Silviase/jgov_v1.4_full_ann huggingface dataset:
【3K123001】使用済み自動車（ELV）の資源ポテンシャルと環境負荷に関するシステム分析に関する研究（H24～H26；累計予算額95,788千円）

酒井 伸一（京都大学）

### 1. 研究開発目的

使用済み自動車（ELV: End-of-Life Vehicles）は自動車の世界的普及に伴い、世界共通の関心の対象となっている。全世界の自動車保有台数は人口を上回る勢いで増加しており、2010年には10億台を超えており、自動車保有台数の増加に合わせてELVも増加している。しかしながら、ELVリサイクル制度や技術の現状に関して世界の動向を比較検討することや、ELVが有する資源性や有害性を念頭においたリサイクルの議論もほとんど行われていない。そこで、(1)ELVリサイクル制度を有する国や地域の制度比較を行うことにより、それぞれの制度の特徴や効果を考察するとともに、3R適用技術やASR管理についての現状を整理し、ELVリサイクルシステムの現状と将来を論考する。また、(2)ELVを循環資源およびヒトに対する有害物質の曝露源という視点から調査対象とし、自動車部材に含有される資源性物質、および車内ダストに含まれるBFRsおよび臭素化ダイオキシン類（PBDD/Fs）等を定性・定量し、その含有傾向と動態推定を試みる。さらに、(3)ELVの解体調査や化学分析結果、ならびに既往研究情報を基礎として、ELV1台当たりの元素含有量の推定と破砕後の自動車シュレッダー残渣（ASR）への移行量推定を行い、資源性物質である磁石中のレアアース類を対象として物質フロー推定モデルを策定し、将来予測を含む動的物質フロー解析を実施することとした。また、アジア地域の自動車リサイクルの展開はこれから本格化するため、鉛バッテリーリサイクル施設などのリサイクルの実態調査を試みた。

### 2. 本研究により得られた主な成果

## (1) 科学的意義

ハイブリッド車 (HEV) と普通乗用車の使用済み自動車 1 台当たりの元素含有量を比較したところ、ハイブリッド車で多く確認された元素は、国家備蓄レアメタルの Co、Ni、レアアースの Y、La、Ce、Pr、Nd、Dy であった。これらの違いはハイブリッド車特有の部品である NiMH 電池やハイブリッドトランスミッション中のレアアース磁石に由来することが確認された。

自動車部材中のポリ臭素化ジフェニルエーテル（PBDEs）や臭素化ダイオキシン類（PBDD/Fs）の濃度を測定したところ、27 試料のうち 16 試料において、部材中の臭素は PBDEs 等の臭素系難燃剤（BFRs）もしくはヘキサブロモドデカン製剤に由来することが明らかとなった。分析に供した全ての床面ダストおよび座面ダストから難燃剤や PBDD/Fs が検出された。

## (2) 得られた成果の実用化

元素別のELVからASRへの移行量について検討したところ、レアアース類についてはHEVの駆動用モーター磁石が、Pdは触媒、電子基板が、Auは電子基板が主に寄与した。このうち触媒の解体段階での取り外し率は比較的高いことから、取り外し率が低いレアアース磁石や電子基板の取り外し率を高めることが重要と示唆される。

資源性物質の代表例として実施した次世代車特有部品由来のレアアース類の動的フロー解析から、2030年には廃棄 HEV 中のハイブリッドトランスミッションからの回収ポテンシャルは 220 ton/yr と推定された。また、NiMH 電池に 2,900 ton/yr の回収ポテンシャルとなった。そして、それぞれの同年の需要量に対して 35.4%、92.1% に相当するレアアース類が使用済部品中に含まれていることが明らかになった。

## (3) 社会への貢献の見込み

有害物質の代表例として実施した鉛の動的フロー解析から、国内保有車の1台当たり鉛含有量
590–890 g/台に対し、ELV 中の含有量は 1,200–1,500 g/台と高いことが明らかとなった。そして、ASR 中の鉛含有濃度が低減するまでには時間を要することが推定により示唆された。一層の低減のためには ELV 処理段階での部品回収など対策が考えられ、取り外しの実態把握が重要となる。

### 3. 委員の指摘及び提言概要

これまでほとんど調査されていなかったアジア地域のELVリサイクルの重要な基礎的データの収集を得たのは大きな成果であり、同地域の自動車リサイクルの制度確立、および我が国内での自動車リサイクルの高度化に活用できよう。ただし、地域の特徴を考慮した考察が必要である。

### 4. 評点

総合評点：A
# CPRC Discussion Paper Series Competition Policy Research Center Fair Trade Commission of Japan

The Stability of Market Leadership Positions in Japanese Manufacturing Industries

Masatoshi KATO

Competition Policy Research Center,

Fair Trade Commission of Japan

Graduate School of Commerce and Management,

Hitotsubashi University

Yuji HONJO

Faculty of Commerce,

Chuo University

CPDP 15-E April 2005

1-1-1, Kasumigaseki, Chiyoda-ku, TOKYO 100-8987 JAPAN

Phone:+81-3581-1848 Fax:+81-3-3581-1945

URL:www.jftc.go.jp/cprc.html

E-mail:cprcsec@jftc.go.jp
# The Stability of Market Leadership Positions in Japanese Manufacturing Industries $ ^{*} $

Masatoshi Kato $ ^{\dagger} $

Yuji Honjo $ ^{\dagger} $

## Abstract

Using a newly constructed panel data set, we investigate the stability of market leadership positions as a measure of market mobility. This paper not only shows the extent of stability of leadership positions across industries over time, but also empirically examines the impacts of industry-specific characteristics and macro-economic conditions on the stability of leadership positions. It is found that leadership positions are more stable in highly concentrated industries. In addition, this paper provides evidence that leadership positions are sensitive to macro-economic conditions, and that high economic growth tend to induce the turnover of market leaders.

Keywords: Binary choice model; Concentration; Market leadership position; Panel data.

JEL Classification: L13, L60
### 1. Introduction

Market leadership positions are stable over a long period of time in some industries, while the positions are unstable in others. What causes the difference in stability of market leadership positions among industries?

This paper explores the stability of market leadership positions as a measure of market mobility. Using a newly constructed panel data set, we show the extent of stability of leadership positions across industries over time, and empirically examine the impacts of industry-specific characteristics and macro-economic conditions on the stability of leadership positions. We employ data in the Japanese manufacturing industry over the period 1977 to 2001, and attempt to identify significant changes in the process of dynamic competition. Our long-term sample unlike previous studies, it is expected, will provide important evidence to assess the competitive process in industries over business cycles.

As Geroski and Toker (1996, p.141) noted, many managers are concerned with their firms' rank at the top of the markets they operate in. Obtaining or sustaining the leadership positions may be a key managerial objective in order to exploit market power and to gain the competitive advantage in the markets. Moreover, as Geroski and Toker pointed out, the turnover of market leaders provides some useful information on the dynamics of the competitive process more accurately than static measures of competition. Until now, some empirical studies have devoted to examining the turnover measure based on
firms’ positions in industries in order to capture market mobility as a reflection of competition. For example, Joskow (1960) proposed the turnover measure by means of the rank correlation coefficient. In addition, Mueller (1986) and Kambhampati (2000) examined the stability of market leadership positions using a binary choice model, respectively. $ ^{1} $

On the other hand, some empirical studies (e.g., Caves and Porter, 1978; Sakakibara and Porter, 2001) have used the stability of market shares as a measure of market mobility. This measure that is a continuous variable needs data on changes in top-ranked firms' market shares between two points of time. However, if a top-ranked firm falls substantially from the top position or exits from the market, the firm is sometimes subject to disappearing in the data source at the following year and the measure based on changes in top-ranked firms' market shares cannot be calculated. Thus, the sample size is considerably reduced, particularly when we construct balanced long-term panel data in order to control the possible existence of unobservable industry-specific characteristics. $ ^{2} $ By contrast, the turnover measure based on top-ranked firms' positions allows us to obtain larger sample size over a long period
of time, since we simply compare the identities of top-ranked firms from year to year. Using the measure based on firms' positions, in this paper, we examine the leadership stability over a long period of time in Japanese manufacturing industries.

The plan of this paper is as follows. Section 2 describes data used in this paper. Section 3 shows the extent of stability of market leadership positions across industries over time, and presents an empirical model for the determinants of stability of leadership positions. Section 4 shows empirical results. Finally, we conclude our findings.

### 2. Data

This section describes data used in this paper. We constructed a new panel data set in Japanese manufacturing industries over the period 1977 to 2001. As a data source, the Market Share in Japan (Nihon Market Shea Jiten) is used to collect data on market shares and industry concentration. $ ^{3} $ The Market Share in Japan has been annually published since 1973 by a Japanese marketing research company, Yano Research Institute Ltd. $ ^{4} $ With respect to industry’s shipments, the Report by Commodity of the Census of Manufactures, which
are compiled by the Research and Statistics Department, Economic and Industry Policy Bureau, the Ministry of Economy, Trade and Industry, is used as another data source.

There are, however, several measurement problems to be discussed. First, the industrial classification in the Market Share in Japan is not necessarily consistent in each year; that is, some categories have been changed or eliminated. Thus, if we cannot constantly obtain data on market shares in an industry during the observation period, then the industry is excluded from the sample. Then, mergers and acquisitions (M&A) or spin-offs arise during the period in several industries. The industries in which the market shares of top-ranked firms are changed by means of M&A or spin-offs are also excluded from the sample. In addition, we match data on market shares to the six-digit standard industrial classification (SIC), corresponding to the categories used in the Report by Commodity of the Census of Manufactures. The industries in which data are not available at the six-digit SIC level are also excluded from the sample.

While the Census of Manufactures covers all establishments until 1976, it covers only establishments with four or more employees in 1977 and afterwards. Therefore, our sample is restricted to the period of 1977 to 2001. As a result, our panel data set consists of 60 manufacturing industries during 24 years. The industries in the sample are shown in Appendix.
### 3. Model

We explain our empirical model to estimate the determinants of stability of market leadership positions. In order to measure the stability of leadership positions, three variables are presented in this paper. Let STAB1, STAB2 and STAB3 denote dummies for the stability of positions of the first-ranked firm, the two top-ranked firms and the three top-ranked firms in an industry, respectively. These variables are defined as follows:

$$ \begin{aligned}STAB1\quad=\quad\left\{\begin{array}{ll}1&if\#1\longrightarrow\#1\\0&otherwise\end{array}\right.\end{aligned} $$

$$ \begin{aligned}STAB2\ =&\left\{\begin{array}{ll}1&if\#1\longrightarrow\#1and\#2\longrightarrow\#2\\0&otherwise\end{array}\right.\end{aligned} $$

$$ \begin{array}{rcl}STAB3&=&\left\{\begin{array}{ll}1&if\#1\longrightarrow\#1,\#2\longrightarrow\#2and\#3\longrightarrow\#3\\0&otherwise\end{array}\right.\end{array} $$

where $ \#1 $ , $ \#2 $ and $ \#3 $ indicate the first rank, the second rank and the third rank, respectively. These dummy variables take a value of one if the positions are stable between periods t-1 and t, and zero otherwise. Apparently, STAB1 is more likely to take a value of one than STAB2 and STAB3. In practice, about 94 percent of STAB1 took a value of one in the sample, and the first-ranked firms in 20 of 60 industries continued to keep their positions during the observation period.

Figure 1 presents the sums of STAB1, STAB2 and STAB3 for 60 industries in each year, respectively. It is found that the stability of market
leadership positions is not constant over time. In particular, the leadership positions appear to be more stable at the stagnant periods such as 1979-80, 1985-86 and the 1990s. Figure 1 suggests that macro-economic conditions are important as factors affecting the stability of leadership positions. In addition, Table A (in Appendix) presents the sums of STAB1, STAB2 and STAB3 for 24 years in each industry, respectively. While leadership positions are fairly rigid, for example, in the oil paints industry, they are relatively instable in the finishing machines industry. Since the extent of stability of leadership positions differs across industries, industry-specific characteristics also seem to be important to determine the stability of leadership positions.

In this paper, we estimate the determinants of stability of market leadership positions in the industries. With respect to independent variables, industry concentration (CONC) is used to identify the relationship between the stability of leadership positions and concentration. Here, we measure the degree of concentration by the sum of the squares of each market share for the three top-ranked firms. $ ^{5} $ From the traditional viewpoint, it has been considered that the collusion among top-ranked firms is more likely to occur and their market shares or positions are more stable in highly concentrated industries.
dustries. $ ^{6} $ Therefore, it is predicted that the effect of concentration on the stability of leadership positions is positive. On the other hand, Davies and Geroski (1997) emphasized that it is difficult to accept the traditional view that high concentration implies the lack of competition. Sakakibara and Porter (2001) also found that the relationship between market share instability and concentration is positive. In this respect, there remains a possibility that the effect of concentration on the stability of leadership positions is negative.

As other independent variables, industry size (LNSZ) is included in the model. Doi (2001) argued that market leadership in larger sized industries are more stable than in smaller sized ones. This variable, however, is used to control the difference of industry size rather than to identify the determinants of stability of market leadership positions. In addition, we use industry growth (GRS) as an independent variable. High growth of market demand may provide potential entrants more opportunities for new entry, and accelerate the disequilibrium among incumbents including leading firms. $ ^{7} $ It is predicted, therefore, that industry growth has a negative impact on the stability of leadership positions. $ ^{8} $
Moreover, the variable for the real growth rate of gross domestic product (GDP) (GDPGR) is included to control the time-specific effects due to macroeconomic conditions. As already argued, the extent of stability of market leadership positions is not constant over time in the sample. Therefore, there may be a significant relationship between the stability of leadership positions and macro-economic conditions. As mentioned earlier, the leadership positions indeed seem to be more stable at the stagnant periods. Also, Yamawaki (1991) found that real GDP growth is positively associated with the net entry rate in Japanese manufacturing industries. In this respect, it is predicted that the effect of real GDP growth on the leadership stability is negative.

All monetary values are converted into real values with the use of the deflator of gross domestic expenditures. The definitions of these variables and the descriptive statistics are shown in Table 1. By using the variables, the model is written as follows:

$$ \begin{aligned}Prob(STAB_{it}=1)=f(\beta_{0}+\beta_{1}CONC_{it-1}+\beta_{2}LNSZ_{it-1}+\beta_{3}GRS_{it}\\+\beta_{4}GDPGR_{t}+u_{i})\end{aligned} $$

where $ STAB_{it} $ represents $ STAB1_{it} $ , $ STAB2_{it} $ or $ STAB3_{it} $ , $ f(\cdot) $ is the cumulative distribution function of the standard normal or logistic distribution, $ u_{i} $ is an industry-specific term, and $ \beta_{0}, \beta_{1}, \ldots, \beta_{4} $ are parameters to be estimated. The independent variables, $ CONC_{it-1} $ and $ LNSZ_{it-1} $ , are measured at period t-1 in order to clarify the causality. The variables, $ GRS_{it} $ and $ GDPGR_{t} $ ,
take the value of changes between periods t - 1 and t, respectively.

### 4. Empirical results

As is shown in Appendix, the number of industries is 60 in the sample. The observation period for the dependent variable is 1977-2001. As a result, we obtain 1440 observations, and the sample consists of balanced panel data.

Tables 2, 3 and 4 show the estimated results for the determinants of stability of market leadership positions. The dependent variables are binary, and the parameters are estimated by a random-effects probit model and a random-effects logit model. The component error terms including $ u_{i} $ are correlated within an industry, but not across industries. The correlation coefficient between the error terms is denoted by $ \rho $ . Furthermore, following Fisman and Raturi (2004), we use Chamberlain's (1980) conditional fixed-effects logit model to estimate the parameters. In this regression, some industries are dropped out of the sample due to all positive outcomes during the observation period, which results in the reduction of the sample size. By using these possible econometric models, we attempt to identify more robust relationships. $ ^{9} $

With respect to industry concentration (CONC), the coefficients on stability of market leadership positions are positive and statistically significant in Tables 2, 3 and 4, although the coefficient is not statistically significant in column (i) of Table 4. These findings indicate that leadership positions are
more stable in highly concentrated industries, which is consistent with that of Mueller (1986). The result suggests that concentration is positively related to the stability of leadership positions. Given that the instability of leadership positions is associated with competition, the result implies that highly concentrated industries are not effectively competitive, although we must identify the relationship between the leadership stability and performance in order to clarify more accurately the meaning of leadership stability.

The effects of industry size (LNSZ) are positive and statistically significant in Table 4, implying that market leadership positions in industries with larger demand size are more likely to be stable than relatively small sized industries. The result is consistent with Doi's (2001) findings, although rather this variable is used to control the difference of sizes among industries. In Tables 2 and 3, the effects of STAB2 and STAB3 are not found, but the coefficients of STAB1 are statistically significant. On the other hand, the coefficients of industry growth (GRS) on the stability of leadership positions are negative as our expectation. The result implies that uncertainty in market demand leads to more turnover of market leaders, although the coefficients are not statistically significant.

Finally, with respect to the real growth rate of GDP (GDPGR), its effects on the stability of market leadership positions are negative and statistically significant. $ ^{10} $ It is found that leadership positions tend to be less
stable during the periods of high economic growth. As mentioned earlier, leadership positions appear to be more stable during the periods of 1979-80, 1985-86 and the 1990s. These periods correspond to recession years due to the second oil crisis, the Plaza Accord and the so-called bubble economy burst in Japan. Therefore, industries tend to have less mobility during the recessions. The findings imply that the mobility in industries is sensitive to macro-economic conditions, and that policy makers should take into account the importance of macro-economic conditions in evaluating the dynamics of competition. Furthermore, from the viewpoint of management strategy, the findings may indicate that firms have pursued their growth during the high economic growth period, but they have pursued other performance such as profitability than firm growth during the recession.

### 5. Conclusions

Using a newly constructed panel data set, we investigated the stability of market leadership positions as a measure of market mobility. This paper not only showed the extent of stability of leadership positions across industries over time, but also empirically examined the impacts of industry-specific characteristics and macro-economic conditions on the stability of leadership positions. It was found leadership positions are more stable in highly concentrated industries. In addition, this paper provided evidence that leadership positions
are sensitive to macro-economic conditions, and that high economic growth tend to induce the turnover of market leaders.
## References

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Caves, R. E. and M. E. Porter (1978) Market structure, oligopoly, and stability of market shares, Journal of Industrial Economics, 26, 289-313.

Caves, R. E. (1998) Industrial organization and new findings on the turnover and mobility of firms, Journal of Economic Literature, 36, 1947-82.

Chamberlain, G. (1980) Analysis of covariance with qualitative data, Review of Economic Studies, 47, 225-238.

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Davies, S. W. and P. A. Geroski (1997) Changes in concentration, turbulence, and the dynamics of market shares, Review of Economics and Statistics, 79, 383-91.

Doi, N. (2001) Market leadership volatility in Japanese industries, Review of Industrial Organization, 18, 427-44.
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Kambhampati, U. S. (2000) Industry competitiveness: leadership identity and market shares, Applied Economics Letters, 7, 569-573.

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Figure 1: Stability of market leadership positions
Table 1: Variable definitions and descriptive statistics

Variable	Definition	Mean	S.D.
(Dependent variable)
STAB1	Dummy variable: 1 if the position of the first-ranked firm is stable, 0 otherwise.	0.935	0.247
STAB2	Dummy variable: 1 if the positions of the two top-ranked firms are stable, 0 otherwise.	0.843	0.364
STAB3	Dummy variable: 1 if the positions of the three top-ranked firms are stable, 0 otherwise.	0.766	0.424
(Independent variable)
CONC	Sum of the squares of each market share for the three top-ranked firms.	0.119	0.081
LNSZ	Logarithm of the value of industry's shipments.	11.497	0.967
GRS	Difference of the value of industry's shipments, divided by the value of industry's shipments.	0.011	0.154
GDPGR	Real GDP growth rate.	0.028	0.020

Note: All monetary values are millions of yen. S.D. indicates standard deviation. The number of observations is 1440.
Table 2: Estimated results: random-effects probit regression

(i)	(ii)	(iii)
STAB1	STAB2	STAB3
CONC	1.722*	2.419***	2.503***
(0.907)	(0.740)	(0.702)
LNSZ	0.141*	0.059	0.051
(0.074)	(0.059)	(0.060)
GRS	-0.048	-0.059	-0.081
(0.373)	(0.272)	(0.254)
GDPGR	-9.600***	-7.013***	-3.558*
(2.931)	(2.209)	(2.000)
Constant term	0.095	0.307	-0.010
(0.854)	(0.685)	(0.697)
$ \rho $	0.103	0.090	0.108
(0.049)	(0.033)	(0.033)
LR test( $ \rho=0 $ )	6.44***	15.96***	29.41***
$ \chi^{2} $	18.20***	21.43***	16.10***
Log likelihood	-334.016	-605.637	-758.489
Number of observations	1440	1440	1440

Note: Standard errors in parentheses. $ *\*\* $ , $ *\* $ and $ * $ indicate significance at the 1%, 5% and 10% level, respectively.