1981. Kkt is the difference between aggregate gross worker
flow of male and female, and Zkt are dummies for industry and firm size. St1981 is defined asfollows:

St1981 =∑ F (θ# + γ#K + Z δ# ) M
∑M
k

3

3

kt

k

kt

3

k1981

.

[18]

k1981

We refer to this first simulation as the within-group transition and report our result in
Figure 1a. To be complete, we also report in Figure 1b the total share of type-I firms whenallowing the composition of industry and size to evolve over time (total transition).19 In thissecond simulation, the evolution of the total share is affected by the distributional shift ofworkers from sector to sector and from size to size. As, according to our results, firms in theservice sector are more likely to adopt the efficiency wage scheme, the increasing share of theservice industry over time is likely to lead to a larger total share of type-I firms.
Figure 1a shows the evolution of the simulated share of type-I firms between 1981 and 2005
based on a fixed sectoral and size composition (St1981).20 The simulated share of type-I firms has
decreased within group at the beginning of the Lost Decade, which confirms the prediction of
the model.
Figure 1b shows the evolution of the simulated total share of type-I firm when allowing forchanges in the industry-size composition. The total share in the simulation is about 25 per centin 2005, which is slightly higher than the share directly computed with the micro data, 19 per
cent. This difference, which could be due to aggregation errors, should make us cautious wheninterpreting the simulated probability based on aggregate data. The figure shows that changes
in the composition of industry and firm-size led to an overall increase in the share of type-I
firms within whole economy. Although the first simulation provides an explanation for the riseof within-group wage differentials, this second simulation could explain widening betweengroup wage differentials.
5.5 Robustness of switching regression
The existence of efficiency wages is not contradicted by our estimations so far. However, itis necessary to conduct some robustness checks to make this conclusion stronger. We first© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Wage and Productivity Differentials in Japan

Figure 1. Predicted share of type-I firms worker: (a) within group transition; (b) totaltransition

discuss the robustness of the switching equation, especially with regard to the identification
strategy; then we discuss the robustness of the wage premium equation.
5.5.1 Gross flow difference between regular workers and part-timers. An alternative choice forthe variable Kj in equation [16] is to use the gross flow difference between regular workers andpart-timers. As we already explained, the drawback of this approach is the drastic decrease of
the sample size. Table 5 shows the distribution of part-timer ratio.
The sample mean of part-timer ratio per establishment is about 23 per cent. However 44
per cent of establishments do not use part-timers at all. Some service industries such as
hotels and restaurants uses part-timers much more than manufacturing industries do. Moreover, part-timers are likely to be concentrated in the very large and very small firms.21
Therefore, if we use the difference between gross flows of full-timers and part-timers, welose much of the sample size and the remaining sample will be biased towards some particular sectors.
Regardless of these drawbacks, Table 6 displays the estimated results of equation [16],when using the difference between gross flows of full-timers and part-timers. As we
expected, with one third less of sample size, the resulting estimation does not distinguish aswell as before type-I from type-II firms. However, results from of pooled ordinary least© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier

Table 5. Distribution of part-timer ratio (BSWS 2005–9)

Total
Industry
Manufacturing
Hotels and restaurants
Firm size
Over 5000
100–299
5–9

Obs.

Mean

Std.
Dev.

Min.

MedianMax.

Fractionof zero266,563

0.232

0.315

0

0.059

1

0.439

50,98033,411

0.115
0.623

0.207
0.328

0
0

0
0.737

1
1

0.541
0.099

29,72538,94538,765

0.326
0.180
0.249

0.390
0.268
0.326

0
0
0

0.075
0.049
0.111

1
1
1

0.411
0.454
0.488

Table 6. Summary of estimated results of switching regression: effect of gross flow
difference between regular workers and part-timers: 2005–9 BSWS and ETS
matched sample

Dependent variables
Outflow ratio
Gross flow ratio difference between
regular and part-timer
Constant
Observations
R-squared

(6d)

(6a)

(6b)

(6c)

Pooled OLS

Type-II firms

Type-I firms

SwitchPredicted establishment fixed effect-0.096(0.020)***-0.030(0.013)**0.152(0.010)***

0.109
0.202
(0.007)***(0.025)***
5,898
0.46
0.16

0.27

-0.202(0.067)***(latent)-0.228(0.002)***-1.059(0.008)***0.97

Notes: Standard errors in parentheses, and *** p < 0.01, ** p < 0.05, * p < 0.1. Estimation includes industry, firmsize, year dummies, and overtime ratio. Gross flow ratio means inflow ratio plus outflow ratio.

squares (OLS) (6d) still show the slightly negative relation between the inflow ratio and thewage premium as a whole. The switching equation (6c), which uses the gross flow difference
between regular workers and part-timers, yields a similar result as in Table 3. Contrary to
the results we showed in Table 3, the outflow ratio in type-II firms now also negatively
affects the wage premium in (6a). Nonetheless, the magnitude of the effect is much smaller
than for type-I firms. Overall, the change of the identification key does not substantiallyalter our results.
5.5.2 Wage premium equation: on-the-job search and composition effect. The estimation of
the wage premium equation may have been affected by two other mechanisms. First of all,as recalled in Section 3, a mechanism of on-the-job search may also explain the observed
negative relation between wage premium and gross flows at the micro level. Because incumbents can look for a job elsewhere, employers have an incentive to retain them with higher
wages, which results in both a higher wage premium and a lower labor flow. To control for© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

533

Wage and Productivity Differentials in Japan

Table 7. Robustness check of switching regression: 2005–9 BSWS and ETS matchedsample
(7a)

(7b)

(7c)

(7d)

Type-II firms

Type-I firms

Type-II firms

Type-I firms

Dependent variables
Outflow ratio
Difference of average age
Ratio of voluntary quit
Constant
Observations
R-squared

Predicted establishment fixed effect-0.018(0.012)-0.001(0.001)-0.150(0.042)***0.009(0.002)***0.123(0.006)***0.241(0.024)***

0.46

0.16

9,007

-0.010(0.013)-0.105(0.031)***-0.040(0.005)***0.132(0.006)***-0.071(0.011)***
0.239(0.014)***

0.47

0.17

Notes: Standard errors in parentheses, and *** p < 0.01, ** p < 0.05, * p < 0.1. Other explanatory variable includes
overtime ratio, four firm size dummies, nine industry dummies, and year dummies. The probability weight tobelong to each sector is re-estimated as in (3c).

this potential bias, we use the outflow ratio of voluntary quits as an additional control
variable.
Second, in the actual workplace, fluctuations in gross flows may introduce a change in thecomposition of the company’s workforce; for example, when hiring decreases, the average age
and tenure of workers may increase as a result of reducing younger new-comers. In theestimation, we assume that the observable attributes of human capital solely determine thehourly base wage, and any change in the workforce composition does not affect the residualof wage equations. However, if the company uses deferred payment schemes, in which
workers receive wage premium in the later period of career, any temporal decrease of younger
workers’ share in hiring will cross-sectionally produce a higher average wage premium, eventhough this change in the composition of the workforce will not alter the aggregated wage
premium through lifetime. As our data set is built on cross-sectional survey, the unobservabledeviation of each establishment from the cross-sectional mean may come from a temporal
imbalance of workers’ composition. To account for this, we use the difference between the
average age of stock and inflows.
Results are shown from columns (7a) to (7d) of Table 7. It appears that extra variables,
which control for on-the-job search incentives and potential composition change, do not alter
the main part of the results. The coefficients of gross inflow remain negative in type-I firms andclose to zero in type-II firms. In (7b), the coefficient of the composition effect is positive, whichmeans that when incumbents are older than newly hired employees, the average wage
premium grows. Interestingly, the estimated coefficient is only statistically significant in type-I
firms, showing that this effect matters only for a specific type of firms. This finding is consistent
with the long-term employment practices in Japan (Ishikawa and Dejima, 1994). The ratio ofvoluntary quits negatively affects the wage premium, which is consistent with on-the-job
search models. After controlling for on-the-job search incentives, gross inflow still affects thewage premium negatively and significantly in type-I firms. Overall, these results show therobustness of our estimation.
© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Yannick Kalantzis — Ryo Kambayashi — Sébastien Lechevalier

6. Conclusion
In this paper, we have proposed a framework aiming at connecting two major stylized
facts that characterized the Japanese economy during the Lost Decade (1992–2004): rising
wage inequalities and increasing productivity differentials. After having documented thesestylized facts and having reviewed possible theoretical explanations, we have proposed a
simple efficiency wage model with one sector but two types of firms of similar size: in one
type of firms, which provide job security and adopt an efficiency wage scheme with endogenous effort, the productivity is assumed to depend on the effort provided by workers,
whereas in the other type of firms, characterized by a competitive labor market, productivity
is exogenous. The prediction of the model is that a negative aggregate productivity shock
produces increasing productivity and wage differentials, as well as a falling share of type-I
firms.
The core of our paper is then an empirical investigation of the Japanese labor market withmicro data. For the first time, we merge two databases, the BSWS and the ETS for the years
2005–9. This matched worker-firm cross-sectional data set allows us to get information on(hourly) wages, hiring, and separation rates and provides many control variables for firms(size, sector) and for workers (age, gender, education). The existence of efficiency wages
mechanisms on average, detected by a negative correlation between the firm-specific wage
premium and workers flows, is not contradicted by our data. Moreover, in dividing oursample of establishments into two groups by using the unknown regime switching regression
à la Dickens and Lang, we find that one group of establishments can be characterized by
efficiency wages, whereas the other group cannot. Further robustness checks and a simulation
confirm that efficiency wages and the heterogeneity of firms with regards to their workorganization is a plausible explanation for the joint rise in productivity and wage differentials
in Japan in recent years.
Important implications can be drawn from this paper. First of all, we confirm that rising
wage inequalities in Japan can be related to increasing productivity dispersion among firms.
Second, we show that developments related to the labor market can generate rising wage
inequalities, without resorting to hypotheses regarding skill-biased technical change or
globalization.
At the same time, several limitations of the paper open future avenues for research. First,
data availability limited our study to cross-sectional data and prevented us to directly observe
the evolution of between-firm wage and productivity differential. Second, although the paper
focuses on between-firm wage differentials, a future extension should decompose precisely theoverall wage differentials into within-firms and between-firms components. Last, the aim of
the simple model we introduced in this paper was to illustrate how an efficiency wage mechanism may generate productivity and wage differentials in the case of an exogenous aggregate
shock. A natural next step would be to conduct a quantitative analysis in an extended andmore realistic model. This is left for future studies.
Appendix A: Derivation of some results of the model
Wages in type-I and type-II firms
Here, we display dynamic equations for the utilities of shirking (V1S) and non-shirking
workers (V1NS ) employed in type-I firms, along with the utilities of the unemployed (V U) andworkers employed in type-II firms (V2):© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

535

Wage and Productivity Differentials in Japan

rV1NS = w1 (1 − t ) − e + s(V U − V1NS )rV1S = w1 (1 − t ) + ( s + q )(V U − V1S )rV U = wu + a[max(V1NS, V1S ) − V U ]rV2 = w2 (1 − t ).
As it is optimal for firms that workers never shirk (otherwise, production would be zero), theychoose a wage such that V1NS = V1S. From this no-shirking condition, we obtain the standard
incentive-compatible real wage schedule (the efficiency wage) applying to workers in type-I
firms:w1 =e( a + s + r + q ) wu+.
1− tq(1 − t )Because jobs in type-II firms are perfectly competitive, workers are indifferent between
working in a type-II firm or being unemployed. Therefore, V u = V2, which gives:

w2 =

ae
w
+ u .
q(1 − t ) 1 − t

Proof of proposition 1
The free entry conditions [13] and [14], together with the expression of the wage differential[11] give the following equation:

m1 ( e ) − m2 =e( s + r + q ),q(1 − t )[A1]

where e, which is a function of a, follows from [8]. Equation [A1] determines the equilibrium
value of a. To solve it, it is useful to introduce the following reduced variables: ηµ 
A$ = 
 1 − η 
a$ =

−

1−η
η

(1 − t )q
A
s+r+q

a
.
s+r+q

Then, substituting [8] into [A1] and using the definition of the reduced variables gives:
η

1

−
−
 1

η
(1 + a$ ) 1−η −(1 + a$ ) 1−η 
A$ = 
η
η
1
1
−
−



−

1−η
η

.

© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

536

Yannick Kalantzis — Ryo Kambayashi — Sébastien LechevalierThis equation implicitly defines a function f such that a$ = f ( A$ ) − 1. It is easy to show that f
is strictly increasing and maps (1, +•) into (1, +•). When A$ > 1, the hiring rate a$ is strictlyf ( A$ )is also strictly
positive and so is employment in type-I firms, L1. In addition,A$
increasing.22
Then, from [8], the effort is given by:11− η  η  f ( A$ )  1−ηe=
µ  $  , 1− η   A 

which is a decreasing function of A$ (and hence of A). The wage rates w1 and w2 follow
from [13] and [14]. From [11], the wage differential w1 - w2, which is also equal to the
productivity differential m1 - m2, is strictly increasing in e and therefore strictly decreasing
in A.
The unemployment allowance wu follows from [9] and can be shown to be equal to(1 - t)(1 - h)w1. Finally, L1, L2, and U follow from [2], [3], and [4]. In particular, we have:−ηL2
1  f ( A$ )  1−η  1 − ts1(1 − η )
=
−1 .



$
$
L1 1 − η  A 
s + r + q f ( A) − 1 
 t.

The second factor on the right-hand side is strictly decreasing and goes to +• when A$ goesto 1 and to -1 when A$ goes to +•. Therefore, it has a unique zero A on (1, +•). When A$ < A,
L2 is strictly positive. The ratio L2/L1 is a strictly decreasing function of A$ on 1, A , so that L1/L2
is strictly increasing in A$ and A.

Appendix B: The data set-matching BSWS individual survey and ETS establishment survey
The key issue in the construction of our matched employee–employer data set is the size ofthe sample after matching.
BSWS individual survey is a sample survey of individual workers conducted by MHLW(Ministry of Labor, Health, and Welfare), once a year, at the end of June. It covers private
establishments over five employees and public establishments over 10 employees. All industries other than agriculture are surveyed. Workers are re-sampled within an establishment.
The sample size is about 78,000 establishments and 1.6 million workers per year. The mostimportant feature for us is rich data on wages.
ETS is an establishment survey conducted by MHLW, twice a year, at the end of June andDecember. It covers public and private establishments with more than five employees in allindustries, except agriculture. Individual recently separated and newly hired workers (within
the sampling period) are re-sampled within an establishment. The sample size is about 10,000
establishments, with 80,000 hirings and 90,000 separations per year. The survey gives detailed
information on new entrants and separations.
We match these two surveys, year by year, at establishment level by using a key provided by
the Ministry. Although the size of the matched sample is 12,100, we found some possible
inconsistencies in the data. The data point of BSWS is the end of June, and that of the second© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

537

Wage and Productivity Differentials in Japanwave of ETS is the beginning of July (day following the data point of BSWS). We proceed toa sample restriction as follows:• 9 establishments are excluded due to the negative employment stock at the beginning of July;• 1,209 establishments are excluded due to the inconsistency of industry classification between
BSWS and the second ETS;
• 1,747 establishments are excluded due to the inconsistency of firm size and establishment sizeclassifications between BSWS and the second ETS;
• 126 establishments are excluded due to the inconsistency of employment data between thefirst and the second ETS.
• 2 establishments are excluded due to perfectly predict success or failure in the dependent
variable along with their associated observations. Dropping perfectly predicted observations
has no effect on the likelihood or estimates of the remaining and increases the numericalstability of the optimization process.
As a result, the final size of the matched sample is 9,007 establishments. For the BSWS,
the matching rate is only 3.4 per cent but from the point of view of ETS, it is 15.9 per cent,which is quite acceptable. Finally, please note that this restriction is very conservative inthat there is a possibility for an establishment to move to another classification at the
beginning of July.

Appendix C: Summary statistics for the data set
Full sample used in the regression
Variable

Mean

Std. Dev.

Min.

Max.

Predicted establishment fixed effect
Outflow ratio
Gross flow ratio difference between male and female
Overtime ratio
Firm size dummies(Over 1000)(300–999)(100–299)(30–99)Industry dummies(Mining)(Manufacturing)(Electric and utilities)(Transportation andcommunication)(Retail, wholesales, andrestaurants)(Finance and insurance)(Real estates)(Service)Year dummies
2005
2006
2007
2008
2009
Observation

0.060
0.137
-0.054
0.080
0.558
0.196
0.155
0.090
0.013
0.508
0.038
0.086

0.274
0.153
0.502
0.058

-1.2500.000-16.000
0.000
0
0
0
0
0
0
0
0

1.641
6.000
9.414
0.446
1
1
1
1
1
1
1
1

0.084

0

1

0.047
0.026
0.198
0.219
0.219
0.204
0.168
0.202
9,007

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

538

Yannick Kalantzis — Ryo Kambayashi — Sébastien LechevalierAppendix D: Summary statistics for gross flow rate by gender, 2005–9 BSWS and ETS
matched sample

Male
FemaleObs.

MeanStd. Dev.

Min.

Median

Max.

9007
9007

0.278
0.332

0.312
0.570

0
0

0.212
0.237

12
16

Notes
1
Thus, the difference between the two types of firms corresponds to different organizational models,
as in Oï (1983), rather than to different monitoring technologies, as in Bulow and Summers (1986).
2
For the sake of simplicity, in this paper, we indifferently use the terms ‘firms’ and ‘establishments’,
although we are aware of the differences between these two units of analysis.
3
There have been debates on the origin of the productivity slowdown — demand (Yoshikawa, 2008)
versus supply side (Hayashi and Prescott, 2002) — and its extent — manufacturing (Fukao et al., 2004)
versus non-manufacturing (Yoshikawa, 2008).
4
As in Caselli (1999). See the next section for other references.
5
First, not all firms have tried to introduce such systems. Second, among those who have tried, many
have abandoned them after a while because of negative externalities. For example, in the case of Fujitsu,
the introduction of individual performance-based scheme has been detrimental to the overall performance of the company due to the fact that individual performance is all the more difficult to observe ina working environment characterized by the pre-eminence of team work. The current attempts atreforming the wage system focus less on setting individual performance-based wage schemes than on
modifying deferred compensation schemes to allow employees to get short-term reward for their engagement in the firm, in a context of rising risks and uncertainty (Fujimura, 2003).
6
According to 2007 Employment Status Survey, female workers represented 74.3 per cent of nonregular workers aged 15–59 and 30.3 per cent of regular workers.
7
The focus on wage differential between firms of different size is understandable in a country that has
been (and still is to a certain extent) characterized by a dual structure along the firm size.
8
The increased number of hours has sometimes been proposed as an alternative proxy for work effort.
This has been criticized by various authors, including Green (2004) or Askenazy (2004), who point out
that a reduction in working hours can actually be associated with the intensification of work effort. Thisis particularly relevant for Japan. The typical response of Japanese firms to negative shocks is indeed
labor hoarding and reduction of working hours (Abraham and Houseman, 1989), as has been observed
during the Lost Decade. Using the number of hours worked as a proxy for effort would lead to theconclusion that work effort has decreased whereas our indicators suggests the opposite.
9
Our main inspiration is Amable and Gatti (2004).
10
The model then abstracts from the size of firms. As explained in Section 2, the wage differential is
not explained by heterogeneity between firms of different sizes.
11
To write this equation, we have already taken into account the fact that workers will not shirk inequilibrium, hence the flow out of type-I firms is equal to sL1.
12
The empirical strategy to detect efficiency wages may drastically vary depending on the exact natureof the model. For example, Abe and Ohashi (2004) confirm the existence of efficiency wage model inJapan by analysing the steepness of wage profiles (the idea is that firms raise not only wage levels but also
the steepness of wage profiles to prevent workers from shirking); Fuess and Millea (2002) use the Geweke
linear feedback method to overcome a basic identification problem regarding the direction of causality
between productivity gains and wage hikes.
13
The rate of inflow a affects the efficiency wage through the value of unemployment, and hence
stands for the probability of finding a job in any type-I firm, whereas the rate of outflow s affects theefficiency wage through the probability that the current employment relationship ends, and hence stands
for the probability of being fired from by the current employer. See Appendix A.

© 2012 CEIS, Fondazione Giacomo Brodolini and Blackwell Publishing Ltd

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Wage and Productivity Differentials in Japan

14
From the assumption of perfect mobility between type-II firms and unemployment, transitions
between jobs in type-II firms and unemployment occur at a potentially infinite rate.
15
The Japanese legal regulation requires almost every employer to prepare ‘Workplace Rule’ (Shugyo
Kisoku), which specify ex ante the base wage and the number of hours worked in a written contract.
Because it is not easy to modify the written contracts, ex post adjustments to temporary shocks areusually made by using bonus payments and overtime work. The base wage and hours reported in thesurvey are based on the wage and hours of the Workplace Rule. Therefore, the hourly base wages in ourdata are not directly affected by temporary shocks.
16
Construction industry was not covered by ETS until 1990. In published tables, the disaggregatedemployment flows for different industries and -firm sizes are only available for firms with more than 30
employees.
17
A well-known limit of this classical methodology, which has been already applied to the Japaneselabor market by Ishikawa and Dejima (1994), is that it provides a test for dual labor markets and does
not recognize the possibility of three segments. For the question we address in this paper, it is not a
problem as we explicitly focus on the difference between two types of productive models.
18
Because of the lack of data, it is not possible to simulate the evolution of other variables such as
wage differentials.
∑ k F θ#3 + γ#3K kt + Zkt δ#3 M kt .
19
The total share St is defined as∑ k M kt()

20

Data for 2003 are missing.
This result is confirmed by the Diversification of Employment Forms survey, an establishment-based
administrative survey conducted by the MHLW in 1994 and 2007. In the 75 per cent of establishments
in which full-time regular employees are the major form of employment, they account for 92 per cent of
the total employment; in the 20 per cent of establishments where part-time employees constitute themajor form of employment, they account for almost two-thirds of the establishment employment. To put
it differently, part-time workers are concentrated on few establishments.
22
A formal proof is available from the authors upon request.
21

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