Development Bank of Japan
Research Report
No. 31

Microstructure of Investment and Employment Dynamics:
Stylized Facts of Factor Adjustment Based on Listed Company Data

October 2002

Economic and Industrial Research Department
Development Bank of Japan

Contents
Summary................................................................................................................................... iii
Introduction .............................................................................................................................. 1
I

Trends in Aggregate Data since the 1980s .................................................................... 2
1. Three Phases since the 1980s and the Macroeconomic Environment ............................ 2
2. Diversification in Factor Adjustment Patterns at the Industry Level............................ 2
3. Changes in Inter-Industry Propagation Structure of Investment ................................... 7

II.

Microstructure of Investment and Employment Dynamics ...................................... 10
1. The Significance and Methods of Micro-Data Econometrics Based on Corporate Data ....... 10
2. Weakening Potential for Capacity Expansion ............................................................. 12
3. Changes in Inter-Company Distribution and the Rising Threshold Level................ 16
4. Growing Heterogeneity in Capacity and Employment Increase Factors .................. 20

III. Factor Adjustment Behavior of Listed Firms from the Perspective of Adjustment
Path Analysis ................................................................................................................. 27
1. Concept of Adjustment Path Analysis and Six Major Categories............................... 27
2. Downsizing as Established Trend Among Common Firms ......................................... 29
3. Trends Among Newly Listed Companies...................................................................... 37

Concluding Remarks .............................................................................................................. 42
References ............................................................................................................................... 43

Development Bank of Japan Research Report/ No. 31iMicrostructure of Investment and Employment Dynamics :Stylized facts of factor adjustment based on listed company data
Summary
1. Under the recent deflationary economy,
Japanese corporate factor adjustment behavior(decisions regarding the amount and timing of
capital and labor input) is changing from one thatassumes expansion in scale to one which considers both expansion and contraction with wider
variety, reflecting differences in business environment and history of adjustment path. In orderto understand the dynamics of aggregate investment and employment, it is essential to explore
the microstructure regarding investment andemployment as a set of factor adjustment behavior. This study investigates the histories andresults of Japanese corporate factor adjustmentsin the domestic market since the 1980s and extracts the stylized facts.
2. We divided the time span since the 1980s
into three phases (Phase I: FY1980-86, Phase II:
FY1987-93 and Phase III: FY1994-2000) andsurveyed the changes in factor adjustment patterns based on aggregate corporate statistical data.
Along with a growth setback of both capital andlabor in Phase III from the macroeconomic perspective, evolving diversity between industries
was apparent as indicated below. First, interindustry correlation of factor adjustment declined
broadly in Phase III for both capital and labor in
manufacturing industries, and for capital in nonmanufacturing industries. Secondly, a positive
correlation between the capital growth rate andlabor growth rate in each industry was observed
throughout the three phases, though significanceevidently declined in Phase III. Thirdly, regarding the structure of the inter-industry correlation of capital spending, the clearly-delineatedpropagation process from manufacturing to nonmanufacturing industries seen in Phase I had
weakened considerably by Phase III after passing
through the so-called “unanimous” type investment boom in Phase II.

3. In order to investigate the microstructure ofcapital and labor growth setback and diversity
seen in the aggregate data, we examined thecharacteristics of factor adjustment behavior at
the corporate level based on longitudinal data ofapproximately 1,400 companies listed continuously since FY1980 (henceforth “common
firms”). The net growth rate of capacity dropped
to near zero in Phase III and the downward momentum continued in gross positive contribution(sum of contribution of firms with positive net
investment), which expresses potential for capacity expansion. Employment trends indicate
that, while the net growth rate in Phase III was
persistently negative, gross positive contribution
does not necessarily decline tied to temporary
employment.
4. The inter-company distribution of the net
growth rate of capacity viewed every five years
indicates that the center of the distribution remained in the negative zone since FY1995 and,in terms of the distribution configuration, theright-hand tail, which expresses the abundance ofgrowth drivers, narrowed greatly, indicating thatthe conditions of capacity in Phase III were almost as stagnant as those of employment.
Meanwhile, the distribution of return on investment (rate of return on tangible assets minus
interest cost), helped by a drop in interest cost,
did not change that greatly, which suggests thatthe growth setback of capital in Phase III should
be attributed to change in investment behavior(response to return on investment). Calculating
the threshold rate of return of capacity expansion
under the appropriate hypotheses, it can be seen
that, following a period of notably low threshold
during the latter half of Phase II, Phase III shiftedto a trend of increase and, especially in FY1999
and 2000, the threshold reached the highest level
since the early 1980s. Comparing this to thethreshold of employment increase, though thetrend is generally similar, the correlation between the two was not as strong in Phase III as it

Development Bank of Japan Research Report/ No. 31

iii

continuing in Phase III), the share by number ofcompanies is small accounting for only 8% andthere are comparatively few capital-intensive
companies among them. Companies of the capital-intensive type that also steadily expanded inscale during Phase III are of the quasi-sustained
growth type that expanded only in capacity (21%in the share by number of companies); however,their performance is generally equivalent to thegrowth setback type and they may be unable tosustain capacity expansion in the future.

had been earlier and that there was a far morecautious attitude toward capacity than employment in FY1999 and 2000.
5. Decomposing the net growth rate of capacity by industry indicates that the gross positive
contribution dropped broadly overall in Phase III
while the gross negative contribution became
more discernable in virtually all industries. Observing the correlation between capacity andemployment by phase in terms of gross contribution, the correlation of positive contribution
gradually declined while the correlation of negative contribution became noticeably higher inPhase III. In summary, while decrease factors inPhase III are similar for both capacity and employment, the differences in increase factors
between capacity and employment are greater. In
addition, when the gross positive contribution of
capacity is decomposed into industry factors andindividual company (idiosyncratic) factors, individual company factors have recently come to
explain virtually everything, as in the case of
employment.
6. Analyzing the history of factor adjustment
path of the common firms in each phase since the1980s on the two axes of capacity and employment, 90% of the firms can be grouped into six
major types: sustained growth, quasi-sustained
growth (capacity only), quasi-sustained growth(employment only), growth setback, early contraction, and Heisei boom. Of these, the growth
setback type (increase in capacity or both capacity and employment in Phase I and II and contraction in both in Phase III) accounts for 28%,
the largest share, of the number of companies.
Especially in manufacturing industries, this category includes leading firms in Japan, indicating
the extent to which downsizing has prevailed in
the domestic market since Phase III. Meanwhile,
although high-profit, high-growth firms in eachof the industries are listed among the sustained
growth type that are expected to drive the growthof aggregate investment (on the same path as
growth setback type firms through Phase II but
with expansion in both capacity and employmentiv

7. Regarding the importance of non-common
firms (newly listed or delisted in or after
FY1981) in the gross positive contribution of
capacity for all listed companies, the share of
non-common firms is steadily expanding but its
impact is small compared to employment. By
industry, those with a notably large positive contribution compared to the common firms aremainly retail industries, services industries andother labor-intensive industries that actively use
temporary and part-time employment.
8. Thus, in the factor adjustment behavior ofJapanese firms, diversity is becoming significant
at both the industry and company level and factors in the expansion of capacity and employment are becoming more idiosyncratic. In addition, based on listed common firm data, we confirmed that the potential for capacity expansion
is limited given the increasingly cautious attitude
toward expansion in scale and the establishmentof downsizing trends, especially among manufacturing industries. Meanwhile, trends among
newly listed companies indicate latent growthpotential and the existence of entrepreneurial
capabilities in non-manufacturing industries andit is hoped that the contribution will expand in
the future, especially in employment. When considering introducing some macroeconomic policy to stimulate investment, it is important to target indirect, medium- to long-term effects taking
into consideration the need for business reorganization among manufacturing industries andfor business expansion among non-manufacturing industries.
[Junichi Nakamura (e-mail: junakam@dbj.go.jp)][Michinori Takahashi (e-mail: mctakah@dbj.go.jp)]

Development Bank of Japan Research Report/ No. 31

Introduction1
As it becomes increasingly difficult to anticipatea macroeconomic environment of constantly
increasing growth and as competition intensifies
within the deflationary economy, corporate factor adjustment behavior2 (decisions regarding theamount and timing of capital3 and labor input) is
also changing from one that assumes expansion
in scale to one which considers both expansion
and contraction with wider variety, reflecting
differences in business environment and historyof adjustment path. In order to understand the
dynamics of aggregate investment and employment, it is essential to explore the microstructure
regarding investment and employment as a set offactor adjustment behavior. This study investigates the histories and results of Japanese corporate factor adjustments in the domestic market
since the 1980s and extracts the stylized facts.4
This study uses two techniques to explore
factor adjustment behavior under a deflationary
economy. The first is micro-data econometrics,
which sheds light on gross flows of investment
and employment at the individual level5 without
offsetting the dynamics in both expansion andcontraction by aggregation. The importance of
micro-data analysis is today widely recognizedeven by macroeconomic researchers due to therelevance of microeconomic heterogeneity toaggregate dynamics and there have been many
empirical studies regarding employment, investment, productivity and the like.6 The second
1

In the preparation of this study, Takahashi was primarily in charge of Chapter I and Nakamura was in chargeof Chapters II and III as well as the Concluding Remarks.
2
Factor adjustment is a generic term for capital stock(capacity) adjustments, labor (employment) adjustments andother adjustments of input to optimize production. Thoughalso used for adjustments of raw materials, energy and soforth, it is used in this study only for adjustments of capitalstock and labor.
3“Capital” in this study refers to real capital stock.
4
Refer to Nakamura (2000), p.5, footnote 1, for the significance of focusing on the period from FY1980 onward.
5“Individual” is a generic term for firms, establishments,
households and so forth. This study considers only firms.
6
Refer to Haltiwanger (1997, 2000) for a survey of research in the U.S., where establishment-level data is wellorganized and there have been more empirical studies basedon micro-data analyses than in Japan. This study is similarin theme and methodology to those of Tanaka (2000) andNakamura (2000, 2001).

technique is the dual approach7, which considers
investment and employment as one set of adjustment behavior. The dual approach is effective
for describing actual corporate behavior in which
it is difficult to adjust capacity and employment
independently.
The study is organized as follows. In Chapter I, we survey changes in factor adjustment
patterns at the industry level based on aggregate
corporate data as an introduction to micro-data
analysis and verify evolving growth setback andheterogeneity during the latter half of the 1990s.
In Chapters II and III, we examine factor adjustment at the corporate level based on longitudinal
data of 1,400 firms listed continuously since
FY1980 (“common firms”). In Chapter II, we
first analyze the microstructure of capacity andemployment growth setback and heterogeneityfrom various angles using the dual approach. By
focusing on gross flows and transitions in distribution at the corporate level, we discuss interesting problems such as differences and interactions between capacity and employment in adjustment patterns. In Chapter III, we conduct
analyses that focus on the history of factor adjustment paths. It is possible to roughly groupsome 90% of the common firms into six patternsof adjustment paths since FY1980 and this taxonomy provides a new understanding ofdownsizing trends in the manufacturing sector
and other characteristics of factor adjustment.
We also review the trends among newly listedcompanies, which we were unable to treat in theanalyses of common firms, based on data for all
listed firms. Finally, in the Concluding remarks
we discuss macroeconomic implications and theresults of the analyses of common firms.

7“Dual approach” is a term coined by the authors. While
there are relatively few examples of micro-data analysis
based on this approach, Sakellaris (2001), for example,
carried out detailed fact-finding from a similar perspective
regarding dynamic choices and their consequences at thetime of lumpy adjustment in capacity and employment
based on plant-level data of U.S. manufacturing industries.

Development Bank of Japan Research Report/ No. 31

1

I Trends in Aggregate Data sincethe 1980s
1.

Three Phases since the 1980s andthe Macroeconomic Environment

In this study, we analyze the changing mode offactor adjustment during the 21-year period since
FY1980 through FY2000 divided into threephases of seven years each. This periodization is
not only for convenience of statistical comparison but each phase has the further significance ofcorresponding to changes in the business cycle
and macroeconomic environment and provides
the basic framework to interpret the results ofanalyses throughout this study.
Phase I (FY1980-86) corresponds to a period of contraction (36 months, 2/80 – 2/83) in the
ninth cycle, a period of expansion (28 months,
2/83 – 6/85) and recession (17 months, 6/85 –11/86) in the tenth cycle. During the prolonged
adjustment after the second oil shock and the socalled “strong yen recession”, the economy experienced its greatest stability of the three phases.
Phase II (FY1987-93) corresponds to a period of expansion (51 months, 11/86 – 2/91) andcontraction (32 months, 2/91 – 10/93) in theeleventh cycle, during which the so-called bubble economy emerged and subsequently collapsed.
Phase III (FY1994-2000) corresponds to aperiod of expansion (43 months, 10/93 – 5/97)
and contraction (20 months, 5/97 – 1/99) in thetwelfth cycle and a period of expansion (21months, 1/99 – 10/00) in the thirteenth cycle.
Although this phase encompassed two periods ofexpansion, the growth rate was low overall,characterized by stagnant investment, rising unemployment and the emergence of structural
deflation.
We will proceed with the analyses in thefollowing sections with reference to the macroeconomic environment during these three phases.
2.

Diversification in Factor Adjustment
Patterns at the Industry Level

In this and the following sections, we review changes in factor adjustment at the industry
level using the dual approach based on aggregate
2

Development Bank of Japan Research Report/ No. 31

corporate statistical data as an introduction to themicro-data analysis of Chapter II and provide
evidence of capital and labor growth setback andevolving diversity in factor adjustment in thelatter half of the 1990s.
We first confirmed the trend of the growthrate of the capital and labor 1 (against the previous year)2 in large corporations capitalized at ¥1billion or more3 (Fig. 1-1).
In Phase I, the growth rate of capital gradually dropped from 10% in FY1980 through 1983
and in FY1984-85, backed by the high-tech
boom, rebounded somewhat, but declined again
in FY1986 mainly due to the effects of the strong
yen. The growth rate of labor remained generally
stable at 2% until FY1985, falling subsequently
below 1% in FY1986.
In Phase II, the growth rate of capital surged
from FY1988, exceeding 13% at maximum in
FY1991, the highest of all three phases. Alongwith the demise of the Heisei boom, however, it
plunged to around 2% in FY1993. The growthrate of labor also increased beginning in FY1988,
peaking at near 6% in FY1990 and then dropping
again below 1% by FY1993.
In Phase III, the slowdown continued forboth capital and labor. Though the growth rate ofcapital, having hit bottom in FY1994, rosesomewhat in FY1995-97 reflecting economic
expansion, it remained negative for three years insuccession beginning in FY1998. The growthrate of labor was even more severe, negative forfour straight years beginning in FY1997.

1“Capital” and “labor” are used in this study to indicate
theoretical concepts or aggregate statistics while “capacity”
and “employment” indicate individual company figures or
behavior. In the analyses, capital (capacity) is considered tobe the book value of tangible fixed assets (excluding land)
and labor (employment) is considered to be the number of
employees.
2
When calculating growth rates (against the previousyear), the major discontinuity caused by the privatization of
publicly-held corporations has been adjusted.
3
In regards to time-series data of small- and mediumsized firms (capital from ¥10 million up to ¥100 million),
the impact of the discontinuity that followed the creation of
the minimum capital system, etc. is great.

(%)
16
Capital growth rate
14
Labor growth rate
12
10
8
6
4
2
0
-2
Ⅰ
Ⅱ
Ⅲ
-4
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscal year

Fig. 1-1

Aggregate Transitions in Capital and Labor Growth Rates

Notes: 1. The major discontinuity due to public corporation privatization has been adjusted.
2. Based on tangible fixed assets (ex-land) at the end of the term for capital and number of employees at the end of
the term for employment (likewise below). Refer to the text for details.
Source: Prepared based on the Quarterly Report on Financial Statements Statistics of Corporations, Ministry of Finance.
Total of large corporations.

We next verified the changes in factor adjustment patterns from the perspective of interindustry comovement. Specifically, we assessed
the similarity of the patterns of capital and labor
input in each industry during the three phases by
the size of the correlation coefficient (i.e., comovement) with the reference pattern of the totalsum of all industries based on industry-specifictime-series data4 of corporate statistics (quarterly,
actual amount). If we find a significantly positive
correlation with the reference pattern in manyindustries, it will indicate similarity of factor
adjustment patterns and, if we do not, it will indicate heterogeneity.
Figure 1-2a shows the arithmetic mean of
the correlation coefficient with the reference
pattern calculated for capital and labor adjustment patterns in 35 industries. Though the correlation coefficient of capital was stable and highat about 0.8 in Phases I and II, it declined
broadly to 0.3 in Phase III. The correlation coefficient of labor, which was originally low, was4

A total of 35 industries, 18 manufacturing and 17 nonmanufacturing industries, from among the 37 categories of
the classification of Financial Statements Statistics of Corporations statistics are analyzed in this chapter with agriculture, forestry, fisheries and aquaculture industries, which
are of lesser importance, consolidated.

0.3 in Phase I, climbed to 0.4 in Phase II, thendropped to 0.1 in Phase III. In other words, theheterogeneity in both capital and labor adjustment tended to become pronounced during Phase
III. Furthermore, calculating the average correlation coefficient for the 18 manufacturing and 17
non-manufacturing industries respectively (Figs.
1-2b, 2c), the direction of change in the case of
manufacturing is the same as for the average of
all industries, though the drop in the correlation
coefficient in Phase III appears even more conspicuously in both capital and labor. In the caseof non-manufacturing, there was downward
momentum in the correlation coefficient of
capital and that of labor also declined in Phase
III, though the drop was narrower for both thanin the manufacturing industries.
Thus, in order to clarify the background of
the changes in inter-industry comovement, we
calculated the same correlation coefficient for
time-series data of sales5 (quarterly, actual
amount seasonally-adjusted) as a proxy variable
for the business environment, and compared itwith the correlation coefficients for capital andlabor in terms of their time-series changes. If a
5

Sales are an important precondition, at least in terms ofbusiness practice, when companies decide the scale of capacity and employment.
Development Bank of Japan Research Report/ No. 31

3

Fig. 1-2a All industries

Fig. 1-2b Manufacturingindustries

Fig. 1-2c Non-manufacturingindustries

1.0

1 .0

1.0

0.8

0 .8

0.8

0.6

0 .6

0.6

0.4

0 .4

0.4

0.2

0 .2

0.2

0.0

0 .0

0.0

-0 .2

-0.2

-0 .2
P hase I

Capital
Labor
Sales

P hase II

Phase I
0.78
0.28
0.58

Phase II
0.82
0.44
0.67

P hase III

Phase III
0.33
0.13
0.45

Comovement of capital
資本成長率の連動性

P hase I

Capital
Labor
Sales

P hase II

Phase I
0.72
0.14
0.52

P hase III

Phase II
0.87
0.39
0.63

Comovement of labor
労働成長率の連動性

Phase III
0.18
-0.05
0.50

P hase I

Capital
Labor
Sales

P h ase II

Phase I
0.85
0.42
0.64

P hase III

Phase II
0.78
0.48
0.70

Phase III
0.49
0.32
0.39

Comovementof sales
売上高成長率の連動性

Fig. 1-2 Transitions by Phase of Inter-Industry Comovement of Capital, Labor and Sales
Note:

The correlation coefficients of quarterly time-series data (sales only seasonally adjusted) of capital, labor and sales forthe 18 manufacturing and 17 non-manufacturing industries to the total of all industries were calculated and the arithmetic means are shown.
Source: Prepared based on Financial Statements Statistics of Corporations, Ministry of Finance. The total of all sizes.

significant change occurred in the average correlation coefficient for either capital or labor andit was a change similar to that of the averagecorrelation coefficient for sales, we can say that achange in the business environment was the primary cause of the change in comovement offactor adjustment. If it was obviously different
from a change of the average correlation coefficient for sales, a change in the response of eachindustry to the business environment was theprimary cause. Taking these points into consideration, first we focus on manufacturing industries (Fig. 1-2b).The correlation coefficient forsales changed little throughout the three phases
and there was no prominent drop in Phase III as
in the case of capital and labor. Consequently,
changes in the factor adjustment patterns of
manufacturing industries in Phase III were likely
caused by changes in the response to the business environment (factor adjustment behavior)rather than changes in the business environment
itself. On the other hand, the conditions of nonmanufacturing industries (Fig. 1-2c) indicate thatthe correlation coefficient of sales was correspondingly low in Phase III and similar to the4

Development Bank of Japan Research Report/ No. 31

changes of the correlation coefficients of capitaland labor. Therefore, changes in the businessenvironment probably played a major role inchanges in the factor adjustment patterns of nonmanufacturing industries, including as well thedecline in the correlation in Phase III.
The analyses heretofore have looked at thecorrelation of short-term changes on a quarterly
basis with capital and labor viewed independently. To complement those, next we analyze
capital and labor as a set of factor adjustment andprovide evidence of growth setback and heterogeneity based on medium-term trends.
We examine the relationship between industries that play a considerable role in capital
growth and those that play a considerable role inlabor growth by expressing the medium-term
growth rate6 of capital and labor of each industry
in each of the three phases in scatter diagrams.
For example, as indicated in the conceptual diagram of Fig. 1-3, if an upward sloping relation6

The figures for the first quarter and the final quarter of
each phase are compared and annualized figures are used
for the growth rate. As in Fig. 1-1, the effect due to privatization has been adjusted.

ship (positive correlation) in inter-industry factor
adjustment is recognized to be significant, itmeans that the more an industry expands (contracts) capital, the more it also expands (contracts) labor. In other words, it is possible to reduce differences in inter-industry factor adjustment patterns to the axis of expansion and contraction in scale. On the other hand, if a downward sloping relationship (negative correlation)is recognized as significant in inter-industry
factor adjustment patterns, it means that the more
an industry expands (contracts) capital, the more
it also contracts (expands) labor. In other words,
the axis of substitution of capital and labor is the
focal point in differences in inter-industry factor
adjustment patterns. In some cases there may be
no significant correlation in inter-industry factor
adjustment patterns. That means that the axis inwhich differences between industries is the focal
point as in the two cases described above does
not exist, that is, that factor adjustment patterns
are diversifying in a manner different from thatof Fig. 1-2.

The conditions of distribution of industryspecific data taking this into account indicate that,in Phase I, a significant upward sloping relationship is apparent7, suggesting that the expansion
and contraction axis is the focal point for differences in inter-industry factor adjustment patterns(Fig. 1-4a) The inclination of the regression line
is 0.8 and, in industries in which the capital
growth rate is 1 percentage point higher than the
average, that means that the labor growth rate is
statistically expected to be 0.8 percentage points
higher than the average. When factor adjustment
patterns are divided into four types by the increase and decrease of capital and labor (refer toconceptual diagram Fig. 1-3), 23 industries,
about 70% of the total, belonged to the scale
expansion type in which both capital and labor
expanded. Electrical equipment, precisionequipment and transport equipment had doubledigit capital growth rates, expanding with broad
divergence from the schedule suggested by the
regression line and showing their role in driving
growth at this time in terms of capital.

Scale axis
Scale expansion type(capital＋, labor ＋)

Scale contraction type(capital −, labor −)Capital-intensive orientation
type (capital ＋, labor −)Labor
growth rate

Labor-intensive orientation
type (capital −, labor ＋)

００

7
Significant level 0.1% (coefficient of determination
Capital growth rate0.60) Substitution axis

Development Bank of Japan Research Report/ No. 31

Fig. 1-3

Conceptual Diagram

5

Labor growth rate (%)

Fig. 1-4a
10
8
6
4
2
0
-2
-4
-6
-8

Labor growth rate (%)(outside the diagram) Business services（18.7,17.2）
Electrical equipment

Transport equipment
Precision equipment

0

5

10

Fig. 1-4b

10
8
6
4
2
0
-2
-4
-6
-8

15

Standard deviat
3.9
3.8

20
25
Capital growth rate (%)

Phase IIBusiness services
Hotelsy = 0.7672x - 3.7205
R2 = 0.6038

Avg.
6.6
1.4

Capital growth rate
Labor growth rate

-5

Retail Construction

Personal services

Real estatey = 0.4724x - 1.8877
R2 = 0.6391

Capital growth rate
Labor growth rate

-5

Labor growth rate (%)

Phase I

0

5

10

Fig. 1-4c

10
8
6
4
2
0
-2
-4
-6
-8

15

Avg.
7.7
1.8

Standard deviation
5.0
3.0

20
25
Capital growth rate (%)

Phase III

Retaily = 0.4564x - 0.1898
R2 = 0.2366

Other transportation& communication
Transport
Chemicals equipment-50Electrical equipment

5

10

Capital growth rate
Labor growth rate

15

Avg.
0.3
0.0

Standard deviatio
3.2
3.0

20

25

Capital growth rate (%)

Fig. 1-4

Distribution and Correlation of Capital and Labor Growth Rates
by Industry in Each Phase

Notes: 1. Distribution charts indicate the annual average growth rates of capital and labor for 18 manufacturing industries
and 17 non-manufacturing industries in each phase. The major discontinuity of public corporation privatization
has been adjusted.
2. Regression lines are the results of regressing the labor growth rate against the capital growth rate. The dotted
lineused in Phase III expresses low significance.
Source: Prepared based on the Quarterly Report on Financial Statements Statistics of Corporations. The total of all sizes.

6

Development Bank of Japan Research Report/ No. 31

A significant upward sloping relationship in
distribution is also evident in Phase II (Fig. 14b).8 On the other hand, the inclination of theregression line is 0.5, smaller than the value of
0.8 of Phase I. The expansion of the capital
growth rate and the contraction of the capital
growth rate were more dispersed compared toPhase I. Of the four types, 24 industries indicatedthe scale expansion type of factor adjustment
pattern, much the same as in Phase I. Service,retail, construction and other industries indicateda high labor growth rate diverging from theschedule suggested by the regression line and it
is possible to see the remnants of an economic
environment led by vigorous domestic demand.
In Phase III, though there is an upward
sloping regression line, given the strict criteria, it
can no longer be considered a significant relationship.9 It is thought that differences in interindustry factor adjustment patterns assumed adiversified form in which the scale axis and thesubstitution axis are intermingled with each other(Fig. 1-4c). The average of capital and labor
growth rates were essentially zero for all industries. In terms of the four types, 13 industries,
including chemicals and transport equipment,
were of the scale-contraction type with a decrease in both capital and labor (0 industries inPhase I, and 2 in Phase II).
3.

Changes in Inter-Industry Propagation
Structure of Investment

In this section, we analyze the inter-industry
comovement or propagation structure, including
leading and lagging relationships, with the focus
on investment.
Quarterly time-series data of the total of allindustries as the benchmark, we calculated thecorrelation coefficient of simultaneous time-series
data of each industry and also the correlation
coefficient when shifted one quarter ahead andbehind. This method is based on that of Hornstein (2000), who analyzed the conditions ofinter-industry comovement for various aggregate
statistics. We then defined industries as “leading
8

Significant level 0.1% (coefficient of determination
0.64)
9Significant level 0.1% (coefficient of determination
0.24)

industries” if the correlation with a one-quarterlead was highest, “lagging industries” if the correlation with a one-quarter lag was highest and“simultaneous industries” if the simultaneous
correlation was the highest. If the correlation
coefficient was less than 0.510 in each case, industries were classified as “low-correlation industries”11 Such definitions basically assumed
that all industry and individual data have a positive correlation. However, if a negative correlation is found, it is deemed to be a “lowcorrelation industry.” Figure 1-5 indicates theconditions of the correlation of each industry
based on this method for the three phases. The
numbers displayed in the lower right-hand cornerindicate the number of relevant industries and,among them, the names of the industries are displayed individually if either the investment issignificantly large or considered qualitatively
important regarding comovement.12
Analyzing changes in the inter-industry
propagation structure of investment with thedefinitions as set out above, there is a distinctive
structure that propagates from manufacturingindustries to non-manufacturing industries inPhase I. Among leading industries are sevenmanufacturing industries including electricalequipment and general equipment while there are
no relevant non-manufacturing industries.
Meanwhile, simultaneous and lagging industries
are mainly non-manufacturing industries, with asmaller proportion of manufacturing industries
that have a strong relationship to consumers suchas foods. This appears to be consistent with themacroeconomic situation at the time, with exports of high-tech industries creating transition
points in the business climate and domestic demand following behind.

10

Corresponds generally to a threshold value with a significance level of 0.1%.
11
Since industries with a negative correlation to the total
amount of all industries all had a correlation coefficient ofless than 0.5 in terms of the absolute value, they were classified as “low-correlation” industries.
12
There were 11 manufacturing industries and 10 nonmanufacturing industries with names listed separately.
Development Bank of Japan Research Report/ No. 31

7

Phase I (2nd Qtr 1980 – 1st Qtr 1987)
Leading

Simultaneous

Lagging

Low-correlation industries

●
●
●
●
●

● Transport equipment○ Real estate○ Overland transportation○ Business services○ Cinema & amusement● Foods● Paper & pulp
○ Wholesale○ Retail○ Other transportation &communication
manufacturing: 3
non-manufacturing: 4● Ceramic, stone & clay
● Iron & steel● Nonferrous metals○ Construction○ Electricity○ Gas & water
manufacturing: 6
non-manufacturing: 8

Chemicals
Metal products
General equipment
Electrical equipment
Precision equipment

manufacturing: 7
non-manufacturing: 0

manufacturing: 2
non-manufacturing: 5

Phase II (2nd Qtr 1987 – 1st Qtr 1994)
Leading

Simultaneous

Lagging

Low-correlation industries● Chemicals● Ceramic, stone & clay
● General equipment● Electrical equipment● Transport equipment● Precision equipment○ Wholesale○ Real estate○ Gas & water○ Business services● Metal products● Nonferrous metals○ Construction○ Retail○ Overland transportation○ Cinema & amusement● Foods● Iron & steel○ Other transportation &communications○ Electricity● Paper & pulp

manufacturing: 9
non-manufacturing: 6

manufacturing: 2
non-manufacturing: 5

manufacturing: 4
non-manufacturing: 2

manufacturing: 3
non-manufacturing: 4

Phase III (2nd Qtr 1994 – 1st Qtr 2001)
Leading

Simultaneous

Lagging

Low-correlation industries● Ceramic, stone & clay
● Electrical equipment● Metal products● Precision equipment○ Wholesale○ Other transportation &communications○ Business services● Foods● Paper & pulp
● Chemicals● General equipment● Transport equipment● Iron & steel● Nonferrous metals○ Construction○ Retail○ Real estate○ Overland transportation○ Electricity○ Gas & water○ Cinema & amusement

manufacturing: 3
non-manufacturing: 1

manufacturing: 2
non-manufacturing: 3

manufacturing: 8
non-manufacturing: 1

manufacturing: 5
non-manufacturing: 12

Fig. 1-5 Inter-Industry Propagation Structure of Investment and Its Changes
Notes:

1. In order to comprehend the correlation including leading and lagging relationships, the correlation coefficients of quarterly timeseries (seasonally adjusted) data of the investments of the 18 manufacturing industries and 17 non-manufacturing industries tothe total of all industries were calculated for the three cases, including the addition of one quarter of lead and lag. Industries
were classified as low correlation industries if the maximum absolute value is less than 0.5 and as industries with a significant
correlation if the maximum value was 0.5 or more. Since the absolute values of correlation coefficients of industries indicating anegative correlation were all less than 0.5, the industries were classified as low-correlation industries.
2. The figures at the lower right express the number of relevant industries and, of these, industries with a large share in investment
or qualitative importance in the propagation process (manufacturing industries: 11, non-manufacturing industries: 10) are listed
by name (● = manufacturing industry, ○ = non-manufacturing industry).
Source: Prepared based on the Quarterly Report on Financial Statements Statistics of Corporations. The total of all sizes.

8

Development Bank of Japan Research Report/ No. 31

In Phase II, there were seven lowcorrelation industries, the smallest number in all
three phases, that did not correspond to leading,simultaneous or lagging industries and the only
major industry was paper & pulp. Another characteristic at this time was the appearance of nonmanufacturing industries among the leading industries and, rather than propagation, this reflects
a unanimous type dynamic structure tied to vigorous domestic demand.
In Phase III, in contrast to Phase II, 17 industries, about half, corresponded to lowcorrelation industries, the most in any of thethree phases. In a breakdown, there were five
low-correlation manufacturing industries, not aparticularly large number compared to Phase II,
but the propagation structure changed greatly
among industries with a significant correlation.
Leading industries declined to only three including electrical equipment, while there was aconsiderable increase in lagging industries toeight, reflecting the conditions of weak growth
drivers other than IT-related industries in termsofinvestment. On the other hand, in non-manufacturing industries, 12 industries including retail,which had until then been a typical correlatedindustry, amounting to 70% non-manufacturing
total, corresponded to low-correlation industries.
Thus, the clearly-defined process of propagation
seen in Phase I from manufacturing industries tonon-manufacturing industries, through the unanimous type boom in Phase II, weakened considerably by Phase III.
In this chapter, we reviewed changes infactor adjustment patterns at the industry levelbased on aggregate corporate data. We confirmed the evolving trends of diversity in Phase
III, under the circumstances of capital and labor
growth setback, in which inter-industry comovement declined while industries that played
a role in growth were divided by capital and labor. In the following chapter, we will empiricallyclarify the microstructure of growth setback andheterogeneity in capacity and employment basedon longitudinal data of firms listed continuously
since FY1980 (“common firms”).

Development Bank of Japan Research Report/ No. 31

9II. Microstructure of Investment
and Employment Dynamics
1. The Significance and Methods ofMicro-Data Econometrics Based on
Corporate Data
In this and the following chapter, we investigate
factor adjustment behavior at the corporate level
using the technique of micro-data econometrics
based on longitudinal data. As stated in the Introduction, micro-data analysis enables the underlying gross microeconomic changes to be
identified without offsetting them by aggregation.
Assume, for example, that, at the beginning of
the term, all companies have the same scale oftangible fixed assets. If at that time we aggregate
both the case in which no firms changed theirassets and the case in which half of the companies doubled assets while the other half disposed of
all of their assets, then the growth rate of tangible fixed assets is zero in both cases and it is
impossible to distinguish between them. Though
this may be an extreme case, generally speaking,the greater the heterogeneity between individuals(the stronger the tendency to deviate from theaverage) the more important the technique of
micro-data econometrics for analyzing the dynamics of the real economy). Actually, according
to research in the U.S., it has been pointed outthat employment, capital stock, production, productivity and other indices in manufacturingindustries have tremendous heterogeneity even if
detailed industry properties are controlled.1 In
addition, analyses of the ROA of listed companies in Japan have shown that the disparities between firms can largely be explained by disparities between firms within the same industry.2
Thus, micro-data analysis is indispensable inprecisely analyzing the factor adjustment behavior in Phase III when the trend toward constantly increasing growth and uniformity had
weakened.
The “individual data” used for analysis in
this paper are the non-consolidated financial dataof listed firms based on securities reports. The
primary subjects of analysis are 1,418 firms (9811210Refer to Haltiwanger (1997, 2000).
Refer to Nakamura (2001).
Development Bank of Japan Research Report/ No. 31

manufacturing firms and 437 non-manufacturingfirms), excluding financial and insurance industries, with stock listed continuously fromFY1980 through FY2000 in the first or second
section of the Tokyo, Osaka or Nagoya Stock
Exchanges (henceforth called “common firms”).
In the U.S., there is a publicly available database3 containing establishment-level longitudinal data of manufacturing industries, so most of
the empirical studies based on micro-data focus
on establishments as the analytical unit. However,in Japan, firm-level data are often used in suchanalyses due to differences in accessibility toestablishment-level data. The reason for focusing
on continuously listed firms in this study is because we are interested in the history of factor
adjustment path, and continuous data are required for the analysis period in question(FY1980 and later). Since this study does notmake comparisons with preceding studies andresults, detailed explanations of the relative advantages and disadvantages due to differences indata characteristics are omitted.4 However, wheninterpreting the analysis results below, it is necessary to consider the following two points.
First, micro-data analyses focusing on employment generally consider the effects of thestartup and shutdown (or entry and exit) of business establishments (or firms). However, when
listed firms are the subject of analysis as in thisstudy, only the expansion and contraction offirms that remain in business are considered.
Secondly, listed firms are not necessarily typicalof firms in Japan. Likewise, continuously listed
firms (common firms) are not necessarily typicalof listed firms. In this sense, sample bias does
exist to some extent, but is not crucial. A generalcharacteristic of common firms is that many ofthem have a long history, are large in scale, andare manufacturing firms.5 The differences between common firms and all listed firms will beanalyzed further in Chapter III, Section 3.
3
Referred to as the Longitudinal Research Database(LRD).
4
Tanaka (2000), who also used non-consolidated data oflisted companies like in this study, gives a general examination of the sample bias problems on pp. 17-18.
5Supporting evidence will be given from time to time toshow that the problem of sample bias and the properties of
the common firms do not have a particularly large impact
on the results of the analyses of this study.

In preparation for the analyses, this section
explains the data used and the key concepts. The
listed company data used in the study (nonconsolidated account settlements) was takenfrom the Corporate Financial Data Bank of theDevelopment Bank of Japan, and certain adjustments have been made to enable comparison of
figures in the case of irregular settlements andmergers before and after (normalized data). In
regard to differences in settlement month, allcommon firms are analyzed as balanced panel
data by treating the data of firms other than thosewith March settlements as data of the fiscal year
in which the end of the settlement period falls.
In the financial data, the book value of tangible fixed assets (excluding land) is used as theconcept of “capital stock” or “capacity.” Thebook value is the balance after deducting the
cumulative depreciation amount in terms of accounting from the historical cost and, in principle,
it can also be considered as the net capital stock
based on historical cost. To ensure consistency
with the concept of capital stock in economics,
reassessment on a current cost basis as well as
adjustment of the difference between depreciation cost recognized in accounting and physicaldepletion are needed. This study, however, is a
simple fact-finding study and the book value isprobably a more practical yardstick used in thedecision-making processes of business operations. Therefore, the analyses are provisionallycarried out without making special adjustments.
As the concepts of “labor” or “employment,” we used the total number of employees,
which is equal to the “number of employees as ofthe end of the term” plus the “number of temporary, contract and other personnel” and, following Tanaka (2000), we considered the former tobe regular employment and the latter to be temporary employment. This assumption raises various points of controversy, such as the ambiguityof the definition of “number of temporary, contract and other personnel” and of the borderline
with the “number of employees as of the end of
the term”6. In addition, “number of temporary,contract and other personnel” is measured as the6

For example, it is thought that both firms that include
regular part-time workers among employees at the end of
the term and firms that include them among temporary andpart-time employees can exist concurrently.

average during the term, not as of the end of theterm, and is provided only when it exceeds acertain number. However, as labor demand has
shifted in recent years to temporary, part-timeand other non-regular labor, the loss caused bytheir elimination would probably be greater. As
with capital stock, adjustments for the sake ofconsistency with the concept of labor in
economics are not carried out in this study.
Next, as flow variables corresponding to theconcepts of capital and labor as stock variables,in the case of capital, the difference in book value at the beginning and end of the term of tangible fixed assets is referred to as “net increase incapacity (namely, net investment)” and, in thecase of labor, the difference in total employees atthe beginning and end of the term is referred toas “net increase in employment.”7 What corresponds to “investment” or “capital expenditure”
in the usual sense is the concept of gross investment with the depreciation (capital depletion)
portion added to net increase in capacity. In thisstudy, however, the focus is solely on the netincrease in capacity from the perspective of thedual approach, which treats investment and employment constantly in parallel. A negative netincrease in capacity means that gross investment
does not exceed the depreciation amount, and apositive figure indicates that gross investment
does exceed the depreciation amount. The ratioof net increase in capacity to the book value oftangible fixed assets (excluding land) at the beginning of the term8 is referred to as “net growthrate of capacity” and the ratio of net increase inemployment to the total number of employees atthe beginning of the term9 is referred to as the“net growth rate of employment.”
The terms “job creation” for the gross positive contribution made by a group of individuals
that increased employment and “job destruction”
for the gross negative contribution (absolutevalue) made by a group of individuals that decreased employment have become established in
the field of labor economics in which micro-data7

It is also possible to obtain negative values for either;
however, the term “net increase” is used whether positive or
negative.
8
I.e., it corresponds to the growth rate of the book valueof tangible fixed assets (excluding land).
9I.e., it corresponds to the growth rate of total employees.
Development Bank of Japan Research Report/ No. 31

11analysis techniques are most frequently applied.10
The rates derived by dividing the number of jobs
created and those destroyed by the number of
employees at the beginning of the term arereferred to, respectively, as the job creation rate
and the job destruction rate and the figure derived by subtracting the job destruction rate fromthe job creation rate corresponds to the net
growth rate of employment defined above. However, there is no appropriate established terminology for capital stock or capacity. Therefore,
in this study, the concept corresponding to creation rate is referred to as “gross positive contribution rate” regardless of whether it is capacity
or employment and the concept corresponding to
the destruction rate is referred to as the “grossnegative contribution rate.” To reiterate, thegross negative contribution is calculated by theabsolute value of the sum total decrease (andtherefore the sign is always positive), and the net
growth rate is derived by subtracting the grossnegative contribution from the gross positive
contribution. Since the gross positive contribution rate expresses the margin of increase of the
net growth rate when the gross negative contribution rate decreases, it serves as an index
indicating the potential for increase in capacity
or employment, in other words, the latent vitalityof the economy or the industry.11
In addition, following preceding studies, thesum of the gross positive contribution rate andthe gross negative contribution rate (the size ofthe fluctuation in both the direction of increase
and decrease) is referred to as the “reallocation
rate” and the value derived by subtracting theabsolute net growth rate from the reallocation
rate is referred to as the “excess reallocation
rate.” The latter is called the excess reallocation
rate because the shift of resources that are absolutely essential for realizing the net increase or
decrease is a shift across the border of the population. This is considered to be the “minimum
reallocation” and is based on the idea that a shift
within the population is that portion that exceeds
the minimum.12
The various concepts defined in this section
and the symbols that correspond to them aresummarized in Table 2-1. Details of how to use
and interpret those concepts will be explained as
required.
2.

Weakening Potential for Capacity
Expansion

In this section, we confirm the sample characteristics of the common firms while observing
the transitions in the basic figures relating tofactor adjustments since FY1980.
Figure 2-1 shows the transitions of the grossinvestment rate (ratio of gross investment to thebook value of tangible fixed assets (excludingland) at the beginning of the term) based on the

Table 2-1 Concepts Used in Micro-Data Analysis and Notation in this Study
Capacity

KDR

Employment Remarks
In relation to employment, generally referred
LCR
to as the job creation rate
In relation to employment, generally referred
LDR
to as the job destruction rate

Net growth rate

KNCR

LNCR

NCR ＝ CR − DR

Reallocation rate

KGRR

LGRR

GRR ＝ CR ＋DR

Excess reallocation rate KERR

LERR

ERR ＝ GRR − ｜NCR ｜Gross positive
contribution rate
Gross negative
contribution rate

Note:

KCR

“Capacity” means the book value of tangible fixed assets (ex-land) and “employment” means all employees including temporary, contract, etc.

10Refer, for example, to Davis et al. (1996).
Discussions here tacitly assume that the gross positive
contribution rate and the gross negative contribution rate are
mutually independent. However, this assumption is notstrictly true (an individual defeated in competition with anindividual that made a gross positive contribution made agross negative contribution).

11

12

Development Bank of Japan Research Report/ No. 31

12

Excess reallocation rate is close in a sense to the original meaning of the word “reallocation” and it is probably
possible to consider it as reallocation defined in the narrow

(%)
30
25

Ⅰ

Ⅱ

Ⅲ

20
15
10
5
0
-5
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscal year
depreciation ratio
net growth rate of capacity
gross investment ratio

Fig. 2-1 Transitions in the Gross Investment Ratioof Common Firms

aggregate statistics of the common firms alongwith decomposition to depreciation portion andnet investment.13 The gross investment rate washigh, exceeding 20% during the first half ofPhase I and in the middle of Phase II. It then fell
rapidly during the latter half of Phase II, andremained low at around 10% in Phase III.
Though the depreciation ratio (ratio of depreciation to the book value of tangible fixed assets(excluding land) at the beginning of the term)
was similar to the gross investment rate regarding the direction of changes, the range offluctuation was narrow, from a maximum of
13% to a minimum of 11%, and stable. If theamount of depreciation is considered to beequivalent to replacement investment, replacement investment can be interpreted to have been
carried out in a stable manner. Meanwhile, theportion for the net growth rate of capacity,
namely the gross investment rate excluding depreciation, fluctuated greatly and explains the
majority of gross investment dynamics. The net
growth rate of capacity was virtually zero inPhase III in particular, and was negative for threestraight years beginning in FY1998. Comparing
the transitions in the net growth rate of capacity
sense.
13
By definition, “gross investment = net investment +capital depletion” is always true; here, however, it is assumed that it is possible to consider physical capital depletion and depreciation expenses in accounting terms to beequal.

of the common firms with the tangible fixed asset (excluding land) growth rate of the large corporations based on the corporate statistics ofChapter I (Fig. 1-1), they show a similar trend,
though there are some differences such as thelatter peaked at a higher level in Phase II. In regard to the representativeness of large corporations, the common firms are a sample with limited bias.
When the net growth rate of capacity foreach firm is calculated, positive and negative
firms are tabulated separately and the contribution rate (gross positive or gross negative contribution rate) of each is determined, three general characteristics emerge (Fig. 2-2). First, thegross negative contribution rate was extremely
small through Phase II compared to the size ofthe gross positive contribution. Virtually all of
the changes in the net growth rate of capacity canbe explained by the change of the gross positive
contribution rate. Secondly, in the midst of thedownward trend in the gross positive contribution rate continuing from Phase II, in Phase III,with the size of the gross negative contribution
rate overtaking that of the gross positive contribution rate, the phase of recovery in the net
growth rate of capacity in FY1995-97, in particular, is explained primarily by the decline in
the gross negative contribution rate. Thirdly,transitions in the gross positive contribution rate
in Phase III were weaker than that of the number
(%pt)
15

Ⅰ

Ⅱ

Ⅲ

10
5
0
-5
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscal year
gross positive contribution
gross negative contribution (reverse symbols)
net growth rate of capacity

Fig. 2-2 Decomposition of the Net Growth Rate of
Capacity by Gross Flows

Development Bank of Japan Research Report/ No. 31

13

of increasing firms, suggesting a weakening in
the potential for increase in investment due to thedecline in major driving forces.
Next, in order to view these characteristics
from a different angle, we made estimates using
the simple linear regression equations given below based on time-series data of gross positive
contribution rate, gross negative contribution rate
and net growth rate of capacity while referring to
the employment analysis examples of Foote(1997) and Higuchi and Shinpo (1998).

The relationships a = g and b = 1 + d arealways binding since KNCR º KCR – KDR.
The results of estimations by Equations (1)and (2) for each of the three phases are given in
Table 2-2. The estimated value of coefficient βof net growth rate of capacity KNCR when grosspositive contribution rate KCR is the dependent
variable gradually decreases from a level near 1
in Phase I to 0.8 in Phase II and to 0.5 in Phase
III, while the estimated value of coefficient δ ofnet growth rate of capacity KNCR when grossnegative contribution KDR is the dependent variable was not significant in Phase I but gradually
increased in magnitude (increase in the absolutevalue) to –0.2 in Phase II and to –0.5 in Phase III.
This is consistent with the rough observations ofFig. 2-2 and, if the estimated results are interpreted to be caused by net growth rate of capacity on gross contribution rates14, then it turns outthat the gross positive contribution rate (rather

than gross negative contribution rate) clearly
responded more sensitively (had greater elasticity) to the net growth rate of capacity in Phase I.
However, this tendency weakened with the passage of time and the gross positive contribution
and gross negative contribution demonstrated
approximately the same degree of elasticity inPhase III. In other words, if the fluctuations in
net growth rate of capacity are considered toreflect changes in the business climate (or, to be
the proxy variable for shock in the macroeconomy), then in the past, company groups that increased their capacity responded sensitively tothe business climate and varied the rate of increase while company groups that reduced capacity did not do so. However, in Phase III, bothcompany groups increasing and decreasing capacity responded in the same manner to the business climate though not as sensitively as company groups that increased capacity had previously done. In summary, Japanese corporations
now no longer naturally increase capacity to theextent that they previously did in times of goodbusiness and, conversely, show little resistanceto flexibly reducing capacity if business is poor,
even if it is not a so-called structural recession.
The estimated value of the constant term was 1.2
in Phase I and somewhat larger, 2.3, in bothPhase II and Phase III. These figures suggest the
anticipated average gross positive and grossnegative contribution rates in the event that the
net growth rate of capacity is zero in the economy overall (macroeconomic environment in aneutral state), that is, they indicate the vigor ofstructural reallocation.

Table 2-2 Relationship between Gross Contribution Rates andthe Net Growth Rate of Capacity (time series)
Phase I
Constant term (common)

1.17(4.02)

Phase II
2.26(10.7)

Phase III2.28(17.0)

Coefficient β of Equation (1)0.959(23.2)

0.786(24.8)

0.518(5.55)

Coefficient δ of Equation (2)−0.041(0.99)−0.214(6.73)−0.482(5.17)

Note: 1. The figures are estimated values (figures in parentheses are t values).
2. The data unit used in estimation is % point.
14

14

Estimation Equations (1) and (2) do not mean a one-

Development Bank of Japan Research Report/ No. 31

way causal relationship from explanatory variable to depen-We will proceed next with a similar analysis of employment. First, in regard to the transitions in the net growth rate of employment (Fig.
2-3), though it has an overall high correlation
with the net growth rate of capacity, we see an
early cautious stance toward expansion15, falling
into the negative range in FY1986 and 87 andremaining in the negative in FY1993 and during
Phase III. However, focusing on the transitions
in gross positive contribution, which expresses
potential for increase, there is no downward
trend as seen in capacity and, though the level islower in Phase III than in Phases I and II, thedifference is not as extreme as with capacity.
When decomposing this into regular and temporary employment (Fig. 2-4), we confirmed thatthe gross positive contribution rate in Phase III,in particular, was largely supported by temporary
employment. Focusing on the relationship between the gross positive and gross negative contribution rate, it is difficult to identify
characteristics that are as clearly defined as in thecase of capacity. However, changes of the grosspositive contribution rate were not that great,with the exception of Phase II, and it appears thatthe changes of gross negative contribution rate
generally explain the changes of net growth rate.
Thus, as in the case of capacity, we madeestimates using the simple linear regression
equations given below based on time-series dataof gross positive contribution rate, gross negative
contribution rate and net growth rate of capacity
for all three phases.

The relationships a = g and b = 1 + d arealways binding since LNCR º LCR – LDR.
The results are given in Table 2-3. First, theestimated value of coefficient β of net growthrate of employment LNCR when gross positive

(%pt)
4

4

2

2

0

0

-2

-2

-4

-4

-6

Ⅰ
I

-8

Ⅱ
II

Ⅲ
III

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscalyear
year
Fiscal

(%pt)

-6
-8

I
Ⅰ

II
Ⅱ

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
FiscalFiscalyear
year

gross positive contribution

Temporary employment gross positive contribution

gross negative contribution (reverse symbols)

Permanent employment gross positive contribution

net growth rate of employment

III
Ⅲ

Permanent employment gross negative contribution (reverse symbols)
Temporary employment gross negative contribution (reverse symbols)
Net growth rate of employment

Fig. 2-3 Decomposition of the Net Growth Rate of
Employment by Gross Flows
Note:

Fig. 2-4 Decomposition of the Net Growth Rate of
Employment by Gross Flows and Status

Gross flows of permanent and temporary employment are identified and tabulated separately and, since there
are firms that are increasing one while decreasing the other, the sum of the positive (negative) contribution inpermanent employment and temporary employment does not coincide with the positive (negative) contribution
in Fig. 2-3.

dent variable but a two-way interpretation is possible.
15
This point is evidently expressed in the differences
between the level of the net growth rate of capacity in
FY1986 and 87 and the level of the net growth rate of capacity in FY1993 and later when the net growth rate of

employment again shifted to a reduction.
Development Bank of Japan Research Report/ No. 31

15

Table 2-3 Relationship between Gross Contribution Rates and the Net Growth
Rate of Employment (time series)

Constant term (common)
Coefficient β of Equation (3)Coefficient δ of Equation (4)Phase I
1.99(27.0)
0.273(3.38)−0.727(9.00)

Phase II
1.99(18.5)
0.454(7.31)−0.546(8.80)

Phase III1.10(4.29)−0.048(0.55)−1.048(11.9)

Notes: 1. The figures are estimated values (figures in parentheses are t values).
2. The data unit used in estimation is % point.

contribution rate LCR is the dependent variable
was smaller than the counterpart in the case of
capacity, 0.3 in Phase I and 0.5 in Phase II, andwas not significant in Phase III, while the estimated value of coefficient δ of net growth rate of
employment rate LNCR when gross negative
contribution rate LDR is the dependent variable
was constantly larger than the estimated value ofcoefficient β in terms of absolute value and became –1.0 in Phase III. In other words, in thecase of employment, the gross negative contribution rate (rather than gross positive contribution rate) constantly responded sensitively tothe business climate, varied greatly and exerted
the major influence on the net growth rate of
employment16 and that, in Phase III, the effect of
the gross negative contribution rate in the case of
capacity rose generally to the same level as thegross positive contribution rate, approaching thesituations seen in employment flow. The existence of labor hoarding, which is the tendency to
avoid dismissal at all costs even during recessionary periods based on the lifelong employment system, has been pointed in the case ofJapanese corporations. However, the empirical
16

These results are similar to those of preceding researches in the U.S. such as Davis et al. (1996) and Foote(1997). However, in the estimates of Higuchi and Shinpo(1998) using data of establishments continuously in business in the Survey of Employment Trends of the Ministry of
Health, Labor and Welfare, the results show that it is the job
creation rate that is more strongly affected by businessconditions (to use the symbols used in this study: β = 0.54, δ= -0.46). However, considering that there is only a small
difference between the absolute values of β and δ in the
study of Higuchi and Shinpo, and that the estimates for thesame estimation period of FY1986-95 applied to the data of
this study, namely β = 0.45 and δ = -0.55 are close to thoseof Higuchi and Shinpo (1998), it is thought that the differences in results between Higuchi and Shinpo and this study
are largely due to differences in the estimation period.
16

Development Bank of Japan Research Report/ No. 31

evidence of this section for listed firms suggeststhat there has been rather flexible adjustment
behavior in employment since the 1980s in linewith the business situation. In addition, capacity
increased steadily like an inviolable rule up to
the early 1990s, but such special treatment has
recently become unsustainable and the adjustment behavior is gradually shifting toward flexible reductions.
3.

Changes in Inter-Company Distribution
and the Rising Threshold Level

In this section, we will explore changes in corporate behavior behind the aggregate data, focusing
on the inter-company distribution of factor adjustment rates and return on investment as a basic approach to micro-data analysis that ranks
with the perspective of gross flow.
First, in order to survey inter-company disFY80 of
tribution and changes in the net growth rate
capacity, the relative frequency distributionFY85
chart
FY90
tabulated at intervals of 5 percentage points
FY95forfiscal years 1980, 85, 90, 95 and 2000 (Fig.
2-5)
FY00
shows that there were considerable changes in
the period up to FY1990 and from FY1995 onward. Specifically, the center of the distribution(interval of highest frequency) made a major
shift into the negative region from FY1995, andNet growth rate indicatesof capacity (%)
the configuration of the distributionthat the relative frequency of the area of highest
frequency rose from around 10% to 30% andthe number of intervals with a relative frequency of 5% or higher (expressing the width of thezone with comparatively thick distribution) also
decreased from 6-8 intervals to 5, demonstratinga general tendency to concentrate near the center.
Comparing this to the relative frequency distribution chart of the net growth rate of employ-

60
80FY

50

85FY
40

Relative frequency (%)

90FY
95FY

30

00FY

20
10

80FY

50

85FY

40

90FY
95FY

30

00FY

20
10

Average
MedianStandard deviationinterval
Relative frequency ofhighest frequency interval
No. of intervals of 5%+

FY85
8.0
4.8
14.5
0〜5

FY90
17.0
12.4
20.8
5〜10

FY95
-0.2
-2.4
9.4
-5〜0

FY00
-1.6
-3.6
10.4
-5〜0

16.1
7

18.7
6

13.1
8

31.5
5

29.3
5

50〜

45〜50

40〜45

35〜40

30〜35

25〜30

20〜25

15〜20

10〜15

0〜5

5〜10

-5〜0

-10〜-5〜-20

N et g ro w th ra te o f c a p a c ity (% )
FY80
9.7
6.8
15.6
5〜10

-15〜-10

0

50〜

40〜45

45〜50

30〜35

35〜40

20〜25

25〜30

10〜15

15〜20

0〜5

5〜10

-5〜0

-10〜-5-15〜-10〜-20-20〜-150-20〜-15

Relative frequency (%)

60

N et g row th rate o f em p loym en t (% )Average
MedianStandard deviationinterval
Relative frequency ofhighest frequency interval
No. of intervals of 5%+

Fig. 2-5 Relative Frequency Distribution of
the Net Growth Rate of Capacity

FY80
0.8
0.3
4.6
-5〜0

FY85
0.3
0.0
5.0
-5〜0

FY90
2.7
2.2
4.7
0〜5

FY95
-2.6
-2.2
4.5
-5〜0

FY00
-3.8
-3.5
8.6
-5〜0

38.9
4

40.3
4

45.7
3

54.2
3

44.4
4

Fig. 2-6 Relative Frequency Distribution of
the Net Growth Rate of Employment

Notes: 1. The basic statistics are calculated with the 20 upper Notes: 1. The basic statistics are calculated with the 20
and lower outliers in each year eliminated from theupper and lower outliers in each year eliminatedpopulation.
from the population.
2. The number of intervals of 5%+ means the number
2. The number of intervals of 5%+ means the numof intervals with a relative frequency of 5% or more.
ber of intervals with a relative frequency of 5%
or more.
FY80
FY85
FY90
FY95
ment (Fig. 2-6), the interval of highest frequency
FY00
in the case of employment, with the exceptionof
FY1990, was consistently –5 to 0% and, though
there was a slight shift to the left in the overall
distribution, the magnitude was smaller than thatof the net growth rate of capacity. In addition,
the configuration of the distribution indicates
Net growth rate of employment (%)
that the relative frequency of the interval ofhighest frequency was about 40% even in
FY1980 when it was at its lowest level and there
was a maximum of four intervals with a relative
frequency of 5% or more. Thus, the degree ofconcentration near the center was higher overallthan with the net growth rate of capacity. However, the structural changes seen in the case of
net investment were not apparent. The trends ofgreater restraint in investment and employment
in the 1990s occurred in common. It is worth
noting, however, that, in terms of the positionand configuration of the distribution in the 1980s,
the adjustment patterns of net investment, whichshowed considerable potential for expansion in
the 1980s, rapidly came to exhibit resemblance
to the employment situation in the 1990s.
In order to analyze this point further, we
now focus on changes in the return on investment (tangible asset profit rate less standard in-terest cost17). Theoretically, investment are determined by the discounted present value of thecurrent and future cash flows generated by implementing a project, that is, primarily by the
relationship of the expected profit rate and discount rate.18 The concept of return on investment
for empirical analyses below is composed of therealized profit rate and loan interest rate. This
concept does not correspond completely to thetheory in the strict sense, but is expected to havea strong correlation with investment. Actually,
the aggregate data shows a stable relationshipbetween investment and its profitability (namely,
return on investment).19 The following can bepointed out based on the inter-company distribution of this return on investment using a relative
frequency distribution chart tabulated at intervalsof 5 percentage points for fiscal years 1980, 85,
90, 95 and 2000 in the same manner as factor
adjustment ratios (Fig. 2-7). First, the center of17

Defined as tangible asset profitability rate = operating
profit/loss / average (tangible fixed assets + inventory assets) at the beginning and end of the term, standard interest
cost = average loan contract interest rate of domestic bank(new loans, total).
18
It is a famous theorem that these factors can be summarized in just one variable referred to as Tobin’s q undercertain conditions (in other words, q is the sufficient statisticof investment).
19
For example, the Development Bank of Japan (2001),
p.13
Development Bank of Japan Research Report/ No. 31

17

60
FY
8 0FY
80

Relative frequency (%)

50

8 5FY
FY
85

40

FY
90
9 0FY
FY
95
9 5FY

30

FY
00
0 0FY

20
10

Average
MedianStandard deviationinterval
Relative frequency ofhighest frequency interval
No. of intervals of 5%+

50〜

45〜50

40〜45

35〜40

30〜35

25〜30

20〜25

15〜20

10〜15

0〜5

5〜10

-5〜0

-10〜-5-15〜-10〜-20-20〜-150

R eturn on in vestm ent (% )
FY80
11.4
8.4
13.8
0〜5

FY85
8.0
6.3
10.7
0〜5

FY90
8.0
5.5
11.3
0〜5

FY95
5.5
4.2
9.2
0〜5

FY00
6.6
4.6
9.8
0〜5

18.3
7

23.9
5

21.9
6

34.0
4

33.9
5

Fig. 2-7 Relative Frequency Distribution of the
Return on Investment
Notes:

1. The basic statistics are calculated with the 20 upperand lower outliers in each year eliminated from the
population.
2. The number of intervals of 5%+ means the number ofintervals with a relative frequency of 5% or more.

actually do confront discrete decision-making
such as whether to increase or decrease the capacity (whether to expand or contract the scale oftheir tangible fixed assets excluding land). Figure
2-8 compares the relative frequency distributionof return on investment of the group of firms
with a positive net growth rate of capacity (positive net investment) in FY2000 (439 firms) andthe group of firms with a negative net growthrate of capacity (negative net investment, 979
firms). As theoretically anticipated, the group ofcompanies with positive net investment had ahigher return on investment (distribution is positioned on the right-hand side) overall than thegroup of companies with negative net investment,
and so we can identify the critical value of returnon investment where the relative frequency of
the former exceeds that of the latter (intersectionof distribution curves).20 This critical rate of return, even if it happened to be the result of an
ideally controlled experiment regarding the same
company, corresponds to the threshold of the60

18

Development Bank of Japan Research Report/ No. 31

50
Relative frequency (%)

The point where the relative frequency of
positive net investment exceeds negative
relative frequency (calculated by linear
interpolation) is considered to be the threshold.

40
30
20
10

50〜

45〜50

40〜45

35〜40

30〜35

25〜30

20〜25

15〜20

10〜15

0〜5

5〜10

-5〜0

-10〜-5-15〜-10〜-20

0-20〜-15the distribution (interval of highest frequency) is
consistently in the range of 0-5% and there is no
substantial change in the position of the distribution. Although there has clearly been downward
momentum in the tangible asset profit rate since
FY1980, the decline in the interest rate has offset
this. In regard to the configuration of the distribution, the number of intervals with a relative
frequency of 5% or more declined somewhat
after FY1995 and, though more gradual than the
net growth rate of capacity, the tendency toward
concentration in the center is clearly becomingstronger. However, seen from the perspective ofinvestment fundamentals or investment environment, the conditions of return on investment
during the late 1990s would have been unlikely
to cause a large change in the distribution of the
net growth rate of capacity.
In other words, the response of firms to thelevel of return on investment, namely a change ininvestment behavior, is thought to have had abackground effect on the stagnation in the net
growth rate of capacity in the latter half of the
1990s. Accordingly, in order to examine thechanges in investment behavior, we prepared aframework as follows. Suppose that companies

Return on investment (%)

positive net investment

negative net investment

Fig. 2-8 Comparison of the Distribution of the
Return on Investment Relating to Increase andDecrease in Capacity (FY2000)

discrete decision-making whether to expand or
contract the capacity and is a parameter that expresses investment behavior characteristics (theseverity of standards of whether or not to adopt
20

It is possible that more than one point could emerge
depending on the conditions of distribution and the way ofdefining the interval (width of the notch); however, no suchcases existed in the analyses below.

(%)
12
10

Ⅰ

Ⅱ

Ⅲ

8
6
4

investment projects). Below, we assume that asimilar threshold derived from the distribution of
actual values of different companies is aparameter that expresses the average investment
behavior characteristics of the population (common firms). We then derive the threshold foreach fiscal year by the linear interpolation
method from relative frequency distribution
charts with an interval of 5 percentage points.
Specifically, if (α -5, a] is the interval immediately prior to the interval in which the distribution intersects, x is the relative frequency difference (absolute value) between companies withpositive net investment and companies withnegative net investment, (α, α+5) is the interval
immediately after the interval in which the distribution intersects and y is the relative frequency
difference (absolute value), then threshold θ canbe determined using the following equation==a - 2.5 and a + 2.5 represent the median valuesof (α -5, a] and (a, a + 5], respectively.
We organized the changes in decisionmaking standards of the common firms and theeffect on the actual investment level based on
threshold value θ thus derived and the changes of
the net growth rate of capacity based on aggregate data for each period (Fig. 2-9). First, θ inPhase I remains broadly constant until FY1984,
assuming that the irregularities of FY1981 and82 are leveled, and then drops in FY1985 and 86,
which corresponds to the so-called strong yen
recession. During this time, the net growth rateof capacity declined broadly in FY1980-84 but
did not drop in FY1985 and 86 in spite of adverse economic conditions. In other words, during
the strong yen recession the net growth rate ofcapacity was maintained under relaxed (or lowered) investment decision-making standards. In
Phase II, the good business climate encouraged
firms to restore θ to its original level until
FY1988, nevertheless the net growth rate of capacity rose. To sum up, changes in investment

2
0
-2
-4
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

Fiscal year
threshold of increase and decrease in capacity
純投資増減の閾値
net growth rate of capacity
純投資比率

Fig. 2-9 Changes in the Threshold Relating toIncrease and Decrease in Capacity and
the Net Growth Rate of Capacity

behavior during the strong yen recession andfollowing recovery suggest that there was a tendency towards smoothing investment in spite of
the business fluctuations. However, θ declined
sharply to around 0% and the net growth rate ofcapacity reached a high peak and then dropped
rapidly in the latter half of Phase II. The fact thatθ approached zero suggests that there would be
an expansion in scale if the tangible asset profit
rate exceeds the loan interest rate even by theslightest margin. In other words, the risk premium required of investment was extremely small21
as the Heisei boom continued its unprecedented
expansion. As a result, the net growth rate ofcapacity, which should have shifted earlier to a
decline given the level of θ in Phase I, remained
high until about FY1991, possibly making subsequent adjustments more severe. In Phase III, in
FY1994-97 θ rose to a level similar to that inPhase I and the net growth rate of capacity
slumped in spite of economic expansion. In addition, although θ was at the same or lower thanin Phase I, the net growth rate of capacity was far
below its level in Phase I and, therefore, there
was a decrease in investment projects that would
satisfy the threshold, thus exacerbating the decline of the net growth rate of capacity. After the
disturbance to the financial system and concerns
about a deflationary spiral in FY1998, θ tended
to rise more strongly in FY1999 and 2000. This
was likely due to the increase in the risk premium for fixed asset holdings resulting from themore severe market assessment of firms with
21

The discussion in this section assumes regulated interest
rates in which loan interest does not reflect individual company or investment project risk. This assumption is also
thought to be approximately appropriate even in Phase III.

Development Bank of Japan Research Report/ No. 31

19

(%)
12
10

Ⅰ

Ⅱ

Ⅲ

8
6
4

deteriorated capital efficiency and financial ratio.
In regard to employment, though it is difficult to define an appropriate profitability index
corresponding to return on investment, we cancalculate threshold λ for the net growth rate of
employment using the same return on investment
for a comparison with capacity and observe thetransitions (Fig. 2-10, 11). In Phase I, both
capacity threshold θ and employment threshold λwere similar both in level and trends. After entering Phase II, however, λ rose conspicuously
above θ and exceeded its own level of the early
1980s in the phase during which θ reverted to its
original level after dropping in FY1987 and 88
and during the strong yen recession. Thus, in the
period of expansion during the Heisei boom,
firms were more reluctant to expand employment
than capacity. However, λ did decline broadly inthe same manner as θ from FY1989 onward and,like capacity, the risk premium became extremely small. In Phase III, the correlation between θ and λ was weaker than in Phase I and II
and there were years, for example, during which
the two moved in different directions. First, inthe process of upward revision of θ in FY199497, initially (FY1994-95) λ rose faster than θ andthen declined in FY1996 and 97. In addition,with the exception of FY1981, λ consistently
exceeded θ every year until FY1998 and the remarkable rise of θ above λ in FY1999 and 2000
was a major feature of Phase III. Although employment behavior was generally severe in Phase
III, the threshold level remained within the scopeof experience of FY1980 onward, while certain
aspects of investment behavior were conspicuously different from those of Phases I and II.
Combining this point with the observationsof inter-company distribution, the investment
behavior of the common firms underwent a major transformation in the latter half of the 1990s,
and its relationship with employment began to
exhibit entirely new characteristics.
(%)
12
10

Ⅰ

Ⅱ

Ⅲ

8
6
4
2
0
-2
-4
-6
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscal year
20
Development Bank of Japan
Research Report/ No. 31
threshold of increase and decrease in employment
雇用純増減の閾値
net growth rate of employment
雇用純増率Fig. 2-10 Changes in the Threshold Relating toIncrease and Decrease in Employment andthe Net Growth Rate of Employment

2
0
-2
-4
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

Fiscal year
threshold of increase and decrease in capacity
純投資増減の閾値
threshold of increase and decrease in employment
雇用純増減の閾値Fig. 2-11 Comparison of Thresholds of Changes in
Capacity and Employment

4.

Growing Heterogeneity in Capacity and
Employment Increase Factors

In this section, we conduct further analyses of
the gross flow of capacity and employment observed in Section 2 by industry along with thefactor decomposition of aggregate trends, anddiscuss the background of capital and labor
growth setbacks in Phase III, particularlychanges in adjustment patterns of capacity.
We will start by explaining the methods ofcalculating the gross positive contribution rate,
gross negative contribution rate, net growth rateand excess reallocation rate by industry. In thissection, we group common firms into 20 industries (manufacturing: 13, non-manufacturing: 7)
according to the primary business category andtabulate the medium-term factor adjustment
amount in Phases I, II and III calculated by firm
and the sign of the amount of change (increase or
decrease) for each of the seven-year periods.
Specifically, KNCij as the net increase in capacity of Firm i affiliated with Industry j, LNCij as
the net increase in employment, Kij as capitalstock or capacity (book value of tangible fixed
assets, excluding land, at the end of the term) and
Lij as labor input or employment (total numberof employees at the end of the term including
temporary, contract, etc.) are defined as follows(Arabic numerals express fiscal year and Roman
numerals express phase)

Then KCRj I, which is the gross positive
contribution rate of the capital stock of Industry j
in Phase I, for example, as well as gross negative
contribution rate KDRj I, net growth rate KNCRj
I and excess reallocation rate KERRj I are calculated as follows.

gross positive contribution and gross negative
contribution essentially offset one another inPhase III. In addition, the excess reallocation rate
in Phase III increased in 18 industries comparedto Phase I and in 19 industries compared toPhase II and so the focus of capacity adjustmentsin Phase III seemed to shift from industry growth
to intra-industry reallocation.
We thus applied the following regression
analysis conducted in Section 2 of this chapter toindustry-specific cross-sectional data in eachphase (the subscript j expresses industry).

As can be seen based on these equations, thedefinitions of the various rates in this section are
cumulative values for seven-year periods. It isalso possible to define the rates for Phases II and
III in the same manner. Based on the factor adjustment ratio of each industry in each phasedefined above, the following points are noted(Fig. 2-12; capacity on the left, employment on
the right). First, in regard to capacity trends, as
can also be surmised from the gross flow analyses of aggregate time-series data in Section 2of this chapter (Fig. 2-2), the gross negative
contribution rate was extremely small comparedto the size of the gross positive contribution rate
through Phase II and the level of the net growthrate of capacity of each industry was basicallydetermined by the gross positive contribution
rate. Iron and steel as well as the wholesale industry in Phase I are exceptions. In Phase III, thegross positive contribution rate decreased in 19
industries compared to Phase I and in all industries compared to Phase II, while the gross negative contribution rate rose in all industries compared to both Phases I and II. As a result, theDevelopment Bank of Japan Research Report/ No. 31

21

Phase I (FY1980-86)

(%pt）
250
200

gross positive contribution

gross negative contribution

net growth rateexcess reallocation rate

Capacity

Employment

150
100
(%)
50
0
-50
s
s
s
y
p
ls
le
el
ing
ent
cts
les
ent
ion
tals
ile
od
cal
cla
ica
pul
ste
esa
du
xti
tur
pm
Fo
uct
me
uti
ipm omob
&
&
em
Te
hol
fa c
pro
str
n & rousqui l equ
t
ne
ace
u
n
l
e
o
Ch
W
per
u
o
a
o
n
r
l
t
m
t
I
a
r
s
A
a
C
P
ar
ma
ica
Me
nfe
i c,
ner
Ph
er
ctr
No
Ge
ra m
Oth
Ele
Ce
Tra

s
l
te
gas turing
t ai
ion
sta
&
Re
cat
al e
fa c
ity
uni
c
i
Re
r
m
anu
t
lec
com
n-m
E
o
n
&
on
her
ati
Ot
ort
nsp

Phase II (FY1987-93)

(%pt）
250
gross positive contribution
net growth rate

200

Capacity

gross negative contribution
excess reallocation rate

Employment

150
(%)

100
50
0
-50
s
s
s
y
ls
ls
ts
el
lp
les
ing
ent
ent
ale
ion
ile
tal
od
cla
ica
pu
ste
tica
duc
xti
tur
les
uct
Fo
me
ipm omob
ipm
Te
ho
r&
ceu one & on &
str
fa c
pro
hem
qu
qu
us
a
e
t
n
u
l
e
e
o
C
W
p
u
a
o
n
r
r
m
l
l
t
t
I
r
s
A
a
C
Pa
ar
ma
ica
Me
n fe
ic,
ner
Ph
ctr
her
No
Ge
ram
Ot
Ele
Ce

ns
gas turing
tio
&
i ca
fac
ity
un
c
i
r
m
anu
t
lec
n-m
com
E
o
n
n&
her
tio
Ot
rta
po
s
n
Tra
ta
Re

il

al
Re

est

ate

Phase III (FY1994-2000)

(%pt）
250
200
150

gross positive contribution

gross negative contribution

net growth rateexcess reallocation rate

Capacity

Employment

100
50
0
-50
s
s
y
ls
le
el
lp
als
cts
les
ent
ing
ent
ion
ile
tal
ods
cla
i ca
pu
ste
esa
tic
du
xti
tur
uct
Fo
me
ipm quipm omob
Te
hol
r&
pro
ceu one & on &
str
fa c
hem
us
qu
e
t
a
u
n
l
e
e
o
C
W
p
u
a
n
r
o
r
l
t
m
l
t
I
r
s
A
a
C
Pa
ar
ma
ica
Me
nfe
ic,
ner
Ph
ctr
her
No
Ge
ram
Ot
Ele
Ce
nsp
Tra

s
te
tail
gas turing
ion
sta
&
Re
cat
al e
fac
ity
uni
c
i
Re
r
m
anu
t
lec
n-m
com
E
o
n
&
on
her
ati
Ot
ort

Fig. 2-12 Decomposition of the Industry-Specific Factor Adjustment Rates by Gross Flows
left side: net growth rate of capacity, right side: net growth rate of employment,
7-year cumulative growth rate

22

Development Bank of Japan Research Report/ No. 31

Table 2-4 Relationship between Gross Contribution Rates andthe Net Growth Rate of Capacity (cross-section by industry)Phase I
Constant term (common)

Phase II

5.13(3.12)

2.09(3.02)

Phase III11.9(14.0)

Coefficient β of Equation (5)0.972(42.3)

0.989(144 )0.582(8.06)

Coefficient δ of Equation (6)−0.028(1.23)−0.011(1.63)−0.418(5.80)

Notes: 1. The figures are estimated values (figures in parentheses are t values).
2. The data unit used in estimation is % point.

The relationships a = g and b = 1+d arealways binding since KNCR º KCR – KDR.
The results of the estimation are shown in
Table 2-4. Estimated valueβwas essentially 1 inboth Phases I and II, reflecting the dominance ofthe gross positive contribution in the trend of the
net growth rate of capacity. Alternatively, if theequations are interpreted to be the effect of net
growth rate of capacity on gross contribution
rates with the idea that the net growth rate ofeach industry is a semi-macro shock (industrylevel shock), then virtually all aspects of the behavior of firms that expanded their capacity canbe explained by the trend of the relevant industry(semi-macro shock), and the behavior of firmsthat reduced their capacity was due almost entirely to idiosyncratic factors unrelated to thetrend of the relevant industry. In addition, theestimated value of the constant term was 5.1 inPhase I, thereafter dropping to 2.1 in Phase II,suggesting that the movements of structural intra-industry reallocation were small in Phase II,
namely that the tendency toward unanimous
scale expansion was strong.22 Meanwhile, inPhase III, the estimated value of β dropped
broadly to 0.6 and, though somewhat larger than
the absolute value of δ, the effect of the grosspositive and gross negative contribution rate
gradually equalized, suggesting the decline inindustry factors with respect to capacityexpanding firms and the rise of industry factors
with respect to capacity-contracting firms. In
addition, the estimated value of the constant termin Phase III was the largest through the threephases at 11.9 while significance also increased
broadly, providing further evidence that the focus of capacity adjustments shifted from industry
growth to intra-industry reallocation.
We next carried out a similar analysis of theconditions of employment adjustment in comparison with capacity adjustments. As indicatedin Fig. 2-11, one clear characteristic during all
three phases is that the gross positive contribution rate was lower than the case of capacity in a
majority of the industries while the gross negative contribution rate was higher and that overall,there was greater restraint compared to the caseof capacity. The change from Phase II to Phase
III was not as extreme as in the case of capacity,although in manufacturing industries in Phase III,the gross positive contribution rate decreased tonear zero in almost all industries and, ignoring
the steady intra-industry reallocation, downsizing
came to play the major role. To confirm this, we
carried out an estimation using the same equation
as for capacity (subscript j expresses industry).

22

Since, as indicated previously, the estimated value ofthe constant term corresponds to the average anticipatedgross positive and gross negative contribution rates when
the net growth rate of capacity is zero, the entire picture ofinter-company reallocation is considered to be comprehended when using time-series data, while inter-company reallocation within the same industry is comprehended in the caseof cross-sectional data by industry.

The relationships a = g and b = 1+d arealways binding since LNCR º LCR – LDR.
The results of the estimation are shown in
Table 2-5. Estimated value β was 0.8 in Phase I,

Development Bank of Japan Research Report/ No. 31

23

(Table 2-6) show that the correlation for the net
growth rate was essentially constant throughout
all three phases. However, the correlation coefficient for the gross positive contribution rate decreased from one phase to the next while thecorrelation coefficient for the gross negative
contribution rate rose conspicuously in Phase III
and exceeded that for the gross positive contribution rate. In other words, gross flows basedon micro-data indicate that the tendency for industries with a large positive contribution to capacity to differ from those of employment, andfor industries with a large negative contribution
for capacity to coincide with those of employment, became stronger in Phase III. This is
closely related to the fact that idiosyncratic factors unrelated to industry trends (semi-macro
shock) came to play the key role in the case ofincrease factors and that the industry trends became dominant in the case of decrease factors.
To wrap up this section, we decompose
gross contribution rates into the industry factor
and the individual company (idiosyncratic) factor
as follows and observe year-by-year transitions.
We first decompose the net increase in ca-

0.6 in Phase II and 0.4 in Phase III and was consistently lower than value β in the case of capacity (Table 2-4) and progressively declined.
Namely, amid the broad trends that gross positive contribution was replaced by gross negative
contribution as the dominant force as well as thedecline of industry factors in the case of firmsthat were expanding employment and rise ofindustry factors in the case of firms that werereducing employment, the momentum of scale
expansion weakened overall compared to capacity. However, since the changes from Phase II to
Phase III were not as drastic as those of capacity,
it is also true that the conditions of capacity andemployment converged in a relative sense. The
estimated value of the constant term was essentially constant during all three phases and phenomena such as the tendency toward unanimous
expansion in Phase II as with capacity and theintensification of intra-industry reallocation in
Phase III were not observed.
Furthermore, focusing on the relationshipbetween capacity adjustment and employment
adjustment, the transitions in the correlation coefficient between capacity and employment for

Table 2-5 Relationship between Gross Contribution Rates andthe Net Growth Rate of Employment (cross-section by industry)Phase I
Constant term (common)

Phase II

9.17(7.07)

Phase III8.89(10.9)

10.2(6.28)

Coefficient β of Equation (7)0.822(7.87)

0.587(10.8)

0.359(4.82)

Coefficient δ of Equation (8)−0.179(1.71)−0.413(7.60)−0.641(8.61)

Notes: 1. The figures are estimated values (figures in parentheses are t values).
2. The data unit used in estimation is % point.

Table 2-6 Correlation Coefficients of the Factor Adjustment Rates by Industry(capacity to employment)
Net growth rate

Gross positive contribution rate

Phase II

Phase III

Phase I

Phase II

Phase III

Phase I

Phase II

Phase III

0.594

0.427

0.621

0.610

0.404

0.401

0.327

0.367

0.578

the net growth rate and gross contribution rates
24

Gross negative contribution rate

Phase I

Development Bank of Japan Research Report/ No. 31

pacity KNCijt = Kijt – Kijt-1 and net increase inemployment LNCij = Lijt –Lijt-1 of Firm i affiliated with Industry j into the industry factor (1stterm) and the individual company factor (2ndterm) as indicated in the following equations (theoverline expresses weighted average).

KNCRji and LNCRji express the averagevalues of Industry j (based on weighted average)
respectively.
Next, we decompose the net increase incapacity of the entire population tabulated bygross flows into the industry factor (1st term)
and the individual company factor (2nd factor)
using the following equations (indicated only forthe case of capacity).
Factor decomposition of the portion of increase････････････････････

Industry factor

Individual company (idiosyncratic) factor

Factor decomposition of the portion of decrease････････････････････

Industry factor

Individual company (idiosyncratic) factor

Finally, we obtain contribution rates bydividing the industry factor and individual company factor defined above by the stock amount at
the beginning of the term (end of the previousterm) for the entire population.
Figure 2-13 shows the transitions in the
gross contribution rates decomposed into the in-dustry factor and the individual company factor
by this procedure; the size of the gross contribution prior to decomposition coincides with Figs.
2-2 and 2-3 by definition. Based on the transitions in the net growth rate of capacity, we findthat the industry factor explains the majority of
the gross positive contribution and the individualcompany factor explains the majority of thegross negative contribution through Phase II. In
Phase III, however, the industry factor weakens
in the gross positive contribution while the importance of the individual company factor increases. This tendency is especially strong fromFY1998 while, conversely, the industry factor
becomes dominant in the gross negative contribution. These are all consistent with the resultsof regression analysis based on Equations (5) and(6). Meanwhile, in regard to transitions in the net
growth rate of employment, the individual company factor in the gross positive contribution
from Phase I is greater than capacity and, in
FY1986 and 87, in particular, the majority of thegross positive contribution is explained by theindividual company factor, though the industry
factor was dominant for a time during the Heiseiboom. From FY1993, however, the individualcompany factor again explains the majority of
the gross positive contribution. In contrast to thegross positive contribution, the importance of theindustry factor in gross negative contribution
was greater than capacity from the early 1980s
and the individual company factor was dominant
for a time during the Heisei boom. However, theindustry factor again played the most importantrole in Phase III.
In light of the fact that the individual company factor in the gross positive contribution of
capacity increased in importance and the overallsituation that the capacity adjustment gradually
approached that of employment adjustment inPhase III, it is necessary for gaining an insight on
future trends of aggregate investment to understand the diverse paths of factor adjustment behavior followed by individual firms and to comprehend their fundamental characteristics, correlation with business performance and the like.
We will address such issues in the next chapter.

Development Bank of Japan Research Report/ No. 31

25

(%pt)
12
10
8
6
4
2
0
-2
-4
-6
-8

(%pt)

Net growth rate of capacity

2
0
-2
-4
-6

Ⅰ

Ⅱ

Ⅲ

-8

Ⅰ

Ⅱ

Ⅲ

-10
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

Fiscal year

Fiscal year

Individual
Individualcompany
companyfactors
factor(gross(grosspositive
positivecontribution)
contribution)

Industryfactors
factor (gross(gross positive
positivecontribution)
contribution)
Industry
Industryfactors
factor (gross(gross negative
negativecontribution,
contribution,reverse
reversesymbols)
symbols)
Industry
Individual
factors
reversesymbols)
Individualcompany
company
factor(gross(grossnegative
negativecontribution,
contribution,
reversesymbols)Fig. 2-13

26

Net growth rate of employment

4

個別企業要因(減少寄与、逆符号)
Individual company factor (gross positive contribution)
Industry factor (gross positive contribution)
個別企業要因(増加寄与)
産業要因(減少寄与、逆符号)
Industry factor (gross negative contribution, reverse symbols)
Individual company factor (gross negative contribution, reverse symbols)
産業要因(増加寄与)

Factor Decomposition of the Net Growth Rate of Capacity and Employment into
Industry Factor and Individual Company Factor (by Gross Flows)

Development Bank of Japan Research Report/ No. 31

III. Factor Adjustment Behaviorof Listed Firms from the Perspective of Adjustment Path Analysis
1.

Concept of Adjustment Path Analysis andSix Major Categories

There are two possible approaches to deriving
implications regarding movements of the overall
population based on micro-data. The first is what
we have worked with in the previous chapter,
namely to investigate gross flows and intercompany distribution for each year (phase) andtheir time-series changes. With this approach,however, it is difficult to shed light on dynamic
aspects such as the effect of past factor adjustment behavior on that in the subsequent years(phases) through the stock adjustment mechanism.
Generally, the impact of the results of past
factor adjustment behavior on the future increases as unforeseen changes in the macroeconomic environment as well as the irreversibility of capacity and employment increase. We
must therefore analyze dynamic aspects in orderto precisely comprehend the conditions of Japanese firms, which experienced the emergenceand collapse of the bubble economy, and thecapacity and employment of which have little
mobility. In this chapter, we carry out analyses
using the simple second approach, that is, thehistory of factor adjustment path followed bycompanies.
Since an analysis of the factor adjustment
behavior of each company which varies with theflow of time involves an enormous number ofpaths, some means for simplifying or stylizing isrequired for the sake of efficiency. In this study,
the factor adjustment paths are stylized as follows.1 First, as in the analyses so far, the period
from FY1980 through FY2000 is divided into
three phases, namely, Phase I (FY1980-86),
Phase II (FY1987-93) and Phase III (FY19942000). The factor adjustment behavior in eachphase is then classified into the four types below

taking into account whether capacity and employment were increased or decreased during theseven-year period.2 The four types are A (= increase capacity and increase employment), B (=increase capacity and decrease employment), C(= decrease capacity and increase employment)
and D (= decrease capacity and decrease employment) (Fig. 3-1).3 Thus, the number of factor
adjustment paths throughout the three phases isfour to the power of 3, or 64 types. From theperspective of the share by number of firms andthe similarity of behavioral changes, the 64 types
of factor adjustment paths were reduced to 6
typical patterns (plus those that cannot be classified in any of them), as described below.
What to use in order to determine a typical
pattern is a conceptual problem based primarilyon the purpose of the analysis. However, this ismeaningless unless we appropriately understandthe basic facts regarding changes in adjustment
behavior. To prepare for the reduction, we therefore reviewed the characteristics and changes inadjustment behavior in each of the phases fromthe perspective of the share by number of firms(Fig. 3-2).
Forty-four percent of the adjustment behavior of Phase I was of the scale expansion type(A) with expansions of both capacity and employment, 43% was of the quasi-scale expansion
type with expansion of either capacity or emExpansion in employment

C

A

Contraction
in capacity

Expansionin capacity

D

B

Contraction in
employment

Fig. 3-1 Conceptual Diagram of
Factor Adjustment Patterns

1

Unlike gross flow analyses, there are probably few
examples of the analysis of adjustment paths in the field of
micro-data analysis and so there was no established, dependable method for stylizing.

2

For example, comparing the balance at the end of
FY1986 and at the end of FY1979 in Phase I.

Development Bank of Japan Research Report/ No. 31

27

Factor adjustment patterns in Phase I(share by number of firms, %)Factor adjustment patterns in Phase II
and changes from Phase I(share by number of firms, %)

4

2

44

M, N, A

61

Factor adjustment patterns in Phase III
and changes from Phase II(share by number of firms, %)

5

7

31
21

6

M

9

13

39

8

5

10

N

14

A
18

33

29

56

17

8

29

Fig. 3-2 Long-Term Transitions in Factor Adjustment Patterns of Common Firms
Notes: 1. Phase I (FY1980-86), Phase II (FY1987-93), Phase III (FY1994-2000)
2. The abbreviations M, N and A indicate the movements, respectively, of the total of manufacturing industries, the total of non-manufacturing industries and the total of all industries.
3. Movements with a ratio of less than 5% are not shown.

ployment (B+C), both of essentially equal scale,with 13% of the scale contraction type with
contraction of both (D). The characteristic differences between capacity and employment are
that, in regard to capacity, scale expansion(A+B) accounted for the majority at 83%, while,in regard to employment, contraction (B+D), at
52%, barely exceeded half. There was no great
difference in these characteristics when the
population was divided into manufacturing andnon-manufacturing; however, the tendency toward expansion of scale was stronger among
manufacturing industries during this period(A-D: manufacturing: 35, non-manufacturing:25).
Sixty-one percent of adjustment behavior
during Phase II was scale expansion type (A),almost twice as much as the quasi-scale expansion (B+C), indicating a strong tendency overall
toward expansion. In regard to capacity, the scale
expansion type (A+B) reached a level of 90%
and, even in the case of employment, the scale
expansion (A+C) type, at 63%, was well overhalf. In terms of the changes from Phase I, of the
61% scale expansion type (A), 31%, about halfof the companies, were type A in Phase I, while
21% shifted from B and 3% from C. Meanwhile,
6% shifted all the way from the scale contraction
type (D) in Phase I to type A. Though there were
3

28

Level growth is classified as a decrease.
Development Bank of Japan Research Report/ No. 31

no great differences in these changes when the
population was divided into manufacturing andnon-manufacturing, the tendency toward scale
expansion was stronger among non-manufacturing industries (A-D: manufacturing: 50, nonmanufacturing: 60).
Regarding adjustment behavior in Phase III,the scale expansion type (A) declined to a mere
10% while the scale contraction type (D), at 56%,
accounted for the majority. In addition, with thequasi-scale expansion type (B+C) at essentiallythe same level as in Phase I (34%), there is aconspicuous trend toward contraction differingfrom Phase II. In regard to capacity, the scale
expansion type (A+B) accounted for 39% and, inregard to employment, the scale expansion type
(A+C) dropped to a mere 15%. In terms ofchanges from Phase II, 33%, or more than half,of the 61% of the scale expansion type (A) in
Phase II fell abruptly to type D. Following that,
18% shifted to B while 7% remained in A during
Phase III. In addition, companies mainly in
manufacturing industries that moved from B toscale contraction (D) increased to 17% and thetendency toward scale contraction was morenotable among manufacturing industries (D-A:
manufacturing: 51, non-manufacturing: 34).
Next we examine the approach of reducing
the 64 types of factor adjustment paths throughout the three phases into six major categories
taking these basic facts into account. Given thecharacteristics of each phase, the key point inelucidating the dynamic aspects of the factor
adjustment behavior of Japanese corporations isprobably the difference between firms that did
not alter their tendency to maintain and expand
scale in spite of the adverse conditions resulting
from the collapse of the bubble economy during
Phase III and firms that shifted to scale contraction. Thus, of those firms that ranked as type A
or B in both Phases I and II (75% of the common
firms), those firms in which the adjustment pattern in Phase III was not type D (33%) and thosethat become type D in Phase III (42%) are categorized, respectively, as type 1 and type 2. Type
1 firms are then further segmented into firms that
expanded both capacity and employment in Phase
III as sustained-growth type 1a (8%), firms that
expanded only capacity as quasi-sustained growth
type 1b (21%) and firms that expanded only employment as quasi-sustained growth type 1c (4%).
Type 2 was likewise segmented into firms that
expanded both capacity and employment inPhase II but abruptly shifted to contraction in
Phase III as growth setback type 2a (28%) andthose that expanded capacity in Phase II while
shifting to contraction in employment as early
contraction type 2b (14%). Besides these, firmsthat could not be classified as either type 1 ortype 2, namely those were positioned as C or D
in Phase I or Phase II (at least capacity contraction), accounted for 25% of the common firms.
Firms among them that were C or D in Phase I
and then shifted to A or B in Phase II (15%)
were positioned as type 3, the sixth type, regardless of the conditions in Phase III (Heisei
boom type). The 10% balance is considered to be
simply “other.”
2. Downsizing as Established Trend Among
Common Firms
In this section, we classify the factor adjustment
paths of common firms based on the six major
types defined in the foregoing section and highlight each type focusing on the major aggregate
indicators of business performance, various
shares by industry4 and so forth, and look at the4

The industry classification is based on the same 20
industrial
categories(manufacturing:
13,
nonmanufacturing: 7) as Chapter II, Section 4.

implications for aggregate investment trends(Tables 3-1 to 3-3).
Type 1a: Sustained growthFirms associated with this type make up the second smallest group of the six types, consisting of
7.8% in the share by number of firms. The shareby number of firms by industry shows that theweight of non-manufacturing industries (9.2%)
was somewhat greater than that of manufacturingindustries (7.1%), exceeding 20% in the case ofpharmaceuticals and retail. Based on the 64 individual paths, A-A-A at 4.2% accounts for morethan half, followed by B-A-A (1.8%). Other
paths associated with this type include A-B-A
and B-B-A.
The average growth rate of sustained growthfirms from FY1980 through FY2000 was thehighest of the six types not only in capacity (tangible fixed assets excluding land) and employment (total number of employees) but also insales and profit. Furthermore, ROA (return on
assets) in FY2000 was 7.0%, far higher than the
average 4.3% for all types, and, looking at theindividual firms, many are leading high-profit
high-growth firms in their respective industries.5
These results naturally indicate the desirable
movement of resource allocation.
The distribution in FY2000 indicates thatsustained growth type firms clearly had a higher
share of employment and profit than the ratio
based on the number of firms while in terms of
capacity and sales, the ratio was essentially thesame as that based on the number of firms, thus
demonstrating a relatively strong labor-intensive
characteristic. Based on industries excluding
electricity & gas, which have a strong effect on
the shares of each type, the share of capacity
increases somewhat, though there is no change in
the general tendency. The labor-intensive character of type 1a probably reflects the propertiesof the retail industry, which alone is associatedwith this type at the industry level. Even withretail excluded, however, the general tendency
5

Since this study does not discuss the relative superiority
of individual firms, we refrain from naming specific firms.
We referred to the lineup of individual firms in assessing
each type of factor adjustment path, however, we make noungrounded assertions other than the lineup of individual
firms.
Development Bank of Japan Research Report/ No. 31

29remains unchanged.

30

Development Bank of Japan Research Report/ No. 31

Table 3-1 Industry-Specific Shares by the Number of Firms of the 6 Major Types ofFactor Adjustment Patterns
6 major types

All industries
manufacturing industries
Foods

TextilesPaper &
pulp

Chemicals

Pharmaceuticals

Ceramic, stone& clay1a Sustained-growth

7.8

7.1

5.7

4.2

−

8.7

21.2

14.6

1b Quasi-sustained growth (capacity only)

21.2

19.5

27.3

19.7

34.8

19.4

39.4

25.0

1c Quasi-sustained growth (employment only)

4.2

3.5

6.8

2.8

−

2.9

−

2.1

2a Growth setback

28.1

30.3

25.0

12.7

39.1

39.8

12.1

20.8

2b Early contraction

13.8

16.4

6.8

26.8

17.4

15.5

6.1

18.8

3

14.6

12.0

18.2

18.3

4.3

6.8

6.1

12.5

Others

10.4

11.2

10.2

15.5

4.3

6.8

15.2

6.3

Total

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

Heisei boom

6 major types

Iron &
steel

Nonferrous Metal
metalsproducts

GeneralElectrical Automobiles
equipment equipment

Other
manufacturing

1a Sustained-growth

3.8

2.9

2.1

6.5

6.3

11.1

7.4

1b Quasi-sustained growth (capacity only)

9.6

20.6

16.7

11.5

23.4

18.1

17.3

1c Quasi-sustained growth (employment only)

1.9

5.9

2.1

5.5

1.6

4.2

2.5

2a Growth setback

15.4

35.3

45.8

29.5

32.0

45.8

33.3

2b Early contraction

36.5

8.8

14.6

19.0

12.5

16.7

12.3

3

25.0

11.8

16.7

17.0

0.8

−

16.0

Others

7.7

14.7

2.1

11.0

23.4

4.2

11.1

Total

100.0

100.0

100.0

100.0

100.0

100.0

100.0

Heisei boom

6 major types

Non-manufacturing industries
Construc- Wholesale Retail
tion

Real estate Transportation Electricity & gas Other non& communimanufacturing
cations

1a Sustained-growth

9.2

4.2

4.8

24.5

10.5

11.2

16.7

5.9

1b Quasi-sustained growth (capacity only)

24.9

18.5

21.7

12.2

47.4

34.7

61.1

17.6

1c Quasi-sustained growth (employment only)

5.7

7.6

3.6

10.2

5.3

4.1

−

5.9

2a Growth setback

23.3

31.1

24.1

34.7

15.8

12.2

11.1

21.6
13.7

2b Early contraction

7.8

5.0

7.2

2.0

10.5

10.2

11.1

3

20.4

28.6

31.3

12.2

5.3

13.3

−

17.6

Others

8.7

5.0

7.2

4.1

5.3

14.3

−

17.6

Total

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

Heisei boom

Development Bank of Japan Research Report/ No. 31

31

Table 3-2a Business Performance Indices Tabulated by the 6 Major types ofFactor Adjustment Patterns (the total of all industries)
6 major types

1a
1b
1c
2a
2b
3

Share by each item, FY2000 (%)

Sustained-growth
Quasi-sustained growth (capacity only)
Quasi-sustained growth (employment only)

Growth setback
Early contraction
Heisei boom

OthersTotal

No. of firms

Capacity

Employment

7.8
21.2
4.2
28.1
13.8
14.6
10.4
100.0

6.4
54.9
1.3
18.1
11.1
5.1
3.1
100.0

16.2
26.6
4.5
31.4
9.3
8.0
4.1
100.0

Sales

8.6
29.4
2.9
27.9
10.6
16.3
4.2
100.0

Profit

12.8
37.8
3.4
28.0
7.9
5.8
4.2
100.0

Average growth rate,
FY1980-2000 (annual rate, %)Capacity Employment
Sales
Profit

7.9
5.1
7.4
4.9
3.3
0.0
-0.1
4.3

4.0
-0.3
2.4
-0.2
-2.6
-2.2
-2.8
-0.3

5.7
2.7
4.7
3.6
1.1
-0.3
0.6
2.2

5.7
3.6
4.1
2.0
-0.6
-2.6
-3.6
1.7

ROA,
FY2000
(%)

7.0
4.4
4.4
4.2
3.3
2.7
4.8
4.3

Table 3-2b Business Performance Indices Tabulated by the 6 Major Types ofFactor Adjustment Patterns (excluding electricity & gas)
6 major types

1a
1b
1c
2a
2b
3

Share by each item, FY2000 (%)

Sustained-growth
Quasi-sustained growth (capacity only)
Quasi-sustained growth (employment only)

Growth setback
Early contraction
Heisei boom

OthersTotal

No. of firms

Capacity

Employment

7.6
20.6
4.2
28.4
13.8
14.8
10.6
100.0

10.7
33.2
2.2
29.6
10.5
8.6
5.2
100.0

16.9
24.0
4.7
32.5
9.2
8.4
4.3
100.0

Sales

9.1
26.1
3.1
29.3
10.6
17.3
4.5
100.0

Profit

15.7
27.0
4.1
33.8
7.0
7.2
5.2
100.0

Average growth rate,
FY1980-2000 (annual rate, %)Capacity Employment
Sales
Profit

7.9
6.5
7.4
5.0
2.6
0.0
-0.1
4.2

4.1
-0.3
2.4
-0.2
-2.7
-2.2
-2.8
-0.3

5.7
2.5
4.7
3.6
0.9
-0.3
0.6
2.1

5.7
2.4
4.1
2.0
-1.7
-2.6
-3.6
1.1

ROA,
FY2000
(%)

7.0
4.1
4.4
4.2
3.0
2.7
4.8
4.1

Table 3-2c Business Performance Indices Tabulated by the 6 Major Types ofFactor Adjustment Patterns (excluding retail)6 major types

1a
1b
1c
2a
2b
3

Sustained-growth
Quasi-sustained growth (capacity only)
Quasi-sustained growth (employment only)

Growth setback
Early contraction
Heisei boom

Others
Total

Share by each item, FY2000 (%)
No. of firms

Capacity

Employment

7.2
21.5
3.9
27.9
14.2
14.7
10.7
100.0

5.3
56.2
1.3
18.0
11.3
4.8
3.1
100.0

12.2
29.4
4.1
32.6
10.2
7.1
4.5
100.0

Sales

6.8
30.7
2.7
28.0
11.0
16.3
4.4
100.0

Profit

11.4
38.8
3.4
28.2
8.1
5.8
4.4
100.0

Average growth rate,
FY1980-2000 (annual rate, %)Capacity Employment
Sales
Profit

7.7
5.1
7.6
4.9
3.3
-0.3
-0.1
4.2

3.7
-0.3
2.1
-0.2
-2.6
-2.7
-2.9
-0.5

5.8
2.7
4.7
3.6
1.1
-0.4
0.6
2.1

6.0
3.6
4.2
2.1
-0.6
-2.6
-3.6
1.7

ROA,
FY2000
(%)

7.4
4.4
4.5
4.3
3.3
2.8
4.8
4.3

Notes: 1. In calculation of shares, capacity means tangible fixed assets excluding land, employment means total number of
employees and profit means operating profit.
2. In regard to the average growth rate figures, the 21-year average through the end of FY2001 with the end of
FY1979 as the standard is used for capacity and employment, and the 21-year average until FY2001 with
FY1979 as the standard is used for sales and profit.
3. ROA = (operating profit/interest dividends received)/average total assets at the beginning and end of the term;
based on weighted average

32

Development Bank of Japan Research Report/ No. 31

Table 3-3 Definition of the 6 Major Types of Factor Adjustment Patterns and Correspondence tothe 64 Individual Paths Along with Business Performance Indices
6 major types and theirdefinitions

Relevant paths (based on
all 64 individual paths)
Phase I Phase II Phase III

1a Sustained-growth

Share by each item, FY2000 (%)

No. offirms

capaci- employty
ment

Avg. growth rate, FY1980FY2000 (annual rate, %)sales

profitcapaci- employty
ment

sales

profit

ROA, FY2000 (%)
excludingexcludingallelectricity
retailindustries
& gas

A

A

A

4.2

4.0

12.5

5.1

9.8

9.6

5.2

6.9

7.1

8.1

8.2

8.8

I A or B

B

A

A

1.8

0.6

1.5

1.7

0.9

7.3

1.7

4.6

2.6

3.5

3.5

3.8

II A or B

A

B

A

0.9

0.6

1.6

1.3

1.3

5.4

1.6

4.7

3.3

8.0

8.0

8.8

III A

B

B

A

0.9

1.2

0.5

0.5

0.9

5.8

-0.1

2.9

2.5

4.0

4.0

4.0

A

A

B

8.4

26.2

14.9

13.4

21.4

5.2

0.7

4.7

4.5

5.0

4.9

5.0

I A or B

B

A

B

6.4

20.4

5.3

6.3

11.0

4.8

-0.7

3.2

3.7

4.2

3.3

4.2

II A or B

B

B

B

3.9

2.8

3.9

5.0

2.6

4.8

-2.2

0.1

0.4

3.6

3.6

3.6

III B

A

B

B

2.5

5.5

2.5

4.7

2.8

5.8

-1.0

1.5

2.0

3.4

3.0

3.4

A

A

C

1.9

1.0

3.5

2.2

2.3

8.1

3.7

5.5

4.5

4.1

4.1

4.1

Ⅰ A or B

B

A

C

1.2

0.2

0.5

0.4

0.7

6.3

1.3

3.6

6.0

7.7

7.7

7.7

Ⅱ A or B

B

B

C

0.6

0.0

0.1

0.1

0.0

4.1

-1.2

3.0

-0.4

2.9

2.9

2.9

ⅢC

A

B

C

0.5

0.1

0.5

0.3

0.3

5.3

-1.8

2.3

0.1

3.5

3.5

3.5

2a Growth setback

A

A

D

16.8

12.4

23.2

21.1

20.2

5.6

0.3

4.2

2.0

4.1

4.1

4.1

Ⅰ A or B

B

A

D

11.4

5.7

8.2

6.8

7.8

3.7

-1.2

2.0

2.2

4.6

4.6

4.7

ⅡA

(only 2 relevant
individual paths)

1b

1c

Quasi-sustained growth(capacity only)

Quasi-sustained growth(employment only)

ⅢD
2b Early contraction

B

B

D

8.9

6.1

6.9

8.3

4.4

2.4

-3.0

0.6

-2.0

2.8

2.7

2.8

Ⅰ A or B

A

B

D

4.9

5.0

2.3

2.3

3.5

4.6

-1.2

3.1

2.2

4.6

4.3

4.6

ⅡB

(only 2 relevant
individual paths)

ⅢD
3

D

B

D

3.0

2.9

2.2

8.5

2.3

-1.3

-4.4

-0.6

-4.4

2.6

2.6

2.6

Ⅰ C or D

D

A

D

3.0

0.5

1.2

1.3

1.0

0.6

-2.7

-1.5

-1.4

3.6

3.6

3.6

Ⅱ A or B(13 other individual
paths omitted)

Heisei boom

Ⅲ All
Other

A

D

D

1.8

0.2

0.9

0.6

0.1

2.4

-1.1

3.2

-7.2

2.3

2.3

2.3

B

D

D

1.7

0.6

0.7

0.9

0.9

-0.4

-5.0

-0.2

-1.0

4.8

4.8

4.8

(20 other individual
paths omitted)

Notes: 1. In regard to the relevant paths (based on all 64 individual paths), there were only 53 paths with an actual record
among the common firms.
2 In calculation of shares, capacity means tangible fixed assets excluding land, employment means total number of
employees and profit means operating profit.
3. In regard to the average growth rate figures, the 21-year average through the end of FY2001 with the end of
FY1979 as the standard is used for capacity and employment, and the 21-year average until FY2001 with
FY1979 as the standard is used for sales and profit.
4. ROA = (operating profit/interest dividends received)/average total assets at the beginning and end of the term;
based on weighted average

Development Bank of Japan Research Report/ No. 31

33

Type 1b: Quasi-sustained growth (capacity only)
Firms of this type make up the second largest group of the six categories in the share by
number of firms. The industry-specific tabulation
indicates that the share by number of firms of
non-manufacturing industries (24.9%) was somewhat greater than that of manufacturing industries (19.5%), while those of electricity & gas
exceed 60%. Based on all 64 individual paths, AA-B was 8.4% and B-A-B was 6.4% and thesetwo paths account for the majority of this type.
In addition, paths B-B-B and A-B-B are also
associated with this type.
Average growth rates from FY1980 throughFY2000 of firms of the quasi-sustained growth
type that maintained growth only in capacity
indicate that employment was negative, albeit
only slightly. The growth rate of capacity was
also less than that of firms of the quasi-sustained
growth type for employment only, and similar tothat of growth setback type firms. That is, eventhough these firms expanded capacity throughout
the three phases, they are characterized by slow
expansion or gradual and stable adjustments. In
addition, the average growth rates for sales andprofit were both positive, though they ranked nohigher than fourth among the six types and ROA
in FY2000 was 4.4%, essentially the same as theaverage of 4.3% for all types, indicating that thequasi-sustained growth type for capacity showed
mediocre performance.
Shares in FY2000 indicate that, based on thenumber of firms, they ranked second, following
the growth setback type, while accounting for ahigh 55% of capacity, 29% of sales and 38% ofprofit, the highest figures of the six types. Thesefirms are characterized by large scale and acapital intensive character even among listed
firms. Even though this characteristic and especially the high share for capacity are strongly
dependent on the fact that most of the firms affiliated with the electricity & gas industries areclassified as this type, the basic orientation remains unchanged even if electricity & gas firms
are excluded. Industries in which the pattern of
total industry values is associated with this typeinclude, among manufacturing industries, foods,
paper & pulp, pharmaceuticals, ceramic, stone& clay and electrical equipment and, among

34

Development Bank of Japan Research Report/ No. 31

non- manufacturing industries, real estate, transportation & communication and electricity &gas.
Type 1c: Quasi-sustained growth (employment
only)
Firms associated with this type are the fewest of
the six types, consisting of 4.2% in the share by
number of firms. The industry-specific tabulation
indicates that the share by number of firms in
non-manufacturing industries (5.7%) was greaterthan that of manufacturing industries (3.5%),
exceeding 10% in the case of retail. Based on all
64 individual paths, A-A-C accounts for 1.9%
and B-A-C for 1.2%, these two accounting forthe majority of this type. In addition, paths B-BC and A-B-C are also associated with this type.
In regard to the average growth rates offirms of the quasi-sustained growth type thatmaintained growth only in employment fromFY1980 through FY2000, though it is probably
natural that the growth rate of employment
would be second only to the sustained growth
type (only the sustained growth and this typewere positive), it is noteworthy that this type also
surpassed the quasi-sustained growth type forcapacity only in the growth rate of capacity andwas at a level approaching that of the sustainedgrowth type. In terms of performance, ROA in
FY2000 was 4.4%, the same level as the quasisustained growth type for capacity only, while
exceeding the quasi-sustained growth type forcapacity only in the average growth rate for sales
and profit.
In regard to the shares in FY2000, thoughemployment alone slightly exceeded the share by
number of firms, capacity, sales and profit all
dropped below the share by number of firms,
showing that firms of this type are relatively
labor intensive and small in scale for a listedcompany. There were no firms in which the pattern of total industry values was associated withthis type.
Type 2a: Growth setback
Firms of this type, which accounted for 28.1% in
the share by number of firms, were the most numerous of any of the six types. The industry-

specific tabulation indicates that the share by
number of firms of manufacturing industries(30.3%) was somewhat greater than that of nonmanufacturing industries (23.3%) and almost
half of automobiles and metal products related
firms were associated with this type. Based on all
64 individual paths, only two paths were associated with this type, A-A-D, which accounted for
16.8%, and B-A-D, which accounted for 11.4%,
the highest and second highest ranking shares
among all individual paths, which can be considered a typical pattern for Japanese firms.
In regard to the average growth rates offirms of the growth setback type from FY1980
through FY2000, capacity grew by about 5%
while employment declined slightly, indicating
similar conditions to those of the quasi-sustained
growth type that maintained growth only in capacity. In addition, ROA in FY2000 was 4.2%,
slightly below the quasi-sustained growth type
(4.4% for both types in capacity and employment) and, though the growth rate of profit was
smaller somewhat, the growth rate of sales exceeded that of the quasi-sustained growth type(capacity only). In terms of performance, there
are no essential disparities between the growth
setback type and the quasi-sustained growth type(especially the quasi-sustained growth type forcapacity only), and many of the growth setback
firms are considered corporate leaders in Japan,
especially in manufacturing industries.
Firms associated with the growth setback
and quasi-sustained growth (capacity only) types
make up about 50% of the share by number offirms and are thought to reflect an image of theaverage Japanese firm through Phase II. It isinteresting to note, however, that these company
groups (in spite of similarities in performance)
were dissimilar in their response in Phase III,
some with a tendency toward increasing capacityand others shifting to a contraction in scale.
Shares in FY2000 indicate that employment,
sales and profit were at about the same level as
the shares by number of firms and even capacity,
the only small figure, was at a level ranking withthe share by number of companies when electricity & gas were excluded. Therefore, overall,they approach the average configuration of listed companies. Industries in which the pattern of
total industry values is associated with this typeinclude, among manufacturing industries, chemicals, nonferrous metals, metal products, generalequipment and automobiles and, among nonmanufacturing industries, construction.
Type 2b: Early contraction
Firms of this type accounted for 13.8% in theshare by number of firms. The industryspecific tabulation indicates that the share by
number of firms of manufacturing industries(16.4%) was considerably greater than that ofnon-manufacturing industries (7.8%) and thoseof iron & steel, textiles and other industries inwhich demand is in a structural trend toward
contraction are conspicuously high. Among all
64 individual paths, B-B-D (8.9%) and A-B-D(4.9%) are the only two paths that are associated with this type.
The average growth rates of firms of theearly contraction type from FY1980 throughFY2000 indicate that, though capacity showed
positive growth, this type ranked the fifthsmallest among the six types while employment
showed negative growth, the most restrained ofall six types. The growth rate for sales was narrowly positive but that of profit was negative and,
together with ROA in FY2000, all ranked fifthamong the six types. Though the factor adjustment patterns themselves of the early contraction
type do not appear to differ greatly from those of
the growth setback type, the business conditions
for this group of companies were harsher thanthose for the average Japanese corporations of
the growth setback and quasi-sustained growth(capacity only) types.
Shares in FY2000 indicate that capacity,
employment, sales and profit all dropped slightly
below the share by number of companies. Firms
in which the pattern of total industry values is
associated with this type are textiles and iron &steel.
Type 3: Heisei boom
Firms of this type account for 14.6% of the distribution by number of firms. The industryspecific tabulation indicates, contrary to the early
contraction type, that the share by number offirms of manufacturing industries (12.0%) was
Development Bank of Japan Research Report/ No. 31

35

surpassed in large measure by nonmanufacturing industries (20.4%) and those ofwholesale, construction and iron & steel were
notably high. Based on all 64 individual paths,
D-B-D and D-A-D both posted large ratios of
3% and, besides these, a diversity of other paths
also existed, including C-A-D, D-A-B, D-B-B,
C-A-B, D-A-A, D-A-C, C-B-D, C-B-B, D-B-A,
D-B-C, C-A-A, C-A-C and C-B-C.
The average growth rates of firms of the
Heisei boom type from FY1980 through FY2000
indicate that the growth rate of capacity leveled
off, the most restrained among the six types,
while that of employment was negative, ranking
fifth among the six types. The growth rate ofsales and profit were both negative and, together
with ROA in FY2000, all faced the most severe
conditions of all six types. The Heisei boom typeof firms, though experiencing the same severe
conditions as the early contraction type in business performance, expanded in scale more cautiously in Phase I and shifted to a more aggressive stance in Phase II.
Shares in FY2000 indicate that capacity,
employment and profit all dropped below theshare by number of firms while only sales exceeded the share by number of firms. Thisstrongly reflects the characteristic of the wholesale industry in which the pattern of total industry value is associated with this type.
Firms not corresponding to any of the six types
Firms that do not correspond to any of the six
types account for about 10% of the share by
number of firms. Relatively common paths
among them show the characteristic of shifting toclearly-defined contraction in scale from Phase II
onward like A-D-D (1.8%) and B-D-D (1.7%).
The average growth rates of firms of this typefrom FY1980 through FY2000 indicate that they
are relatively close to the Heisei boom type andare thought to have suffered severe businessconditions on a par with the early contraction
and the Heisei boom types. There are no industries in which the pattern of total industry values
is associated with this type.

Sub-conclusion: Characteristics and interpre36

Development Bank of Japan Research Report/ No. 31

tation of the six types
To conclude this section, we again summarize
the implications of the analyses of the six types
relating to future trends in the dynamics of aggregate investment. First, in regard to type 1a,the sustained growth type, although it includes
high-profit high-growth firms that play leadingroles in their respective industries, there are few
capital-intensive firms among them, so they cannot be expected to drive aggregate investment. In
addition, many of the groups of firms that
stimulated investment were divided in Phase III
between type 1b, quasi-sustained growth typethat maintained growth only in capacity (expansion of capacity also in Phase III), and type 2a,
the growth setback type (contraction of capacity
in Phase III). There are no disparities between
the two types in terms of performance, andleading Japanese firms, especially in manufacturing industries, were also included in type 2a.
This suggests that downsizing has become anestablished trend in the domestic market since
Phase III and that the sustainability of future
capacity expansion by type 1b firms is uncertain.
Going forward, therefore, common firms arevery unlikely to drive aggregate investment.
How should we respond to this reality?
Companies with good performance tend to have
optimized their activities in a broad sense. The
fact that companies with relatively good performance exist not only in type 1a but also in types
1b, 1c and 2a suggests that the optimal selectionof investments and other factor inputs resulting
from profit-earning opportunities (business opportunities), retained resources, risk and so forth
can be diverse (not only expansions in scale).
From the perspective of macroeconomic policy,
given the strong hope for expansion in capacityand employment in the corporate sector, the expansion of capacity is increasingly considered to
be indispensable for revitalizing the corporate
sector (including so-called improvement in competitiveness). However, before arguing about theneed for introducing some macroeconomic policy to stimulate investment, we must closely examine the following points based on the microeconomic reality that the optimal selection of acompany does not necessarily mean expansion incapacity: (1) expansion of capacity is the effect,rather than the cause, of revitalization of the corporate sector and so regardless of investment, it
is important to consider why the corporate sector
has not been revitalized and (2) to what extent
externalities (technology spill-overs, etc.) result
from expansion of capacity.

expand the targets of analysis to all listed firms
in order to address this issue, and survey trends
in factor adjustment of newly listed firms mainly
in non-manufacturing industries.
We first confirm the positioning of noncommon firms among listed firms (Fig. 3-3). The
time-series transitions in the number of listed
firms as the result of newly listed and delisted
firms6 indicate that there was a total of 1,577
listed firms as of FY1979. There were 1,845
newly listed firms during the subsequent 21

3. Trends Among Newly Listed Companies
From Chapter II through the foregoing section,
we have analyzed factor adjustment behavior
Transitions in the number of firms(firms)

3500
3000
2500
2000

(%)

Ⅰ

Ⅱ

(%)

70 40

Ⅲ

Transitions in the share of non-common firms
relating to scale

Ⅰ

Ⅱ

Ⅲ

30

Share of manufacturingindustries (right-handgradations)

60 20

1500
1000

10

500
0

50

0

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

Fiscal year

Fiscal year

Non-manufacturing, new
非製造業新規Manufacturing, new
製造業新規
Non-manufacturing, continuous
非製造業継続Manufacturing, continuous
製造業継続
系列

Sales
売上高
Operating profit
営業利益
Tangible fixed assets at the end of the term (ex-land)
期末有形固定資産（除く土地）
Total number of employees at the end of the term
期末総従業員数

Fig. 3-3 Transitions in the Status of Non-Common Firms in All Listed Firms
Notes: 1. In regard to the transitions in number of firms, “continuous” coincides with the 1,418 common firms, which, as of
FY2000, had been listed continuously since FY1980 and “new” represents 1,749 firms, which, as of FY2000, hadbeen listed continuously since FY1981 or later. However, since all of them are counted among the number ofcompanies recorded in the Corporate Financial Data Bank, they do not strictly coincide with the number of listed
firms in each year and an increase in “new” becomes apparent in 1988 due to the commencement of recording ofover-the-counter listed firms.
2. In regard to the share of non-common firms relating to scale, the discontinuity resulting from the recording ofprivatized firms (generally the year prior to listing) is apparent in FY1985 (NTT, JT), FY1992 (East Japan Railway), FY1994 (West Japan Railway) and FY1996 (Central Japan Railway) and the impact of the conversion of
NTT to a holding company in FY1999 is especially strongly expressed in the movements of tangible fixed assets.

focusing solely on firms that have been continuously listed since FY1980. However, as stated in
Chapter II, Section 1, listed firms are not always
typical firms of Japan and continuously listed
firms are not always typical of listed firms. In
recent years in particular, trends among nonmanufacturing industries, which are increasing
their presence both in number of companies andin shares by capacity and employment, have notbeen sufficiently understood. In this section, we

years as well as 255 delisted firms7, giving a total
6
Due to the dependence on the Corporate Financial Data
Bank at the Development Bank of Japan, strictly speaking,it is not the number of listed firms but transitions in “thenumber of firms that released financial statements in Securities Reports as the result of listing.” In spite of this, however, the time of “new listing” in the explanations below is
considered to be the time of appearance in the Data Bankand the time of “delisting” is considered to be the time ofremoval from the Data Bank.
7
Including 94 firms newly listed in or after FY1980.

Development Bank of Japan Research Report/ No. 31

37

of 3,167 listed firms as of FY2000, about 2.2
times the 1,418 common firms. Some 60% of
firms newly listed since FY1980 were in nonmanufacturing industries and the ratio of firms in
non-manufacturing industries to total listed firms
rose from 31% in FY1979 to 47% in FY2000(31% of common firms). Meanwhile, since
newly listed firms are small in scale on averagecompared to continuously listed firms8, the shareby number of firms of non-common firms as of
FY2000 was 55%, though the ratio of tangible
fixed assets (excluding land), sales and so forth
was no more than about 20%. Since the ratio was
30% for operating profit and 35% for the numberof employees, profits and labor intensiveness of
non-common firms tended to be large given theirsize. We analyzed the factor adjustment patternsof non-common firms with continuous data
available during Phase III (FY1994-2000) andconfirmed the degree of relative growth potentialof newly listed companies. According to Fig. 3-4,
sustained growth type companies that expandedboth capacity and employment in Phase III accounted for no more than 10% of common firms
while the ratio of the same type to the populationof non-common firms exceeded one-third (33%).
To what degree then can we expect changes
to occur in the transitions of factor adjustment
rates that have been analyzed thus far if the horizon is broadened to include non-common firms?
Fig. 3-5 indicates the transitions in net growthrate of capacity and net growth rate of employment according to the tabulations of all listed
firms for which a comparison with the previous
year is possible9 and the contribution of commonand non-common firms to that by the degree ofincrease and decrease10. The net growth rate ofcapacity first shows that, although the net growthrate of capacity tended to remain below that ofcommon firms (refer to Fig. 2-2) through Phase
II and to exceed it in Phase III, the difference issmall in both cases and, in recent years, the gen8
The listing of very large firms as in the case of communications or railways, for example, through privatizationand new entries is apparent as an exception.
9
In the simple tabulated values of listed firms, there is anincrease if there are newly listed firms (decrease if there aredelistings). Here, however, such fluctuations unrelated tothe actual situation of the firm are excluded.
10
Excluding special factors arising from communication
career regrouping.

38

Development Bank of Japan Research Report/ No. 31

eral tendency is similar, with the level dropping
notably in FY1998 and shifting into the negative
thereafter. Still, looking only at the moves of thegross positive contribution, excluding the effects
of restrained investment by delisted or privatizedbusinesses, within the context of the abrupt dropin the gross positive contribution of common
firms in recent years, there has been a relative
increase in the presence of non-common firms
and the ratio of the non-common firms to thegross positive contribution of all listed firms rosefrom 4% in the early 1980s to as high as 43%.
Next, transitions in the net growth rate of
employment indicate that, though not dominating
the group, a clear difference from common firms
has emerged in recent years. The net growth rateof employment based on common firms dropped
to –5.8% in FY1999 but to only –4.3% in thecase of all listed firms and, though negative in
FY2000, the extent of the decline was thesmallest since FY1993 (the second most severe
year for common firms after FY1999). The ratioof the non-common firms to the gross positive
contribution of all listed firms also increased inimportance to a level exceeding capacity and was
at a level of about two-thirds from FY1998.
Newly listed firms gradually increased in importance in both capacity and employment crea10
34

A

29
5

B
20
11

C

34

D

55

Fig. 3-4 Comparison of Factor Adjustment Patterns in Phase III (%)(Left: common firms, right: non-common firms, for which
continuous data was available for the period FY1994-2000)

tion in Phase III. However, due in part to thelarge ratio of firms in non-manufacturing indus-

Net growth rate of capacity

(%pt)
12

Net growth rate of employment

(%pt)
4

10

2

8
0

6

-2

4
2
0
-2
-4

-4
Non-common (gross positive contribution)
Common (gross positive contribution)
Common (gross negative contribution, reverse symbols)
Non-common (gross negative contribution, reverse symbols)
Net growth rate of capacity

Ⅰ

Ⅱ

Ⅲ

-6
-8

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscal year

Ⅰ

Non-common (gross positive contribution)
Common (gross positive contribution)
Common (gross negative contribution, reverse symbols)
Non-common (gross negative contribution, reverse symbols)
Net growth rate of employment

ⅡⅢ

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00
Fiscal year

Fig. 3-5 Decomposition of the Contribution of Common and Non-Common Firms to
the Factor Adjustment Rate of All Listed Firms
Notes: 1. Produced with a tabulation of all data comparable to the previous year targeting all listed firms including overthe-counter and new markets (FY2000, about 3,200 firms). However, NTT in FY1999 (conversion to holding
company) and KDDI in FY2000 (merger) were excluded from the tabulation because of the strong impact ofthe discontinuity.
2. Non-common firms include firms newly listed in FY1981 or later as well as firms that were delisted due tomerger, bankruptcy, etc.

tries, the contribution to employment was more
prominent than that to capacity.
Accordingly, we tabulated all listed firms
with continuous data available in Phase III(FY1994-2000)11 and compared the contribution
by industry of the factor adjustment rate with thecase of common firms (Fig. 3-6). As expected,
the large size of the gross positive contribution,
especially in employment, in retail, real estateand other non-manufacturing industries (service,etc.) is an obvious difference from common
firms. Specifically, based on individual firms, thelineup of non-common firms among the top
thirty firms with a large gross positive contribution in capacity and employment in Phase III12 is
as indicated in Table 3-4. In terms of capacity,
there were no more than four non-common firms
among the top thirty, while in terms of employment, there were 18, that is, more than half, andthey are mainly in retail, restaurants, service andother industries that are labor-intensive and that
actively employ temporary and part-time workers.

12

11

Same as footnote 10.

Based on all listed firms; however, the recently listed
firms with continuous data unavailable in Phase III were
included in the tabulation based only on the data subsequent
to listing.

Development Bank of Japan Research Report/ No. 31

39

(%pt)
50
gross positive contribution

40

gross negative contribution

net growth rateexcess reallocation rate

30
20
10
0
-10
-20
-30
-40
-50
od
Fo

s

s
y
n
p
ls
ls
le
el
les
cts
e nt
e nt
iles turing
cal
tio
cla
pul
ica
eta
ste
esa
xti
ob
pm
pm
uti
odu
&
c
&
ruc
&
em
sm
Te
hol
qui
qui
er
ace
ne
tom
ufa
n st
l pr
on
e
e
Ch
ou
W
p
o
u
a
r
n
o
r
m
t
l
l
t
a
I
r
a
r
s
A
a
C
P
a
m
ica
Me
nf e
ic,
ner
Ph
er
ctr
No
Ge
ram
Ele
Oth
Ce
Tra

ns

s
te
ng
tail
gas
ion
sta
uri
Re
cat
and ufact
al e
uni
ity
Re
n
c
m
a
i
r
m
com Elect
onrn
n&
tio
the
a
O
t
por

Fig. 3-6 Decomposition of the Industry-Specific Factor Adjustment rates by
Gross Flows of All Listed Firms (Phase III = FY1994-2000)
Left side: net growth rate of capacity, right side: net growth rate of employment,
7-year cumulative growth rate
Note:

Tabulation targeting all listed firms with continuous data available for the period FY1994-2000. NTT and KDDI
were excluded for the reasons given above.

(%pt)

T

s
te
ng
tail
gas
ion
sta
uri
Re
cat
and ufact
al e
uni
ity
Re
n
c
m
a
i
r
m
com Elect
on&
rn
on
the
ati
O
t
r
sp o
ran

他非製

電ガ

運通

不動産

小売

s
g
y
n
p
ls
ls
le
el
es
les
cts
e nt
e nt
rin
cal
tio
cla
pul
ica
eta
bil
ste
esa
xti
uti
odu
&
ipm
ipm
ctu
&
ruc
&
em
mo
sm
Te
hol
er
ace
ne
l pr
ufa
n st
on
equ al equ Auto
Ch
ou
W
p
o
a
r
n
o
r
m
t
l
t
a
I
r
a
r
s
a
C
P
a
m
ic
Me
nf e
ic,
ner
Ph
er
ctr
No
Ge
ram
Ele
Oth
Ce

卸売

excess reallocation rate
超過再配分率

建設

他製造

自動車

電機

net growth rate
純増率

機械

金属

非鉄

gross negative contribution
減少寄与（逆符号）

窯土

医薬

化学

紙パ

繊維

s

食品

od
Fo

gross positive contribution
増加寄与

鉄鋼

50
40
30
20
10
0
-10
-20
-30
-40
-50

(Reference) Decomposition of the Industry-Specific Factor Adjustment Rates by Gross Flows of Common
Firms (Phase III = FY1994-2000)
Left side: net growth rate of capacity, right side: net growth rate of employment,
7-year cumulative growth rate
Note:

40

Though this is a repetition of Fig. 2-12, the gradations are different for comparison purposes.

Development Bank of Japan Research Report/ No. 31

Table 3-4 List of Non-Common Firms with Large Positive Contributions to
Capacity and Employment Increase in Phase III
Capacity

Rank Company Name

Employment

Industry

Rank Company Name

Industry

6 NTT DoCoMo

Communications

2 Nichii Gakkan Company

Service
Restaurant

8 Oriental Land

Service

4 Skylark

14 Japan Telecom

Communications

5 LIFE Corporation

Retail

24 Ricoh Leasing Company

Service

6 BELLSYSTEM24

Service

7 N.I.C. Corporation

Service

11 Fast Retailing

Retail

14 Ten Allied

Restaurant

15 Yaoko

Retail

16 Yamada Denki

Retail18 Aim Services

Service19 Shimamura

Retail

21 Kuraya Sanseido

Wholesale

22 SMC

General machinery

23 Fuji

Retail25 Heiwado

Retail26 Jonathan’sRestaurant

28 Kohnan Shoji

Retail

30 Kojima

RetailNotes: 1. The ranking by contribution was calculated by including also the newly listed firms after FY1994 in the tabulation.
2. Only non-common firms were extracted and listed in the top 30 ranking by contribution. However, companies towhich special factors are thought to apply, such as mergers, regroupings, etc., were excluded.

In this section, we examined the dynamismof Japanese corporations, which cannot be fully
grasped by analyzing continuously listed companies, and showed that newly listed firms, especially in non-manufacturing industries, made a
steady contribution especially in terms of employment in the latter half of the 1990s. Never-theless, when retail, service and several otherindustries are excluded, there is no great change
in the established trends of downsizing seen previously even if the scope is broadened to include
newly listed firms, and even the contribution ofretail and service industries was limited in termsof capacity.

Development Bank of Japan Research Report/ No. 31

41

Concluding Remarks
Given the fact that investment does not have thestrength that it once had as a macroeconomic
driving force as well as the increasing structural
severity of the employment environment, thechanges occurring in corporate behavior andtheir background are now of immense concern
for macroeconomic policy. However, theeconomic environment is no longer marked byconstant growth and, as corporate behavior becomes more heterogeneous, monitoring only a
few industries and firms or focusing either oncapacity or employment may lead to incorrectconclusions.
This study therefore analyzed the overall
recent trends of capacity and employment adjustment behavior of Japanese firms, especiallylisted firms, based on the techniques of microdata analysis, which focuses on gross flows at
the level of individual firms along with the dual
approach, which treats both capacity and employment as a single set of adjustment behavior.
The various topics addressed in this study andtheir implications are given below.
1.

2.

3.

42

The factor adjustment patterns at the industry level showed two-fold diversity, namely
the decline of inter-industry comovementand divided growth-driving industries bycapital and labor.
Factor adjustment behavior at the company
level indicates that a prominent weakeningof the potential for expansion has been more
serious in capacity than in employment inrecent years. The transitions in the thresholdrate of return of capacity expansion suggest
dramatic changes in investment behavior
since the mid-1990s in terms of greater caution and a decline in the correlation to employment.
Capacity expansion behavior is no longer as
sensitive to trends in the business climate(macro) and in industry (semi-macro) andthe tendency toward domination by individual company (idiosyncratic) factors is
becoming stronger. Meanwhile, trends in

Development Bank of Japan Research Report/ No. 31

4.

5.

the business environment and industry are
gaining in importance in the behavior of capacity contraction. This suggests the establishment of downsizing trends in capacity asin employment overall.
Since the mid-1990s, there have been frequent cutbacks in capacity and employment
in the domestic market by leading firms inJapan in terms of track record and scale especially in manufacturing industries, and diversification is evident in the optimal selection of factor input.
The trends in newly listed firms indicate theexistence of latent growth potential and entrepreneurial strength in non-manufacturingindustries which are expected to makegreater contributions in the future, especially in employment.

Investment and employment do not represent a promise that firms will acquire profitearning opportunities (business opportunities)
but definitely generate costs on the other hand.
This is the undeniable reality in business, thougheasily forgotten when the business climate remains favorable for a long time. The most important implication of this study is that the factor
adjustment behavior of Japanese companies in
the current deflationary economic environment
has changed significantly in line with muchgreater awareness of this undeniable reality.
When considering whether to introduce somemacroeconomic policy to stimulate investment,
taking these structural changes in corporate behavior into consideration, it is essential to remember that policies that target direct short-term
effects could seriously distort optimal corporate
behavior. It is thus preferable to aim for indirect
medium- to long-term effects by creating an environment (by improving legal systems, regulations, consumption stimulation measures) thatenables firms to easily pursue profit-earning opportunities, taking into consideration the needs ofmanufacturing industries for restructuring andthe needs of non-manufacturing industries forbusiness expansion.

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Development Bank of Japan Research Report/ No. 31

43

List of Back Numbers (Including JDB Research Report)
No. 31 Microstructure of Investment and Employment Dynamics: Stylized Facts of Factor Adjustment Basedon Listed Company Data. (this issue)
No. 30 Recent Trends in the Japanese Economy: Globalization and the Japanese Economy, August 2002
No. 29 Corporate Financing Trends in Recent Years: Fund Shortages and Repayment Burden, August 2002
No. 28 Urban Renewal and Resource Recycling: For the Creation of a Resource Recycling Society, July 2002
No. 27 Labor’s Share and the Adjustment of Wages and Employment, June 2002
No. 26 Survey on Planned Capital Spending for Fiscal Years 2001 and 2002, May 2002
No. 25 Environmental Information Policy and Utilization of Information Technology: Toward a Shift in Environmental Policy Paradigm, March 2002
No. 24 The Changing Structure of Trade in Japan and Its Impact: With the Focus on Trade in Information
Technology (IT) Goods, March 2002
No. 23 Microstructure of Persistent ROA Decline in the Japanese Corporate Sector: Inter-company Disparties
and Investment Strategies, March 2002
No. 22 Recent Trends in the Japanese Economy: The Japanese Economy under Deflation and Prospects ofEvolution, February 2002
No. 21 Survey on Planned Capital Spending for Fiscal Years 2000, 2001 and 2002, December 2001
No. 20 Current Situation and Challenges for Cable Television in the Broadband Era, October 2001
No. 19 Recent Trends in the Japanese Economy: The Japanese Economy under Deflation, August 2001
No. 18 Introduction of a Home Appliance Recycling System: Effects & Prospects: Progress towards Utilisationof Recycling Infrastructure, June 2001
No. 17 Survey on Planned Capital Spending for Fiscal Years 2000 and 2001, June 2001
No. 16 Revitalization of Middle-aged and Elderly Workers in Japan’s Labor Markets: Requiring the Expansionof the Vocational Training Functions, March 2001
No. 15 Risk-Averting Portfolio Trends of Japanese Households, March 2001
No. 14 Consumption Demand Trends and the Structure of Supply: Focus on Retail Industry Supply Behavior,
March 2001
No. 13 Recent Trends in the Japanese Economy: Weakness of Current Economic Recovery and Its Background,
March 2001
No. 12 Empirical Reassessment of Japanese Corporate Investment Behavior: Features and Changes since the1980s, based on Micro-level Panel Data, March 2001
No. 11 Survey on Planned Capital Spending for Fiscal Years 1999, 2000 and 2001, October 2000
No. 10 Job Creation and Job Destruction in Japan, 1978-1998: An Empirical Analysis Based on EnterpriseData, September 2000
No. 9

Recent Trends in the Japanese Economy: Information Technology and the Economy, September 2000

No. 8

Trend of International Reorganization Affecting the Japanese Automobile and Auto Parts Industries,
June 2000

No. 7

Survey on Planned Capital Spending for Fiscal Years 1999 and 2000, June 2000

No. 6

Current Status and Future Perspective of the Japanese Remediation Industry: Technology and Market
for Underground Remediation, May 2000

No. 5

Recent Trends in the Japanese Economy: The 1990s in Retrospect, March 2000

No. 4

Destabilized Consumption and the Post-bubble Consumer Environment, February 2000

No. 3

The Slump in Plant and Equipment Investment in the 1990s: Focusing on Lowered Expectations, theDebt Burden and Other Structural Factors, January 2000

No. 2

Survey on Planned Capital Spending for Fiscal Years 1998, 1999 and 2000, November 1999

No. 1

Corporate Strategies in Japan’s Semiconductor Industry: Implications of Development in Other AsianCountries, November 1999_________________________________________________________________________________________JDB Research Report

No. 96 Recent Trends in the Japanese Economy: Focused on Fixed Investment and Capital Stock, August 1999
No. 95 Efforts to Protect the Natural Environment in the United States and Germany: Environmental Mitigation and Biotope Conservation, July 1999
No. 94 Survey on Planned Capital Spending for Fiscal Years 1998 and 1999, June 1999
No. 93 Towards the realization of ‘environmental partnership’: A survey of Japan’s environmental NPO sector
through comparison with Germany, June 1999
No. 92 The Impact of Demographic Changes on Consumption and Savings, March 1999
No. 91 Recent Research and Development Trends in Japanese Enterprises: Technology Fusion, March 1999
No. 90 Recent Trends in the Japanese Economy: Prolonged Balance Sheet Adjustment, January 1999
No. 89 Impact of Asset Price Fluctuations on Household and Corporate Behavior: A Comparison between
Japan and the U.S., December 1998
No. 88 Survey on Planned Capital Spending for Fiscal Years 1997, 1998, and 1999, December 1998
No. 87 Foreign Exchange Rate Fluctuations and Changes in the Input-Output Structure, November 1998
No. 86 Structural Changes in Unemployment of Japan: An Approach from Labor Flow, November 1998
No. 85 Recent Trends in the Japanese Economy: Characteristics of the Current Recession, August 1998
No. 84 R&D Stock and Trade Structure in Japan, August 1998
No. 83 Survey on Planned Capital Spending for Fiscal Years 1997 and 1998, August 1998
No. 82 The Significance, Potential, and Problems of DNA Analysis Research: Establishing Public Acceptanceis Essential, May 1998
No. 81 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1996, 1997 and1998 Conducted in August 1997, March 1998
No. 80 Recent Trends in the Japanese Economy: Growth Base of the Japanese Economy, January 1998
No. 79 Information Appliances: The Strength of Japanese Companies and Tasks for the Future, January 1998
No. 78 Challenges and Outlook for the Japanese Machinery Industries: Impact of ISO14000 Series and Environmental Cost, January 1998
No. 77 Current Conditions and Issues of Computerization in the Health Care Industry: For the Construction of
a Health Care Information Network, January 1998
No. 76 Household Consumption and Saving in Japan, December 1997
No. 75 The Direction of Japanese Distribution System Reforms: Strengthening the Infrastructure to Support
Diverse Consumer Choices, November 1997
No. 74 Foreign Direct Investments by Japanese Manufacturing Industries and Their Effects on International
Trade, October 1997

No. 73 The Impact of the Changing Trade Structure on the Japanese Economy: With Special Focus on the
Effects on Productivity and Employment, October 1997
No. 72 An Analysis of Foreign Direct Investment and Foreign Affiliates in Japan, August 1997
No. 71 Recent Trends in the Japanese Economy: Stock Replacement and New Demand as Aspects of
Economic Recovery, August 1997
No. 70 Corporate Fundraising: An International Comparison, June 1997
No. 69 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1996 and 1997
Conducted in February 1997, May 1997
No. 68 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1996 and 1997
Conducted in August 1996, August 1997
No. 67 An International Comparison of Corporate Investment Behavior: Empirical Analysis Using Firm Data
from Japan, France and the US, April 1997
No. 66 Housing in the National Capital: Analysis of the Market for Housing using Micro Data, March 1997
No. 65 The Environment for Locating Business Operations in Major East Asian Cities, January 1997
No. 64 Direction of Reconstruction of Business Strategy in the Chemical Industry, January 1997
No. 63 Reflection on Discussions Concerning Regulation of the Electric Power Industry: Deregulation of theElectric Power Industry in Japan and Implication of Experiences in the United States, December 1996
No. 62 Current Status and Future Perspective of the Japanese Semiconductor Industry, November 1996
No. 61 A Breakthrough for the Japanese Software Industry?: Responsiveness to Users’ Needs is the key, October 1996
No. 60 Recent Trends in the Japanese Economy Focusing on the Characteristics and Sustainability of the Current Economic Recovery, September 1996
No. 59 Analysis of the Primary Causes and Economic Effects of Information: Related investment in the UnitedStates and Trends in Japan, August 1996
No. 58 Selected Summaries of Research Reports: Published in FY1995, June 1996
No. 57 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1995 and 1996
Conducted in February 1996, May 1996
No. 56 Recent Trends in the Japanese Economy, May 1996
No. 55 Issues Concerning the Competitiveness of the Japanese Steel Industry, February 1996
No. 54 Changes in the Financial Environment and the Real Economy, January 1996
No. 53 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1994, 1995 and1996 Conducted in August 1995, October 1995
No. 52 Current Economic Trends: Focusing on the Appreciation of the Yen and its Effects, October 1995
No. 51 Problems Concerning the International Competitiveness of the Petrochemical Industry, October 1995
No. 50 An Economic Approach to International Competitiveness, October 1995
No. 49 Selected Summaries of Research Reports Published in FY1994, July 1995
No. 48 Strategies for Improving the Efficiency of the Japanese Physical Distribution System: Part 2, July 1995
No. 47 Issues on International Competitive Strength of the Auto Industry, June 1995
No. 46 Problems Concerning the International Competitiveness of the Electronics and Electric Machinery Industry, June 1995

No. 45 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1994 and 1995
Conducted in March 1995, June 1995
No. 44 Strategies for Improving the Efficiency of the Japanese Physical Distribution System, March 1995
No. 43 Capital Spending Recovery Scenario Cycle and Structure, August 1994
No. 42 Progress of International Joint Development between Industrialized Countries on the Private Level,
May 1994
No. 41 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1993, 1994 and1995 Conducted in August 1994 , November 1994
No. 40 Selected Summaries of Research Reports Published in FY1993, June 1994
No. 39 Recent Trends in Japan’s Foreign Accounts, April 1994
No. 38 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1993 and 1994
Conducted in March 1994, April 1994
No. 37 Economic Zones and East Asia Prospect for Economic Activity Oriented Market Integration, December
1993
No. 36 Japanese Corporate Responses to Global Environmental Issues, December 1993
No. 35 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1992, 1993 and1994 Conducted in August 1993, September 1993
No. 34 Structure of Profit to Capital in the Manufacturing Industry, September 1993
No. 33 Comparison of the Japanese and The U.S. Labor Markets, October 1992
No. 32 The Relative Competitiveness of U.S., German, and Japanese Manufacturing, March 1993
No. 31 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1992 and 1993
Conducted in March 1993, April 1993
No. 30 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1991, 1992 and1993 Conducted in August 1992, December 1992
No. 29 Flow of Funds in the 80s and Future Corporate Finance in Japan, November 1992
No. 28 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1991 and 1992
Conducted in February 1992, April 1992
No. 27 An Analysis of Foreign Direct Investment in Japan, March 1992
No. 26 Projection of Japan's Trade Surplus in 1995: Analysis of Japan's Trade Structure in the 80s, February
1992
No. 25 Intra-Industry Trade and Dynamic Development of The World Economy, November 1991
No. 24 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1990, 1991 and1992 Conducted in August 1991, September 1991
No. 23 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1990 and 1991
Conducted in February 1991, March 1991
No. 22 Trends of the Petrochemical Industry and its Marketplace in East Asia, March 1991
No. 21 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1989, 1990 and1991 Conducted in August 1990, September 1990
No. 20 Deepening Economic Linkages in The Pacific Basin Region: Trade, Foreign Direct Investment andTechnology, September 1990
No. 19 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1989 and 1990

Conducted in February 1990, March 1990
No. 18 Petrochemicals In Japan, The US, and Korea an Assessment of Economic Efficiency, February 1990
No. 17 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1988, 1989 and1990 Conducted in August 1989, October 1989
No. 16 Impact of the EC Unification on Japan’s Automobile and Electronics Industries, August 1989
No. 15 Industrial and Trade Structures and the International Competitiveness of Asia’s Newly Industrializing
Economies, August 1989
No. 14 The Japan Development Bank Reports on Capital Investment Spending: Survey for Fiscal Years 1988
and 1989 Conducted in February 1989, March 1989
No. 13 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1987, 1988 and1989 Conducted in August 1988, September 1988
No. 12 Growing Foreign Investors’ Activities and the Future of Internationalization, September 1988
No. 11 Futures Tasks and Prospects for the Japanese Machine-Tool Industry, July 1988
No. 10 International Division of Labor in the Machine Industries Among Japan, Asia’s NICs and ASEANCountries, June 1988
No. 9

Trends of the Petrochemical Industry and its Marketplace in East Asia around Japan, May 1988

No. 8

The International Competitiveness of Japan and U.S. in High Technology Industries, April 1988

No. 7

The Japan Development Bank Reports on Private Fixed Investment in Japan, March 1988

No. 6

Economic Projections of the Japan’s Economy to the Year 2000, February 1988

No. 5

The Japan Development Bank Reports on Private Fixed Investment in Japan, September 1987

No. 4

Current Trends in the Japanese Auto Parts Industry in Overseas Production, July 1987

No. 3

Current Moves for Foreign Direct Investment into Japan, May 1987

No. 2

Overseas Direct Investments by Japanese Corporations, May 1987

No. 1

Current U.S. Consumption Trends, May 1987