Development Bank of Japan
Research Report
No. 38Decline in Productivity in Japan andDisparities Between Firms in the 1990s:
An Empirical Approach Based on Data
Envelopment Analysis

April 2003

Economic and Industrial Research Department
Development Bank of Japan

This report was originally published in Japanese as Chosa No. 44
in August 2002.

Contents
Summary ............................................................................................................................................... iii
Abstract.......................................................................................................................................................... 1

I

Introduction ................................................................................................................................... 2
1. Stagnation in Productivity in Japan from the Perspective of Total Factor Productivity .......... 2
2. Technological Efficiency and TFP........................................................................................... 4

II

Technological Progress and Efficiency......................................................................................... 5
1. Definition of Efficiency and Technological Progress .............................................................. 5
2. Methods for Measuring Efficiency .......................................................................................... 5
3. DEA Method and Technological Efficiency ............................................................................ 6
4. Technological Change and the Malmquist Productivity Index ................................................ 7
5. Foregoing Research in the Analysis of Productivity Using the Malmquist
Productivity Index.................................................................................................................... 9

III Development of Data for Calculating the Malmquist Productivity Index.............................. 10
1. Labor and Capital Productivity at the Firm Level ................................................................. 10
2. Value Added........................................................................................................................... 10
3. Amount of Labor Input .......................................................................................................... 10
4. Amount of Capital Input ........................................................................................................ 11
IV Calculation of the Malmquist Productivity Index by Industry ............................................... 13
1. Chemicals............................................................................................................................... 13
2. Iron and Steel ......................................................................................................................... 15
3. Electrical Machinery.............................................................................................................. 16
4. Automobiles and Auto Parts .................................................................................................. 17
5. Construction........................................................................................................................... 17
6. Retail...................................................................................................................................... 18
7. Real Estate ............................................................................................................................. 19
8. Services (excluding leasing and private sector broadcasting)................................................ 20
V

Conclusion .................................................................................................................................... 21

Supplementary Note .......................................................................................................................... 27
References ............................................................................................................................................ 33

Development Bank of Japan Research Report/ No. 38iDecline in Productivity in Japan and Disparities Between Firms in the
1990s: An Empirical Approach Based on Data Envelopment Analysis
Summary
1. The Japanese economy has suffered a low
level of growth since the 1990s and the decline
in productivity is often pointed out as the primary cause in growth accounting. Along withcapital and labor input, productivity is anothermajor factor that supports economic growth.
However, since it can be calculated as the residual derived by subtracting growth in capitaland labor input from the growth rate of theeconomy overall (the so-called Solow residual),increased productivity is not only the result of
direct technological progress based on researchand development and the like but also includes
economies of scale, learning effect and various
other elements. It is therefore difficult but important to clarify the factors that are involved in
producing changes in productivity.
This study, based on individual corporate
financial data, classifies changes in productivity
as (1) technological progress of firms with thehighest level of productivity within an industry
and (2) improvements in productivity of otherfirms and conducts analyses focusing on the
differences that are evident within major industries. Taking into account the intra-industry
differences in productivity thus identified, possible directions of future policies are proposed.
2. First of all, in terms of changes in totalfactor productivity (TFP), although manufacturing industries suffered stagnant growth after
the collapse of the bubble economy, they succeeded in maintaining positive growth due toexpansion in electrical machinery and other
IT-related industries. There was a broad decline
in productivity in non-manufacturing industries,
however, especially construction and other industries that were strongly impacted by the collapse of the bubble, and the TFP of
non-manufacturing industries overall turned
negative.
3.

It is not possible to clarify the backgroundcauses of the slump in productivity by the TFP
estimated based on aggregate data. The study
therefore sought to verify whether or not disparities exist between firms by gauging disparities in productivity between firms within thesame industry, using the Malmquist productivity index (MPI) calculated by data envelopment
analysis (DEA). The MPI is divided into (1)changes (technical changes) in efficiency offirms that are making the most effective use of
the input factors of capital, labor and so forth(i.e., that have the highest productivity) (technological frontier) and (2) improvements in efficiency (technical efficiency) of other less efficient companies. The amount of capital andlabor input and value added of each firm are
estimated using financial data of listedcompanies, then the MPI, technical change andtechnical efficiency in major industries are calculated.
4. The MPI of major manufacturing industries indicates growth in electrical machinery
since the latter half of the 1990s while growthin chemicals, iron & steel, automobiles &components has been persistently low. There
was a strong tendency toward continued positive growth in technical change throughout the
1990s, contributing considerably to improved
corporate productivity. However, some firms
were slow to introduce efficiency improvements.
5. The MPI of major non-manufacturing industries indicates a persistent overall decline inproductivity in the construction, retail and services (excluding leasing and private sector
broadcasting) industries. There was a broad
expansion in productivity in the constructionindustry during the time of the bubble economy,
but, after that, there has subsequently been astrong tendency toward negative growth intechnical change. In the retail industry, there
was a notable improvement in productivity insupermarkets, convenience stores and other re-

Development Bank of Japan Research Report/ No. 38iii

tail outlets from the 1980s through the early
1990s, though other industries failed to keeppace. But growth has remained low in recent
years. While there has been modest change inproductivity in the service industry, there is little prospect of an improvement in productivity
due to economies of scale since the collapse of
the bubble economy.
[Naoki Shinada (e-mail: nashina@dbj.go.jp)]

iv

Development Bank of Japan Research Report/ No. 38

Abstract
The Japanese economy has suffered a low levelof growth since the 1990s and the decline inproductivity is often pointed out as the primarycause. In regard to the characteristics of productivity at the corporate level in Japan, theanalyses in this study focus on clarifying disparities between firms within the same industry.
Specifically, the value added, labor productivity
and capital productivity of each firm were
measured using individual financial data oflisted companies and the Malmquist productivity index (MPI) in eight major industries wascalculated by data envelopment analysis (DEA).
The results showed that the disparities between
the two have been expanding, especially in
manufacturing industries, since the latter half ofthe 1990s, through the decomposition of trends
in productivity into (1) the technological progress of firms with the highest productivity inthe industry and (2) the improvement of productivity in other firms.

Development Bank of Japan Research Report/ No. 38

1I

Introduction
1. Stagnation in Productivity in Japan
from the Perspective of Total Factor
Productivity

According to an estimate by the Cabinet Office(2001), the potential growth potential in Japan
has been declining since the Heisei recession(Fig. 1-1). Besides a decline in capital and labor
input reflecting restricted capital spending and a
decline in working hours as well as stagnationin the growth in the number of employed persons, there has been notable stagnation in thegrowth of productivity (Fig. 1-2).
Productivity as calculated here is the totalfactor productivity (TFP). Essentially, TFP indicates total output per total input, that is, productivity in relation to the overall input; however,there is a problem with, the method of tabulating

7

multiple inputs and outputs and gauging productivity. Here, assuming a production function with
one output and two inputs, the growth of production volume and the growth of the two inputs ofoperating capital and labor were compared, andthe portion that cannot be explained by growth in
the two inputs was calculated as the TFP.
TFP trends by industry indicate thatgrowth slumped in virtually all industries during the 1990s compared to the 1980s during thetime of the bubble economy (Fig. 1-3). TFP in
manufacturing industries maintained positive
growth even during the 1990s due to technological progress in electrical machinery andother industries. In non-manufacturing industries, however, TFP growth was negative reflecting a decline in construction and other industries that were strongly impacted by the collapse of the bubble economy.

(%)
Actual growth rate
Potential growth rate

6
5
4
3
2
1
0
-1
-2

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

Fig. 1-1

Potential Growth Rate and Actual real GDP Growth Rate

Note:

Productivity (TFP) is calculated from the disparity (so-called Solow residual)
between the GDP growth rate and capital and labor input growth (multiplied by
the allocation rate of each).
Source: Cabinet Office, “Fiscal 2001 Economic Outlook and Basic Stance for Macroeconomic & Fiscal Management.”2

Development Bank of Japan Research Report/ No. 38

7

(%)

6
5

Productivity (TFP)
Labor input(number of employees)

4

Potential growth rate

3
2
1
0
-1

Capital input
Labor input (working hours)

-2
81-85

Fig. 1-2

86-91

92-95

(year)
96-00

Factors in the Decline of the Potential Growth Rate

Note:

Productivity (TFP) is calculated from the disparity (so-called Solow residual)
between the GDP growth rate and capital and labor input growth (multiplied by
the allocation rate of each).
Source: Cabinet Office, “Fiscal 2001 Economic Outlook and Basic Stance for Macroeconomic & Fiscal Management.”

1.5

(%)
1981-911.0

1992-98

0.5
0.0
-0.5
-1.0
All industries Manufacturingindustries

WholesaleFinancial andinsurance

ElectricalmachineryNonmanufacturingindustries

Fig. 1-3

Transition in TFP by Industry

Construction

Real estate

Note:

Productivity (TFP) is calculated from the disparity (so-called Solow residual) between the GDP growth rate andcapital and labor input growth (multiplied by the allocation rate of each).
Source: Cabinet Office, “Fiscal 2001 Economic Outlook and Basic Stance for Macroeconomic & Fiscal Management.”

Development Bank of Japan Research Report/ No. 38

32.

Technological Efficiency and TFP

In terms of the efficient use of technology, it ispossible to comprehend the rise in productivity,that is, technological progress, as the degree of
efficiency with which companies use their production factors.
Technological progress can be summarized
as the following two types. The first is the efficiency of technology consisting of whether or
not a given level of technology is being usedefficiently. The other is the change in the levelof technology itself. Even if a certain level ofsophisticated technology exists, when no entity
is able to use it with full efficiency, there will
be no improvement in technological progress
overall. The extent of the technological progress of society as a whole is thought to consistof a combination of both efficiency and change
in the technological level.
Can TFP thus be considered an appropriate
index of technological progress? In order toclarify the efficiency of technology, it is necessary to compare the state in which certain technology is used to the maximum and the state inwhich it is not. In order to do this, it is necessary to establish an appropriate classificationand tabulation of inputs such as value added,
operating capital and labor input as well as
outputs. Various problems have been pointed

4

Development Bank of Japan Research Report/ No. 38

out in proving this, and the so-called productivity paradox occurs in which the calculated TFPmoves differently from that expected. Nakajima(2001) points out the problem of the precisionof the input and output data. Canter and Hanusch (1999) state that it is difficult to assume
an appropriate production function for diverse
technological progress. The TFP is calculatedusing outputs and inputs aggregated at the industry level. Due to the difficulty of incorporating all of the costs of all goods and qualitative changes, the TFP does not indicate content
in detail. In addition, when clarifying changes
in the technological level itself, similar problems arise when conducting analyses using aggregated values.
Therefore, this study sought to prove how
efficiently firms within the same industry use
production factors or if disparities in efficiency
arise between firms by measuring the productivity at the level of each firm and using that toestimate the technological frontier (isoquantcurve). Specifically, the disparities in productivity between firms within the same industry
are clarified by calculating the Malmquist productivity index using data envelopment analysis
procedures in estimating the technological frontier. The next chapter examines how productivity is comprehended by this approach.

II Technological Progress andEfficiency
1.

Definition of Efficiency andTechnological Progress

The definition of efficiency used in this paper isthe extent to which output is generated by using
production factors efficiently. Generally, when
production factors are used most efficiently andit would be impossible to produce the same
volume of output with fewer production factors,
the isoquant curve (a curve indicating the combination of input volumes required to produce agiven volume of output) can be expressed as thetechnological frontier. It furthermore indicatesthe degree of efficiency (or inefficiency) due todeviation from the technological frontier. For
example, in Fig. 2-1, if the output is y and there
are two inputs (production factors), x1 and x2,
the isoquant curve is depicted as SS′. If we assume that it is not possible to produce the same
volume of output with input less than SS′, SS′
then becomes the technological frontier. In thesimplest case not taking the cost of production
factors into account, Firm Q on curve SS′ ismost efficient while Firm P, which deviatesfrom SS′, is inefficient because it uses moreproduction factors.
The above definition of efficiency is ex-pressed by the technological frontier and deviation from it at that point in time and is comprehended statically. Technological progress,
however, is subject to changes in the technological level with the passage of time. Technological progress is divided into technologicalefficiency at a given time and changes in thetechnological level that serves as the standard.
Consequently, in order to express technologicalprogress using the technological frontier, it isalso necessary to consider changes in the technological frontier itself.
Section 2 below explains the means thatare available for measuring the isoquant curve
at a given time and the extent of the deviationfrom that, that is, the former point. Meanwhile,the latter, changes in the technological frontier
itself, is considered from Section 3 onward.
2.

Methods for Measuring Efficiency

There are two primary methods for measuring
the technological frontier. The first uses parametric estimation methods, such as stochastic
frontier analysis. This method considers thedeviation from the technological frontier to be
the distribution of stochastic variables, applies afunction type to the frontier and econometrically estimates parameters. However, the number of parameters increases and multicollinearity readily occurs when formulating translog

x2/y
S
●

●

P

Q

S′
0

A′
Fig. 2-1

x1/yTechnological Frontier and Technological Efficiency

Development Bank of Japan Research Report/ No. 38

5

type or other functions.1 Accordingly, this study
uses a method that derives the technologicalfrontier and the extent of deviation from observations without any parameter estimations.
Data envelopment analysis (DEA) is a typicalmethod of this type, and enables non-parametricestimates to be made.
When making parametric estimations, the
TFP of a certain firm j, for example, would
generally be defined as indicated below.
hj =uT y jvT x j(2.1)

n : number of firms (j = 1,…, n)
hj : TFP
yj : output
xj : input
When considering the production of s
number of outputs from m number of inputs, yj
is the s-vector (r=1,…, s) and xj is the m-vector(i=1,…, m); however, the weight of the outputs
and inputs is exogenous and, in order to estimate the parameters, it is necessary to hypothesize certain special function types.
With a non-parametric approach such as
the DEA method, however, it is possible toeliminate the restriction of applying such special function type to all and making the parameters the same. Since parameters are determined endogenously with the DEA method,
individual observed objects have different parameters.
3.

DEA Method and Technological
Efficiency

The following is a simple outline of the DEA
method. With the DEA method, the technological frontier envelope is estimated by applying a linear programming procedure such asthat below.2 Refer to Supplementary Note 2.1.
1
Nakajima (2001) points out that it is possible to avoid
this problem by introducing a cost function; however, that
requires input cost data. Estimations in this study wereconducted using the DEA method described above due inpart to the unavailability of input cost data of individualcompanies.
2
This is the simplest case that does not take economies

6

Development Bank of Japan Research Report/ No. 38

for the calculation method using DEA.
Since the technological efficiency of firm j
is expressed as u′yj/v′xj, the technological frontier maximizes this. Since technological efficiency is less than 1:
 u ′y j ,max 
u ,v  v ′x 
j 

s.t.
u ′y j≤ 1,
v ′x ju, v ≥ 0(2.2)

The problem of maximization is thus resolved. This can be modified as indicated below.3

min θ j ,
θ ,λ

s.t.
− y j + Xλ ≥ 0,(2.3)

θ j x j − Xλ ≥ 0,λ≥0
θj : technological efficiency of firm j(j=1,…, n)
λ : n×1 absolute term vector
X : input vector of the industry overall
xj : input vector of firm jyj : output vector of firm j
By implementing this linear programming
problem repeatedly for n number of firms, it ispossible to calculate the technological efficiency of each n firm. Since the technologicalfrontier is estimated as an envelope, even if thenumber of observable firms is small, it is possible to specify the distance from observable objects and clarify the standards for promoting
efficiency using production factors as long as it
is possible to estimate the technological frontier.
The content covered thus far is summaof scale into account. In the estimations in this study, calculations are carried out using a model that takes economies of scale into account. Refer to Supplementary Note
2.2.
3
Based on Coelli (1996).

rized using Fig. 2-2. In Fig. 2-2, assuming afirm that produces one type of output using two
types of inputs, the efficiency of the technologyof that firm is clearly specified. The labor andcapital productivity of various firms at a given
point in time are calculated, the reciprocals oflabor productivity and capital productivity are
determined and plotted, respectively, on thevertical and horizontal axes. The bold line is thetechnological frontier. The technological frontier is the combination of capital productivity
and labor productivity of a firm that produces
the most outputs with the fewest production
factors and is a line formed by connecting theobservation points that are closest to the origin.
Firms on the technological frontier are thosethat are capable of using technology the mostefficiently.
Meanwhile, firms located above the technological frontier are technologically inefficientand the extent of deviation from the frontier isexpressed by b. Consequently, to generally express the degree of efficiency, this b is relativized by the distance from the origin to the object. This represents the simplest case and themethod of calculating efficiency differs depending on the configuration of the envelope,the existence of economies of scale and otherfactors.

(labor productivity
reciprocal)

4.

Technological Change and the Malmquist Productivity Index

As seen thus far, technological efficiency indicates the degree of deviation from the most efficient technological frontier at a given point intime and is therefore static in nature. However,technological progress changes in time-series
and, in order to comprehend it dynamically,changing the technological frontier itself must
be considered.
In Fig. 2-2, looking first at changes in thetechnological frontier itself, the technologicalfrontier of period t is the continuous bold line
while the technological frontier of period t+1
moves on the dotted line. The shift width afrom the continuous line to the dotted line is atime-series change in the technological frontier
itself.
If the technological frontier shifts, thetechnological efficiency of the firms that deviate from that line also changes. The width ofthe deviation of the technological frontier atperiod t was b; however, it shifted to deviation
width c due to the shift in the technologicalfrontier. Since deviation widths b and c are relativized in actual measurements by the distance from the origin, the difference in the relative ratio expresses time-series change.
Changes in total dynamic technologicalprogress include the two elements of the abovechanges in the technological frontier itself andchanges in the technological efficiency of firms
deviating from there. The Malmquist productiv-

Technological frontier curve
Firms with highest productivity inthe industry in period t(period t)

Inefficient firms deviating fromthe production frontierba(periodt+1)c(period t)(period t+1)
(capital productivity reciprocal)

Fig. 2-2

Malmquist Productivity Index (MPI) Concept
Development Bank of Japan Research Report/ No. 38

7ity index indicates changes in productivity by
combining these two.
In Fig. 2-3, the technological frontier isexpressed as F and a given firm located in aposition deviating from that is expressed as A.
The technological efficiency of At during period t is expressed as the ratio of 0At and 0B. If
At shifts to At+1 during period t+1, the changes
in technological efficiency can be expressed as:
0 B / 0 At
0 D / 0 At +1

The Malmquist productivity index determines the geometrical average of (2.4) and (2.5).
That is,0C / 0 At
0 E / 0 At +1(2.5)

If this is also less than 1, that indicates that
there has been an improvement in technologicalefficiency.

(2.6)

Equation (2.6) can be decomposed as indicated below.
 0 B / 0 At ⋅MI At +1 = 
 0 E / 0 At +1 

(2.4)

The technological efficiency of A in relation to technological frontier Ft rises if this isless than 1. However, since the technologicalfrontier also shifts to Ft+1 during period t+1, it isnecessary to take into account the changes intechnological efficiency of A in relation totechnological frontier Ft+1, that is,

0 B / 0 At
0C / 0 At⋅
0 D / 0 At +1 0 E / 0 At +1

MI At +1 =

 0 E / 0 At +! 0C / 0 At 
 0D / 0 A ⋅ 0B / 0 A 
t +1
t +1 
= MC ⋅ MT(2.7)

Thus, the Malmquist productivity index
can be decomposed to the product of MC,
which expresses changes in technological efficiency, and MT, which expresses changes in thelevel of the technological frontier. If MC is
more than 1, that indicates that it has approached the technological frontier and, if it isless than 1, it indicates that it lags behind thetechnological frontier. If MT is more than 1, thelevel of the technological frontier itself is rising
and, if less than 1, it is declining.

x2/y

●
●
●

D

At+1

E

●
●
●

C

At

B
Ft
Ft+1

0
Fig. 2-3

8

x1/yTechnological Efficiency, Technological Change and the Malmquist Productivity Index

Development Bank of Japan Research Report/ No. 38

5.

Foregoing Research in the Analysis of
Productivity Using the Malmquist
Productivity Index

By using the Malmquist productivity index, it ispossible to divide technological progress, thepurpose of this study, that is, increased productivity, into changes in technology and changes
in technological efficiency. The technologicallevel of firms with the highest productivity
within an industry, how that changes over time
and to what extent firms with lower productivity are catching up with firms with the highest
productivity are clarified by calculating the
Malmquist productivity index for firms of agiven industry as well as MC and MT. It is possible to pursue the substance of productivity
increases by focusing on the nature of thechanges within individual firms that result inchanges in productivity in a given industry
overall.
Malmquist (1953) and Solow (1957) presented the theoretical basis for the Malmquist
productivity index and it was used by Caves,
Christensen and Diewert (1982a, 1982b) as amethod for measuring productivity. Canter andHanusch (1999), in particular, attempted comprehensive calculations of the Malmquist productivity index by the DEA method at the national, industry and firm level.
However, the DEA method, which is thebasis for calculating the Malmquist productivity
index, has been frequently discussed in the fieldof operations research and has been used more
frequently for analyzing the efficiency of firms
in individual industries than for comprehensive
empirical analysis. Berger and Humphrey(1999) refer to the use of the DEA method inanalyzing management efficiency of financial
institutions and later produced a number of empirical research studies relating to financial deregulation in South Asia and the management
efficiency of financial institutions.
Empirical research in Japan has also
focused on analyses of the management efficiency of firms in separate industries. These
include Niimi (2000), who analyzed financial
deregulation and the management efficiency ofbanks using the banking industry of Korea as acase study, and Kitamura and Tsutsui (1996),
who measured and compared management efficiency in the electrical industry in Japan and the
U.S. However, there have been few such studies. Much of the foregoing research has notgone beyond analyses of individual industries
and, in addition, due in part to the short analysis
periods, they have not calculated real operating
capital stock, amount of labor input or realvalue added that would be frequently used in
the field of empirical economic analysis using
capital investment or productivity functions.
Consequently, compared to foregoing research, the analyses in this study are characterized by (1) using a comprehensive approachtargeting the major industries of Japan and (2)being empirical analyses based on production
functions after estimating real operating capitalstock or the amount of labor input based on
firms. It should therefore be possible to compare productivity trends between major industries and clarify the characteristics of theirtime-series changes.
The following chapter explains the development of data necessary before the Malmquist
productivity index can be calculated at the firmlevel.

Development Bank of Japan Research Report/ No. 38

9Nominal value added =operating profit + labor expenses + rental expenses + taxes and public charges + patent li(3.3)
cense fees + depreciation expenses

III Development of Data for
Calculating the Malmquist
Productivity Index
1.

Nominal value added calculated in thismanner is deflated by the commodity price index of the industry to which the firm belongs.6

Labor and Capital Productivity
at the Firm Level

A model with two inputs and one output is usedin measuring the Malmquist productivity index
at the firm level. The labor and capital productivity of each firm are calculated with labor andcapital as the inputs and value added as theoutput.
The labor productivity and capital productivity of each firm are as indicated below.
Labor productivity =real value added/amount of labor input(3.1)

Capital productivity =real value added/amount of capital input(3.2)

The target firms are listed firms excluding
the financial and insurance industries and theperiod of data acquisition is 1980-2000.4 The
sources of the data used are as indicated laterand the corporate financial database of the Development Bank of Japan was used for the financial data of firms, the primary data.
All financial data used in this chapter was
extracted from the corporate financial databaseof the Development Bank of Japan for
1980-2000 for the firms for which the Malmquist productivity index was actually measured.
The number of firms involved is indicated in
Figs. 4-1 to 4-8.
2.

Value Added

The nominal value of value added, the denominator of labor and capital productivity, wascalculated based on the definition in the Financial Data Handbook of the Development Bankof Japan.5
4The settlement of firms with a settlement term extending from April of the relevant year through March of
the following year was converted to settlement as of theend of the relevant year.
5 The items are calculated based on the following data
recorded in the corporate financial database of the De10

Development Bank of Japan Research Report/ No. 38

3.

Amount of Labor Input

The amount of labor input is calculated as
number of workers multiplied by number ofworking hours. Number of workers is the aggregate total of the number of employees ofeach company at the end of the term, temporary
and contract employees not included in thenumber of employees at the end of the term,
employees on temporary transfer, on leave, etc.
and company officers. The index of actual total
working hours of the Monthly Labor Survey(Ministry of Health, Labor and Welfare) wasused for working hours and, for industry, theintermediate sector classification was matchedto the industry classification of the Monthly
velopment Bank of Japan.
* Operating profit* Labor expenses: officer compensation + employee salaries and allowances + welfare expense + transfer to reserve for retirement allowances + corporate pensions +cost of labor* Rental expenses = rental expenses in the cost of goods
manufactured + rental expenses in sales and management expenses* Taxes and public charges: taxes and public charges in
the cost of goods manufactured + taxes and publiccharges in sales and management expenses* Patent license fees: patent license fees in the cost of
goods manufactured + patent license fees in sales andmanagement expenses* Depreciation expenses: depreciation expenses in the costof goods manufactured + depreciation expenses in sales
and management expenses
6
The intermediate sector classification of the Development Bank of Japan is used for industry classifications andthe wholesale price index of the Bank of Japan is appliedin principle for the price by goods as the corresponding
price index. The corporate services price index (Bank ofJapan), consumer price index (Ministry of Public Management, Home Affairs, Posts and Telecommunications)and construction price deflator (Ministry of Land, Infrastructure and Transport) are used for that portion of
non-manufacturing industries to which the wholesale priceindex does not correspond (intermediate sector classification for construction, wholesale and retail, real estate,transportation and communications and service industries).

Labor Survey (mining, construction, manufacturing, electricity and gas, transportation andcommunications, wholesale and retail, real estate and service).
4.

Amount of Capital Input
4.1

Depreciable Assets

When calculating the amount of capital
input, assets are divided roughly into (1) depreciable assets, (2) real estate and (3) inventory
assets. The amount of capital input of depreciable assets is calculated as indicated below.
Amount of capital input of depreciable assets = real capital stock of depreciable assets
× capacity utilization rate
(3.4)
Real capital stock of depreciable assets is
calculated by the benchmark year method basedon the relationship of:

K t = (1 − σ )K t −1 + I t(3.5)

Kt : real capital stock
It : read capital investment
σ : depreciation rate
Capital stock here is not gross capital stockas indicated in the Private Sector Corporate
Capital Stock of the Cabinet Office but net capital stock. The former is a physical concept deducting asset retirement and so forth due to thesale or disposal of fixed assets while the lattertakes into consideration real value in production
activities that assumes capital wastage, also including the decline of production capacity due toasset superannuation or attrition.
a. Nominal Capital Investment
It is determined by deflating nominal capital
investment by the price of capital goods. The
acquisition price of new tangible fixed assets
during the term is used in principle for nominal
capital investment.7

7

Refer to Supplementary Note 3.1 for details regarding
the calculation method.

b. Calculation of the Price of Capital Goods by
Industry and by Asset
The price of capital goods used in deflating isbased on domestic wholesale prices. Tangible
fixed assets are first divided into (1)non-residential construction, (2) structures, (3)machinery and equipment, (4) motor vehicles
and transportation equipment and (5) tools, furniture and fixtures. The price of construction
materials of the composite price index is used
for items (1) and (2) and the price of transportation equipment of the domestic wholesale
prices is used for (4). For (3) and (5),weight-averaged domestic wholesale prices using the weight for equipment and other investment goods in each industry given in the fixed
capital matrix of the Input-Output Tables of the
Ministry of Public Management, Home Affairs,
Posts and Telecommunications are used for the
price of capital goods.8

c. Preparation of Benchmarks for Real
Capital stock
Real capital stock is calculated based on equation (3.5) using real capital investment deflatedby the price of capital goods by asset, though it
is necessary to estimate the initial value ofbenchmark K. Real capital stock by industry asof 1980, the benchmark, is thus estimated bydetermining the market value/book value ratio
by industry as of 1980 and multiplying that by
the book value in 1980. The market value/bookvalue ratio is:Market-to-book-value ratio =real net capital stock/capital stock book value (3.6)

Real net capital stock by industry is estimated based on the National Accounts and Private Sector Corporate Capital Stock of the
Cabinet Office.9
8If, for example, 70% of the machinery and equipment(excluding transportation equipment) included in the fixed
assets of Industry Z in the fixed asset matrix for 1995 weregeneral equipment, 20% were electrical equipment and
10% were precision equipment, the prices of machineryand equipment (excluding transportation equipment) capital goods of Industry Z in 1995 = domestic wholesale priceof general equipment in 1995 x 0.7 + domestic wholesale
price of electrical equipment in 1995 × 0.2 + domestic
wholesale price of precision equipment in 1995 x 0.1.
9
Refer to Supplementary Note 3.2 for details.
Development Bank of Japan Research Report/ No. 38

11

The book value of tangible fixed assets(excluding land and construction suspense accounts) of non-financial corporations from theAnnual Report of Corporate Statistics (Ministryof Economy, Trade and Industry) is used forcapital stock book value by industry.
Real (net) capital stock was estimated bydetermining the market value/book value ratio
by industry in 1980 using the above data andmultiplying that by the asset book price of eachcompany in 1980.

MV1 = (BV0 − Ld ) ⋅ (MV0 / BV0 ) ⋅ P1 / P0 + La

d. Capital Fepreciation Rate
The capital depreciation rate differs from theasset retirement rate in the gross capital stock
described above. The wastage rate by asset usedin Hayashi and Inoue (1991) was used for thisestimate.

The market price in 1980, the benchmark,was determined by multiplying the market
value/book value ratio derived from thenon-financial corporation land asset value of
the National Accounts (Cabinet Office) andland book value of the Annual Report of Corporate Statistics (Ministry of Economy, Tradeand Industry) by the book value in 1980.

e. Capacity Utilization Rate
It is possible to calculate the real capital stockof depreciable assets using a. to d. In order todetermine the amount of capital input of depreciable assets, it is also necessary to multiply theutilization rate of depreciable assets by realcapital stock. In regard to the utilization rate in
manufacturing industries, capital stock based on
utilization rate can be calculated by using theutilization rate indices by industry included in
Indices of Industrial Production (Ministry of
Economy, Trade and Industry) statistics.
Meanwhile, for non-manufacturing industries, the utilization rate was estimated bynon-manufacturing industry capital investment
judgment BSI and business-use electrical power
consumption rate of the Business Outlook Survey (Ministry of Finance) based on the methodof Kamada and Masuda (2000).10
4.2

Land

The market price of land was estimated by themethod below by referring to the Economic
Planning Agency (1997).

10
The business-use electrical power consumption rate
was regressed by capital investment judgment BSI andtime trends and the sum total of the absolute term and BSI
term divided by their maximum value was set as the utilization rate for non-manufacturing industries.

12

Development Bank of Japan Research Report/ No. 38

When:Period t

Period t+1

Urban land price index(Real Estate Research Institute)
Increase during the term

P0

P1

-

La

Decrease during the term-

Ld
BV1＝BV0＋
La－Ld
MV1

Term-end book price

BV0

Term-end market price

MV0

4.3

Inventory Assets

Inventory assets were divided into (a) products
and manufactured goods, (b) sales-use real estate, (c) intermediate and partially-finishedgoods, (d) disbursements for work in progressand (e) raw materials. When deflating, the deflator used in section 2 was used for (a), theurban land price index was used for (b), theconstruction deflator was used for (d), the domestic wholesale price index was used for (e)and the average of (a) and (e) was used for (c).
Items 1. to 3. derived above were totaled
and used as the amount of capital input. It istherefore possible to calculate value added andamount of labor input as well as the Malmquist
productivity index using the DEA method.

IV Calculation of the Malmquist
Productivity Index by Industry
The Malmquist productivity index was calculated using the value added, labor input andcapital input for each company determined in
the previous chapter. Though the DEA methodwith linear programming is used for the calculation, this study used DEAP Ver. 2.1, a dataenvelopment analysis (computer) program developed by Prof. Coelli of the University of
New England, Australia.11 The production
function was an input-oriented model with two
inputs (average labor input and capital input atthe beginning and end of the term12) and one
output (value added).
Based on this, the Malmquist productivity
index, technological change and technologicalefficiency were calculated for eight majorindustries: chemicals, iron and steel, electricalmachinery, automobiles and auto parts, construction, retail, real estate and services (excluding leasing and private sector broadcasting).
Figures 4-1 to 4-8 plot the calculated
technological change and technological efficiency in 3-year central moving averages aswell as the Malmquist productivity index.

Graphs of the original figures are given in Supplementary Figs. 4-1 to 4-8.
1.

Chemicals

It can be seen that the Malmquist productivity
index (MPI) of chemicals stayed slightly above
1.0 during the 1980s until about FY1987 andthat productivity rose gradually. Dividing the
MPI between technological change and technological efficiency, technological change rose
above 1.0 especially during the latter half of the
1980s while technological efficiency remained
below 1.0. This indicates changes in the technological level of the group of companies withthe highest productivity in this industry and an
expansion in the gap due to other companies
catching up.
Large-scale capital adjustments were madeat this time, particularly among the leading
petrochemical producers. Japanese petrochemical companies suffered from the structural chronic overcapacity stemming from continuous capital spending competition throughthe first half of the 1980s. In 1983, however,the Law Concerning Temporary Measures for
the Structural Improvement of Specified Industries was enacted, promoting adjustments to

Fig. 4-1a Chemical Industry (135 firms) MPI (3-year moving average)

11

Other computer programs include Warwick DEA,IDEAS and Frontier Analyst.
12
In both cases, the beginning of the current term isdeemed to be the same as the end of the previous term.
Development Bank of Japan Research Report/ No. 38

13

balance supply and demand through thelarge-scale disposal of facilities in 1984-85. As
a result, the capacity utilization rate of ethylene
plants improved and, with the rationalization of
personnel and other measures, the leading ethylene centers improved revenues. However,pharmaceuticals and other business categories
with higher productivity are also included inchemicals. Whereas the petrochemical companies improved their productivity through capitaladjustments even if sales peaked, pharmaceuticals saw sluggish growth during the first half ofthe 1980s due to measures to restrain medical
costs but midway through also managed to expand production and increase the utilization rate.
Pharmaceuticals, etc. were thus largely responsible for stimulating the improvement in productivity in chemicals overall, especially interms of technological change. The technological efficiency of the top petrochemical companies actually declined during this period while
the technological efficiency of companies near
the technological frontier of pharmaceuticals,
etc. showed an improvement.
Technological change and technologicalefficiency both approached 1.0 in 1989-90 and
MPI growth was no longer evident but, later,
though growth was seen in technologicalchange in 1993-94, technological efficiency
dropped far below 1.0. This is the result ofpressure on profits due to the increased burdenof high-level investments made during the bub-ble period when the advent of the Heisei Recession brought a rapid drop in demand as well asa decline in productivity and other factors, eventhough (1) growth in pharmaceutical production,
which had been stagnant until about 1990,
started to rise due to the introduction of new
drugs and other reasons while (2) the leading
petrochemical companies enjoyed high profitability until 1989 due to the increased demandduring the bubble economy.
In the latter half of the 1990s, technological change, which had grown until then at alevel in excess of 1.0, dropped below 1.0. In the
background, there was a series of NHI pricerevisions in the 1990s which had a considerable
impact on pharmaceutical companies. The expansion of sales networks due to the development of new drugs and deregulation did notoffset the negative factors and so productivity
slumped. Meanwhile, productivity improved
among petrochemical firms as demand recovered from about 1994. Since no rise in the levelof the technological frontier centered on pharmaceuticals, etc. was evident, the slump intechnological efficiency showed a relative improvement and a level above 1.0 was maintained for a time. In recent years, however,oversupply conditions due to the prolonged recession have persisted and the productivity of
petrochemicals remains stagnant and the MPI
for chemicals overall has remained at a low
level. The transitions in the MPI of chemicals

1.4
Technological efficiency (A)Technological change (B)MPI (A×B)

1.3
1.2
1.1
1.0
0.9
0.8
83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Fig. 4-1b Chemical Industry (excluding pharmaceuticals, 102 firms) MPI (3-year moving average)

14

Development Bank of Japan Research Report/ No. 38

excluding pharmaceuticals (Fig. 4-1b) during
the 1990s indicate consistently low growth and,especially in recent years, the level has been
about 1.0.
The difference in the growth of technological change and that of technological efficiency reflects the degree of expansion of disparities in technology within the industry. In
chemicals overall, significant disparities occurred in the first half of the 1990s due to thegrowth of pharmaceuticals and the stagnation of
petrochemicals, though the gap later narrowed.
However, in chemicals excluding pharmaceuticals, the disparities instead were narrower during the early 1990s, expanding somewhat during the latter half of the decade. Thus, it is not
only chemicals excluding pharmaceuticals thathave demonstrated gradual inter-industry disparities in recent years but rather this characteristic can be seen in a number of industries as
discussed below.
2.

constitute the technological frontier and therevenues of those producers slowed during theearly half of the 1980s and, in 1986, five producers posted broad losses in ordinary profit
due to the downturn in exports as the yen appreciated. Later, while promoting restructuring
measures, there was a broad expansion in domestic demand due to the bubble economy
during the latter half of the 1980s, enhancing
company revenues and significantly improving
productivity.
In order to respond to the outlook for internal demand, firms began increasing capitalspending again starting in 1989 and investments
continued above the level of the previous year
for four straight years through 1992. Actually,
however, since internal demand reached a peakin about 1990, the burden of these investments
and labor costs put pressure on revenues andcompany productivity began to slide in 1991.
The revenues of steel producers other than blast
furnace operations also deteriorated due to theeffects of sagging internal demand following
the collapse of the bubble economy. Since thedrop in productivity was relatively smaller thanthat of blast furnace producers, however, theirdistance from the technological frontier shortened. Still, the MPI of the overall iron and steel
industry remained consistently below 1.0 during the first half of the 1990s.
During the latter half, in spite of a certain
degree of technological change, the MPI con-

Iron and SteelThe MPI of iron and steel generally remained
above 1.0 during the 1980s and productivity
increased. Technological change showed considerable growth especially during the latter
half of the 1980s and there was a boost in thetechnological level of firms on the technological frontier. There is a relatively large numberof blast furnace producers among the firms that
1.3

Technological efficiency (A)Technological change (B)MPI (A×B)

1.2
1.1
1.0
0.9
0.8
0.7
83

84

Fig. 4-2

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Iron and Steel Industry (52 firms) MPI (3-year moving average)

Development Bank of Japan Research Report/ No. 38

15

The growth of technological change was
especially strong from 1993 through 1996. The
reason, however, was the improvement in productivity of companies on the technologicalfrontier within the context of the broad rise indemand for IT-related goods in the 1990s. On
the other hand, technological efficiency was
below 1.0 and it is clear that the disparity between firms that are above and those that are
below the technological frontier is now widening. In productivity analyses based on general
TFP, it is frequently claimed that electricalmachinery is one of the industries that have
driven the rise in productivity in Japan in recent
years and indeed, in the MPI-based analyses inthis study, it was above 1.0 in most years during
the 1990s and growth in productivity was confirmed to be higher than in other industries. The
breakdown, however, suggests it was due to thecontinued technological progress of firms above
the technological frontier and advancing disparities with technologically inefficient firms.

tinued to expand above the 1.0 mark. This
likely reflects the integration of production facilities or withdrawal from unprofitable operations by firms on the technological frontier or
perhaps the implementation of employment adjustments or other management efforts. The
overall MPI also rose to some degree in 2000,
but technological efficiency dropped below 1.0
in 1997 and 1999 and the disparity between thetwo widened.
3.

Electrical Machinery

The MPI of electrical machinery indicates thatgrowth above 1.0 continued throughout the
bubble economy from the latter half of the
1980s with a subsequent drop coinciding with
the Heisei recession. In this respect, this industry demonstrates movements similar to chemicals and steel. One characteristic, however, isthat the MPI remained relatively firm during therecovery from the Heisei recession.

1.4
Technological efficiency (A)Technological change (B)MPI (A×B)

1.3
1.2
1.1
1.0
0.9
0.8
83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Fig. 4-3 Electrical Machinery and Equipment (123 firms) MPI (3-year moving average)

16

Development Bank of Japan Research Report/ No. 38

4.

5.

Automobiles and Auto Parts

Construction

After rising sharply during the latter half of the
1980s, the MPI of the construction industry has
been held in check since the Heisei recession.
During the bubble period from the latter half ofthe 1980s, there was a notable rise in technological change, which is thought to be due to an
upswing in productivity supported by expansion
primarily on the demand side, such as robust
growth in private-sector demand for both residential and non-residential construction.
However, as the Heisei recession deepenedin the 1990s, technological change dropped
sharply, lowering the MPI. Firms with highproductivity on the technological frontier,greatly burdened by capital and labor accumulated during the bubble, were compelled torestructure: the number of employees of listed
construction companies declined from 337,000
in FY1990 to 313,000 by FY2000, while fixed
assets increased from 19% of total assets in
FY1990 to 35% in FY200013, indicating increased burden in terms of capital. In single
years, though technological change was above
1.0 in 1996 and 1999, it has generally been below 1.0, dragging down productivity overall.

Automobiles and auto parts also indicated thesame tendencies as electrical machinery until
the mid-1990s. Productivity rose strongly
throughout the bubble period from the latter
half of the 1980s, reflecting the favorable conditions of the industry at that time. Since thelatter half of the 1990s, the MPI has weakened
while productivity has slumped. Entering theHeisei recession, technological change dropped
below 1.0 and the productivity of firms above
the technological frontier diminished, restraining the overall MPI. Along with the recovery indemand in the mid-1990s, the restructuring ofoperations was promoted, especially by finishedautomobile manufacturers, stimulating anotherincrease in technological change. On the other
hand, auto parts producers positioned awayfrom the technological frontier experienced depressed profits and technological efficiency declined. Both have suffered in recent years andthe growth of the overall MPI has been minimal.

1.4
Technological efficiency (A)Technological change (B)MPI (A×B)

1.3
1.2
1.1
1.0
0.9
0.8
83

Fig. 4-4

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Automobile and Auto Parts Industry (73 firms) MPI (3-year moving average)

13

Source: Research Institute of Capital Formation, Development Bank of Japan (2002)
Development Bank of Japan Research Report/ No. 38

17

1.15
1.10
1.05
1.00
0.95
Technological efficiency (A)Technological change (B)MPI (A×B)

0.90
0.85
83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Fig. 4-5

6.

Construction Industry (117 firms) MPI (3-year moving average)

RetailThe MPI of retail showed growth exceeding 1.0
during the 1980s until about 1987, followed bya slowdown that continued until about 1992.
Though growth resumed in 1993-95, it is noteworthy that technological change consistently
exceeded 1.0 from the latter half of the 1980s
until the mid-1990s, whereas technological efficiency dropped far below 1.0.
Growth in technological change at thistime was primarily due to supermarkets andconvenience stores. The technological frontier
improved due to the emergence of new businesscategories different from department stores.
This deviation between technological change
and technological efficiency persisted throughout the bubble period, which was due to the fact

18

Development Bank of Japan Research Report/ No. 38that the productivity of department stores thatwere not on the technological frontier not only
deteriorated but also some department storesthat had been on the technological frontier during the 1980s later fell below as the result ofinvestment failures during the bubble period.
MPI growth remained low from the latter
half of the 1990s onward. Technological change
by year indicates that, in spite of growth in
1999, growth was depressed in the other years
and technological efficiency stayed below 1.0.
Thus, growth in the productivity of supermarkets, convenience stores and other businesseson the technological frontier was restrained
amidst the prolonged stagnation in personal
consumption while the productivity of othercompanies, which never caught up, was evenlower.

1.15
1.10
1.05
1.00
0.95
Technological efficiency (A)Technological change (B)MPI (A×B)

0.90
0.85
83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Fig. 4-6 Retail Industry (51 firms) MPI (3-year moving average)

7.

ing the Heisei recession and firms not on thetechnological frontier suffered from a decline inproductivity. The productivity of firms on thetechnological frontier, though in excess of 1.0,
showed slower growth and the MPI experienced a considerable slump in the first half ofthe 1990s. Due to differences in type of operation, the range of the fluctuation in productivityof mainstay real estate firms with fewer leased
properties was greater than that of the large
general real estate companies with many leased
properties and the degree of decline was especially great during this period.

Real Estate

In regard to the real estate industry, the burdenof investments of some companies on the technological frontier became heavier as officefloor space increased in the mid to late 1980s
and technological change dropped below 1.0.
There was an improvement in technologicalefficiency, however, and overall the MPI remained above 1.0. However, technological efficiency plunged when office floor space dropped
below the level of the previous year after enter1.15

Technological efficiency (A)Technological change (B)MPI (A×B)

1.10
1.05
1.00
0.95
0.90
0.85
83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

(FY)

Fig. 4-7 Real Estate Industry (18 firms) MPI (3-year moving average)
Development Bank of Japan Research Report/ No. 38

19

In the latter half of the 1990s, all of thefirms undertook restructuring measures such as
asset and staffing reductions to improve productivity, which had worsened. From about
1999, both technological change and technological efficiency improved and recovery in the
MPI is also apparent.14
8.

on the technological frontier have data, engineering and many other business services;
however, productivity declined due to constrained demand during the recession in the
1990s. Meantime, the productivity of companies not on the technological frontier,particularly hotels and inns, cinema and entertainment and other industries, was no longer
evident due to the prolonged slump in personal
consumption. The drop in technological efficiency was especially notable in 2000 and aglance at the breakdown shows that this is due
largely to a decline in economies of scale. Although firms made efforts to reduce assets andrationalize staffing, such measures did not leadto any notable recovery in profitability which
remains depressed.

Services (excluding leasing and private
sector broadcasting)

For services, the MPI was calculated with theexclusion of leasing and private sector broadcasting. There was not much fluctuation in the
MPI compared to the other industries describedabove and, overall, it remained near 1.0. In the
post-bubble period, however, the tendency todrop below 1.0 become stronger and companies

1.15
Economies of scaleTechnological efficiency (A)Technological change (B)MPI (A×B)

1.10
1.05
1.00
0.95
0.90
0.85
83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Fig. 4-8Service Industry (excluding leasing and private sector broadcasting, 35 firms)
MPI (3-year moving average)

14
According to the Research Institute of Capital Formation of the Development Bank of Japan, the rate of returnon assets of listed firms in the real estate industry was
1.8% in 1996, which rose to 2.5% in 1999 and 2.9% in
2000.

20

Development Bank of Japan Research Report/ No. 38

VConclusion

The MPI trends in the eight industries describedabove are summarized below.
•
The MPI of both manufacturing andnon-manufacturing industries increased
throughout the bubble period from the latter
half of the 1980s and declined after the bubble burst. However, disparities are evident
depending on the industry in the degree ofthe decline and moves toward recovery during the economic rebound in the latter halfof the 1990s.
•
Disparities in productivity emerged between
firms on the technological frontier and otherfirms in most manufacturing industries fromthe latter half of the 1990s, which continued
to expand.
•
In non-manufacturing industries, disparities
in productivity between firms are not as apparent as in manufacturing industries but

there was an overall downturn in the MPI in
most industries.
It is necessary to take into account various
problems relating to the estimates in these results. For example, the effects of economic
fluctuations on the changes in productivity must
be excluded from those changes. To do this,capital and labor input calculated in this study
are based on actual utilization, but they arestrictly industry-specific data and not individual
firm data. This means that the effects of economic fluctuations are not entirely removed.
Consequently, there are probably areas in which
economic fluctuations greatly affect productivity trends. However, since the relationship between value added, capital input and labor input
of individual firms is taken into consideration,such effects are perhaps less than, for example,
TFP calculated using capital and labor input
aggregated the macro-level.15

15
It is possible to cite cases in which favorable businessconditions only affect some firms and not others for one
reason or another. For example, even if growth in value
added evidently exceeds the growth of some firms as theresult of large-scale capital input by those firms and aggregate-measured TFP also increases during an economic
boom, technological efficiency could decline and growthof the MPI could be hindered if there are a considerable
number of other firms that are not able to catch up withthem.

Development Bank of Japan Research Report/ No. 38

21

1.4
Technological efficiency (A)Technological change (B)MPI (A×B)

1.3
1.2
1.1
1.0
0.9

Supplementary Fig. 4-1 MPI of the chemical industry (135 firms)

0.8
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-1 MPI of the Chemical Industry (135 firms)

1.6

Technological efficiency (A)Technological change (B)MPI (A×B)

1.4
1.2
1.0
0.8
0.6

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-2 MPI of the Iron and Steel Industry (52 firms)

22

Development Bank of Japan Research Report/ No. 38

1.4Technological efficiency (A)Technological change (B)MPI (A×B)

1.3
1.2
1.1
1.0
0.9
0.8

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-3 MPI of the Electrical Machinery and Equipment Industry (123 firms)

1.4
Technological efficiency (A)Technological change (B)MPI (A×B)

1.3
1.2
1.1
1.0
0.9
0.8

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-4

MPI of the Automobile and Auto Parts Industry (73 firms)

Development Bank of Japan Research Report/ No. 38

23

2.0
Technological efficiency (A)Technological change (B)MPI (A×B)

1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99
(FY)

Supplementary Fig. 4-5 MPI of the Construction Industry (117 firms)

1.15
1.10
1.05
1.00
0.95
Technological efficiency (A)Technological change (B)MPI (A×B)

0.90
0.85

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-6 MPI of the Retail Industry (51 firms)

24

Development Bank of Japan Research Report/ No. 38

1.2

1.1

1.0

0.9

0.8

Technological efficiency (A)Technological change (B)MPI (A×B)

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-7 MPI of the Real Estate Industry (18 firms)

1.15
1.10

Economies of scaleTechnological efficiency (A)Technological change (B)MPI (A×B)

1.05
1.00
0.95
0.90
0.85
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00(FY)

Supplementary Fig. 4-8 MPI of the Service Industry(excluding leasing and private sector broadcasting, 35 firms)

Development Bank of Japan Research Report/ No. 38

25

Supplementary Table 4-1 Inter-industry Disparities in Technological Efficiency(manufacturing industries)
Chemicals
Technological Technologicalchangeefficiency
1.01

Chemicals (excluding pharmaceuticals)
Disparity
0.04

Technological Technologicalchangeefficiency

FY86-90

1.07

0.98

0.09

1.03

0.99

0.04

1.21

0.89

0.32

FY91-95

1.11

0.94

0.16

1.00

1.02

-0.02

0.86

1.15

-0.29

FY96-00

1.00

1.02

-0.02

1.05

0.96

0.09

1.07

0.96

0.12

FY81-85

1.01

1.01

0.00

1.03

1.02

Disparity

1.05

Electrical machinery

1.04

Technological Technologicalchangeefficiency

FY81-85

Technological Technologicalchangeefficiency

1.04

Disparity

Iron & steel

0.00

Automobiles and auto parts
Disparity
0.00

Technological Technologicalchangeefficiency
1.04

1.01

Disparity
0.02

FY86-90

1.04

1.01

0.02

1.05

0.99

0.06

FY91-95

1.07

0.98

0.09

1.00

1.02

-0.02

FY96-00

1.08

0.98

0.10

1.05

0.97

0.08

Supplementary Table 4-2 Inter-industry Disparities in Technological Efficiency(non-manufacturing industries)
Construction
Technological Technologicalchangeefficiency

RetailDisparity

Technological Technologicalchangeefficiency

Real estate
Disparity

Technological Technologicalchangeefficiency

Disparity

FY81-85

1.02

0.99

0.03

1.00

1.02

-0.02

1.01

0.99

0.02

FY86-90

1.09

0.98

0.11

1.04

0.95

0.09

0.96

1.05

-0.09

FY91-95

0.97

1.03

-0.06

1.05

0.95

0.10

1.03

0.94

0.09

FY96-00

1.00

1.00

0.01

1.02

0.98

0.04

1.02

1.03

-0.01

Service (excluding leasing and private sector
broadcasting)Technological Technologicalchangeefficiency
FY81-85

0.97

FY86-90

1.01

0.99

0.02

FY91-95

0.98

1.00

-0.02

FY96-00

1.01

0.98

0.03

26

1.03

Disparity-0.06

Development Bank of Japan Research Report/ No. 38

Supplementary Note 2.1
Efficiency

Technological Efficiency, Allocative Efficiency and Economic

x2/y
S
P
A
●
●

R

Q

●

Q′

0

S′

A′

x1/y

where, y is output, x1 and x2 are inputs, SS′ is technological frontier and the deviation of P from SS′,that is, technological efficiency, is expressed as:

TE = 0Q / 0 P = 1 − (QP / 0 P )TE is a value between 0 and 1. If TE=1, P is above SS′, which is the level of greatest efficiency.
Furthermore, considering the price of the inputs, if the input price ratio is expressed as AA′, theallocative efficiency of the input of Firm P is expressed as:
AE = 0 R / 0Q
Economic efficiency taking both TE and AE into account is expressed as:

EE = TE ⋅ AE = (0Q / 0 P ) ⋅ (0 R / 0Q ) = 0 R / 0 P
EE is a value between 0 and 1.

Development Bank of Japan Research Report/ No. 38

27

Supplementary Note 2.2

DEA Method Approach and Numerical Sequence

x2/y

S
●

●

●
●
●

●

S′

●

x1/y

0

With the DEA method, the technological frontier SS′ is estimated from actual plotted company data.
In the case above, SS′ is determined by linking to the point where there are no more observable objects
on either the x axis (left) or y axis (bottom). This line is referred to as a piecewise linear convex isoquant.
A number of problems arise, however, when estimating this piecewise linear convex isoquant.
For example, let us consider the example below.
x2/y

S
●

●

A′

●

C

A

●
●

B′

B

D

●

0

S′
x1/y

The efficiency of Firm A is expressed as OA/OA1 if A′ of SS′, which has the greatest efficiency,is the standard. However, since it is possible to shift A′ to point C by decreasing the amount of input x2
without changing the output, it is not necessarily an efficient point. A′C is referred to as input slack
and, in order to resolve this problem of slack with DEAP used for calculation in this study, the peersof B (C and D) are determined by repeating linear programming problems a number of times, and the
distance between B′, where CD and OB intersect, and B is measured and efficiency is measured usingthat weight.

28

Development Bank of Japan Research Report/ No. 38

The following is an attempt to express the numerical sequence.1 The assumption is that Firms 1-5
produce y using inputs x1 and x2, as described below.
Firm
1
2
3
4
5

y
1
2
3
1
2

x2
5
4
6
2
2

x1
2
2
6
3
6

x1/y
2
1
2
3
3

x2/y
5
2
2
2
1

x1/y
6

1

5
4
3

1′ ●
2

2

3′
●

1

3

4

●

5

4′

0
0

1

2

3

4

5

6
x2/y

Linear programming problems are resolved for each firm based on this data. The linear programming problem in the case of Firm 3 is solved as indicated below based on Equation (4.3) of the maintext.

min θ 3θ ,λs.t.
− y j + ( y1λ1 + y 2 λ 2 + y 3 λ3 + y 4 λ 4 + y 5 λ5 ) ≥ 0,

θ 3 x13 − ( x11λ1 + x12 λ 2 + x13 λ3 + x14 λ 4 + x15 λ5 ) ≥ 0,
λ ≥ 0,λ = (λ , λ , λ , λ , λ )′
1

1

2

3

4

5

Based on Coelli (1996)
Development Bank of Japan Research Report/ No. 38

29

The results are indicated in the table below.
Firm
1
2
3
4
5

θ

λ1

λ2

0.5
1.0
0.833
0.714
1.0

-

λ3

0.5
1.0
1.0
0.214
-

λ4
-

λ5
-

IS1

0.5
0.286
1.0

IS2
-

0.5
-

It can be seen that the technological efficiency (θ) of Firms 2 and 5 is 1.0 and that they are on thetechnological frontier. θ of Firm 3 is 0.833. Firms 2 and 5 are peers of 3 and 3′ is the point on the frontier that is the standard when measuring the efficiency of 3. As indicated on the graph, the weight of 2
and 5 at that time is λ2(1.0) and λ5(0.5), respectively.
In addition, since 2 has higher efficiency than 1′, an input slack (IS2) of only 0.5, the difference
between the two, is generated.

Supplementary Note 2.3

Technological Efficiency Taking Economies of Scale into AccountTechnological efficiency has been defined based on the assumption of constant returns to scale in themodel used in Supplementary Notes 2.1 and 2.2. Banker, Charnes and Cooper expanded the model ofconstant returns and have presented a model that incorporates the increase in technological efficiency
due to economies of scale.2
A linear programming problem incorporating economies of scale is derived by adding the condition below to Equation (2.3) of the main text.

n1′λ = 1
where, n1′ is the n×1 vector.
The diagram below shows the technological frontier with one input and one output.

CRS

y

VRS
●
●
●

A

Pc

● ●

Pv

●P

●

0

2

30

Based on Coelli (1996)
Development Bank of Japan Research Report/ No. 38

x

CRS is the technological frontier with constant returns to scale. Technological efficiency TE at
this time is:

TECRS = APc / AP
Meanwhile, if there are economies of scale, the technological frontier is VRS and TE is:TEVRS = APv / AP
It is possible to express economies of scale SE as:

SE = APc / APv
That is:TECRS = TEVRS ⋅ SE
Supplementary Note 3.1

Method of Calculating Nominal Capital Investment

If nominal capital investment is NOMI and the newly acquired amount is ACQ, assuming that asset
sales and other reductions in tangible fixed assets are not considered:
NOMI = ACQ
However, here, if there are asset sales, etc., the relevant amount must be deducted from ACQ andthat must be considered as NOMI. The amount deducted is not the balance of the book value (NR) of
the sold asset but rather the cost of re-procurement (CNR). CNR is calculated by the following method.
PK is cost of capital goods, K is real capital stock and KNB is the book value of the stock.

CNR = NR ⋅ (PK ⋅ K / KNB )That is, CNR is estimated by multiplying the ratio of re-procurement cost/book value by NR. NR
thereby becomes:

NOMI = ACQ − NR ⋅ PK ⋅ K / KNB
However, since K and KNB during the current term are not known, the ratio derived from thevalue of each from the previous term is used.

Supplementary Note 3.2

Calculation of Net Capital Stock by Industry3

In order to determine net capital stock by industry, net capital stock by industry in 1970 indicated in
the National Wealth Survey of the Cabinet Office is converted to a deflation standard and the benchmark is produced.
New capital investments indicated in the private sector capital stock statistics of the Cabinet Office are used for capital investments in each year.
3

With reference to Miyagawa (1996)
Development Bank of Japan Research Report/ No. 38

31Capital depreciation rate is calculated by building, structure, means of transportation, equipment
and other assets from net fixed assets and gross fixed asset formation of the national economic accounting. The capital depreciation rate is weight averaged by the weight of each asset in each industry
using the National Wealth Survey.
Net capital stock by industry is estimated by the benchmark year method from the benchmark,capital investments and capital depreciation rates derived above.

32

Development Bank of Japan Research Report/ No. 38

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Coelli, Tim. 1996, “A Guide to DEAP Version 2.1:
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Program,” CEPA Working Paper 96/08
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Management for FY1997
Hayashi, Fumio and Inoue, Tohru. 1991, “The
Relation between Firm Growth and Q with
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Kamada, Koichiro and Masuda, Kazuto. 2000,“Effects of Measurement Error on the Output
Gap in Japan”, Bank of Japan Research andStatistics Department Working Paper 00-15
Malmquist, S. 1953, Index Numbers and Indifferences Surfaces, Trabajos de Estatistica 4, 1953,
209-42
Solow, R.M. 1957, Technical Change and the Aggregate Production Function, Review of Economics and Statistics 39, 1957, 312-20

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Kenkyu ‘84 (Capital Investment Research1984)”, Keizai Keiei Kenkyu, 7/84
Development Bank of Japan, Research Instituteof Capital Formation, 2002 “Zaimu Data demiru Sangyo no 40nen – FY 1960 – FY 2000
(40 years of industry as seen in financial data –FY1960 – FY2000)”
Kitamura, Mika and Tsutsui Miki. 1996, “DEA ni
yoru Nichi-Bei Denki Jigyo no Keiei
Koritsusei Keisoku to Hikaku Bunseki (Measurement and comparative analysis of the business efficiency of U.S.-Japan electrical operations by DEA)”, Electrical Power Economic
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Miyagawa, Tsutomu. 1996, “Setsubi Toshi Hendo
to Sangyo Rensa (Capital investment trends
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Nakajima, Takanobu. 2001, “Nihon Keizai no Seisansei Bunseki (Productivity Analysis of theJapanese Economy)”, Nihon Keizai Shimbun
Niimi, Kazumasa. 2000, “Kinyu Jiyuka to Ginko
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Development Bank of Japan Research Report/ No. 38

33

List of Back Numbers (Including JDB Research Report)
No. 38 Decline in Productivity in Japan and Disparities Between Firms in the 1990s: An Empirical Approach Based on Data Envelopment Analysis (this issue)
No. 37 Trends in Socially Responsible Investment: Corporate Social Responsibility in a New Phase,
March 2003
No. 36 Recent Trends in the Japanese Economy: A Medium-term Scenario for the Sustainability of theJapanese Economy, February 2003
No. 35 Concerns for the Future and Generational Consumption Behavior, February 2003
No. 34 Prospects and Challenges Surrounding Japan’s Electrical Equipment Industry: General Electrical
Equipment Manufacturers’ Restructuring of Operations and Future Prospects, November 2002
No. 33 Survey on Planned Capital Spending for Fiscal Years 2001, 2002 and 2003, November 2002
No. 32 Behavior Trends of Japanese Banks toward the Corporate Sector and Their Impact on the Economy, October 2002
No. 31 Microstructure of Investment and Employment Dynamics: Stylized Facts of Factor Adjustment
Based on Listed Company Data, October 2002
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 inEnvironmental 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
of Evolution, 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
Utilisation of 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
Expansion of 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 the 1980s, 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 Enterprise Data, 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 andMarket 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,the Debt 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
Asian Countries, 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 Acceptance is Essential, May 1998

No. 81 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1996, 1997
and 1998 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 andEnvironmental 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 and1997 Conducted in February 1997, May 1997
No. 68 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1996 and1997 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: Deregulationof the Electric 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
United States 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 and1996 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
and 1996 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 and1995 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
and 1995 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 and1994 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
and 1994 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 and1993 Conducted in March 1993, April 1993
No. 30 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1991, 1992
and 1993 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 and1992 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
and 1992 Conducted in August 1991, September 1991
No. 23 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1990 and1991 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
and 1991 Conducted in August 1990, September 1990
No. 20 Deepening Economic Linkages in The Pacific Basin Region: Trade, Foreign Direct Investment
and Technology, September 1990
No. 19 The Japan Development Bank Reports on Capital Spending: Survey for Fiscal Years 1989 and1990 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
and 1990 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
and 1989 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
ASEAN Countries, 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