Silviase/jgov_v1.4_full_ann huggingface dataset:
11) 佐藤真奈美、岩本浩二、鈴木石根、白岩善博, 2010: マリンバイオテクノロジー学会 （マリンバイオテクノロジー学会賞受賞講演）

「円石藻Emiliania huxleyiの細胞内石灰化の生理学的制御要因の解析-リン酸欠乏誘導性のココリス形成の低温による促進効果」

12) Shiraiwa, Y. and H. Araie, 2010: The 9th International Symposium on Selenium in Biology and Medicine (Selenium 2010)

“Physiological, Biochemical, and Molecular Aspects of Selenium Utilization by Photosynthetic Microorganisms”

13) Shiraiwa, Y. and H. Araie, 2010: The 13th International Nannoplankton Association Meeting, Yamagata, Japan.

“Strategy for the Utilization of Selenium as a Growth Regulator by the Coccolithophorid, Emiliania huxleyi”.

14) Shiraiwa, Y., 2010: The IX International Marine Biotechnology Conference (IMBC2010), Qingdao, China (Invited keynote speech)

“Impact of ocean acidification on carbon dioxide sequestration by a cosmopolitan marine unicellular calcifying alga, Emiliania huxleyi”

15) 安達大輝、濱健夫、佐藤雄飛、澤井茉利奈、緑川貴、石井雅男、笹野大輔, 2010: 第47回アイソトープ・放射線研究発表会

「海洋酸性化が物質循環に及ぼす影響－安定同位体トレーサ法による実験的解析」

16) 澤井茉利奈、小森真里奈、佐藤雄飛、大森裕子、安達大輝、遠藤寛子、中山剛、井上勲、緑川貴、石井雅男，斉藤秀，笹野大輔、小杉如央、濱健夫,2010：2010年度日本海洋学会秋季大会

「海洋酸性化が植物プランクトンの群集組成に与える影響に関する実験的解析」

17) Hama, T., 2010: Lecture in Hanyan University, Korea

“Production and transformation of dissolved organic matter by microbial activity”

18) Hama, T., 2010: Special Lecture in Korean Oceanic Research Institute, Korea "Effect of ocean acidification on the production of dissolved organic matter"

19) Suzuki, Y., S. Fukuda and S. Shiraiwa, 2011: Side event of IPCC WGII/WGI Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystem, Nago, Japan. "Physiological responses of the coccolithophorid, Emiliania huxleyi to low pH or high CO $ _{2} $ Conditions"

20) Sawai, M., S. Kawashima, M. Komori, Y. Sato, Y. Omori, T. Adachi, H. Endo, T. Nakayama, I. Inoue, Y. Shiraiwa, S. Fukuda, T. Midorikawa, M. Ishii, D. Sasano, N. Koshugi, and T. Hama, 2011: Side event of IPCC WGII/WGI Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystem, Nago, Japan.

“Experimental analysis of the effect of ocean acidification on coastal phytoplankton community and a non-calcified phaptophyte Chrysochromulina sp.”
## （3） 出願特許

特に記載すべき事項はない。

（４）シンポジウム、セミナーの開催（主催のもの）

1）白岩善博、園池公毅 2009：藻類で挑む地球環境問題。日本植物学会第73回大会、山形大学（シンポジウム企画）

2）白岩善博、都筑幹夫 2010：国際微細藻類シンポジウム：微細藻類のバイオテクノロジー～食糧・環境・エネルギー、東京大学、東京.（シンポジウム企画・主催）

（５）マスコミ等への公表・報道等

1）朝日小学生新聞（2009年11月3日、科学欄、朝日新聞社）本研究の成果と培養実験について写真付きで掲載された。

2）科学雑誌ニュートン2010年4月号（2010年4月7日発行）のCO₂の特集記事において、本研究の成果の一部が紹介された。

3）科学雑誌ニュートン別冊（2010年11月15日発行）の地球温暖化の特集記事において、本研究の成果の一部が紹介された。

（６）その他

特に記載すべき事項はない
# CPRC Discussion Paper Series

# Competition Policy Research Center Japan Fair Trade Commission

The turnover of market leaders in growing and declining industries

Yuji Honjo

Faculty of Commerce,

Chuo University

Noriyuki Doi

School of Economics (emeritus),

Kwansei Gakuin University

Yasushi Kudo

Competition Policy Research Center,

Japan Fair Trade Commission

CPDP-63-E March 2016

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

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

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

E-mail:cprcsec@jftc.go.jp
# The turnover of market leaders in growing and declining industries $ ^{*} $

Yuji Honjo $ ^{\dagger} $ Noriyuki Doi $ ^{\ddagger} $ Yasushi Kudo $ ^{\S} $

March 2016

## Abstract

This paper explores the turnover of market leaders in the manufacturing and information and communications industries in Japan. We propose indices for market mobility by focusing on the turnover of market leaders and examine how the likelihood of the turnover of market leaders differs across industries. We provide evidence that market leaders are more likely to be replaced by competitors not only in growing industries but also in declining industries. Moreover, the results reveal that the turnover of market leaders is more likely to occur in research and development-intensive industries. Furthermore, the interaction effects of industry growth and concentration indicate that the turnover of market leaders is more likely to occur in declining industries with high concentration.

JEL Classifications: C25; D43; L13

Keywords: Industry growth; Market leader; Turnover
### 1. Introduction

Many, but not all, managers are concerned about their firms' ranking at the top of the markets in which they operate (e.g., Geroski and Toker, 1996). Some managers make decisions in order to perform well relative to their competitors, while paying attention to their market shares, rather than profits. This is partly because profits are difficult to forecast or because a focus on profits may lead to a short-term orientation at the expense of long-term considerations (e.g., Armstrong and Collopy, 1996). By gaining higher market share, firms can exploit market power and have competitive advantages in the industry. Hence, sustaining a leadership position is considered to be one of the key managerial objectives for market leaders.

If a market leader holds a competitive advantage, the ranking of market shares will be stable in the industry. Conversely, if a competitor against a market leader creates a breakthrough product, the ranking of market shares will be vulnerable in the industry. Therefore, we can say that the ranking of market shares reflects the degree of competition in the industry. In this respect, a change in the ranking of market shares not only attracts managers, but also provides useful information on the dynamics of the competitive process for policy makers.

From the perspective of competition policy, much attention has been paid to how to examine the factors affecting competition in industries. To identify the degree of competition, scholars have proposed some indices for market mobility. $ ^{1} $ Traditionally, the n-firm concentration ratio and the Hirschman–Herfindahl index (HHI) have been regarded as measures of market mobility. However, these indices ultimately tend to be static and ignore the dynamics of the industry because they simply provide a snapshot of market share distribution. More specifically, market mobility may appear high, even though the concentration ratio is high. Indeed, the market shares of oligopolistic firms, which compete with each other fiercely, can change, even in highly concentrated industries. Therefore, further research is needed to explore how
to measure market mobility and to identify the factors affecting the dynamics of the competitive process at the industry level.

This paper explores the turnover of market leaders in the manufacturing and information and communications (ICT) industries in Japan. We propose indices for market mobility by focusing on the turnover of market leaders and examine how the likelihood of the turnover of market leaders differs across industries. We provide evidence that market leaders are more likely to be replaced by competitors not only in growing industries but also in declining industries. Moreover, the results reveal that the turnover of market leaders is more likely to occur in research and development (R&D)-intensive industries. Furthermore, the interaction effects of industry growth and concentration indicate that the turnover of market leaders is more likely to occur in declining industries with high concentration.

The remainder of the paper is organized as follows. The following section discusses how to measure market mobility and reviews the literature on this topic. Section 3 introduces the method used in this paper and Section 4 describes the data and variables. The estimation results are presented in Section 5. Finally, the findings of this paper are concluded.

### 2. Market mobility

#### 2.1. Market share measures

To discuss market structure and competition, a substantial number of studies have explored how to measure market mobility. Mueller and Hamm (1974) and Mueller and Rogers (1980, 1984) used changes in the four-firm concentration ratio as a measure of market mobility. Doi (2001) examined the determinants of market mobility, using data on Japanese manufacturing industries. These studies proposed market share measures, using data on changes in the concentration ratio or in market share (e.g., Caves and Porter, 1978).

However, as Kato and Honjo (2006) pointed out, even if the concentration ratio is found to be almost constant over time, fierce competition may still exist among leading firms. That is, the concentration ratio ignores the shift in market shares
among leading firms. Since earlier studies were based on cross-section data, their results showed only the difference in the concentration ratio or market shares between industries in a year. In this respect, these studies did not lose sight of the dynamics of the industry.

Afterwards, some scholars examined the determinants of market mobility, using panel data on market shares. For instance, Sakakibara and Porter (2001) proposed an index for market mobility (they called this “market share instability”) that represents changes in the market shares of market leaders. They found robust evidence that domestic rivalry has a positive and significant relationship with trade performance measured by world export share, particularly when R&D intensity reveals opportunities for dynamic improvement and innovation. Kato and Honjo (2006) also used some of these indices to measure market mobility, finding a significant relationship between concentration and market share instability and showing that the market shares of leading firms are more stable in highly concentrated industries.

Although these studies employed panel data in their analyses, it is doubtful whether these indices capture market mobility when market leaders change. Without tracing the market shares of the firms themselves over time, we cannot accurately measure changes in the market shares of market leaders. In other words, we may calculate the changes in market shares by matching a first-ranked firm in a year with a different firm that is first ranked in the following year. However, it is cumbersome to trace the market shares over time, mainly because of the limitation of data on market shares. In practice, marketing companies or governments tend to target only major firms, for example, the top 10-ranked firms. Therefore, if a firm drops out of the top ranking in the industry, data on the firm's market share are not obtainable from the data source. This fact indicates that we cannot measure the changes in the market shares of market leaders that are no longer top ranked in the following year, which causes underestimation bias.
#### 2.2. Turnover measures

The market share measures used in the literature may capture changes in the market shares of different firms over time (e.g., Doi, 2001; Sakakibara and Porter, 2001; Kato and Honjo, 2006). To measure market mobility, therefore, some studies have attempted to use turnover measures based on firms' positions (ranking) in the industry. For example, Joskow (1960) proposed a turnover measure by means of the rank correlation coefficient. Mueller (1986) examined the stability of market leadership positions, using a binary choice model. Marlow and Wright (1987) also proposed mobility and turnover measures, using the number of changes in ranking among the top three-ranked firms and the number of times that firms move into the top three ranking. Moreover, Kambhampati (2000) identified market leaders, using a dummy variable for leadership stability, which reflects industry competitiveness. Similarly, Kato and Honjo (2005) examined market mobility in Japanese industries, using the index for market leadership instability, and found that leadership positions are sensitive to macroeconomic conditions. $ ^{2} $ As Geroski and Toker (1996) emphasized, turnover measures reflect the dynamics of the competitive process more accurately than static measures of competition, such as the n-firm concentration ratio.

Turnover measures can capture changes in the ranking of certain firms. In this respect, turnover measures may more accurately reflect market mobility. Additionally, turnover measures have more advantages than market share measures when we construct a data set. Access to a data source is often restricted, and information on firms' market shares tends to be concealed. Compared with information on market shares, however, information on firms' rankings is more obtainable from a data source. More importantly, as already explained, if a firm drops out of the top ranking, we cannot trace changes in the market share of the firm. By contrast, we can obtain information on whether the firm drops out of the top ranking. As a result, turnover measures are more obtainable than market share measures.
#### 2.3. Industry growth patterns

When examining the factors affecting market mobility, some scholars have paid attention to the life cycle patterns of industries (e.g., Gort and Klepper, 1982; Agarwal and Gort, 1996; Klepper, 1997). According to the perspective of the industry life cycle, the number of firms changes over time and increases with rapid growth through innovation in the early stage. Bayus and Agarwal (2007) argued that industries follow the product life cycle pattern: an initial period of intense competition, significant entry and exit of firms, and fragmented market shares is eventually followed by a shakeout in which the number of firms dramatically falls, leading to higher industry concentration. More importantly, as Klepper (1996) emphasized, eventually the rate of change in the market shares of the largest firms is low and the leadership of the industry stabilizes.

Following these arguments, we suggest that market mobility depends on the industry life cycle. However, in practice, identifying the life cycle patterns in each industry can be challenging. Meanwhile, we may be able to classify the growth patterns of industries, using concentration and industry growth, because concentration and industry growth appear to represent life cycle patterns. Specifically, high industry growth tends to be seen in earlier stages, while concentration tends to occur in later stages. In addition, high industry growth may provide more opportunities to gain market share, whereas concentration is often caused through the shakeout in the industry. Therefore, it is predicted that market mobility is higher in industries characterized by high growth and low concentration. Furthermore, market mobility is higher in R&D-intensive industries because discontinuous innovations often disrupt established market positions. Such growth patterns and industry-specific characteristics may thus determine market mobility.

### 3. Method

Because of the advantages of turnover measures, we propose indices to capture market mobility, using information on firms' rankings in the examined industries.
In this paper, market mobility is measured by the turnover of market leaders. Below, we explain the method used to examine the factors affecting the turnover of market leaders.

Consider whether firm j that is ranked highly (more precisely, top three ranked in this paper) in year t will be ranked highly in year $ t+1 $ in industry $ i(=1,\ldots,n) $ . Let $ R_{i} $ denote the set of firm j in industry i, and $ j\in R_{i} $ . We assume that the ranking of firms is determined by firm j's market share (or sales) in industry i and that it is measured by discrete time. Let $ s_{jt}(>0) $ denote firm j's market share in year t. If the ranking of market shares frequently changes in an industry, the industry can be characterized as having higher market mobility. To measure market mobility in industry i, we define three indices, $ Y(1)_{it} $ , $ Y(2)_{it} $ , and $ Y(3)_{it} $ , for the top one-, two-, and three-ranked firms by binary variables based on the turnover of market leaders as follows: $ ^{3} $

$$ Y(1)_{it}\ =\ I\Big(s_{kt+1}<\max_{j\in R_{i}|j\neq k}s_{jt+1}\mid s_{kt}=\max_{j\in R_{i}}s_{jt}\Big) $$

$$ Y(2)_{it}\ =\ I\Big(s_{kt+1}<\max_{j\in R_{i}|j\neq k}s_{jt+1}\cup s_{lt+1}<\max_{j\in R_{i}|j\neq k,l}s_{jt+1}\mid s_{kt}=\max_{j\in R_{i}}s_{jt}, $$

$$ s_{l t}=\max_{j\in R_{i}|j\neq k}s_{j t}\Big) $$

$$ \begin{array}{r c l}{Y(3)_{i t}}&{=}&{I\Big(s_{k t+1}<\max_{j\in R_{i}|j\neq k}s_{j t+1}\cup s_{l t+1}<\max_{j\in R_{i}|j\neq k,l}s_{j t+1}\cup s_{m t+1}<\max_{j\in R_{i}|j\neq k,l,m}s_{j t+1}}\end{array} $$

$$ \mid s_{k t}=\max_{j\in R_{i}}s_{j t},s_{l t}=\max_{j\in R_{i}\mid j\neq k}s_{j t},s_{m t}=\max_{j\in R_{i}\mid j\neq k,l}s_{j t}\Big), $$

where $ I(\cdot) $ represents an indicator function and $ k, l, m \in R_{i} $ . We define that $ Y(\cdot)_{it} = 1 $ if the turnover of market leaders occurs between t and $ t + 1 $ , and $ Y(\cdot)_{it} = 0 $ otherwise.

These indices, $ Y(1)_{it} $ , $ Y(2)_{it} $ , and $ Y(3)_{it} $ , indicate the state of the turnover of firm j's ranking and therefore reflect market mobility. That is, $ Y(1)_{it} $ indicates whether the first-ranked firm changes and $ Y(2)_{it} $ indicates whether either the first- or second-ranked firm changes in industry i between t and $ t+1 $ . To identify the
factors affecting these probabilities, we formalize $ Y(\cdot)_{it} $ as the following function:

$$ \Pr(Y(\cdot)_{it}=1)=F\left(x^{\prime}_{it}\beta+\mu_{i}\right), $$

where $ x_{it} $ indicates a vector of the independent variables, $ \mu_{i} $ is an industry-specific term, and $ \beta $ is a vector of the coefficients for $ x_{it} $ . In Eq. (4), the industry-specific term, $ \mu_{i} $ , is included to control for unobserved heterogeneity. To estimate the parameters, we specify $ F(\cdot) $ , using a binary choice model such as a logit model, and identify the factors affecting the turnover of market leaders in the industry by maximizing the likelihood function.

Moreover, these indices, $ Y(1)_{it} $ , $ Y(2)_{it} $ , and $ Y(3)_{it} $ , can be regarded as categorical and ordered. More precisely, we find the following relationship for $ Y(1)_{it} $ , $ Y(2)_{it} $ , and $ Y(3)_{it} $ :

$$ \Pr(Y(1)_{it}=1)\leq\Pr(Y(2)_{it}=1)\leq\Pr(Y(3)_{it}=1). $$

By aggregating the information on these indices, we can newly construct an index with an ordered response. Let $ Z_{it} $ denote an observed ordinal response, and we define $ Z_{it} $ as follows:

$$ Z_{it}=\left\{\begin{array}{ll}0&if Y(3)_{it}=0\\1&if Y(3)_{it}=1\cap Y(2)_{it}=0\\2&if Y(2)_{it}=1\cap Y(1)_{it}=0\\3&if Y(1)_{it}=1.\end{array}\right. $$

In this case, the random-effects model can be written as follows:

$$ \mathrm{P r}(Z_{i t}>k)=H\left(x_{i t}^{\prime}\gamma+\nu_{i}-\kappa_{k}\right), $$

where $ k(=1,2,3) $ is a possible outcome and $ \kappa_{k} $ is a cutpoint for k. To estimate the parameters, we specify $ H(\cdot) $ , using an ordered choice model such as an ordered logit model, and identify the factors affecting the turnover of market leaders in the industry by maximizing the likelihood function.

### 4. Data

#### 4.1. Data source

We constructed a new data set for Japanese industries during the period 1991–2010, using the Survey of Concentration Ratio on Production and Shipment (CRPS)
(Seisan Shukka Shuchudo Chosa), which has been surveyed by the Japan Fair Trade Commission (JFTC) to monitor market structure. $ ^{4} $ The CRPS covers a large number of industries and the industries surveyed roughly correspond to the six-digit Standard Industrial Classification level. $ ^{5} $

This source includes data on domestic production, based on quantity of production and value of shipment of the top 10 firms and concentration in each industry during the period 1991–2010. While data on either quantity of production or value of shipment were reported for some industries, data on both quantity of production and value of shipment were reported for other industries. To construct as long a period of panel data as possible, we employed data on quantity of production if the observation period for quantity of production was longer than that for value of shipment; otherwise, we employed data on value of shipment to obtain a larger sample size. $ ^{6} $ As already mentioned, we targeted top three-ranked firms to capture market mobility and collected data on the top three-ranked firms available in the CRPS. We identified which firms are ranked highly and whether market leaders within the top three are replaced by competitors in the examined industries.

Table 1 presents the number of observations in each year and industry. We measured changes in the turnover of market leaders for 669 industries during the period 1991–2010 (19 years). The total number of observations is 5,627. The sample is unbalanced panel data because the JFTC does not always survey market shares for all industries. As shown in Table 1, manufacturing accounts for a major proportion of the observations, followed by ICT. This is because the market shares of these industrial products have been traditionally surveyed by the JFTC. Meanwhile, market mobility depends on the level of R&D intensity in the industries. However, it is not easy to obtain data on R&D expenditure in some industries (e.g., wholesale and retail trade) because such data tend not to be reported for firms. To identify the factors affecting the turnover of market leaders, we therefore focus only on the
manufacturing and ICT industries.

The sample for the estimation is composed of 5,009 observations in these industries. The number of industries is 594. Regarding the turnover of market leaders, Figure 1 describes the means of $ Y(1) $ , $ Y(2) $ , and $ Y(3) $ during the observation period. The means of these indices indicate the ratio (probability) of the turnover of market leaders on average. The means of $ Y(1) $ , $ Y(2) $ , and $ Y(3) $ are 0.121, 0.263, and 0.395, respectively. That is, the turnover of the first-ranked firms occurs with a probability of approximately 12%, while the turnover of the first-, second-, or third-ranked firm occurs with a probability of approximately 40%.

#### 4.2. Variables

When discussing the factors affecting the turnover of market leaders, we first focus on the relationship between the turnover of market leaders and concentration. Concentration, which is often regarded as a static measure of competition, represents market structure and, as discussed, indicates the process of industry growth. The traditional view in competition policy is that high concentration may represent low mobility in the industry. However, the turnover of market leaders substantially differs from concentration because it reflects the dynamics of the industry. In this paper, therefore, we identify whether the turnover of market leaders depends on concentration in the industry. It is plausible that concentration has a negative effect on the turnover of market leaders. In other words, market leaders are less likely to maintain their positions in low concentrated industries. It also seems natural that the likelihood of the turnover of market leaders is negatively related to the difference in market shares between market leaders.

We measure market concentration, using the HHI $ (HHI)^{.7} $ . In addition to HHI, we use a dummy variable for concentration, $ H\_HHI $ , to identify a high concentration level. Besanko et al. (2013) classified the nature of competition into four types—perfect competition, monopolistic competition, oligopoly, and monopoly—and regarded perfect and monopolistic competition as having the HHI below 0.2. In