Discussion Paper No. 1045

WAGE INEQUALITY
DURING THE LONG-TERM STAGNATIONIN JAPAN: CHANGES WITHIN
AND BETWEEN ESTABLISHMENTS

Mika Akesaka
Koyo Miyoshi

November 2018

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Wage Inequality during the Long-term Stagnation in Japan: Changes within
and between Establishments

Mika Akesaka1 and Koyo Miyoshi2
1Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka 567-

0047, Japan; Email: mika.akesaka@gmail.com (corresponding author).
2

Department of Economics, Aichi Gakuin University, 3-1-1 Meijo, Kita-ku, Nagoya, Aichi 462-8739

1

Abstract
Wage inequality between individuals has changed little in Japan in recent times unlike inother developed countries. We examine changes in wage inequality within and between
establishments during the prolonged stagnation period in Japan. Using a micro-level
worker–establishment dataset from 1991 to 2012, we find changes in inequality during the
1990s. First, an increase in the variance in the establishment fixed effects expanded men’s
between-establishment inequality. Meanwhile, a reduction of returns to tenure decreased
men’s within-establishment inequality and thus suppressed rising individual wage inequality.
Second, we find a different trend in women’s wage inequality. Between-establishment
inequality was larger than that of men in the early 1990s. However, since the structure ofwage inequality in men changed significantly, the composition of inequality between men
and women has become similar in recent years. Finally, the assortativeness between workers
and establishment rose for both sexes.

JEL Codes: J21, J31, M52
Keywords: inequality trends, returns to tenure, within- and between-establishment inequality2

1

Introduction
The significant rise in wage inequality in developed countries is associated with an expansion of

wage inequality within skill groups (Katz and Murphy, 1992, Bound and Johnson, 1992). An inequality
expansion within skill groups has also been found for men’s wages in Japan (Kambayashi et al., 2008;
Yamada and Kawaguchi, 2015). However, during this expansion, overall wage inequality between
individuals has changed little unlike in other developed countries.
The Japanese labor market therefore presents an important test case for assessing changes in wage
determination and inequality under a long-term recession. Since the bubble economy collapsed in 1991,
the Japanese economy continued to stagnate throughout the 2000s (Japan’s so-called “lost decades”).
Decisions under such economic fluctuations differ among employers considerably (Fort et al., 2013).
Nevertheless, most previous studies have paid limited attention to workplace differences whenanalyzing wages during recessions.
This study aims to clarify changes in wage inequality in Japan taking into account differing workplace
effects. We examine workplace effects using a dataset with an employer/employee structure in which
an establishment is the unit of employment. By pooling micro-level data from the Basic Survey on
Wage Structure (BSWS) from 1991 to 2012, we can then investigate the changes in Japan’s lost decades
while controlling for the establishment fixed effects. The BSWS is an annual government survey thatcovers workers from a nationally representative sample of establishments in Japan. We analyze wages
using a linear regression model for each year and capture the change in the effects of workers’
heterogeneity and workplace fixed effects on wage inequality.
We make four notable findings. First, we find that wage inequality between establishments is
expanding in Japan. For men, between-establishment wage inequality expanded during the study period,
whereas within-establishment wage inequality shrank over a similar period. The increase in betweenestablishment inequality is largely due to the widening disparity in the establishment fixed effects as inother developed countries. This increase occurred more intensively in the 1990s than in the 2000s. The
rise in the assortativeness between workers and establishments has also affected the expansion of
inequality between establishments. This sorting occurs mainly at lower wages than at higher wages.
3Second, we reveal changes in the wage structure that bring about not only between-establishment
inequality but also within-establishment inequality. Most previous studies that have pointed out the
expansion of inequality between workplaces have paid insufficient attention to the corresponding
changes in the workplace. In Japan, during the country’s long-term economic depression, wage profiles
flattened owing to changes in the internal labor market (Hamaaki et al., 2012). In this study, we confirm
that similar changes are observed even when considering the establishment fixed effects. Moreover, weclarify that the decrease in within-establishment wage inequality is caused by the decline in theinfluence of seniority on wages.
Third, we find different changes among the percentiles of between- and within-establishment
inequality that cannot be observed with variance. Song et al. (2018) show that the expansion of betweenfirm inequality in the United States is caused by a large increase in average wages at higher percentiles.
However, when we examine the change in male between-establishment inequality in Japan, theexpansion was caused by a decrease (increase) in wages at low (high) percentiles.
Fourth, we shed light on the large difference in the change in inequality by sex. Previous studies suchas Card et al. (2013) and Song et al. (2018) have reported the expansion of between- establishment
wage inequality, suggesting few differences between sexes. However, in Japan, the establishment effecton wages was significantly different by sex in the early 1990s, and considerable differences in the trend
remained thereafter. In the early 1990s, the establishment effect on wages was larger for women thanfor men and between-establishment inequality for men (women) expanded (shrank) during our study
period. As a result, the contributions of establishment fixed effects on total wage inequality between
men and women have become approximately equal in recent years.
We contribute to the literature on wage inequality between and within establishments by analyzingchanges in employer-specific wage differentials in a country in which wage inequality has rarelychanged. The workplace plays an important role in wage determination and provides significant
employer-specific wage differentials (Abowd et al., 1999, 2002; Gruetter and Lalive, 2009). The recent
literature, including Card et al. (2013), Barth et al. (2016), and Song et al. (2018), has found that the
expansion of these differentials and sorting of workers to workplaces have widened wage inequality.
4However, these studies have focused on countries in which wage inequality is expanding such as theUnited States and Germany. Thus, there still is little evidence on whether these differentials havechanged in countries with stable wage inequality and on whether the workplace can help explain
structural changes in wage inequality.
When we capture the change in wage inequality, considering employer-specific wage differentials is
more important in Japan than in other countries, as Japanese employment practices serve as an effective
way to foster the skills necessary for each workplace (Carmichae, 1983; Koike, 1988), and thus workers
are influenced more strongly by their workplaces. The traditional Japanese employment based on alonger tenure and a sharper wage profile than that of the United States (Hashimoto and Raisian, 1985)
declined substantially during the lost decades (Ono, 2010; Hamaaki et al., 2012; Kawaguchi and Ueno,
2013; Kambayashi and Kato, 2017). Our findings show that this decline in the internal labor market isobservable even when we control for establishment fixed effects and focus on the wage system in theworkplace.
The remainder of this study is organized as follows. Section 2 summarizes the macroeconomic trends
and changes in the labor market in Japan. Section 3 describes the data. Section 4 presents ouridentification strategy. Section 5 reports our findings. Section 6 analyzes the driving factors of thesechanges. Section 7 concludes.

2

Macroeconomic Trends and Labor Market Changes in the Stagnation
PeriodThis section briefly describes the macroeconomic trends before and after the bubble economy inJapan and summarizes the main changes in its labor market since the early 1990s. Then, we empiricallyexplain the trends in the changes in workers’ attributes and wage inequalities in Japan in the 1990s and2000s.

2.1

Overview of the Macroeconomic Environment

Japan experienced a severe economic contraction in the 1990s and continued stagnation throughout
5the 2000s. This period is widely referred to as Japan’s lost decades. Before this depression, Japan
enjoyed a bubble economy from December 1986 to February 1991, where the economy grew rapidly
and the unemployment rate was low. Figure 1 plots the growth rate of GDP and the unemployment ratefrom 1980 to 2012. The 1980s were characterized by strong growth, with an average annual GDP
growth rate of 3.7%. After the bubble burst, growth slowed and the GDP growth rate was negative in
1993, 1998, 1999, and 2009. During this period, the labor market also suffered one of the worst periods
in recent Japanese history. The unemployment rate reached a historical high of 5.4% in 2002, more than
2.5 times the level in 1990.
In the 1990s, output per capita dropped to 0.5% on average, much lower than the average of theprevious decade (3.2%). The decline in total factor productivity was behind the weak growth in theJapanese economy during these years (e.g., see, Hayashi and Prescott, 2002). The wage level in Japan
was also sluggish. As the left-hand side of Figure A1 in Appendix A shows, while average wages inother developed countries such as the United States, the United Kingdom, and Germany rose more than
1.5 times from 1992 to 2010, the average wage in Japan changed little.
[Figure 1 about here]

2.2

Changes in Japan’s Labor Market

Certain institutional changes in the 1990s affected wage determinations in Japan. First, we explain
the change in Japanese employment practices. Japan has longer job tenures and larger returns to wages(Hashimoto and Rasian, 1985; Mincer and Higuchi, 1988; Clark and Ogawa, 1992). However, as the
economic environment changes, such as the low growth and aging population that has characterized
Japan since the collapse of the bubble economy, Japanese employment practices have altered.
According to Kambayashi and Kato (2011), as far as the change in male tenure is concerned, long-term
employment in Japan is not necessarily declining. On the contrary, Hamaaki et al. (2012) point out thatthe age/wage profile became flatter in the 1990s and 2000s, especially for employees in the middle andfinal phases of their careers.
Second, the Equal Employment Opportunity Law enforced in 1986 aimed to eliminate differences in6

recruitment, placement, training, welfare benefits, retirement, and dismissal for men and women. The
revision in 1997 subsequently prohibited sex discrimination with stiff penalties. Since the enforcementof the law in 1986, the employment rate of women aged 15–64 years in Japan has gradually risen from
57.1% to 67.7% in 2012. Further, although the gender wage gap in Japan remains the second greatest
among OECD countries after South Korea, the gap between male and female wages has steadily reduced(Hara, 2018).
Third, the rise in the minimum wage has also affected wage inequality in Japan. The statutory
minimum wage in Japan increased continuously throughout the 1990s and 2000s. Kambayashi et al.
(2013) show that this increase reduced lower-tail inequality.

2.3

Changes in Workforce Composition

The share of educated and older workers in the Japanese labor market has risen steadily since the1990s. Figure 2 shows the shares by the education, age, and tenure of full-time workers from 1991 to2012 based on data from the BSWS. Between 1991 and 2012 in Japan, the two-year and four-yearcollege graduate share rose among both men and women. The two-year college graduate share rosefrom 4.25% to 9.91% for men and from 17.20% to 28.41% for women. The four-year college graduate
share rose from 20.64% to 36.53% for men and from 4.65% to 22.71% for women. On the contrary, theshares of junior high school and high school graduates fell. The former dropped sharply from 21.44%
to 4.28% for men and from 21.02% to 3.09% for women. The latter dropped from 53.66% to 49.27%
for men and from 57.12% to 47.79% for women. Such high education in Japan has spread more rapidly
than in the United States (Katz and Revenga, 1989; Kawaguchi and Mori, 2016).
There are three noticeable changes in the share of workers’ age. First, the number of young people
entering the labor market declined due to the fall in the birthrate and extension of schooling. Second,
first-generation Baby Boomers after World War II began to retire and second-generation Baby Boomers
reached middle age. Third, the share of women in their thirties and forties in the workforce increased
above that for men, as more women continued to work after marriage and childbirth. Tenure among
women also lengthened, consistent with the findings of Kambayashi and Kato (2011), whereas the job
7tenure of men hardly changed. On the contrary, the share of workers with fewer than 10 years of tenure
declined significantly among full-time female workers. Long-term employment is also becoming
common for women, although the extent remains less than that for men.
[Figure 2 about here]

2.4

Trends in wage inequality in the 1990s and 2000s

Here, we define a skill group by education, experience, and tenure. Figure 3 illustrates the trends intotal, between-group, and within-group wage inequality.1 Although total wage inequality has hardly
changed for either men or women, there is a large difference in their structures. For men, between-groupinequality declined in the 1990s and then fluctuated after 2000, while within-group inequality stayednearly constant in the early 1990s and increased after the mid-1990s. Women showed lower betweengroup inequality and higher within-group inequality than men. This did not change in the 1990s and2000s.
Several studies have discussed the trends in Japan’s inequality. For example, Tachibanaki (2005)
reports that income inequality widened during the 1980s and 1990s. By contrast, Ohtake (2005) claims
that the increase in income inequality is partly due to the aging population because the degree of income
inequality is intrinsically high among elderly people and the aging population mechanically widens
income inequality. Kambayashi et al. (2008) find that the aging population and other changes in thelabor force cannot explain the increase in within-group wage inequality among male workers. They alsofind that the declining between-group wage inequality is largely due to the fall in the returns to educationand job tenure. Yamada and Kawaguchi (2015) point out that the decreased return to tenure is a key
factor preventing a rise in wage inequality. However, it remains unclear whether the declining betweengroup wage inequality for men observed in Kambayashi et al. (2008) and Yamada and Kawaguchi(2015) can be observed robustly when we control for the establishment fixed effects. It is also worth
verifying whether the increase in within-group inequality among male workers is linked to the1

On the right-hand side of Figure A1, while the wage inequality is at a similar level, that in other developed
countries expanded after the 1990s. Japan’s slight increase in 2005 is assumed to be due to the change in thesurvey structure.
8

expansion of inequality between workplaces.
[Figure 3 about here]

3Data
3.1 BSWS Data
This study uses repeated cross-sectional data from the BSWS from 1991 to 2012, which is collected

every year in June by the Ministry of Health, Labor and Welfare in Japan. The BSWS is conducted to
explain the wage structure of Japan’s main industries by surveying both establishments and theirworkers. It is the most appropriate and reliable data on the trend of wage inequality in Japan.
The BSWS adopts a two-stage random sampling procedure. In the first stage, establishments are
randomly chosen from most regions and industries except the agriculture, forestry, and fishery sector inJapan. The sample includes establishments with 10 or more employees in both the private and the public
sectors as well as private establishments with five to nine employees. In the second stage, workers are
selected from the payroll records of the selected establishments, following the uniform sampling
method. 2 Therefore, it is possible for us to identify to which establishment each worker belongs andcontrol for the workplace fixed effects in our estimation.
The survey consists of an establishment form and an employee form. The former provides
information about establishments’ location, industry, and number of regular workers (workers with nonterminated contracts or contracts of at least one month). The latter provides individuals’ sex, contract
type (non-terminated or terminated, full-time or part-time), educational background, age, tenure, andearnings. In addition, we can see their working hours, cash earnings for June including overtime
allowance and commuter allowance, and bonus in the last year.
The BSWS changed the category name to identify the working hours of workers on the survey formin 2005 (i.e., during our observation period). Until 2004, the BSWS classified workers into full-timeand part-time. Since 2005, the BSWS has called part-time workers short-time workers without changing
its definition. As a result, despite the absence of a definition change, the ratio of full-time to part-time2

Board members are not included in this sample, but there is neither top- nor bottom-coding instead.
9

(i.e., short-time) workers has significantly increased since 2005. 3 There might be inconsistency
between the 2004 and 2005 surveys because our sample excludes part-time and short-time workers.
Therefore, when comparing the periods before 2004 and after 2005, we need to pay attention to thispoint.

3.2. What is an Establishment?
Following Card et al. (2013) and Barth et al. (2016), this study focuses on an establishment as theunit of analysis. An establishment is defined as a place of business in which a firm conducts a type of
economic activity. It is an important unit because several taxes and local regulations affect a firm basedon its location. In the dataset, we are unable to identify to which firm each establishment belongs.
Although human resources are managed at the firm level, using an establishment as the unit ofanalysis is not thought to be a serious concern for the following reason. Since all establishments withina firm strictly adhere to the rules determined by their group, the heterogeneity between establishments
is similar to that between firms. From the results verified by Song et al. (2018) using US data, we canthus confirm that rising inequality among establishments is closely mirrored in rising inequality amongfirms. Indeed, they show that 84% of the increase in cross-establishment inequality can be accounted
for by firms.

3.3 Sample and variables
We focus on full-time male and female workers aged 15 to 59. We restrict employees to full-timeworkers because the BSWS does not provide the educational background for part-time workers. In
addition, we exclude those aged 60 and older because the mandatory retirement age is typically 60.
Further, we use the log hourly wage as the main dependent variable. Using the information on monthly
earnings and monthly working hours, the hourly wage is calculated as follows:3

In Japan, some people call those who do not work in permanent positions part-time workers even if they are
working full-time. Shinozaki (2008) points out that a some full-time workers were misclassified as part-timeworkers until 2004.
10

hourly wage = (monthly earnings×12+bonus) / ((scheduled working hours + overtime worked)×12).

As the bonus in Japan is decided systematically and usually considered to be part of one’s wage, weshould include it in our measure of hourly wages. This bonus does not necessarily reflect individual
performance and is regarded as a kind of profit sharing (Freeman and Weitzman, 1987) and/or a return
to job-specific tenure (Hashimoto, 1979).
To capture the trend of wage inequality, our dataset starts from 1992 even though data from 1991 areavailable. The bonus of the previous year (total amount paid from January to December in the previousyear) is reported to the BSWS. Because Japan experienced the bubble economy from December 1986
to February 1991, the bonus in 1991 may have been strongly affected. Hence, we start our dataset in1992 to avoid these bubble effects and use the wages in the other years converted into 1992 real values
based on the consumption price index.
We control for tenure, potential experience (age − educational years − 6), and four categories of
educational attainment (junior high school, high school, two-year college, and university or more) torepresent workers’ characteristics. We use dummy variables for firm size (5–29, 30–99, 100–299, 300–
999, 1,000–4,999, and 5000 or more workers), industrial categories (Group 1: Secondary Industry;
Group 2: Infrastructure, Transport, and Retail; Group 3: Finance and Real Estate and Group 4: Services),
office categories (independent office, head office, branch office), and the 47 prefectures as the variables
to represent establishments’ characteristics.4

3.4 Descriptive Statistics
Table 1 shows the number of employees and establishments in the sample. The yearly sample size
ranges between 534,454 and 830,297. There is no notable change in the number of establishments from1992 to 2012; however, there is a relatively large decline in the average number of female and maleworkers per establishment. Consequently, the number of workers during the study period decreases by

4

The Japan standard industrial classification changed three times during our analysis period. We thus constructconsistent industrial groups for all periods from the detailed industrial classifications (see Appendix B for moredetails).
11

about 200,000 for men and 80,000 for women. 5[Table 1 about here]

Table 2 presents the summary statistics for average hourly wages for an employee and an
establishment. Hourly wages are adjusted for inflation using the Consumer Price Index in 2010 to allow
a comparison among the years. Average wage levels rose from 1992 to 2002 and fell from 2002 and2012 both for individual workers and for establishments. The average wage between 1992 and 2012
stayed almost the same for men, whereas it rose for women.
[Table 2 about here]

Table 3 reports workers’ characteristics in 1992, 2002, and 2012. The changes in workers’
characteristics generally correspond with those in Figure 2. We can find a large increase in educational
levels for both men and women. Tenure for men hardly changed, whereas that for women lengthened
during the study period.
[Table 3 about here]

Table 4 shows the estimates of the Mincer-type wage equation (equation (3)) using 1992, 2002, and2012 surveys. Columns (1), (4), and (7) report the ordinary least squares (OLS) estimates without
controlling for the information on establishments. Columns (2), (5), and (8) control for theestablishment characteristics such as firm size and industrial group dummies. Columns (3), (6), and (9)show the estimates with the establishment fixed effects. Returns to education and tenure change largely
if we control for the establishment fixed effects. This finding implies a correlation between
education/tenure and the establishment fixed effects. An F-test with the null hypothesis of a constantestablishment effect is rejected for both men and women in all sample years.
[Table 4 about here]

5

The decrease in the number of workers at each establishment is assumed to be caused by a decline in theworkforce since the peak in 1997, an increase in workers aged 60 and over, and an increase in the ratio of parttime workers.
12

4

Econometric Framework
4.1

Simple Variance Decomposition

Our first approach is to decompose the variance in log hourly wages into the within- and between,

establishment components. Letin year t. We assume that,be the log hourly wages of worker

can be decomposed into two parts, namely the average establishment,) and the difference in the average establishment wage (wage (
,

≡

,

+

−

employed by establishment j

−

):

. (1)

Then, we can decompose the variance in individual wages as follows:

,

where

=

+

(

,

−

) , (2)is between-establishment inequality. Between-establishment wages are weighted toplace greater weight on establishments with more workers.

(

,

−

) is within-establishment

inequality. Using this method, we decompose wage inequality for each year and show the trends.

4.2

Simple Decomposition across Percentiles

Under our second approach, we decompose individual wages by percentile. While the variancedecomposition presented in Section 4.1 is a useful and easy method, it can mask trends in inequality
across the wage distribution. In this section, we compare the wage distribution in 1992 with that in 2012.
First, we categorize individuals in 1992 into percentile bins based on their wages. Second, we estimate
the average wages for each percentile bin. Let(,) indicate the average log wages at the x-

percentile bin in 1992. We apply the same procedure to the wage in 2012 and calculate(,). The same calculations are repeated for all the percentiles.
13

(

,

)−

The variance in individual wages comprises the average wage of the establishment (difference in establishments’ average wage (,−), as in equation (2).

and,) and the−are basedon the percentile of individual wages ( ). That is, at each x-percentile bin of individual wages, we
calculate the average establishment wage and its difference for each year. Then, we take their difference
between years for the same percentages; hence, the between-establishment change is() and the within-establishment change is

4.3

(

,

−

)−

(

,

−

(

)−

).

Detailed Variance Decomposition

We next investigate the sources of those changes in within- and between-establishment inequalitythat appeared in the simple decomposition. Specifically, we estimate a Mincer-type wage equation withthe establishment fixed effects and then decompose the variance in the predicted wages. Although Cardet al. (2013) and Song et al. (2016) use establishment–worker matched panel data and identify bothestablishment and employee fixed effects using Abowd et al.’s (1999) approach, we employ repeatedcross-sectional data and thus cannot apply their approach.
Under the assumption that the wage determination process is common across establishments, after
we control for the establishment fixed effects, we can write the Mincer-type wage equation for eachyear as in equation (3):

,

where

=

+ ! " , + # , , (3)denotes the establishment fixed effects, while " , denotes the workers’ characteristics

composed of dummy variables of educational attainment, tenure and its squared term, potential
experience and its squared term, and the interaction term of tenure and potential experience. # ,denotes an idiosyncratic shock. We define workers’ observable characteristic effects as " , !$(hereafter worker effects).
For OLS to identify the parameters of interest, we need the following conditions to hold:
14

#,

%

=0 ∀ , % ", #,= 0 ∀ . (4)The assumption that the residuals (# , ) are orthogonal to covariates (" , ) is standard. However, theassumption that residuals are orthogonal to establishment identifiers () depends on the condition thatthe assignment of workers to establishments is strict exogeneity conditional on the covariates (" , ).
Substituting the establishment effects and worker effects into equation (1), we obtain equation (5):,+ !$ " + [!$ (" , − " ) + # , ]. (5)

≡

,

=
+

!$ "

+ 2+,-, !$ "+!$ (" , − " ) +# , . (6)Taking the variance in equation (5), we decompose the variance in log wages into five parts as shownin equation (6): variance in the establishment fixed effects, variance in the predicted wage from theaverage worker effect at each establishment, their covariance, variance in relative worker effects (i.e.
workers’ relative wage at their establishment expected from their observable attributes), and variancein the residual.6

4.4

Detailed Decomposition across Percentiles

Finally, we consider a decomposition across percentiles corresponding to the detailed variancedecomposition. In the detailed variance decomposition, we decomposed individual wages into five6

In our approach, an unobserved individual effect that workers at the same establishment hold commonly is
included as part of the establishment fixed effects. Therefore, if there is sorting between an establishment andworkers, the variance in the establishment fixed effects will be overestimated compared with using the estimation
method of Abowd et al. (1999).
15

parts: the establishment fixed effects (), average workers’ characteristics at establishments (!$ " ),the combination of establishment fixed effects and average workers’ characteristics (, !$ " ), relative

worker effects (!$ (" , − " )), and residual (# , ). We observe the change in each part based on thepercentiles of individual wages (x). We then calculate their average at each x-percentile bin of individual
wages for each year. However, we use .

, !$ " to index the extent of the combination and take thedifferences in the five components between years at the same percentages.

5

Results
5.1

Simple Variance Decomposition

We conduct the model (2) decomposition using data from 1991 to 2012 by sex. Figure 4 reports thetrends in the share of within- and between-establishment inequality in total individual wage inequality.
For men, within- and between-establishment wage inequality in the early 1990s were similar. Betweenestablishment inequality expanded and within-establishment inequality shrunk in the late 1990s.
However, for women, the share of between-establishment inequality was much larger in the early 1990s
than that of within-establishment inequality. Between-establishment inequality in the early 1990s waslarger for women than for men. However, between- and within-establishment inequality for women did
not change (both in its share and in its level) unlike those of men. Consequently, the shares of betweenand within-establishment inequality in individual wage inequality are similar for men and women.
Kambayashi et al. (2008) investigate the increase in wage inequality within skill groups from 1989
to 2003 in Japan, finding a difference in the changes for men and women over time. They suggest thatthe change in the composition of worker characteristics explains the expansion of women’s wage
inequality within the same skill groups. However, the expansion for men did not offer a similar
explanation. Our results suggest that an increase in between-establishment inequality for men does
explain the expansion in wage inequality among men within the same skill groups.
[Figure 4 about here]

16

5.2

Simple Decomposition across Percentiles

Figure 5 reports the wage changes for each percentile in the individual wage distribution. The
horizontal axis indicates the percentiles of an individual worker’s wage and the vertical axis showschanges in wages from 1992 to 2012. The line labeled “total” reports the change in the individual wage
distribution from 1992 to 2012, “between” denotes the change in the average establishment wage, and“within” denotes the changes in the deviation of the individual wage from the average wage of theestablishment to which the worker belongs.
Figure 5 shows that men’s inequality in individual wages is slightly increasing and that betweenestablishment wage inequality is widening more than that of individual wages. Not only the increase at
higher percentiles but also the decline at lower percentiles in the average establishment wage contributeto the increase in between-establishment inequality. Men’s “within” line shows a rise (fall) in thedeviation of the individual wage from the average establishment wage at lower (higher) percentiles.
The difference with the United States (see Song et al., 2018) appears more clearly in the wage
distribution than in the variance. In Japan, there is no jump in the average establishment wage at higher
percentiles as seen in the United States. The significant changes in within-establishment inequality formen are also unique to Japan. Within-establishment inequality remains almost constant in the UnitedStates. The change in the wage distribution of women is considerably different to that of men. In the
distribution of individual wages, the wage level is rising overall. However, the rise in wages is limited
at a particularly high levels (i.e., above the 90th percentile). The rises in individual wages reflect the risein the average establishment wage. There is no remarkable change in within-establishment inequality
as for men.
[Figure 5 about here]

5.3

Detailed Variance Decomposition

To quantify the contributions of changing wage inequality in the worker and establishment effects,
and assortative matching, we conduct a detailed variance decomposition based on equation (6). Table17

5 reports the variance and their share of each component for 1992, 2002, and 2012. Figure 6 shows thetrends from 1991 to 2012.
The male result in Table 6 shows that the variance in the worker effects declined from an average of
0.030 in 1992 to 0.024 in 2012, whereas the variance in the establishment fixed effects increased from
0.085 in 1992 to 0.103 in 2012. The share of the variance in the establishment fixed effects in overall
wage inequality increased from 33.7% in 1992 to 39.5% in 2012. This finding implies that one’s placeof work explains a larger part of the wage determination in recent years. The covariance between theworker and establishment effects also increases from 0.020 in 1992 to 0.032 in 2002. This finding
implies that the rise in assortativeness between workers and establishment contributes to the increase inbetween-establishment wage inequality. On the contrary, the variance in the relative worker effect foreach establishment significantly decreased from 0.079 in 1992 to 0.059 in 2012. The variance in theresidual seems to be increasing, but the rise occurred in 2005 and thus it cannot be distinguished fromthe influence of the change in the survey form.
For women, the variance in the establishment fixed effects accounts for the vast majority of overallvariance, which was 56.4% in 1992 but decreased to 44.7% in 2012. The variance in the relative worker
effect in their establishment among women is lower than that among men. It was 0.032 in 1992,
representing only 17% of overall wage inequality. Moreover, there was no significant change in thevariance in the predicted relative wage from 1992 to 2012. The degree of assortative matching is rising
for women like men. However, as shown in Figure 6, the covariance in the early 1990s for women wasmuch lower than that for men.
[Table 5 and Figure 6 about here]

5.4

Detailed Decomposition across Percentiles

To assess how each part of workers’ wages in each percentile of the wage distribution has changed,
we use the method described in Section 4.4. Figure 7 reports the changes in the five parts of wages foreach percentile of the individual wage distribution. The horizontal axis indicates the percentiles of anindividual worker’s wage.
18

[Figure 7 about here]

The most noticeable changes in the male graph are the changes in the establishment fixed effects andworker effect. Neither changes markedly in a specific wage distribution, whereas the establishment
fixed effects increase more and the worker effect decreases more as wages rise. However, the interactionof the establishment fixed effects and worker effects, which represents the change in assortativenessbetween workers and establishments, suggests a change in a particular wage distribution. The
downward shift at the lower tail of the wage distribution is larger than the upward shift at the higher
tail.
For women, the establishment fixed effects decrease more as wages rise, probably because wages
were raised at low-paying establishments in response to the revision of the Equal Employment Act andrises in the minimum wage. The average worker effect and the interaction of the establishment fixed
effects and average worker effect peak around the 75th percentile, and thereafter their degree of change
is smaller for higher percentiles. The widening variance of these parts is assumed to be caused by theextension of female tenure; however, why the effect is weakened at high percentiles (e.g., above the75th percentile) is unclear.

6

Worker and Establishment Fixed Effects, and Assortative Matching
6.1

Accounting for Within-establishment Inequality

To examine the source of the decreasing within-establishment inequality, we estimate counterfactual
wage inequality using the coefficient of workers’ characteristics in 1992,!$ (" , − " ) . By

comparing them with actual establishment inequality in the worker effects, we can learn how changes
in the coefficient affect changes in within-establishment inequality.
Figure 8 shows counterfactual wage inequality. In this figure, we fix the coefficient of the observables
at 1992 for experience, tenure, and experience × tenure.7 For men (see the left-hand panel), the line of7

We do not report the counterfactual variance with the coefficient of education in 1992 because it is similar to
the actual variance.
19the counterfactual coefficient of tenure follows a different trend to actual wage inequality. This finding
suggests that a decrease in the coefficient of tenure affected the decline in within-establishment
inequality in the 1990s. Further, the contributions of the change in the coefficient of experience increasein the late 2000s. This change implies that wages have been influenced by raising the mandatory
retirement age and the change in the survey form.
As pointed out by Hamaaki et al. (2012), we confirm that there was a decrease in returns to tenure
implied by the flattening of the wage profile even if we control for the establishment fixed effects.
Yamada and Kawaguchi (2015) argue that changes in the coefficients of tenure and experience explain
the decrease in overall individual wage inequality in the 1990s. In our results, the change in thecoefficient of experience hardly affects the variance in wages, whereas tenure’s effects are similar totheir results. As shown in the right-hand panel of Figure 8, counterfactual wage inequality with tenure’s
coefficient in 1992 is rapidly increasing in the 1990s for women, driven by women’s tenure increasing
significantly. On the contrary, the change in the coefficient of experience hardly affects the variance inwages as for men. The gender differences in within-establishment inequality are considered to be causedby the different maturity levels of the internal labor market.
[Figure 8 about here]

6.2

Accounting for Between-establishment Inequality

The results in Sections 5.3 and 5.4 show that a large part of the rise in between-establishment
inequality for men is associated with the rise in the variance in the establishment fixed effects. To
investigate the source of this disparity increase, we decompose the variance in the establishment fixed
effects by their characteristics. We assume that the establishment fixed effects

in equation (3)consist of three components: firm size, prefecture (47 administrative regional units), and industry. The
error term (/ ) represents the difference in productivity and allowance that the three factors cannot
explain, and we suppose E / 12 3 45 ,5657 # 5 , 89:#3

establishment fixed effects is decomposed as in equation (8).

20

) = 0. Then, the variance in the

= ; , + ; , 2 <_> 45 + ; ,=?; , 2 <@ AB C +5657 # 5 + ; , 89:#3;,

5657 # 5+ / , (7)+ Var( ; , 89:#3) + Var( / ).

(8)

Table 6 and Figure 9 report the results of this establishment fixed effects decomposition. The ratiosof the parts that are not explained by firm size, region, and industry in the establishment fixed effects
increase each year. Further, there is almost no common point, such as the breakdown in variance in theearly 1990s and the transition between men and women. For men, about one-quarter of the variance inthe establishment fixed effects is explained by the wage difference according to firm size, while wage
inequality derived from the differences in firm size increased from the late 1990s. For women, thevariance in the establishment fixed effects declined, largely due to the falling variance among firm sizes
and industries.
The existence of a firm size/wage premium has long been studied (Brown and Medoff, 1989; Troske,1999). However, some studies have recently pointed out the relationship between productivity and wage
inequality (Berlingieri et al., 2018). They show that the link between wages and productivity is strong,
especially in the service sector. Unfortunately, no productivity information on each establishment isavailable. Hence, the question of whether the increase in the productivity gap caused the increase in thevariance in the establishment fixed effects is beyond the scope of our study and remains an avenue for
future research.
In Germany, the variance in the establishment fixed effects has doubled over about 30 years (Cardet al., 2013), whereas the change in Japan over 20 years has not been as large. In addition to shrinking
wage inequality in the workplace, this different trend in establishment fixed effects is one of the reasons
why Japan’s wage inequality has expanded little. To clarify the reasons behind this, an analysis using
more information at the establishment level would be necessary.
[Table 6 and Figure 9 about here]

6.3

Change in the Assignment of Workers to Establishments21

To reveal more about the rise in the covariance in the worker effects (" , !$ ) and establishment
fixed effects (), Figure 10 displays the joint distribution among the deciles of worker andestablishment fixed effects in 1992 and 2012, while the third panel shows the difference between them.
There is a rise in assortative matching: workers who may receive high (low) wages are concentrated inan establishment that may pay high (low) wages, which increases the wage differential.
However, the change in matching in Japan is moderate. Using our definitions of worker andestablishment fixed effects, assortativeness does not rise intensively among the very high wage groups
as seen in the United States (Song et al., 2016). This is more remarkable at lower deciles than at higher
deciles, where the middle- and lower-decile workers have substantially shifted to lower-decile
establishments. Larger Japanese firms, which often pay higher wages, tend to have employees withlonger tenures and their employment is less mobile (see Hamaaki et al., 2012). Executives are oftenchosen from among career employees. 8 Therefore, it is unlikely that assortativeness will riseintensively in the high wage distribution. This limited change in matching is one of the reasons why
wage inequality has not increased significantly in Japan.
[Figure 10 about here]

7

Conclusion
This study investigates within- and between-establishment wage inequality from 1991 to 2012 using

Japanese employer/employee data. We find the following three main results. First, the results of thedetailed decomposition of within- and between-establishment wage inequality suggest an expansion of
the latter for men during the study period, while this expansion is offset by the contraction of the former.
The increase in between-establishment inequality stems from the expanding establishment fixed effects.
The decrease in within-establishment inequality is caused by a reduction in returns to tenure estimatedin the wage equation. This decrease is a key factor preventing a rise in wage inequality in Japan.

8

Only managers lower than directors are included in the BSWS sample.
22

Second, we find a different trend in women’s wage inequality. This finding suggests that men andwomen faced different labor markets. The establishment effect on wages was significantly different formen and women in the early 1990s and there were also considerable differences in the trend thereafter.
In the early 1990s, the establishment effect on wages was larger for women than for men and betweenestablishment inequality shrunk. As a result, the impact of establishments on wages between men andwomen has become similar in recent years.
Third, we find that a rise in assortative matching between workers and establishments increases
between-establishment inequality. The rise in assortativeness is larger for women than for men. It isbecause women are more likely to find it easier to change jobs than men. In addition, the share of socalled lifetime employment remains large and retention rates are low for men in Japan compared within other developed countries. Thus, the impact of assortative matching on wage inequality in Japan is
not as large as other countries.
We acknowledge the several limitations of the present study. First, our analysis cannot exclude theeffect of unobserved worker heterogeneity by controlling for the fixed effects because we employ
repeated cross-sectional data. Second, we cannot clarify in detail the factors that changed the variancein the establishment fixed effects because of the limited information at the establishment level. In
particular, the heterogeneous effects of productivity, profitability, and labor’s share of revenue would
be a fruitful topic for future research.

Acknowledgement
This study is based on the second chapter of the Ph.D. dissertation of Akesaka at Osaka University. Thiswas originally published as ISER DP No. 985, entitled “Trends in Wage Inequality Within and Between
Establishments: Evidence from Japanese Employer–Employee Matched Data,” (in Japanese:   ) October 2016.

We are grateful to Junichiro Ishida, Ryo Kambayashi, Nobuyoshi Kikuchi, Miki Kohara, Fumio Ohtake,
Ryosuke Okazawa, Hideo Owan, Masaru Sasaki, Katsuya Takii, Junichi Yamasaki, and Kazufumi
Yugami for their helpful comments and suggestions. We also would like to thank the seminar23

participants of the 2015 Japanese Economic Association Spring Meeting at Niigata University, 18th
Labor Economics Conference at Hitotsubashi University, RIEB Seminar at Kobe University, andEconomic Workshop at Osaka City University for their useful discussions. Any remaining errors are
our own. Akesaka was financially supported by the Grant-in-Aid for JSPS Fellows DC2 (14J07655)
and the Grant-in-Aid for Scientific Research S (15H05728) from the Japan Society for the Promotionof Science. This research was also financially supported by the Grant-in-Aid for Scientific Research(24330074). We use the BSWS based on permission from the Ministry of Health, Labor and Welfare(MHLW). The views expressed are those of the authors and do not necessarily reflect those of the
MHLW.

24

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Table 1: Sample Structure of Full-time Men and Women

Number of observations

Number of workers in an Establishment

Workers

Establishments

Mean

Std.Dev

Min

Max

1992

765,677

52,611

14.55

14.11

1

316

2002

736,390

49,749

14.80

13.84

1

351

2012

565,926

52,006

10.88

11.24

1

279

1992

338,797

49,044

6.91

7.06

1

122

2002

269,31245,091

5.97

6.51

1

103

2012

265,948

46,546

5.71

6.17

1

134

Male

FemaleNotes: Sample includes employees in Japan aged 15–59 working full-time in establishments that hire 10 or more
employees (full- and part-time workers combined) or in single establishments of firms that hire five to nine
employees.

28

Table 2: Summary Statistics for Wages

Average of log hourly wage

Log hourly wagein establishments

Mean Std.Dev

Min

Max

Mean Std.Dev

Min

Max

1992

7.71

0.50

4.22

11.89

7.59

0.39

5.76

10.39

2002

7.80

0.50

4.05

13.22

7.68

0.41

5.72

10.06

2012

7.71

0.51

2.90

11.96

7.59

0.42

5.79

9.76

1992

7.18

0.47

4.02

11.51

7.14

0.39

5.67

10.99

2002

7.37

0.46

3.59

12.78

7.32

0.40

4.62

10.59

2012

7.32

0.45

4.22

11.85

7.27

0.37

5.48

10.64

Male

FemaleNotes: Hourly wages are adjusted for inflation using the Consumer Price Index in 2010.

29

Table 3: Basic Statistics of Full-Time Workers

Male
Sample year

Female

1992

2002

2012

1992

2002

2012

Junior High School

0.21

0.09

0.04

0.16

0.06

0.02

High School

0.54

0.52

0.47

0.63

0.56

0.51

Two year college

0.05

0.10

0.11

0.16

0.25

0.26

University or over

0.20

0.29

0.38

0.05

0.13

0.21

Education

Experience

20.16

19.73

19.53

17.47

17.41

18.37

(11.94)(11.59)(10.93)(13.31)(12.46)(11.69)

11.53

12.34

11.47

7.75

9.41

9.15

(9.95)(10.38)(10.11)(7.96)(8.50)(8.66)

Group 1 (Secondary industry)

0.47

0.40

0.23

0.47

0.40

0.23

Group 2 (Infrastructure, Transport, Retail)

0.26

0.27

0.22

0.22

0.25

0.21

Group 3 (Finance and Real Estate)

0.09

0.08

0.13

0.10

0.09

0.15

Group 4 (Service)

0.18

0.24

0.41

0.20

0.26

0.41

5-29

0.36

0.31

0.27

0.36

0.29

0.27

30-99

0.19

0.19

0.16

0.20

0.20

0.17

100-299

0.13

0.14

0.14

0.14

0.15

0.15

300-999

0.10

0.12

0.12

0.10

0.13

0.12

1000-4999

0.10

0.12

0.14

0.10

0.12

0.14

5000 or over

0.12

0.12

0.16

0.10

0.11

0.14

TenureIndustry

Firm size

Notes: Standard deviations are in parentheses. Although observations are available for every year between 1991
and 2012, the descriptive statistics are reported for three years.

30

Table 4: Estimation Results of the OLS and Establishment Fixed Effects Model
Male

(1)
Base

1992
(2)

(3)

+ Establ.

+ Es tabl.

characteristics

Fixed Effect

(4)
Base

2002
(5)

(6)

+ Establ.

+ Es tabl.

characteristics

Fixed Effect

(7)
Base

2012
(8)

(9)

+ Establ.

+ Establ.

characteristics

Fixed EffectJunior high school
Two year collage
University or over

-0.1820.118
0.368-0.1440.111
0.277-0.1120.075
0.203-0.1680.108
0.356-0.1310.093
0.268-0.0930.073
0.184-0.1400.087
0.361-0.1090.083
0.275-0.066
0.060
0.169

Experience

0.026-0.0420.040-0.0720.043-0.0750.026-0.0370.035-0.061

0.038-0.0620.022-0.0360.028-0.0530.030-0.052

TenureTenure2 /100

0.040
0.010

0.025
-0.011

0.023-0.017

0.039
0.023

0.025
-0.0090.020-0.017

0.039
0.010

0.030
-0.0250.028-0.039Experience * Tenure-0.054-0.0060.002-0.069-0.0090.003-0.054-0.0010.010

Constant

6.983

6.738

6.976

7.008

6.790

7.078

6.959

6.763

7.028

adj. R-sq

0.575

0.675

0.649

0.541

0.642

0.613

0.508

0.606

0.534

Experience2 /100

765,677Obs.
G : Fixed effects are homogeneous
F-value(p-value)

Female73639024.31(0.00)

(1)
Base

1992
(2)

(3)

+ Estab.

+ Estab.

characteristics

Fixed Effect56592630.65(0.00)

(4)
Base

2002
(5)

(6)

+ Estab.

+ Estab.

characteristics

Fixed Effect19.55 (0.00)

(7)
Base

2012
(8)

(9)

+ Estab.

+ Estab.

characteristics

Fixed EffectJunior high school
Two year collage
University or over

-0.211
0.199
0.422-0.1300.113
0.276-0.0770.069
0.179-0.1700.272
0.441-0.1160.170
0.304-0.0630.080
0.170-0.1440.212
0.435-0.1020.181
0.371-0.032
0.071
0.170

Experience
Experience 2 /100

-0.0030.011

0.010-0.0190.010-0.0150.007-0.0100.012-0.0260.013-0.0240.009-0.0160.012-0.0260.007-0.014

TenureTenure 2 /100

0.064
0.027

0.053
-0.0090.053-0.037

0.051
0.030

0.044
-0.0020.037-0.026

0.042
0.034

0.039
0.012

0.039
-0.019Experience * Tenure-0.134-0.079-0.061-0.101-0.054-0.027-0.085-0.055-0.035Constant

6.885

6.509

6.829

6.878

6.608

6.969

6.832

6.635

6.953

adj. R-sq

0.362

0.569

0.349

0.410

0.557

0.336

0.415

0.475

0.276

Obs.
G : Fixed effects are homogeneous
F-value(p-value)

33879726931214.33(0.00)

26594812.95(0.00)

8.56(0.00)

Notes: Robust standard errors are in parentheses. Columns (1), (4), and (7) report the OLS estimates without
controlling for the establishment variables. Columns (2), (5), and (8) control for the establishment characteristics
such as firm size, industrial group, and prefecture dummies. Columns (3), (6), and (9) show the estimates withthe establishment fixed effects. An F-test is for the null hypothesis of a constant establishment effect.

31

Table 5: Decomposition of the Change in Wage Inequality
Male

(1)

(2)

in 1992(3)(4)

in 2002(5)(6)

in 2012

(7)

(8)

(9)

Diff.

Diff.

Diff.

2002-1992

2012-2002

2012-1992

Between Establishment
A. Var(Est. Fixed Effect)

0.085

33.7%

0.099

39.3%

0.103

39.5%

0.014

0.004

0.018

B. Var(Average Worker Effect)

0.030

11.9%

0.024

9.4%

0.024

9.3%

-0.0060.001-0.006

C. 2×Covariance(AB)

0.020

7.7%

0.030

11.8%

0.032

12.2%

0.010

0.002

0.012

0.135

53.4%

0.152

60.5%

0.160

60.9%

0.018

0.007

0.025

D. Var(Worker Effect)

0.079

31.4%

0.064

25.3%

0.059

22.5%

-0.016-0.005-0.020E. Var(Residual)

0.038

15.2%

0.036

14.3%

0.043

16.5%

-0.002

0.007

0.005

0.118

46.6%

0.100

39.5%

0.102

39.1%

-0.0180.003-0.015

0.000

0.010

0.010

Within Establishment

Total

0.252

Fe male

(1)

0.252
(2)

in 1992

0.262

(3)

(4)

in 2002(5)(6)

in 2012

(7)

(8)

(9)

Diff.

Diff.

Diff.

2002-1992

2012-2002

2012-1992

Between Establishment
A. Var(Est. Fixed Effect)

0.123

56.4%

0.116

53.9%

0.103

44.7%

-0.007-0.012-0.020B. Var(Average Worker Effect)

0.020

9.2%

0.019

8.7%

0.024

10.5%

-0.001

0.006

0.004

C. 2×Covariance(AB)

0.002

0.9%

0.017

7.8%

0.032

13.8%

0.015

0.015

0.030

0.145

66.5%

0.151

70.4%

0.160

68.9%

0.006

0.008

0.014

D. Var(Worker Effect)

0.032

14.9%

0.028

13.3%

0.029

12.5%

-0.0040.000-0.003

E. Var(Residual)

0.041

18.6%

0.035

16.4%

0.043

18.6%

-0.005

0.008

0.002

0.073

33.5%

0.064

29.6%

0.072

31.1%

-0.0090.008-0.001-0.004

0.017

0.013

Within Establishment

Total

0.218

0.215

0.232

Notes: See the notes of Table 3 for the sample composition. Calculation based on the estimated fixedestablishment effects models summarized in Table 4. Entries in columns (1), (3) and (5) are the variancecomponents in 1992, 2002, and 2012. Entries in columns (2), (4) and (6) are the ratio of the variancecomponents to the total variance (as a percentage). Entries in columns (7), (8) and (9) are the change in thevariance components from 1992 to 2002, from 2002 to 2012, and from 1992 to 2012.

32

Table 6: Variance Decomposition of the Establishment Fixed Effects

Male

(1)

(2)

in 1992
Var(Est. Fixed Effect)

(3)

(4)

in 2002

0.085

(5)

(6)

in 2012

0.099

0.103

(7)

(8)

(9)

Diff.

Diff.

Diff.

2002-1992

2012-2002

2012-1992

0.014

0.004

0.018

Explained
Var(Firm Size)

0.022

25.4%

0.027

27.1%

0.030

28.8%

0.005

0.003

0.008

Var(Prefecture)

0.011

12.9%

0.007

7.0%

0.008

7.4%

-0.0040.001-0.003Var(Industry)

0.003

3.6%

0.003

3.1%

0.003

2.8%

0.000

0.000

0.000

0.049

58.1%

0.062

62.9%

0.063

61.0%

0.013

0.001

0.014

(1)

(2)

(3)

(4)

(5)

(6)

Unexplained
Var(ν)
Femalein 1992
Var(Est. Fixed Effect)in 20020.123

in 2012

0.116

0.094

(7)

(8)

(9)

Diff.

Diff.

Diff.

2002-1992

2012-2002

2012-1992-0.007-0.022-0.029Explained
Var(Firm Size)

0.023

18.4%

0.016

13.9%

0.009

9.6%

-0.007-0.007-0.014

Var(Prefecture)

0.016

12.6%

0.012

10.6%

0.012

12.3%

-0.003-0.001-0.004Var(Industry)

0.012

9.5%

0.013

10.9%

0.004

4.0%

0.001

-0.009-0.008

0.073

59.5%

0.075

64.6%

0.070

74.1%

0.002

-0.005-0.004

Unexplained

Var( :unexplained)

33

Figure legends
Figure 1: Macroeconomic Indicators in Japan, 1980–2012
Figure 2: Trends in Workforce Share by Education, Age, and Tenure
Figure 3: Trends in the Variance in the Within- and Between-Skill Groups
Figure 4: Simple Variance Decomposition in Wages
Figure 5: Simple Decomposition across Percentiles from 1992 to 2012
Figure 6: Detailed Variance Decomposition in Wages
Figure 7: Detailed Decomposition across Percentiles
Figure 8: Counterfactual Trends in Within-Establishment Inequality
Figure 9: Decomposition of Establishment Fixed Effects
Figure 10: Change in the Joint Distribution of the Worker and Establishment Fixed Effects

34

Figure 1
Unemployment Rate

2

-5

3

0

Economy

4

5

5

10

GDP Growth Rate

1980

1990

2000

2010

1980

1990

2000

2010

year
Graphs by Index

Notes: The GDP growth rate is based on the annual report of the national accounts calculation by the
Cabinet Office. The unemployment rate is based on the Labor Force Survey conducted by the Statistics
Bureau, Ministry of Internal Affairs and Communications.

35

Figure 2
Education

Male

1990

1995

2000

2005

2010

.5
.4
.3
.2
.1
0

.1

.2

.3

.4

.5

.6

Tenure

Male
.6

Age

0

0

.1

.2

.3

.4

.5

.6

Male

1990

1995

2000

2005

2010

1990

2000

9
14
Graphs by gender

2005

12
16+

2010

2005

2010

2005

2010

.6
0

.1

.2

.3

.4

.5

.6
.5
.3
.2
.1
0
1995

2000

Female

.4

.5
.4
.3
.2
.1
0
1990

1995

Female.6Female

1990

1995

15-30
50-59

2000

2005

30-39
60+

2010

1990

40-49

1995

2000

0-4
10-14

Graphs by gender

Graphs by gender

36

5-9
15+

Figure 3

.25
.2
.15
.1
.05

.05

.1

.15

.2

.25

.3

Female

.3

Male

1990

1995

2000

2005

Total

2010

1990

1995

Between Group

2000

2005

2010

Within Group

Graphs by gender

Notes: This figure reports the trends in wage inequality calculated for the datasets of workers classified as
regular and permanent employees aged 15 or over. Skill groups are defined by education, experience, andjob tenure.

37

Figure 4
Female

.2
.15
.05

.1

Variance

.25

.3

Male

1990

1995

2000

2005

Total

2010

1990

1995

Between

2000

2005

2010

WithinGraphs by gender

Notes: See the variance decomposition in equation (2). Between-establishment variance is calculated using
the mean log hourly wage and weighted by the number of employers. Within-establishment inequality is
calculated based on the difference between individual log wages and establishment mean log hourly
wages.

38

Figure 5
Female

.1
0
-.2

-.1

Ln ratio of levels

.2

Male

0

50

100 0

50

100

Individual's percentile
Total

BetweenWithinGraphs by gender

Notes: Between-establishment statistics are based on the average of mean log wages at establishments forindividuals in that percentile of wages in each year. Within-establishment statistics are based on individual
log wages minus establishment mean log wages for individuals in that percentile of wages in each year. All
values are adjusted for inflation using the consumer price index.

39

Figure 6
Female.1.05
0variance and covariance

.15

Male

1990

1995

2000

Var(Establ. effect)
Var(Relative Worker effect)

2005

2010

1990

1995

Var(Establ.Avg. Worker)
Var(Residual)

2000

2005

2010

2Cov(Establ., Establ.Avg.worker)

Graphs by gender

Notes: See the variance decomposition in equation (6). The simple decomposition in Figure 4 is further
decomposed into five parts: variance in the establishment fixed effects, variance in the predicted wage ofthe average observable workers’ characteristics for each establishment, their covariance, variance inworkers’ relative wage expected from their observable attributes at their establishment, and variance in theresidual.

40

Figure 7
Female

.1
0
-.1
-.2

variance and covariance

.2

Male

0

50

Establ. effectWorker effect

100

0

50

Establ.Avg. Worker
Residual

100

Establ.*Establ.Avg.worker^(1/2)

Graphs by gender

Notes: Establishment unit statistics such as the establishment fixed effects, establishment average worker
effect, and their interaction are based on the average of each estimated value at establishments forindividuals in that percentile of wages in each year. Data on the worker effects and residual are based on
the average of each estimated value for individuals in that percentile of wages in each year.

41

Figure 8
Female

.06
.04
.02

variance

.08

.1

Male

1990

1995

2000

2005

2010

1990

1995

2000

year
Coef.Exp in 1992

Coef.Ten in 1992

Coef.Exp,Ten,Exp*Ten in 1992

ActualGraphs by gender

42

2005

2010

Figure 9

Female0.05

Variance

.1

.15

Male

1990

1995

2000

2005

2010

1990

1995

2000

Graphs by gender

43

2005

2010

Establ. effectFirm size
Prefecture
Industry

Figure 10(A) Joint Distribution in 1992 and 2012
in 1992
Female

0

.05

.1

.15

Proportion of Obervations

.2

Male

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

6

7

8

9

10

6

7

8

9

10

Graphs by genderin 2012
Female

0

.05

.1

.15

Proportion of Obervations

.2

Male

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

Graphs by gender(B) Change in the Joint Distribution from 1992 to 2012
Female

.05
0
-.05
-.1

Change in Proportion

.1

Male

1

2

3

4

5

Worker:Decile 1
Worker:Decile 6

6

7

8

9

10

Worker:Decile 2
Worker:Decile 7

1

Worker:Decile 3
Worker:Decile 8

2

3

4

Worker:Decile 4
Worker:Decile 95Worker:Decile 5
Worker:Decile 10

Graphs by gender

Notes: This figure shows the joint distribution of the worker and establishment fixed effects estimatedfrom equation (5). Higher fixed effects deciles are computed with respect to the distribution of
establishments. Since higher fixed effects establishments are usually larger, there are more employees in
the higher establishment fixed effects deciles. Establishment fixed effects deciles are ordered from left toright from 1 to 10. Within each establishment fixed effects decile group, worker effect deciles are orderedfrom left to right from 1 to 10.

44Appendix A
Figure A1: Trends in Average Wages and Inequality across Four Countries
Decile Ratio of Earnings:9/1

.8

.5

.9

1

1

1.5

1.1

2

1.2

Average Hourly Wage

1980

1990

2000

2010

1980

1990

2000

2010

year
Japan
United Kingdom

United States
Germany

Graphs by Index

Notes: Both figures are based on OECD statistics. Average wages are obtained by dividing the national
accounts-based total wage by the average number of employees in the total economy, which is then multipliedby the ratio of the average usual weekly hours per full-time employee to the average usually weekly hours for all
employees. The index of earnings inequality is defined by the ratio of the ninth decile to the first decile in themonthly earnings of full-time dependent employees.

45Appendix B: Additional Information for the Sample
Table B1: Groups for the Industrial Classification
Group 1

Group 3

Secondary industry

Finance, Real Estate

C. Mining and Quarrying of Stone and Gravel

J. Finance and Insurance

D. Construction

K. Real Estate ,Goods Rental and Leasing

E. ManufactureGroup 2

Group 4

Infrastructure, Transport and RetailService

F.Electricity, Gas, Heat Supply and Water

L. Scientific Research, Professional and Technical Services

G. Information and Communication

M. Eating and Drinking services, Accommodations

H. Transport and Postal Activities

N. Living-Related and Personal Services and Amusement Services

I.Wholesale and Retail Trade

O. Education, Learning Support
P. Imedical Services, Public Health and Hygiene
Q. Compound Services
R. Miscellaneous Services. N.E.C.

Exception from the BSWS
A. Agriculture, Forestry
B. Fisheries
S. Government Seriveses(Except elsewhere classified)
T. Industries unable to classify

46

Table B2: Changes in the Industrial Category
9th revision

10th revision

11th revision

12th revision

January, 1984

October, 1993

March, 2002

November, 2007

A. Agriculture

A. Agriculture

A. Agriculture

A. Agriculture, Forestry

B. Forestry

B. Forestry

B. Forestry

B. Fisheries

C. Fisheries

C. Fisheries

C. Fisheries

C. Mining and Quarrying of Stone and Gravel

D. Mining

D. Mining

D. Mining

D. Construction

E. Construction

E. Construction

E. Construction

E. Manufacture

F. Manufacture

F. Manufacture

F. Manufacture

F.Electricity, Gas, Heat Supply and Water

G. Electricity, Gas, Heat Supply and Water

G. Electricity, Gas, Heat Supply and Water

G. Electricity, Gas, Heat Supply and Water

G. Information and Communication

H. Transport and Communications

H. Transport and Communications

H. Information and Communication

H. Transport and Postal Activities

I. Holesale and Retail Trade, Eating andDrinking Survice
J. Finance and Insurance

I. Holesale and Retail Trade, Eating andDrinking Survice
J. Finance and Insurance

I. Transport

I.Wholesale and Retail Trade

J. Wholesale and Retail Trade

J. Finance and Insurance

K. Real Estate

K. Real Estate

K. Finance and Insurance

K. Real Estate ,Goods Reantal and Leasing

L. Services

L. Services

L. Real Estate

L. Scientific Research, Professional and Technical
Services

M. Government Seriveses(Except elsewhereclassified)

M. Government Seriveses(Except elsewhereclassified)

M. Eating and Drinking services, Accommodations

N. Industries unable to classify

N. Industries unable to classify

M. Eating and Drinking services, Accommodations

O. Education, Learning Support

N. Living-Related and Personal Services andAmusement Services
O. Education, Learning Support

P. Compound Services

P. Imedical Services, Public Health and Hygiene

Q. Miscellaneous Services. N.E.C.

Q. Compound Services

R. Government Seriveses(Except elsewhereclassified)

R. Miscellaneous Services. N.E.C.

N. Imedical Services, Public Health and Hygiene

S. Industries unable to classify

S. Government Seriveses(Except elsewhereclassified)
T. Industries unable to classify

Survey Year
1991

1996

2004

2009

1992

1997

2005

2010

1993

1998

2006

2011

1994

1999

2007

2012

1995

2000

2008

2001
2002
2003

47

Table B3: Sample structure for all observation year

Number of observations

Number of Individuals in Establishments

Indiviudals

Establishments

Mean

Std.Dev

Min

Max

1991

766,497

52,706

14.54

14.08

1

304

1992

765,677

52,611

14.55

14.11

1

316

1993

809,831

54,672

14.81

14.33

1

403

1994

775,280

52,263

14.83

14.34

1

360

1995

813,186

55,336

14.70

13.91

1

322

1996

824,047

54,426

15.14

13.86

1

315

1997

830,297

54,778

15.16

13.84

1

262

1998

811,170

53,833

15.07

13.82

1

352

1999

803,240

52,898

15.18

13.89

1

457

2000

767,468

50,640

15.16

13.87

1

344

2001

753,869

49,081

15.36

14.05

1

358

2002

736,390

49,749

14.80

13.84

1

351

2003

726,287

49,002

14.82

13.83

1

365

2004

727,242

49,430

14.71

12.99

1

361

2005

591,42147,812

12.37

11.80

1

217

2006

617,946

50,695

12.19

11.74

1

251

2007

564,255

48,349

11.67

11.61

1

241

2008

564,300

48,800

11.56

11.64

1

232

2009

552,391

50,221

11.00

11.26

1

207

2010

548,687

49,875

11.00

11.17

1

202

2011

534,454

49,326

10.84

11.06

1

290

2012

565,926

52,006

10.88

11.24

1

279

1991

340,671

49,384

6.90

7.13

1

125

1992

338,797

49,044

6.91

7.06

1

122

1993

384,180

51,846

7.41

7.33

1

123

1994

363,897

49,585

7.34

7.32

1

115

1995

375,240

52,403

7.16

7.19

1

111

1996

341,451

51,080

6.68

6.76

1

106

1997

339,072

51,398

6.60

6.69

1

96

1998

318,781

50,167

6.35

6.61

1

126

1999

309,961

49,274

6.29

6.55

1

108

2000

288,908

46,805

6.17

6.54

1

114

2001

275,197

45,231

6.08

6.49

1

152

2002

269,31245,091

5.97

6.51

1

103

2003

262,913

44,151

5.95

6.51

1

117

2004

274,200

45,121

6.08

6.34

1

92

2005

281,894

44,271

6.37

7.12

1

118

2006

292,077

47,181

6.19

6.50

1

209

2007

286,038

44,824

6.38

6.53

1

125

2008

262,019

44,486

5.89

6.27

1

158

2009

264,80245,840

5.78

6.22

1

175

2010

264,623

45,577

5.81

6.17

1

92

2011

256,91444,867

5.73

6.09

1

118

2012

265,948

46,546

5.71

6.17

1

134

a. Maleb. Female48

Appendix C
Figure C1: Simple Decomposition across Percentiles from 1992 to 2002 and 2002 to 2012

1992-2002, Female

2002-2012, Male

2002-2012, Female

-.2
.2
.1
0
-.1
-.2

Ln ratio of levels

-.1

0

.1

.2

1992-2002, Male

0

50

100

0

50

100

Individuals' percentile
Total

BetweenGraphs by year and gender49

Within