MMRC-F-11

Total Factor Productivity in Japanese Information
Service Industries: Firm-Level Analysis
Kiyohiko G. Nishimura
Faculty of Economics, University of Tokyo and Executive
Research Fellow, Economic and Social Research Institute,Cabinet Office, Government of Japan
Futoshi Kurokawa
Graduate School of Economics, University of Tokyo
June 2004

21COE, University of Tokyo

MMRC Discussion Paper No. 11

Total Factor Productivity in Japanese
Information Service Industries: Firm-Level
Analysis
Kiyohiko G. Nishimura
Faculty of Economics, University of Tokyo and Executive Research
Fellow, Economic and Social Research Institute, Cabinet
Office, Government of Japan
Futoshi Kurokawa
Graduate School of Economics, University of Tokyo
June・2004

Abstract
We examine factors determining productivity of firms’ information service activities,
using most comprehensive data of information service industries in Japan.
focus

on

the

degreeofmodularization

andresultingWe

outsourcing,

economies/diseconomies of scale in software development, and firms’ organizationalchanges. Outsourcing has persistent negative effects on total factor productivity,
suggesting not only productivity-enhancing modularization is not fully utilized but
also productivity-hindering remnants of traditional main-contractor-subcontractor
1

Kiyohiko G. Nishimura・Futoshi Kurokawa

relations still prevail in information service industries. We also find diseconomies ofscale in software development, suggesting less efficient communication amongdevelopment team members. Finally, we find a substantial cost on firms’organizational changes on productivity.

JEL classification
L23 - Organization of Production
L25 - Firm Size and Performance
L86 - Information and Internet Services; Computer Software
O33 - Technological Change: Choices and Consequences2

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

1. Introduction
Japan has been suffering from the most severe economic stagnation since 1990.

Among possible

causes of this prolonged recession, a sharp decline in productivity has attracted much attention ofboth economists and policy makers in the past decade.i。In particular, Nishimura et al. (2002)
examine and compare US experience (reported in Jorgenson et al. (2001)) and Japanese experience(reportedinNishimuraand

Shirai(2002)).

They

find

a

sharp

contrast

betweenproductivity-improving manufacturing sectors (though there is a wide variety among them) andproductivity-stagnating non-manufacturing sectors in Japan.

In contrast, no comparable differenceof this magnitude is found in the United States though there is a wide sectoral difference.

Japanese

ICT hardware-producing industries are as productive as US counterpartsii but Japanese ICT using
industries (finance and insurance, trade, and other services) show markedly inferior performancecompared with US counterparts.
In another piece of evidence, Ark et al. (2002) report that in the latter half of the 90s, IT-using
industries (mostly service industries in a broad sense) show a robust 5.4% increase in the UnitedStates, while the Japanese counterparts exhibit disappointing 0.0%.

Especially, a sharp contrast is

notable in Wholesale and Retail Trade industries between Japan and the United States.iii

The White

Paper (2001) of the Cabinet Office on the State of the Economy argues that the TFP growth inJapanese non-manufacturing declines sharply whereas the decline of TFP growth in manufacturingsectors is not so pronounced.

Thus, disappointing TFP growth in non-manufacturing sectors

aggravate, if not cause, Japan’s economic stagnation of the 1990s.iv。
Although their importance is apparent in their large share of GDP and the above-mentioned
disappointing performance, non-manufacturing industries have not been a major subject oflarge-scale firm-level empirical studies.

This is a sharp contrast with manufacturing industries inwhich large-scale firm-level as well as plant-level studies are commonly undertaken.

This scarcity

of research is partly due to data availability.v
In this paper, we fill this huge gap between practical importance and scarcity of firm-level research in
non-manufacturing industries.

Among a variety of non-manufacturing industries, we take up

information service industries as our target industries for the following three reasons.
Firstly, information service industries include the software industry that is an integral part of3

Kiyohiko G. Nishimura・Futoshi Kurokawa

so-called “IT (information technology) Revolution” alongside with the hardware industry. Because ofa prominent role of IT on recent economic development, in-depth analysis of information service
industries is urgently needed. We should remind ourselves that the software industry is among
non-manufacturing industries while the hardware industry is by definition among manufacturingindustries.vi
Secondly, both the Government and business communities consider information service industries as“strategic” industries.

The Japanese government launched an initiative called “e-Japan Strategy II”

in 2003, and clearly targeted information service industries.

Some firms in electronic industries are

shifting their business emphasis from computer hardware to software and information services.vii
Thirdly, however, there is a seemingly high hurdle for the government and firms to achieve theirgoals.

There are several pieces of anecdotal evidence of low potential in Japanese information

service industries.

For example, according to the so-called Capability Maturity Model (CMM), 23

Indian firms are in the top rank at the end of 2000, while no Japanese firm is ranked to thiscategory.viii

Similarly, no Japanese firm is in the top 5 of OECD (2002)’s revenue ranking ofsoftware licensing and service fees.

Only one Japanese firm appears among the top 20.

Thus, it isof utmost importance to analyze factors determining productivity in Japanese information service
industries including the software industry and to discern possible impediments to improve efficiency.
Although there is a sizable literature on firm-level productivity in manufacturing industries, only afew studies have been conducted on non-manufacturing industries and almost none on information
service industries even in the United States, where data are relatively easily available.ix Ehrlich et al.
(1994) examine international airline companies and find an increase in TFP growth after fullprivatization. Gort and Sung (1999) show that increased competition due to deregulation raises TFP
growth in telephone companies in the United States.

To our knowledge, there is no empirical study

investigating TFP growth of information service industries at the firm level in the United States.
Even case studies that examine productivity directly are scarce.
One notable exception is Cusmano et al. (2003), who investigate software-development productivity
based on case studies of 104 software development projects mostly in India, Japan, the United States
and the European Union. Their results indicate that India and Japan are higher development
productivity than the United States and the European Union. However, their productivity measure is
not TFP but several project-oriented measures of performance such as the number of lines of code perprogrammer-month, and defects reported per 1000 lines of code in one year after delivery tocustomers. To interpret their results, we should take account of “cultural difference,” as suggested bythese authors.
4

They point out that US programmers tend to emphasize shorter or more innovative

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

programs, and spend more time in optimizing code, which ultimately reduces the number of lines ofcode, while simultaneously increasing programmer-months.
The research reported here is markedly different from previous researches about information service
industries in Japan and in other countries in two aspects.

Firstly, this research is based onlarge-scale Census-like survey data on all firms and establishments engaged in information services,
called the Survey on Selected Service Industries, Volume of Information Services. In contrast,
existing studies in this field are at most case studies (see, for example, Sano (2001)), and in somecases they may be biased to successful firms (see, for example, Koyama and Takeda (2001)x).
These studies are informative, but insufficient for the purpose of capturing the overall picture of theindustry as a whole.
Secondly, we examine total factor (or multi-factor) productivity of firms.

Previous studies usinglarge-scale data are often concerned only with labor productivity (see, for example, Shintani (1998)).
However, it might be misleading to base one’s argument on labor productivity only, in an industry
using sophisticated IT equipments like computers and servers. The necessity of TFP analysis is
obvious in information service industries.
This paper is organized as follows.

In Section 2, we examine factors determining productivity in

information service industries, with special emphasis on the software industry.

In section 3, weclarify important characteristics of the Survey of Selected Service Industries, and explain data
construction in some details.

Main empirical results are presented in Section 4.

these results and examine what factors are behind these results.
in Section 5.

5

We then discuss

Concluding remarks are presented

Kiyohiko G. Nishimura・Futoshi Kurokawa

2. Factors Determining Productivity of Information-Service Firms
In this section, we examine possible determinants of productivity of information-service firms, which
will be the basis of empirical analysis of Section 4.

Since software products are major outputs in

information service industries, we base our argument mostly on productivity determinants ofsoftware development. xi

In particular, we consider three possible determinants: the degree ofmodularization, the scale of software development, and organizational structure, which have often
been considered as most influential determinants of software development productivity.

2.1. Modularization

Both in popular presses and academic writings, so-called Silicon Valley Model has attracted muchattention.

It is often argued that in the Silicon Valley of the State of California, informationcompanies and engineers form a “community” and collaboration within the community is common
and productive.xii

Based on such collaboration, outsourcing has been considered as one of the mostproductivity- enhancing practices.
However, simple outsourcing is not always likely to be productivity enhancing.

For example, if

new products come out with new production design, outsourcing may cause a serious production
problem when readjustment of production process is necessary and communication between
outsourcers and outsourcees are not so smooth.

In order to facilitate such readjustment andcommunication and to make outsourcing successful, “modularization” of parts has been considered
to play a vital role in the Silicon Valley.xiii
The concept of “modularization” stems from the development of IBM SYSTEM/360.

Computers

before IBM SYSTM/360 had their own specifications with own parts, operating system, andapplication software.

There were few common parts (both hardware and software) between eachgeneration of computers.

Computer manufacturers developed idiosyncratic parts and software

suitable for each generation of computers virtually from scratch.
The development of IBM SYSTEM/360 was revolutionary in the sense that its developers invented
the concept of “family” of computers: compatibility within a family was maintained as much as
possible, and in order to do so, “modularization” had been extensively utilized.

The adoption ofmodularized design and its wide-range application were considered to be one of the most dramatic
6

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

driving forces behind the revolutionary speed of innovations in the computer industry (Baldwin and
Clark (1997)).
Modularization has far-reaching consequences.

Modularization of product architecture allows firms

to modularize production process, which leads to modularization of organization.
modularization is “deepened”, firms can outsource a part of their business.

Thus when

Outsourcees, who aresometimes spinouts of some outsourcer firms, are then able to get outsourcing contracts from various
companies to realize economies of scale and to slide down the learning curve quite rapidly, so thatthey become competitive in prices.

This in turn enables outsourcers to procure parts inexpensively.

It should be noted that software was an integral part of computers at the time of IBM SYSTEM/360.
Thus, it may not be far-fetched to assume the same productivity-enhancing effect of modularization
applies to software development.
The successful hardware modularization, however, may not be duplicated in software andinformation service industries.

Koyama and Takeda (2001) forcefully argue that production

innovation in the software industry is less frequent than the hardware industry, implying these two
industries are not so similar.

Thus simple “transplanting” of modularization may not work well inthe software industry. For example, if OS suppliers do not open source codes of their OS,
modularization is far less complete than in the hardware industry. xiv

Furthermore, human factors

are relatively important in the software industry, since implicit knowledge of engineers may not be
fully “coded” and reproducible.
In sum, there is no theoretically clear-cut conclusion about whether modularization and resulting
outsourcing are productivity enhancing in the software industry.

Thus, whether modularization andoutsourcing are productivity improving or not is in essence an empirical question.

2.2. Scale of Development Organization

The second possible factor determining productivity of information service industries is the scale ofdevelopment organization.

Here development organization includes the basic research anddevelopment division and system software development division (including system integration).
Software development is a complex process involving intensive communication among development
team members.

Thus, smooth communication among development team members is vital,

especially under continuously changing software development environment.

Development

languages are changing quite rapidly, and suppliers have to cater for ever-changing demand of their
7

Kiyohiko G. Nishimura・Futoshi Kurokawa

customers.
This observation suggests diseconomies, rather than economies, of scale in software development.
In fact, Frederick P. Brooks Jr., who is often referred as the “father of IBM SYSTEM/360”, points out
these possible diseconomies of scale, and argues against large-scale development.

He coins the ideathat inputs can be measured in man-months as a “myth” in his renowned book Mythical Man-months(Brooks (1995)). He argues that the time of one programmer is not substitutable by the time ofanother programmer, so that using one hour of 100 programmers cannot finish a project that can befinished by using 100 hours of one programmer.

Consequently, programmers’ labor inputs cannot

be measured by man-hours or man-months.xv
Thus, when communication is insufficient among development team members, an increase in thenumber of development teams may have even negative effects on the productivity of softwaredevelopment.

This possible negative effect may be particularly keen when the development

schedule is suddenly shortened and engineers are added to the development team to compensate thechange.
In contrast, there is one important example of economies of scale in software development, which isthe success of the Linux system development.

As is well known, the development of Linux is madepossible by participation of many engineers and programmers around the world, who form an
Internet virtual community.

They enhance communications among them through various means ofInternet technology. The most important characteristics of the development of Linux is that codes areopen to their community and thus coding and debugging are efficiently undertaken in a rapid manner.
The success of Linux suggests that if communication among developers is smooth, there are notableeconomies of scale in software development.
In sum, the scale of development may negatively affect productivity as in the case of the “mythicalman-month”, or may positively influence productivity as in the success of Linux development,
depending on the smoothness of communication among development team members.

In theempirical analysis, we see which factor dominates in the Japanese information service industries.

2.3. Organizational Development and Productivity

We have so far examined a crucial role of communication in software development. Comparing thephenomenon described in the mythical man-month and the success of Linux, we understandimportance of development-team organization that enhances or hinders effective communication.
8

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

In fact, effectiveness of communication is also closely related to modularization.

If softwaredevelopment is modularized in a way to enhance communication as in the Linux case, we can expect
a large productivity gains.

If modularization is pursued by other motivations than communication,

we are likely to face inefficiency described in the mythical man-month.

In a similar token, one may

argue that firm organization may be as important as development team organization.
Innovations in software development are often described as leap-frogging, not gradual, in which pastexperiences have relatively less value than in other industries.

This means that firm organization

should be sufficiently flexible to accommodate such leap-frogging innovations.

In fact, information

service industries are known for a variety of employment contracts, ranging from standard long-term
contracts for regular employees called “Shain” to short-term ones for workers choosing more flexible
work styles.
One typical example is Elysium, a producer of 3D data transformation software called direct datatranslator.xvi

It was divested from Armonicos, which itself was founded by three spinouts of

YAMAHA as a software firm in 1984.xvii

The packaged software division was divested in 1999 tobecome Elysium. The custom software division remains in Armonicos.

Thus, even though Elysium

and Armonicos are in the same 3D CAD software market, they decide to separate in order to seek
efficiency and to maximize their value-making potential.
In the empirical analysis of Section 4, we examine the effect of organizational changes on
productivity explicitly.

In particular, we distinguish adjustment costs of firm organization fromthose of employment adjustment.

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Kiyohiko G. Nishimura・Futoshi Kurokawa

3. Data: Survey of Selected Service Industries: Volume of Information Service
Industries
In this section, we briefly review and explain our data source, the Survey of Selected Service
Industries, Volume of Information Service Industries.
Economy, Trade and Industry (METI).

This survey is conducted by the Ministry ofHowever, before explaining data source, it may be

worthwhile to briefly peruse Japanese information service industries.

3.1.

Japanese Information Service Industries

Survey of Selected Service Industries, Volume of Information Service Industries is the mostcomprehensive statistics about the Japanese information service industries.

Here information

service industries include information processing service (including application service providers(ASP))xviii, custom software, prepackaged software (business prepackaged software, software game,basic software), system management and administration, data base service (online and offline),
research, and others.
In the empirical analysis we use panel data of firms between 1991 and 1998.xix

However, we use

the most recent aggregate data (2001) here to review the current state of Japanese information service
industries.

The total sales of information service industries are 13.9 trillion yen, a remarkable

18.2% increase from the previous year. Note that the Japanese economy was in the deflationary stage
in 2001, so that this increase in nominal terms is all the more impressive.

Among all information

service industries, custom software development has the largest share, which is 49.4%, and the thirdlargest is prepackaged software development’s 10.8%.

Thus software development including bothcustom and prepackaged accounts for more than 60%. The second largest share is information
processing service’s 19.1%.
Prepackaged software’s growth of sales is an astounding 49.1%, which is the highest among
information service industries.

This high rate of growth is the result of extremely high growth rates

of its subcategories, that is, 162.3% growth in software games and 112.6% growth in basic software.
System management and administration’s growth rate is 44.8%, which is the second highest.

This

may be caused by a rapid growth in network systems in this field. Custom software has a decent
growth rate of 8.4%, though its rate pales before the extremely high growth rate of prepackaged
10

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

software.

Only one category exhibits negative growth, which is –10.5% of data base service.

This

category includes some of Internet businesses, so that this decline may reflect hard time of Internet
businesses after the burst of so-called “Internet Bubble.”Let us now turn to the demand side of information service industries.

In the total contracted sales,

the share of manufacturing firms is 22.6% and that of finance and insurance firms is 17.5%, and these
two industries account for more than 40%.

As for the growth rate, transportation andcommunication have the highest, which grows at the rate of 41.5%.

Wholesale and retail tradeindustries are the second highest, and its growth rate is 30.6%.

3.2.

Characteristics of The Survey of Selected Service Industries

The Survey of Selected Service Industries, Volume of Information Service Industries, is the mostcomprehensive statistics about Japanese information service industries.

This survey, conducted by

the Ministry of Economy, Trade and Industry (METI), is a Census-like one, in which all firms andestablishments engaged in information service industries are surveyed.

Moreover, the coverage ofthe survey is far wider in its scope of information service industries than even the Establishment andEnterprise Census. The Survey collects information about establishments and firms that have somebusiness activities in information service, while the Census gathers information about establishments
and firms whose major business activities are in information service.

This is particularly importantsince information service industries are rapidly expanding ones with many entries from and exits toother industries.
Table 1 shows the number of establishments as a whole and for each sub-categories based on firms’organizational structure, according to aggregate figures published by the METI.

In our sample

period of 1991-1998, the total number of establishments ranges between the smallest number 5,812
of 1995 and the largest 8248 of 1998.

There are three sub-categories: single establishments(without a branch office), headquarters (with branch offices), and branch offices.

The category of

single establishments has the largest share of 50% on the average.
The purpose of our study is to investigate productivity at the firm level and we examine the effects offirm-level characteristics such as firms’ organizational structure on firms’ productivity.
basic units of investigation are firms, rather than establishments.

Thus, ourAmong all establishments listed inTable 1, we examine single establishments and headquarters in our study.
The Survey of Selected Service Industries is particularly suited for firm-level analysis of information
11

Kiyohiko G. Nishimura・Futoshi Kurokawa

service industries in several respects.

Firstly, the Survey meticulously distinguishes revenues, costs,

labor inputs and capital stocks in firms’ information service activities from their other activities.
This is important since some firms in our samples engage only partially in information service
activities.

In our empirical analysis, we use these revenues, costs, labor inputs, and capital stocks

solely in their information service activities to construct value-added and other data necessary for
TFP calculation.

That is, our value-added, labor input and capital stock data are solely of firms’

information service activities, and thus they are not “contaminated” by other activities.xx
A notable characteristic of capital investment data in this data set is many zeros and blanks found in
the category of “acquisition of structure and buildings” and “acquisition of machines andequipments”, on the one hand, and relatively large payments of computer-time lease and other rental
payments that are supposedly building rents.

This implies that in some cases a traditional perpetualinventory method may not be appropriate, especially in the case of capital service inputs ofcomputers, where capitalization is more appropriate.

Thus, in the following analysis, we use theperpetual inventory method as much as possible, but when it is not appropriate, we resort to otherprocedures to get capital service inputs.
Finally, there is no intangible asset data in the Survey.
assets that firms have.
software assets.

In particular, there is no data of software

It is inconceivable that firms engaging information service activities have no

Consequently, we should take this fact in mind and take measures to correct

possible biases stemming from the lack of software asset information.

3.3.

Framework of TFP Growth Measurement

We follow standard neoclassical growth accounting framework.

In particular, we assume constantreturns to scale and flexible factors of production.
3.3.1. Output: Real Value-Addedxxi
Value added of each firm is calculated by the following formula, which follows the definition of thevalue-added in the Survey itself.
Real value-added (index) =(Sales – Operating Expenses + Total Compensation + Rents) / Price Index
Here, as a deflator, we use the Corporate Service Price Index (CSPI) of Information Service Industry, compiled

12

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis
by the Bank of Japan.

We exclude firms having negative value-added in a particular year from our samples of that year.
However, the number of these firms is very small, accounting only 0.88% of total firm-years, so that
this does not cause any significant bias.
3.3.2. Inputs
As for factor inputs, we have labor hoursxxii, equipment stock services, and building and structure
capital services.

(As usual, we assume services are proportional to the amount of stocks throughout

this paper.)
We use perpetual inventory methods as much as possible to construct capital stocks data.

For

depreciation rates we rely mostly on Fraumeni (1997)’s rates. For deflators, we construct deflator
data based on data supplied by P. Shcreyer of OECD, which are harmonized price indexes of capitalstocks.

We construct our depreciation rates and deflators using weights suitable for Japanese

information industriesxxiii.
A distinctive characteristic of information service industries is an importance of computer (andcomputer time) lease.

Fortunately, the Survey has data about computer and computer-time lease.

We capitalize these lease costs to get a computer stock data.

(This conversion is necessary forconsistency since we use stock data as inputs assuming service flows are proportional to stock
values.)
One note should be due for buildings and structure.
structure investment.

We encounter many zeros in building andThis means many small information service firms do not own buildings andstructure but simply rent them.

Since we have information about building rents, we utilize thisinformation to estimate building and structure capital stocks.xxiv

3.3.3. User Cost
To estimate user costs, we use the following standard formula:

UCC it =Here

1 − u t z it
( ρ t + δ i )qit
1 − ut

UCCit is the t -th period user cost of the i-th capital stocks, ρ t is the t -th period dividend
13

Kiyohiko G. Nishimura・Futoshi Kurokawa

yields of the First Division of the Tokyo Stock Exchanges,xxv

δ i is the depreciation rate of thei -th stocks, u t is the effective marginal corporate tax rate of the t-th period, and zit is the capitalconsumption allowance of the i -th capital stocks,xxvi andqit is the price of i th capital stocks.

3.3.4. TFP Growth
Using data described so far, we calculate TFP growth in the following formula.
TFPGrowth = {ln Vt +1 − ln Vt } −

5

∑(

){

1
si ,t +1 + si ,t ⋅ ln Fi ,t +1 − ln Fi ,t
2 i =1},

where V is real value-added, Fi is the i-th inputs and si is their cost share. In particular, weconsider as inputs (a) labor hours, (b) equipment capital stocks, (c) imputed computer capital stocks
by capitalizing lease payments, (d) building and structure stocks estimated by the perpetual inventory
method, and (e) imputed building and structure stocks by capitalizing building rents.

3.4. “Firms with Well-Established Information Service Activities”

It is not possible to compute TFP growth of all firms because very high turnovers. For this reason, we
restrict our attention on firms whose information service activities are well established.

We set thefollowing two criteria, and firms satisfying all these criteria are put in the following empiricalanalysis.
In the first step, if firms whose information service activities yield negative value-added and/ or hire
no worker in a particular year, we exclude these firms from our sample of the particular year.

Ifthese firms show positive value-added after that year, they are included in our sample thereafter.

Asreported earlier, actual occurrence of negative value-added is rather rare, accounting less than 1% of
our total firm-years.
In the second step, if firms do not have five consecutive years of information service-related
equipment investment, we excluded from our sample.

This is necessary since we use the five-year

average investment growth rate to estimate benchmark-year capital stocks.xxvii

By doing this, we

restrict our attention virtually on firms with well-established information service activities.
Through this procedure, we get 1,106 firms with well-established information service activities.
14

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Our sample period is 1991-1998, and our sample is an unbalanced panel.
To discern the characteristics of our samples, “firms with well-established information service
activities”, we compare the whole samples and our truncated samples in the following tables.
Table 2-1 reports the summary statistics of the whole samples after excluding firms with negative
information-service value-added and/or no information-service worker, while Table 2-2 shows that ofour samples.

Here major variables are the ratio of information-service outsourcing to

information-service sales, the number of SE (system engineers)xxviii, the ratio of SE to the total
information-service work force, and the ratio of information-service profits to information service
operating expenses, which we consider later as determinants of TFP growth.
Comparing Table 2-1 and 2-2, we find that quite a similar picture between the total samples(excluding abnormal ones) and our “firms with established information services”, except for thenumber of SE’s and the number of information service workers.

For example, the ratio ofoutsourcing to information service sales, the total samples’ average is 0.1378, while 0.1482 in firms
with established information activities.

Similarly, the ratio of SE’s is 0.2839 in the total sample,

while 0.3789 in our target of investigation.

However, the number of information service workers

and SE’s is substantially higher in firms with established information service activities, so that ourtarget is slanted to larger firms.

In order to interpret our results, these characteristics of our target ofinvestigation should be kept in mind. xxix
Let us now turn to the issue of firms’ organizational dynamics and its effects on TFP growth in our
samples.

In order to discern the effects of firm dynamics, we disaggregate our samples further into

four segments.xxx These four segments are:Group 1 (Single-Establishment Firms): firms that remain single establishments throughout the sample period.
Group 2 (Multiple-Establishment Firms): firms with the headquarters and branch offices throughout the sample
period.
Group 3 (Expanded-to-Multi Firms): firms that start as single establishments and become multi-establishment
firms by the end of the sample period.
Group 4 (Shrunk-to-Single Firms): firms that start as multi-establishment firms and become single
establishments by the end of the sample period.

Table 3 reports the average of major variables for each segment. As for the estimated TFP growth,Group 2 (Multi-Establishment Firms) has the highest TFP growth rate (2.61%), while Group 4(Shrunk to-Single Firms) exhibits the lowest (0.51%).
behind these figures.

We will consider determinants of the TFPGroup 2 (Multi-Establishment Firms) has the highest outsourcing to sales

ratio (17.69%), while Group 1 (Single-Establishment Firms) shows the lowest (13.09%).
15

Kiyohiko G. Nishimura・Futoshi Kurokawa

4. Models, Estimation Results and Discussion
4.1. Choice of Variables

4.1.1. Modularization and Outsourcing
In Section 2, we have examined possible effects of modularization on information service
productivity. There we have pointed out that modularization enables firms to outsource parts of theirproducts in an efficient way. If this is the case, the degree of outsourcing may measure the degree ofmodularization, although the degree of modularization itself is not observable. Consequently, in ourempirical analysis, we use the degree of outsourcing, which is the ratio of outsourcing to total sales in
information service activities, as a variable representing modularization and resulting outsourcing.
According to Fujimoto (2002), there are three kinds of modularization: (a) modularization of productarchitecture (modularization in development process), (b) modularization of production process, and(c) modularization of supplier relations.

Outsourcing is a result of (3), which is based on (b) and (a).

However, outsourcing may not solely be motivated by modularization, and other factors may prompt
firms to outsource their business activities. We will explicitly consider the latter possibility in ourlater interpretation of empirical results obtained in this paper.
4.1.2. Scale of Development Organization and the Number of SE’s
The second determinant we have examined in Section 2 is the scale of software development.
Ideally, if a measure of the average quality-adjusted scale of software development in a firm isavailable, this is the variable we should use in our empirical analysis.

However, since the average

scale of software development is not directly observable, we need a proxy for this variable.
Software development is skilled-labor intensive, and thus the scale of development is likely to behighly correlated with the size of skilled work force.

In the context of information service

industries and especially the software industry, skilled work force include SE’s (system engineers),
programmers, and research scientists, about which we have data in the Survey of Selected Service
Industries, Volume of Information Service Industries. Among them, SE’s have a pivotal role insoftware development, especially in custom software one, which dominates Japanese softwaredevelopment.
16

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

In custom software development, to tailor software to the demand of customers is of utmost
importance.

SE’s in the worker classification of the Survey are those who responsible to listen

customers’ needs and to interpret them into schemes which are programmable.

They are also

responsible to select most suitable development languages and to organize programmers to get thebest out of them. Taking account of SE’s importance in Japanese software development, we use their
number as a proxy of the scale of software development.xxxi
4.1.3. Other Possible Determinants(1) Unobservable Software Capital Stocks and the SE ratio.
As explained before, there is no information about non-tangible software capital stocks in the Survey.
However, it is inconceivable that there is no software capital stock in information service firms. To
ignore this unobservable capital stock may lead to overestimation of TFP.

Thus, we need to control

these unobservable software capital stocks using a proxy variable.
Presumably the more a firm is technology-oriented, the more its unobserved software capital stocks
are. In this sense, the ratio of the number SE’s in the total number of information service workers,
representing the firm’s technological orientation, is likely to be a good measure of unobserved
software capital stocks.
(2) “Profit-driven R&D Investment” Hypothesis and the Profit-to-Sales Ratio.
It has often been argued that the higher a firm’s profits are, the higher the level of research anddevelopment activities is, leading to a higher TFP. This “profit-driven R&D investment” may be
relevant in information service industries in Japan since relatively small firms in our samples may beliquidity-constrained in our sample period of 1991-1998. To control this possibility, we consider theratio of gross profits of information service activities to their operation costs as one determinant of
TFP.
(3) Employment Adjustment Costs
In Section 2, we have pointed out a possibility that changes in firm organization affect TFP such astransformation from a single-establishment firm to a multi-establishment-firm.

In our empiricalanalysis, we consider the effects of firms’ organizational dynamics on TFP by disaggregating oursamples in four groups explained in Section 2.
Beside these “adjustment costs” of organizational changes, there are other important adjustment costs
in Japan, which are employment adjustment costs.

Although information service industries are

relatively new industries and that we find rather flexible labor contracts in these industries compared17

Kiyohiko G. Nishimura・Futoshi Kurokawa

with other industries, there remains a sizable cost of employment adjustment in information service
industries like other Japanese industries.

Such adjustment costs may lower overall productivity of afirm, leading to a lower TFP.
To take account of these employment adjustment costs, we consider the squared rate of change in thenumber of information service workers in the following empirical analysis.

4.2. Models and Estimation

Taking account of arguments in previous sections, we formalize that the level of TFP in information
service industries is determined in the following way:
J

T

j =1

k = t +1

ln TFPi ,t = α 0 + α1i t + ∑ β j xij ,t + ∑ γ k d k ,t +ε i ,t(1)

Here i denotes the i-th firm and t denotes the year.

i = 1,L I ; t = 1,LT

Equation (1) implies that the total factor

productivity of the i-th firm’s information service activities in year t,

constant

TFPit , is determined by a

α 0 , a time trend α 1t t , microeconomic variables xij ,t discussed in the previous sections,time dummy

d k ,t representing macroeconomic conditions, which is 1 if t = k otherwise 0, anddisturbances

ε i ,t . In this formulation, the time trend is constant but it varies across firms

representing firm heterogeneity.
As for microeconomic determinants

xij ,t, we consider the followings.xxxii

1) OUT: the outsourcing-to-sales ratio in information service activities, which is used as an index ofmodularization.
2) SE: the number of SE’s, which is used as an index of the scale of development organization.
3) SE-RATIO: the ratio of the number of SE’s to the total information service workers, which is used
to take account for unobservable software capital stocks.
4) PROFIT: the ratio of gross profits to operating expenses in information service activities.

In some cases, we consider the effects of employment adjustment costs on TFP.
18

There we will add

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

the following variable.

5) ADJUST: the squared rate of employment change.

We use a standard growth accounting procedure to get firms’ TFP growth, so that we base ouranalysis in the following “growth” or first-difference formulation (2).xxxiii.
J

T

j =1

k =t +1∆ ln TFPi ,t = α1, j + ∑ β j ∆xij ,t + ∑ γ k ∆d k ,t + ei ,t
(2)
where

∆Yt = Yt +1 − Yt,

andxxxiv

ei ,t = ε i ,t +1 − ε i ,tIn addition, we assume that the time trend,

α1,i, is the sum of a constant and an unobservable

individual specific effect:

α1,i = α1 + ui

where

,

α1 denotes the constant and ui denotes the unobservable individual specific effect. Thenthe equation (2) can be written as ordinary one-way error component regression model with time
dummy (3). xxxv
J

T

j =1

k =t +1∆ ln TFPi ,t = α1 + ∑ β j ∆xij ,t + ∑ γ k ∆d k ,t + ui + ei ,t(3),

It is well known that to apply standard procedures such as OLS causes problems if variables are
non-stationary.xxxvi

However, to apply tests of stationarity, we should take into account oursamples’ characteristics of large cross-sectional but small time dimensions (at most nine years) and aunbalanced panel. To check stationarity of variables in our panel, we carry unit root testsxxxvii
developed by Breitung and Meyer (1994) [BM] and Bond et al. (2002) [OLS1, OLS2 (t-test based on
OLS estimation)] for panels with large cross-sectional dimensions but small time dimensions such as
ours. xxxviii The results of these tests are reported in Table 4. The unit root hypothesis is soundly
rejected in all unit root tests for the first differenced variables.
It is uncertain that whether

ei ,t = ε i ,t +1 − ε i ,tsatisfies standard liner regression model assumptions

about disturbances.xxxix Consequently, we estimate (3) for cases of (a) error terms without
19

Kiyohiko G. Nishimura・Futoshi Kurokawa

autocorrelation, and (b) error terms with autocorrelation. For simplicity, we consider AR (1) in thecase of autocorrelation.
Furthermore, there is a good possibility that error terms of each individual firm may haveheteroscedastic structure. To take a proper account of this possibility, we also estimate (3) by FGLS
(Feasible General Least Squares) for the case that error terms in regressions are autocorrelated andheteroscedasticity is present across firms and years, where autocorrelation coefficients are not
identical across panels.
So, we consider three estimation procedures to take account of them and to check robustness.
Panel OLS: (random-effects model/fixed-effects model).
Panel AR1 (random-effects model/fixed-effects model): assuming error terms are first-order
autoregressive.
FGLS (Feasible General Least Squares): assuming error terms are AR (1), where first-order
autocorrelation coefficients are heterogeneous and heteroscedasticity is present across firms andyears.

4.3. Results and Discussion

4.3.1. A Brief Summary
The results of estimation is reported Table 5 through Table 10. Table 5 is the results of Panel OLSxl,
Table 6 is those of Panel AR(1), and Table 7 is those of FGLS. Cases with employment adjustment
costs are shown in Table 8 (Panel OLS), Table 9 (Panel AR(1)), and Table 10 (FLGS). In addition,
each table shows the difference among firms with different organizational dynamics over time.

We

show the results of all firms in our samples (“firms with well-established information service
activities”) in the column of “All”.

The results of Group 1 (single-establishment firms), Group 2(multiple-establishment firms), Group 3 (expanded from single to multiple), and Group 4 (shrunkfrom multi to single) are shown respectively in these tables. xli
The Sample period is 1991-1998 for Table 5 through Table 7, and 1991-1997 for Table 8 throughTable 10.xlii

Although the number of firms varies among tables, roughly 1,100 firms are in our

samples.
As shown in Tables 5 and 7, the null hypothesis of no autocorrelation in error terms is rejected in allcases (see All, Groups 1, 2, 3 and 4) at the 1% significance level, we hereafter concentrate our
discussion mostly on Tables 6 and 9 (Panel AR(1)) and Tables 7 and 10 (FGLS), with some reference20

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

to Tables 5 and 7 to check robustness.
Let us consider estimation results of all firms, which are reported in the column “All” of Tables 5through 10.

In all these tables, we have fairly consistent and significant results: the coefficient of

the d-OUT, d-SE, d-SE-RATIO and d-PROFIT is significant at the 1% level, where “d-“ means thefirst difference.

The sign of d-OUT and d-SE is negative, while that of d-SE-RATIO and d-PROFIT

is positive. Period dummies are all significant at the 1% level when employment adjustment costs are
not incorporated in Tables 5 through 7.

However, their effects are substantially reduced whenemployment adjustment costs are explicitly considered in Tables 8 through 10.
4.3.2. Outsourcing and Remnants of Traditional RelationshipA remarkable result in these tables is that the outsourcing-to-sales ratio (OUT) has a significant
negative effect on TFP. This result is quite robust in all specifications. This robust negative effect ofoutsourcing is striking. As in the Section 2, modularization behind outsourcing should improve
productivity, rather than reduce productivity. Thus, a robust negative result of outsourcing on TFPsuggests that outsourcing in Japanese information service industries has a different origin frommodularization, which hinders productivity.
In fact, in-depth analysis of industrial structure of the Japanese software industry suggests that thenegative effect of outsourcing stems from a remnant of traditional subcontracting practices found in
this industry. In the traditional relationship, there are main contractors on the one side that get
contracts from customers being often large corporations and/or central as well as local governments.
On the other side of the relationship, there are sub-contractors that depend mostly on main
contractors to allocate business to them.
Main contractors outsource their business not to promote the efficiency in software development but
to make sub-contractors as “buffers” of economic fluctuations to reduce costs of adjustment
necessitated by such fluctuations. It is sometimes argued that this cost consideration of main
contractors leads to “over-outsourcing” to sub-contractors in the sense that programming expertise is
not properly retained in these main contractors. If so, they violate the dictum of “do not outsource thecore of your competence.”
For sub-contractors, their poor financial positions make them unable to take advantage of suchoutsourcing. Outsourced sub-contractors have little human-capital investment as well as little
physical-capital investment.
This inefficient subcontracting system, however, cannot survive if new efficient firms enter the21

Kiyohiko G. Nishimura・Futoshi Kurokawa

market.

Unfortunately, strong preferences of buyers (consisting of local and central governments

and large corporations) to “established vendors” make entry of new, especially small, firms very hard
and thus this industrial structure is sustained for a long time.
4.3.3. Mythical Man-Month and Communication Problems
In Section 2, we have argued that the scale of development may affect productivity positively insome cases and negatively in others, depending on the smoothness of communication between
customers and development team members and among development team members themselves.
The results of our study strongly suggest diseconomies of scale in software development: the scale ofdevelopment organization affects firms’ productivity negatively.
Thus, the phenomenon of mythical man-month dominates Japanese information service industries.
Effective communication to reduce costs between SE’s and their customers and among SE’s
themselves is lacking, and this is likely to be one of major obstacles of Japanese firms to improvetheir efficiency in information service provision.
In this respect, central and local governments should play an important role to reverse this tendency.
Central and local governments are major customers of information service firms. However, thesegovernments are often pointed out to be rather incompetent to articulate their real needs when theyplace orders to information service firms. Because of this lack of articulation, communications
between these governments as customers and SE’s in supplier firms are not smooth and in some cases
this leads to last-minute specification changes that are very costly in software development.
To test the magnitude of this “incompetent government” effect, we put a dummy variable
representing a particular firm’s dependency on government contracts as one explanatory variable in
the FGLS estimation of Table 7.

Table 12 shows the results for the case in which the government

dummy is 1 when the ratio of government contracts to the total information service contracts exceeds
30%, 40% and 50%, respectively.

In all cases, the dummy of firms’ dependency on government

contracts is statistically significant and has a negative sign. Clearly, governments themselves should
act to rectify this problem.
4.3.4. Other Determinants
As for other determinants, we have statistically significant positive effects of the SE-RATIO as
expected, which is the proxy of unobservable software capital stocks in firms.
statistically significant positive effects of the PROFIT.
firms may be liquidity constrained.
22

We also getThis suggests many of information service

However, there may be other possible explanations of theseTotal Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

positive effects of profits on productivity, and thus the results should be considered at most as
suggestive.
4.3.5. Strong Effects of Employment Adjustment
Let us now turn to the issue of employment adjustment costs.

Tables 8, 9 and 10 report the results.

For all firms (see “All” category), we see a substantial downward effect of employment adjustment
on productivity. This result is pretty robust across estimation methods we take, and consistent withthe commonly held view of high employment adjustment costs in Japan.
However, more in-depth analysis of data shows variation across firms’ organizational changes. In
particular, while we have a strong negative effect of employment adjustment for firms of
multiple-establishment firms (Group 2) and expanded firms from single to multiple (Group 3), we
obtain no significant effect for single establishment firms (Group 1) and a significantly positive andlarge effect for firms shrunk from multiple to a single establishment (Group 4).
This last finding may be particularly interesting. This shows that when a firm takes a drastic
measure to restructure itself to trim out all subsidiaries to become a single establishment, the morethoroughly its accompanying employment adjustment (that is, employment reduction) is, the higher
its productivity is.
4.3.6. Effects of Firms’ Organizational Changes (Group1--4)
We now examine the effects of firms’ organizational changes on firms’ productivity. The basic
econometric picture of all subcategories (Groups 1 through 4) is qualitatively the same as “All”: In
the OLS Panel, the Hausman test leads to random-effect models, and there is serial correlation inerror terms so that we use AR(1) and FGLS results mostly in our discussionxliii.
Group-wise estimation results are similar among these Groups.

OUT, SE, SE-RATIO and PROFIT

have mostly statistically significant coefficients at either 1% or 5% level and the same sign as in thecase of “All”.

However, the magnitude of their effects varies among these Groups, suggesting astrong effect of firms’ organizational changes on productivity.
As for the negative effect of outsourcing, Table 6 (Panel AR(1)) shows the following order ofmagnitude:
Group 1 (single) < Group 4 (shrunk) < Group 2 (multi) <Group 3 (expanded)
Similarly, Table 7 (FGLS) has comparatively smaller effects and shows the following order ofmagnitude
23

Kiyohiko G. Nishimura・Futoshi Kurokawa

Group 1 (single) < Group 2 (Multi) < Group 4 (shrunk) < Group 3 (expanded).
Thus in both tables, the negative effect of outsourcing is the smallest in single establishments and thelargest in firms expanded from a single establishment to multiple establishments.
A particularly interesting result is that the negative effect of outsourcing is far lesspronounced in single establishments (Group 1) than multiple establishment (Group 2). This result is apiece of supporting evidence for the hypothesis we developed in Section 4.3.2.

There we haveargued that traditional relationship between large main contractors and small subcontractors leads toinefficiency in outsourcing.

This argument is more relevant to large firms in Group 2, than small

firms in Group 1, so that this observed sharp difference strengthens our argument. However, negative
effects remain in Group 1 though their magnitude is far smaller than in Group 2. This may reflect
small-scale traditional subcontracting systems even in Group 1.
Negative effects of outsourcing are the largest in expanded firms (Group 3). When organization is
expanding, there exists larger demand for outsourcing not from efficiency consideration but fromkeeping up increasing demand.

Since outsourcing here is not from modularization, resulting effectsof outsourcing are rather negative. This shows the existence of organizational adjustment costs
distinct from employment adjustment costs.
Let us now turn to the issue of diseconomies of scale in software development. Table 6 shows thefollowing ranking in the order of magnitude:
Group 3 (Expanded) < Group 1 (Single) < Group 2 (Multi) < Group 4 (Shrunk),
while Table 7 reveals
Group 3 (Expanded) < Group 1 (Single) < Group 2 (Multi) < Group 4 (Shrunk).
Thus, a consistent picture emerges.
A remarkable result is that negative effects diseconomies of scale in development organization are farless pronounced in single establishments (Group 1). (Group 3, which are expanded from single
establishments, are likely to have much in common with single establishments with respect tosoftware development organization.) Since single establishments have the simplest organization
structure that facilitate relatively smooth and efficient communication both inside and outside, thisresult suggests our communication argument about the cause of diseconomies of scale is in a righttrack. Moreover, we find that negative effects of outsourcing are the largest in expandingorganization (Group 3), where smooth communication is rather difficult.

In sum, our results

strongly suggest smooth communication is very important to achieve higher productivity.

24

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

5. Concluding Remarks
In this paper, we have examined factors determining productivity of information service activities offirms, using most comprehensive data of information service industries in Japan.

Among possible

determinants, we have focused on (1) the degree of modularization and resulting outsourcing, (2)economies/diseconomies of scale in software development, and (3) firms’ organizational changes.
We have found somewhat disturbing results for Japanese government officials promoting “e-Japan
Strategy II” initiatives to revitalize the Japanese economy. Firstly, we have shown that outsourcing
haspersistent

negative

effectsontotal

factorproductivity,

suggesting

not

only

productivity-enhancing modularization is not fully utilized but also productivity-hindering remnantsof traditional main-contractor-subcontractor relations still prevail in information service industries.
This shows that to increase in productivity is not a simple task to be achieved by introducing
productivity-enhancing modularization into information service industries. A fundamental
restructuring of business practices is necessary to transform traditional main-contractor-subcontractor
relations into modularized, horizontal as well as vertical, mutually supportive community-likerelations. To achieve this goal, governments as buyers should play a crucial role.
Secondly, we have found diseconomies of scale in software development, suggesting less efficient
communication among development team members. Communication gaps are also pointed out
between information service providers (particularly system engineers who are in charge of
coordinating development) and their customers.

Among these customers, central and local

governments are most important, and our preliminary empirical study shows that these governments
hinder information service firms’ productivity, rather than enhance it, contrary to the presumption of“e-Japan Strategy II” proponents. Lack of information service expertise on the side of central andlocal governments is the cause of these problems, and these governments should rectify their own
problems when they promote productivity-enhancing practices in the industries at the same time.
Although these tasks seem formidable, these governments have no other choice than transforming
themselves into informationally and technologically advanced customers of information service firms,
to promote true modularization and efficient communication between customers and information
service providers.

25

Kiyohiko G. Nishimura・Futoshi Kurokawa

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29

Kiyohiko G. Nishimura・Futoshi Kurokawa

i There has been a sizable literature on productivity growth or technological progress. Hayashi and
Prescott (2002) examine the movement of the TFP (total factor productivity) growth relying on neoclassicalframework and argue that declined supply of labor coupled with declined TFP growth is one of the culprits ofdisappointing economic growth of Japan. Fukao et al. (2003), based on detailed industry-wise data as well as
macro data, reveal a sharp decline in the TFP growth rate in aggregates and in many industries.
ii In the same token, Ark et al. (2002) show that the labor-productivity growth in Japanese IT-producing
industries in the late 90s is 19.5%, which is roughly in line with 23.7％ in the United States and 13.8％ in EU.
iii In fact, US wholesale and retail trade sectors contribute greatly to overall productivity. Basu et al.
(2003) claim that almost 70% of the overall TFP growth of the United States can be explained by the TFP
growth of the wholesale and retail trade sectors.
iv However, Fukao and Kwon (2003) report that when they adjust quality of labor inputs and utilizationof capital stocks in the traditional growth accounting framework, TFP growth rates in manufacturing sectors arerather low compared with rather high TFP growth rates in service sectors. They attribute rather good
performance to deregulation in service industries. However, their procedure of “adjustment” seems inconsistent
with basic assumptions of the growth accounting framework, notably perfect utilization, so that their result
should be evaluated cautiously.
v Ahn (2001) touches service industries briefly in his survey of empirical results in productivity basedon firm dynamics. He points out several problems in productivity analysis of service industries such as
appropriate measurement of outputs, although importance of service industries is obvious in viewing its large
share in GDP.
vi The JSIC (Japanese Standard Industry Classification) System was revised substantially in March 2002
(called 11th Revision). Before this revision, information service industries were classified to service
industries, but after the revision, they now belong to a new 1-digit industry classification called “Information
and Communication Industries”.
vii Most prominent is IBM. IBM’s divisional revenues now reveal that revenues from software andinformation service divisions exceed those from hardware divisions. IBM’s move prompts several Japanesefirms to follow it.
viii CMM evaluates maturity or capability of software development in five levels. See Conference for
Improving Software Development and Procurement Processes (2001).
ix Ahn (2001) and Bartelsman and Doms (2000) survey firm-level empirical analyses. Ahn examines
non-manufacturing industries extensively.
x Sano (2001) examined the effect of outsourcing on information service industries based on case studies.
Koyama and Takeda (2001) investigated software-development productivity, with special emphasis on
modularization, referring to various cases in the industry.
xi In 1999 the share of custom software and prepackaged software is more than 60% of the total
value-added of information service industries.
xii See Aoki and Okuno (1996) for an example of Japanese academic writing emphasizing thiscollaboration positively.
xiii Here “modularization” is to design complex products and processes consisting of small-scale
subsystems that are independently designed (see Baldwin and Clark (1997)). The “small subsystems
independently designed” are “modules”. So long as the “integrability” is maintained, each subsystem can be
designed independently.
xiv API (application programming interface) is a part of OS that is used in application softwaredevelopment. Since Microsoft does not make source codes open for its WINDOWS OS, developers of
WINDOWS application software cannot develop their software independently, which implies insufficientmodularization. Such problems do not exist in computer hardware.
xv This is a clear warning against common practices that estimate the value of software products by wage
times man-hours of programmers and system engineers who engage in developing these software products.
xvi See http://www.elysiuminc.com/.
xvii See http://www.armonicos.co.jp/english/.
xviii ASP is included if they develop software themselves.
xix This is based on the data set supplied by the Statistics Bureau for our empirical study.
xx There are other notable characteristics. First, the Survey asks the headquarters of firms to report the
total fixed capital stocks and the total operating expenses (and their breakdowns) of information service
30

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis
activities of the firms as a whole. This is very convenient in our constructing firm-level information-service
activities’ value-added and capital stocks. However, categorized information-service activities’ labor input
data (such as the number of system engineers) are available only at the establishment level. Thus, it isnecessary to construct firm-level data of information-service activities’ labor input data from establishment
data.
xxi One might argue, as Baily (1986) and Bartelsman and Gray (1996) do, that outputs of firms should begross outputs rather than value-added. However, gross outputs seem not so appropriate in information service
industries including the software industry, because outsourcing is prevalent in these indusries. Outsourcing is
not usual inputs in production process and whether to outsource some of their business or not is a strategic
decision rather than a technological constraint. In this case, the framework of gross production treating
outsourcing as usual inputs may lead to misrepresentation of production function. See Hulten (2001) on thisissue.
xxii Labor hours are estimated by multiplying the number of workers by the average work hours in
information service industries, using the Monthly Labor Survey.
xxiii As for weights, we use those in the 1995 Input-Output Tables’ fixed-investment matrix data.
xxiv Unfortunately however, we do not have an individual firm’s building and structure rent data directly.
We only have information about the percentage of building and structure rents in the total cost on the average.
We construct imputed building rents relying on this average figure assuming individual building rents are notdifferent from the average.
xxv Here we follow Hall (1990).
xxvi The effective tax rates and the discounted value of future depreciation are calculated by using
information contained in the Annual Report of Incorporated Enterprise Statistics, the Annual Report on Financeof Local Governments, and the Survey on Incorporated Enterprises. We follow here the procedure of Homma
et al. (1984).
xxvii Here we follow the method of the White Paper of the Cabinet Office. However, this five-year
criterion might be too restrictive, so that we plan to relax this assumption in the future.
xxviii Although we have information about SE’s in establishments, we cannot aggregate them to get
firm-level SE’s since the Survey does not collect information about what firm a particular establishment
belongs to. Consequently, we are obliged to assume that the ratio of SE’s to the total work force at theheadquarters is on the average its ratio for the firm as a whole. In particular, the firm-level number of SE’s in
our study is calculated by multiplying the firm’s total number of information service workers by the ratio ofSE’s to the information-service work force at the headquarters. The firm-level ratio of SE’s to the total
information-service workers is calculated by using this figure.
xxix In addition, we show in Tables 2-3, Table 2-4 and Table 2-5 differences in averages for
subcategories of our samples (custom software, prepackaged software, system management and administration,
online database service) for different thresholds. To make comparison comparable, we results about single
establishments in these tables.
xxx Firms that do not conform to any of four types are excluded from segment-wise estimations in thefollowing empirical analysis.
xxxi We tried the number of programmers and that of research scientists in our preliminary analysis, butthey did not have any significant effects on TFP. Thus, we excluded them in the present analysis.
xxxii See footnotes in Section 3.4 for the way these variables are constructed.
xxxiii In firm-level panel cases, we can estimate the TFP level using Caves et al (1982) or Good et al(1997) approach of estimating productivity differentials across the panel. Nishimura et al. (2003) estimate the
TFP level and examine entry-exit dynamics of Japanese manufacturing firms by using a firm-level panel data
based on the Basic Survey of Business Structure and Activities. However, we start here with first differences
for analytic simplicity.
xxxiv In this way, we implicitly assume that TFP levels are non-stationary, which are likely since TFPchanges are in many cases permanent technological changes. However, even if TFP-levels are stationary,
OLS estimators of equation (2) (or (3)) remain consistent. See Maddala (2001).

x

xxxv There might be a possibility that lagged ij may influence TFP in (1) (or (2), (3)). We estimate
correlation matrices of dependent variables and independent variables including lags to check this point.
Estimated correlation matrices, however, suggest that TFP at t is correlated mostly with independent
31

Kiyohiko G. Nishimura・Futoshi Kurokawa

variables at the same period. Furthermore, preliminary estimation results including these lags show
insignificant coefficients for lagged variables in most cases, so that we ignore them in our baseline empiricalresults.
xxxvi However, non-stationarity of the data may not be so problematic. Phillips and Moon (1999) argue
that even if the unit root tests fail to reject the null hypothesis, usual pooled time series and cross section
regression models yield useful information concerning long run regression relationship.
xxxvii Here we should distinguish between unit root tests for panels with large time dimensions andthose for panels with small time dimensions, and between unit roots tests applicable balanced panels andunbalanced ones. For panel unit root tests with small time dimensions, see Bond et al. (2002). Several
major panel unit root tests, such as those of Levin and Lin (1993) and Im et al. (2003), are not directly
applicable for unbalanced panel data like our samples. For unbalanced panel unit root tests, see Maddala andWu (1999).
xxxviii Bond et al. (2002) point out that t-tests based on OLS estimation results provide simple robust
tests with high power in Monte Carlo simulations, in the case that the variance of the unobserved heterogeneity
is relatively small, and in the case of panels with relatively small time series.
xxxix That is,

E ( ei ,t ) = 0

E ( ei ,t 2 ) = Var ( ei ,t ) = σ e2
E ( ei ,t + r e j ,t + s ) = Var ( ei ,t + r , e j ,t + s ) = 0 for i ≠ j , r ≠ s.
xl In Table 5, random effect models are selected by the Hausman test.
xli One may argue there might be simultaneity between TFP growth and first differences of explanatory
variables. To take this in mind, we conducted an instrument variable estimation and found qualitatively thesame results as in reported Tables 5 through 10. The results of instrumental variable estimation results arereported in Table 11.
xlii Data are available between 1991 and 1999. However, we take first differences so that we lose one
year. In addition, we lose one more year in the case of employment adjustment costs to calculate ADJUST
variables.
xliii The Wald test leads to the rejection of heteroscedasticity for all Groups in FGLS.

32

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 1 The Numbers of Establishments in Information Service Industries(Survey of Selected Service Industries )
1991
3563
SingleHeadquarters 1702
Branch Offices 1831
7096
Total

1992
3205
1883
1889
6977

1993
3041
1708
1683
6432

1994
2902
1538
1542
5982

1995
2822
1496
1494
5812

33

1996
3289
1379
1629
6297

1997
3186
1338
1568
6092

1998
4361
1687
2200
8248

Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 2-1 Total Samples Excluding Negative Value-Added and Zero Workers: 1991-1998
Summary Statistics of Major Variables:
Following variables are those of information service activities of firms.
AverageMin.
Max.

# of Firms # of Firm-Year'sLabor Productivity Growth

0.0311(0.4931)-8.09888.4861

7610

32060

Outsourcing to Sales Ratio

0.1378(0.1577)0.00001.0000

10866

38746

31.7126(142.3801)

0.00009425.2220

10866

38746

Number of SE

SE to Total Work Force Ratio

0.2839(0.2567)0.00001.0000

10866

38746

Profit to Operating Cost Rati

0.2827(1.7481)-0.9608

161.0000

10846

38715

96.2852(324.0208)

1.0000

23668

10866

38746

# of Workers

Notes:
Standard deviations in parenthesis. "# of firms" column reports the number of firms with sufficient information
enabling the calculation.

34

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 2-2 Firms with Well-Established Information Service Activities: 1991-1998
Summary Statistics of Major Variables:
Following variables are those of information service activities of firms.
Average

Min.

Max.

Labor Productivity Growth

0.0417(0.3516)-4.19314.24791106

6117

Outsourcing to Sales Ratio

0.1482(0.1442)0.00000.88981106

6117

79.3533(231.8276)

0.0471########

1106

6117

Number of SE's

# of Firms # of Firm-Year's

SE to Total Work Force Ratio

0.3789(0.2247)

0.00311.00001106

6117

Profit to Operating Cost Rati

0.2332(1.3172)-0.6533

89.5953

1106

6117

193.2424(479.3581)

1.0000

9430

1106

6117

# of Workers

Notes:
Standard deviations in parenthesis.

35

Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 2-3 Differences in Averages among Subcategories in Single Establishments:
Case of 90% Criterion
Firms whose particular segment's sales share exceeds more than 90% of their total information service sales

Variable

CustomSoftwareSales Share > 90%
SystemPrepackaged
Management
Softwareand

Online Data
Base Service

TFP growth0.0341(0.3962)0.0644(0.5534)-0.1051(1.1670)-0.0545(0.2027)

Labor Productivity Growth0.0457(0.3177)0.0471(0.4748)0.1365(1.0241)

0.0638(0.1124)

Outsourcing to Sales Ratio

0.1626(0.1502)0.0725(0.1071)

0.0546(0.0760)

0.0635(0.0492)

42.4845(140.5637)

9.9575(25.1370)

10.2602(20.3217)

3.0500(2.4582)

SE to Total Work Force Rati

0.4912(0.2160)

0.3918(0.2428)0.3546(0.2009)

0.1921(0.0637)

Profit to Operating Cost Rat

0.1473(0.4017)0.4267(0.6632)

0.4897(0.6779)

0.0075(0.1306)

69.3514(170.7314)

24.7377(38.2829)

21.4444(32.3246)

16.9000(14.8208)

363
1255

52
122

6
9

4
10

Number of SE's

# of Workers# of Firms# of Firm-Year's
Notes:The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.

36

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 2-4 Differences in Averages among Subcategories in Single Establishments:
Case of 70% Criterion
Firms whose particular segment's sales share exceeds more than 70% of their total information service sales

Variable

CustomSoftwareSales Share > 70%
SystemPrepackaged
Management
Softwareand

Online Data
Base Service

TFP growth0.0281(0.3987)

0.0436(0.5782)-0.0592(0.6637)

0.0612(0.4613)

Labor Productivity Growth

0.0464(0.3182)

0.0265(0.4522)0.0980(0.5967)

0.0398(0.2270)

Outsourcing to Sales Ratio

0.1620(0.1575)0.0805(0.1126)0.1127(0.1472)

0.0509(0.0517)37.3376(124.3926)

12.1866(30.0798)

21.0377(44.3199)

3.0213(2.2421)

SE to Total Work Force Rati

0.4687(0.2164)0.3836(0.2385)0.2948(0.2249)

0.1685(0.0710)

Profit to Operating Cost Rati

0.1774(0.5150)

0.4620(0.7313)0.2234(0.4262)0.2670(0.8301)64.4387(153.3472)

27.0452(40.2962)

100.7742(136.4685)

19.3889(13.3907)

458
1680

74
177

18
31

10
18

Number of SE's

# of Workers# of Firms# of Firm-Year's

37

Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 2-5 Differences in Averages among Subcategories in Single Establishments:
Case of 50% Criterion
Firms whose particular segment's sales share exceeds more than 50% of their total information service sales

Variable

CustomSoftwareSales Share > 50%
SystemPrepackaged
Management
Softwareand

Online Data
Base Service

TFP growth0.0250(0.4040)0.0362(0.5632)

0.0810(0.7895)-0.0010(0.4833)

Labor Productivity Growth0.0425(0.3202)0.0264(0.4457)

0.0765(0.4346)

0.0541(0.2134)

Outsourcing to Sales Ratio

0.1542(0.1500)

0.0894(0.1219)0.1266(0.1243)0.0487(0.0553)35.8617(115.4218)

9.9708(24.8576)

19.5121(35.1353)

3.0284(2.2996)

SE to Total Work Force Rati

0.4532(0.2154)0.3440(0.2243)

0.2686(0.2179)0.1723(0.1109)

Profit to Operating Cost Rat

0.1834(0.5186)

0.3748(0.6261)

0.2191(0.5663)0.4587(1.5229)64.9219(144.3840)

25.4161(35.6407)

103.2206(133.3655)

19.8184(12.6948)

527
2010

113
274

35
68

13
27

Number of SE's

# of Workers# of Firms# of Firm-Year's
Note:

The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.

38

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 3 Differences in Averages Among Different Organizational Changes: 1991-1998
All

Group 1(Single)

Group 2(Multi)

TFP growth0.0197(0.4631)0.0179(0.4164)

0.0261(0.4139)0.0239(0.5017)0.0051(0.5198)

Labor Productivity Growth

0.0417(0.3516)0.0403(0.2972)

0.0433(0.3079)

0.0510(0.4273)

0.0189(0.3943)

Outsourcing to Sales Ratio

0.1482(0.1442)0.1309(1.4703)0.1769(0.1508)

0.1549(0.1465)

0.1325(0.1276)

Number of SE's

79.3533########

19.7731(38.2180)174.3493########

112.1807(308.3249)

34.6971(81.3331)

SE to Total Work Force Ra

0.3789(0.2247)

0.3848(0.2268)

0.3699(0.2160)

0.3713(0.2314)0.3639(0.2170)

Profit to Operating Cost R

0.2332(1.3172)

0.2048(0.6716)0.2215(0.6480)0.3755(3.0485)0.2095(0.6760)# of Workers

193.2424########

50.0860(69.6813)

445.6522########

258.7119(584.3632)

94.4569(160.6008)

6117

1732

1294

899

788

# of Firm-Year'sGroup 3(Expanded)

Notes:The table shows breakdowns of firms with well-established information service activities. Standard
deviations are in parenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period,
Group 2: firms that have headquarters and branch offices throughout the sample period, Group 3:
firms that start as single establishments and become multi-e4stablishment firms by the end of thesample period, Group 4: firms that start as multi-establishment firms and become single
establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.

39

Group 4(Shrunk)Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 4 Unit Root Test：First Difference
TFP Growth
***

d-OUT

d-SE
***

d-SE-RATIO
***

***

d-PROFIT

d-ADJUST***

0.9805 **(0.0083)BM

0.6264(0.0180)

OLS1-0.3467 ***(0.0116)-0.2135 ***(0.0132)-0.1920 ***(0.0108)-0.2982 ***(0.0126)-0.4896 ***(0.0124)-0.0375 ***(0.0055)

OLS2-0.6734 ***(0.0259)-0.3474 ***(0.0233)-0.3758 ***(0.0218)-0.5020 ***(0.0245)-1.6247 ***(0.0350)-0.3809 ***(0.0214)0.5368(0.0138)

0.7763(0.0111)0.5402(0.0138)-0.1946(0.0147)Notes"d-" denotes the first difference. The null is that there is a unit root and "***", "**", and "*" denote significance at 1%, 5%, and10%, respectively, under the null. Standard deviations are in parenthesis. BM is the unit root test of Breitung and Meyer (1994).
OLS1 and OLS2 are those in Bond et al. (2002), where OLS1is without differenced lags, and OLS2 with differenced lags. We test
OLS1 and OLS2 both with and without time-dummies, but results are qualitatively similar. Thus we report here only the resultswith time dummies.

40

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 5 Estimation Results (PANEL OLS): 1991-1998
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi) Group 3(Expanded)Group 4(Shrunk)d-OUT-0.8310 ***(0.0670)-0.2803 **(0.1223)-0.6004 ***(0.1351)-1.2221 ***(0.1807)-0.4295 **(0.1846)d-SE-0.1008 ***(0.0129)-0.0727 ***(0.0274)-0.1685 ***(0.0298)-0.0809 ***(0.0305)-0.2527 ***(0.0344)d-SE-RATIO

0.2838 ***(0.0547)

0.3445 ***(0.0974)

0.2181 *(0.1157)

0.2939 **(0.1378)

0.6840 ***(0.1577)d-PROFIT

0.0753 ***(0.0030)

0.1998 ***(0.0121)0.1417 ***(0.0139)0.0494 ***(0.0035)

0.1704 ***(0.0139)

d-D1992

0.1325 ***(0.0320)0.0003(0.0553)

0.1902 ***(0.0663)0.2909 ***(0.0805)-0.3861 ***(0.0887)

d-D1993

0.0989 **(0.0313)0.0102(0.0540)

0.0914(0.0640)0.2179 ***(0.0785)-0.2886 ***(0.0902)

d-D19940.0402(0.0294)-0.0652(0.0505)

0.0789(0.0593)0.1341 *(0.0741)-0.2836 ***(0.0873)

d-D1995

0.0745 ***(0.0267)

0.0318(0.0455)

0.0844(0.0534)0.1305 *(0.0682)-0.2235 ***(0.0796)

d-D19960.0869 ***(0.0235)

0.0593(0.0396)0.0668(0.0466)

0.1047 *(0.0604)-0.1343 *(0.0704)

d-D1997

0.0630 ***(0.0196)0.0483(0.0327)

0.0512(0.0384)

0.1620 ***(0.0514)-0.1386 **(0.0582)

d-D1998

0.0396 ***(0.0142)0.0216(0.0234)

0.0537 *(0.0277)

0.0976 ***(0.0378)-0.0317(0.0417)

Constant

0.0380 ***(0.0067)

0.0227 **(0.0115)

0.0512 ***(0.0134)

0.0611 ***(0.0176)-0.0531 ***(0.0193)

Hausman Test 15.7500
p-value
0.15089.340012.6300

1.90006.28000.59050.31850.99880.8543fixed or random randomLM####### ***random####### ***random
66.9396 ***random####### ***random
70.3953 ***

318
1732

231
1294

156
899

143
788

# of Firms# of Firm-Year'

1106
6117

Notes:"d-" denotes the first difference. D-year is a year dummy.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firmsthat have headquarters and branch offices throughout the sample period, Group 3: firms that start as single
establishments and become multi-e4stablishment firms by the41
end of the sample period, Group 4: firms that start asmulti-establishment firms and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
LM is joint LM test for serial correlation and random effects. See Baltagi (1995).

Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 6 Estimation Results (PANEL-Random effects with AR1): 1991-1998
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi) Group 3(Expanded)Group 4(Shrunk)d-OUT-0.8499 ***(0.0679)-0.2765 **(0.1232)-0.6086 ***(0.1359)-1.2614 ***(0.1826)-0.4255 **(0.1850)d-SE-0.1162 ***(0.0132)-0.0894 ***(0.0278)-0.1919 ***(0.0306)-0.0784 **(0.0311)-0.2686 ***(0.0348)d-SE-RATIO

0.3221 ***(0.0553)0.3841 ***(0.0982)

0.2611 **(0.1167)

0.2946 **(0.1389)

0.7199 ***(0.1580)d-PROFIT

0.0740 ***(0.0030)0.2006 ***(0.0119)0.1464 ***(0.0141)0.0495 ***(0.0034)

0.1726 ***(0.0139)

d-D1992-0.1849 ***(0.0409)-0.0035(0.0702)-0.2999 ***(0.0837)-0.3672 ***(0.1023)0.4848 ***(0.1118)

d-D1993-0.2090 ***(0.0412)-0.0871(0.0702)-0.2135 **(0.0851)-0.3777 ***(0.1039)0.3929 ***(0.1123)

d-D1994-0.1132 ***(0.0407)0.0883(0.0690)-0.1926 **(0.0841)-0.3011 ***(0.1016)0.4519 ***(0.1118)

d-D1995-0.1376 ***(0.0402)

0.0162(0.0680)-0.2173 ***(0.0832)-0.3195 ***(0.1013)0.4785 ***(0.1097)

d-D1996-0.1730 ***(0.0402)-0.0225(0.0680)-0.2155 ***(0.0832)-0.2383 ***(0.1003)

0.3863 ***(0.1114)

d-D1997-0.1736 ***(0.0404)-0.0391(0.0686)-0.1972 **(0.0833)-0.3595 ***(0.1011)0.4972 ***(0.1116)

d-D1998-0.1891 ***(0.0405)-0.0330(0.0685)-0.2536 ***(0.0835)-0.3908 ***(0.1010)0.4202 ***(0.1117)

cosntant

0.1875(0.0377)0.0345(0.0642)0.2513(0.0783)-0.0991-0.1454-0.1107-0.0869

318
1732

231
1294

156
899

143
788

AR1 Coefficent -0.1273# of Firms# of Firm-Year'
Notes:

1106
6117

***

***

0.3557(0.0940)***-0.4434(0.1020)***d- denotes the first difference. D-year is a year dummy.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firmsthat have headquarters and branch offices throughout the sample period, Group 3: firms that start as single
establishments and become multi-e4stablishment firms by the end of the sample period, Group 4: firms that start asmulti-establishment firms and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
Assuming that the disturbances follow an AR(1) process42

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 7 Estimation Results (FGLS): 1991-1998
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi)Group 3(Expanded)Group 4(Shrunk)d-OUT-0.6283 ***(0.0197)-0.2063 ***(0.0344)-0.3336 ***(0.0640)-1.0684 ***(0.0613)-0.4127 ***(0.0496)d-SE-0.0762 ***(0.0050)-0.0880 ***(0.0104)-0.1156 ***(0.0132)-0.0691 ***(0.0158)-0.1877 ***(0.0191)

d-SE-RATIO

0.2405 ***(0.0153)0.4207 ***(0.0371)

0.2165 ***(0.0436)

0.2622 ***(0.0579)

0.5558 ***(0.0637)d-PROFIT

0.1049 ***(0.0031)0.2153 ***(0.0052)

0.2058 ***(0.0113)0.0595 ***(0.0065)

0.2083 ***(0.0099)

d-D1992

0.0811 ***(0.0113)0.0239(0.0294)

0.1478 ***(0.0120)0.1759 ***(0.0411)-0.2940 ***(0.0441)

d-D19930.0512 ***(0.0101)

0.0274(0.0271)0.0844 ***(0.0125)0.1122 ***(0.0365)-0.2227 ***(0.0424)

d-D1994

0.0193 **(0.0092)-0.0350(0.0237)

0.0624 ***(0.0118)

0.0555 *(0.0308)-0.2289 ***(0.0365)

d-D1995

0.0601 ***(0.0077)

0.0583 ***(0.0191)0.0705 ***(0.0104)

0.0682 ***(0.0260)-0.1795 ***(0.0308)

d-D19960.0715 ***(0.0065)0.0805 ***(0.0150)0.0471 ***(0.0092)

0.0735 ***(0.0207)-0.1035 ***(0.0237)

d-D1997

0.0552 ***(0.0050)

0.0543 ***(0.0118)0.0307 ***(0.0055)

0.1144 ***(0.0163)-0.1186 ***(0.0184)

d-D1998

0.0302 ***(0.0038)0.0143(0.0093)0.0236 ***(0.0066)

0.0725 ***(0.0136)-0.0236 *(0.0131)constant

0.0285 ***(0.0020)0.0237 ***(0.0050)

0.0439 ***(0.0026)

0.0480 ***(0.0076)-0.0387 ***(0.0075)#######
0.0000#######
0.000023.46310.0000#######
0.0000Log likelihood #######Wald Test : p-val 0.0000# of Firms

1086

315

226

154

139

# of Firm-Year's

6097

1729

1289

897

784

Note:
d- denotes the first difference. D-year is a year dummy.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firms thathave headquarters and branch offices throughout the sample period, Group 3: firms that start as single establishments and43
become multi-e4stablishment firms by the end of the sample period, Group 4: firms that start as multi-establishment firms
and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
Wald Test is the test statistic asymptotically distributed as chi-square(k) under the null of heteroscedasticity where kKiyohiko G. Nishimura・Futoshi Kurokawa

Table 8 Estimation Results with ADJUST (PANEL): 1991-1997
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi) Group 3(Expanded)Group 4(Shrunk)d-OUT-0.7918 ***(0.0737)-0.2750 **(0.1374)-0.4778 ***(0.1469)-1.2134 ***(0.1941)-0.3968 **(0.1873)d-SE-0.1049 ***(0.0141)-0.0637 **(0.0299)-0.1891 ***(0.0333)-0.1540 ***(0.0427)-0.3505 ***(0.0402)d-SE-RATIO

0.2686 ***(0.0597)

0.3284 ***(0.1062)

0.2789 **(0.1279)

0.4825 ***(0.1634)

0.9846 ***(0.1672)

d-PROFIT

0.0714 ***(0.0032)

0.1877 ***(0.0128)

0.1992 ***(0.0181)0.0451 ***(0.0035)

0.2760 ***(0.0191)

d-ADJUST-0.0231 ***(0.0063)

0.0060(0.0204)-0.0482 **(0.0236)-0.0274(0.0113)**0.0827 ***(0.0191)

d-D1992

0.0628 *(0.0358)

0.0010(0.0626)

0.0761(0.0814)0.2403 ***(0.0810)-0.2182 **(0.0915)

d-D19930.0329(0.0335)0.0048(0.0584)

0.0008(0.0733)

0.1584 **(0.0770)-0.1657 *(0.0883)

d-D1994-0.0215(0.0301)-0.0744(0.0520)-0.0027(0.0648)0.0670(0.0705)-0.1660 **(0.0812)

d-D1995

0.0166(0.0258)

0.0178(0.0442)0.0061(0.0548)0.0604(0.0625)-0.1428 **(0.0698)

d-D19960.0363 *(0.0209)

0.0422(0.0354)0.0011(0.0431)0.0359(0.0520)-0.0762(0.0572)

d-D1997

0.0181(0.0148)0.0314(0.0247)-0.0088(0.0295)

0.0909 **(0.0383)-0.1000 **(0.0407)

constant

0.0343(0.0080)0.0267 *(0.0139)0.0436 **(0.0171)

0.0694 ***(0.0194)-0.0314(0.0213)5.30000.9157
random
78.7613 ***9.60000.5665
random31.3115 ***4.59000.9493
random
63.4210 ***2.09000.9982
random18.5245 ******Hausman Test 9.8200
p-value
0.5469fixed or random randomLM####### ***# of Firms

1096

318

228

155

140

# of Firm-Year'

5103

1439

1085

752

656

Note:
"d-" denotes the first difference. D-year is a year dummy. ADJUST is the squared rate of change in employment.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firmsthat have headquarters and branch offices throughout the sample period, Group 3: firms that start as single
establishments and become multi-e4stablishment firms by the end of the sample period, Group 4: firms that start as44
multi-establishment firms and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
LM is joint LM test for serial correlation and random effects. See Baltagi (1995).

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 9 Estimation Results with ADJUST (PANEL-Random Effects with AR1): 1991-1997
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi) Group 3(Expanded)Group 4(Shrunk)d-OUT-0.8119 ***(0.0747)-0.2701 *(0.1385)-0.4910 ***(0.1477)-1.2519 ***(0.1983)-0.4050 **(0.1888)d-SE-0.1184 ***(0.0144)-0.0817 ***(0.0305)-0.2045 ***(0.0340)-0.1423 ***(0.0431)-0.3724 ***(0.0410)d-SE-RATIO

0.3009 ***(0.0604)

0.3723 ***(0.1074)0.3022 **(0.1290)0.4573 ***(0.1646)

1.0293 ***(0.1685)

d-PROFIT

0.0707 ***(0.0031)0.1879 ***(0.0127)0.2058 ***(0.0184)0.0459 ***(0.0035)

0.2708 ***(0.0188)

d-ADJUST-0.0245 ***(0.0065)

0.0069(0.0208)-0.0508 **(0.0243)-0.0246 **(0.0115)0.0840 ***(0.0197)

d-D1992-0.1057 **(0.0459)-0.0126(0.0797)-0.1519(0.1030)-0.3291 ***(0.1050)

0.2730 **(0.1172)

d-D1993-0.1294 ***(0.0461)-0.0935(0.0799)-0.0798(0.1020)-0.3384 ***(0.1071)0.2179 *(0.1188)

d-D1994-0.0338(0.0457)

0.0813(0.0788)-0.0663(0.1016)-0.2593 **(0.1052)

0.2536 **(0.1184)

d-D1995-0.0551(0.0454)0.0103(0.0780)-0.0806(0.1017)-0.2726 ***(0.1050)

0.2894 **(0.1161)

d-D1996-0.0914 **(0.0454)-0.0245(0.0783)-0.0857(0.1015)-0.1936 *(0.1043)0.2013 *(0.1166)

d-D1997-0.0927 **(0.0456)-0.0464(0.0784)-0.0675(0.1018)-0.3415 ***(0.1054)0.3253 ***(0.1181)

constant

0.1085(0.0430)

0.0413(0.0744)0.1196(0.0974)

0.3176(0.0975)-0.1083-0.1082-0.1525-0.1230

AR1 Coefficient -0.1263

**

***

-0.2548(0.1092)# of Firms

1096

318

228

155

140

# of Firm-Year'

5103

1439

1085

752

656

*

Note:
"d-" denotes the first difference. D-year is a year dummy. ADJUST is the squared rate of change in employment.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firmsthat have headquarters and branch offices throughout the sample period, Group 3: firms that start as single
establishments and become multi-e4stablishment firms by the end of the sample period, Group 4: firms that start asmulti-establishment firms and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.

45

Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 10 Estimation Results with ADJUST (FGLS): 1991-1998
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi) Group 3(Expanded)Group 4(Shrunk)d-OUT-0.6106 ***(0.0170)-0.3067 ***(0.0382)-0.2908 ***(0.0490)-1.0146 ***(0.0507)-0.2881 ***(0.0519)d-SE-0.0951 ***(0.0030)-0.0708 ***(0.0099)-0.1661 ***(0.0145)-0.1667 ***(0.0209)-0.3081 ***(0.0151)d-SE-RATIO

0.2669 ***(0.0137)0.3764 ***(0.0255)

0.2980 ***(0.0474)

0.5281 ***(0.0475)

0.9632 ***(0.0446)

d-PROFIT

0.0852 ***(0.0025)0.2146 ***(0.0079)

0.2943 ***(0.0173)

0.0464 ***(0.0057)

0.3653 ***(0.0162)

d-ADJUST-0.0281 ***(0.0023)

0.0160(0.0112)-0.0499 ***(0.0088)-0.0393 ***(0.0061)0.1108 ***(0.0127)

d-D1992

0.0101(0.0106)

0.0331(0.0305)

0.0487 ***(0.0181)0.0967 ***(0.0361)-0.1802 ***(0.0343)

d-D1993-0.0144(0.0090)0.0340(0.0268)

0.0085(0.0166)

0.0366(0.0325)-0.1118 ***(0.0340)

d-D1994-0.0465 ***(0.0074)-0.0391 *(0.0220)0.0009(0.0149)-0.0262(0.0269)-0.1091 ***(0.0294)

d-D1995-0.0014(0.0058)

0.0419 **(0.0170)0.0163(0.0127)-0.0168(0.0204)-0.0919 ***(0.0219)

d-D19960.0259 ***(0.0039)0.0657 ***(0.0118)0.0010(0.0093)-0.0070(0.0144)-0.0388 **(0.0153)

d-D1997

0.0091 ***(0.0028)

0.0467 ***(0.0079)-0.0086(0.0072)

0.0572 ***(0.0101)-0.0763 ***(0.0110)

constant

0.0281(0.0019)#######
Log likelihood
Wald Test: p-valu 0.0000# of Firms# of Firm-Year's

1066
5073

***

0.0303(0.0053)***

0.0396(0.0038)***

0.0494(0.0075)***-0.0252(0.0070)#######
0.0000#######
0.000067.56140.000091.28210.0000

310
1431

220
1077

151
748

137
653

***

Note:
"d-" denotes the first difference. D-year is a year dummy. ADJUST is the squared rate of change in employment.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firms thathave headquarters and branch offices throughout the sample period, Group 3: firms that start as single establishments andbecome multi-e4stablishment firms by the end of the sample period, Group 4: firms that start as multi-establishment firms
and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
Wald Test is the test statistic asymptotically distributed as chi-square(k) under the null of heteroscedasticity, where k46

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

Table 11 Estimation Results (IV Method, Random Effects Model): 1992-1998
dependent variable: TFP growth
All
Group 1(Single) Group 2(Multi)Group 3(Expanded)Group 4(Shrunk)d-OUT-1.3809 ***(0.3041)0.3045(0.7727)-1.2141 **(0.4942)-2.4283 **(1.1603)-0.9453(0.9589)d-SE-0.8234 ***(0.1059)-1.4139 ***(0.3643)-1.0901 ***(0.2339)-0.0282(0.3808)-1.6316 ***(0.3120)d-SE-RATIO

2.0495 ***(0.3161)

3.8917 ***(1.0640)

2.2545 ***(0.6611)0.2646(1.1460)

4.0471 ***(0.9808)

d-PROFIT

0.0404 ***(0.0074)

0.2062 ***(0.0436)0.1083 ***(0.0347)0.0404 ***(0.0138)

0.2163 ***(0.0742)

d-D1993-0.0910 **(0.0289)-0.1432 **(0.0641)-0.0723(0.0665)-0.0477(0.0556)-0.1925 *(0.1087)

d-D1994-0.1632(0.0312)-0.2358 ***(0.0688)-0.1497 **(0.0688)-0.1085(0.0716)-0.2985 **(0.1322)

d-D1995-0.1100 ***(0.0301)-0.1467 **(0.0676)-0.1380 **(0.0662)-0.0626(0.0744)-0.2435 **(0.1192)

d-D1996-0.0700 ***(0.0268)-0.1170 *(0.0615)-0.1153 **(0.0580)-0.0487(0.0750)-0.2050 *(0.1105)

d-D1997-0.0255(0.0211)-0.0191(0.0423)-0.0521(0.0444)0.0534(0.0592)-0.1296(0.0863)

d-D1998-0.0079(0.0151)-0.0320(0.0314)0.0130(0.0308)0.0590(0.0411)0.0186(0.0607)

Constant

0.0218 **(0.0089)

0.0288 *(0.0157)

0.0263(0.0342)0.0159(0.0197)-0.0154(0.0341)

1086
5346

315
1535

226
1108

153
786

139
700

# of Firms# of Firm-Year'

Notes:"d-" denotes the first difference. D-year is a year dummy.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2:
firms that have headquarters and branch offices throughout the sample period, Group 3: firms that start as single
establishments and become multi-e4stablishment firms by the end of the sample period, Group 4: firms that start asmulti-establishment firms and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
Instruments are the first lags of explanately variables.

47

Kiyohiko G. Nishimura・Futoshi Kurokawa

Table 12 The Government-Dependency Dummy (FGLS): 1991-1998
dependent variable: TFP growth
Gov_Dummy = 1 Gov_Dummy = 1
If Gov
If Gov
Share>20%
Share>30%

Gov_Dummy = 1
If Gov Share >40%

Gov_Dummy = 1
If Gov Share >50%-0.6113 ***(0.0201)-0.0780 ***(0.0050)
0.2481 ***(0.0154)
0.1067 ***(0.0032)
0.0828 ***(0.0113)
0.0517 ***(0.0101)0.0203 **(0.0092)
0.0580 ***(0.0077)
0.0678 ***(0.0065)
0.0510 ***(0.0051)
0.0269 ***(0.0039)***-0.0174(0.0039)0.0304 ***(0.0020)-0.6011 ***(0.0208)-0.0789 ***(0.0050)0.2494 ***(0.0155)
0.1075 ***(0.0032)
0.0836 ***(0.0113)0.0526 ***(0.0101)
0.0210 **(0.0091)
0.0578 ***(0.0077)
0.0673 ***(0.0065)0.0499 ***(0.0052)
0.0263 ***(0.0039)***-0.0191(0.0048)0.0306 ***(0.0020)-0.6072 ***(0.0202)-0.0784 ***(0.0050)
0.2484 ***(0.0154)0.1071 ***(0.0032)
0.0839 ***(0.0114)0.0528 ***(0.0101)
0.0211 **(0.0092)
0.0584 ***(0.0077)
0.0680 ***(0.0065)0.0508 ***(0.0051)
0.0268 ***(0.0039)***-0.0216(0.0040)0.0302 ***(0.0020)-0.5986 ***(0.0210)-0.0787 ***(0.0050)0.2497 ***(0.0155)
0.1077 ***(0.0032)
0.0831 ***(0.0114)0.0523 ***(0.0101)0.0207 **(0.0092)
0.0575 ***(0.0077)
0.0674 ***(0.0065)0.0497 ***(0.0052)
0.0261 ***(0.0039)**-0.0141(0.0056)
0.0299 ***(0.0020)610.1253
0.0000609.8844
0.0000612.0432
0.0000610.3230
0.0000# of Firms with
Gov_Dummy = 1

111

78

60

44

# of Firms# of Firm-Year's

1086
6097

1086
6097

1086
6097

1086
6097

d-OUT
d-SE
d-SE-RATIO
d-PROFIT
d-D1992
d-D1993
d-D1994
d-D1995
d-D1996
d-D1997
d-D1998
Gov Dummy
Constant
Log likelihood
Wald Test: p-valu

Note:"d-" denotes the first difference. D-year is a year dummy. Gov_Dummy is a dummy that indicates large share of sales
to government.
"***", "**", and "*" denote significance at 1%, 5%, and 10%, respectively.
The table shows breakdowns of firms with well-established information service activities. Standard deviations are inparenthesis.
All: all in our samples, Group 1: firms that remain single establishments throughout the sample period, Group 2: firmsthat have headquarters and branch offices throughout the sample period, Group 3: firms that start as single
establishments and become multi-e4stablishment firms by the end of the sample period, Group 4: firms that start asmulti-establishment firms and become single establishments by the end of the sample period.
Because our samples are an unbalanced panel, the number of firms each year is not equal.
Wald Test is the test statistic asymptotically distributed as chi-square(k) under the null of heteroscedasticity, where kdenotes the number of firms.
48

Total Factor Productivity in Japanese Information Service Industries: Firm‑Level Analysis

49