Silviase/jgov_v1.4_full_ann huggingface dataset:
### I. Introduction

Market leaders are generally regarded as enjoying more market power than their rivals. Indeed, market leaders maintain their positions over a long period in some industries. By contrast, the turnover of market leaders is often observed in other industries. What causes the turnover of market leaders? This paper explores the turnover of market leaders, using data on Japanese manufacturing industries over the period 1975–2002, and describes industry differences in the turnover of market leaders.

For many companies, obtaining or sustaining market leadership positions may be a key managerial goal to exploit competitive advantages and scale economies, which are more likely to be associated with superior performance. In addition, as noted by Geroski and Toker (1996, p.141), many managers are concerned with their rank at the top of the markets they operate in. The findings from research on the turnover of market leaders may provide some insight into the management strategy of firms.

In the field of industrial organization, a number of empirical studies have addressed the turnover of market leaders to measure the extent of market mobility. For instance, Joskow (1960) proposed a turnover measure by means of the rank correlation coefficient. $ ^{1} $ Caves and Porter (1978) examined the market share instability of leading firms as a measure of market mobility. However, most studies on mobility tended to employ cross-section data, which does not reflect intra-industry changes over time. Since market mobility may depend on an adjustment process in the market system over time rather than at a given point in time, we analyze the turnover of market leaders, using a long-term data set. The lack of turnover of market leaders
over time may signal that the adjustment process to a new equilibrium is not sufficiently effective, which, it is expected, would provide an important clue in assessing the dynamics of competition in industries.

Furthermore, as is often argued, Japanese domestic markets appear to have special characteristics. For example, several types of cartels, such as recession cartels, rationalization cartels, and export-import cartels, had been exempted from the application of Antimonopoly Act, in order to protect domestic industries by avoiding overt competition under the rapid macroeconomic growth. $ ^{2} $ The industries where cartels were legally sanctioned may still have a historical legacy, even if the cartels are not exempted from the application of Antimonopoly Act today. Then, the historical background may impede competition between firms. In this respect, research that focuses on Japanese industries might be of some interest to the discussion of competition policy.

The remainder of the paper is organized as follows. Section II describes data employed in the analyses. Section III explains the methods used and discusses the estimation results. The final section includes some concluding remarks.
### I I. Data

The data set employed in this paper comes from the National Survey of Concentration Ratio on Production and Shipment (Seisan Shukka Shuchudo Chosa) (hereafter, the CRPS), which has been surveyed for the purpose of monitoring market structure by the Fair Trade Commission of Japan (JFTC). $ ^{3} $ In the CRPS, most industries surveyed are classified at the six-digit or eight-digit Standard Industrial Classification (SIC) level. The CRPS contains data on concentration and market shares, based on production or shipment since 1975.

Using the CRPS, we attempt to identify market leaders in industries. Most, if not all, market leaders have the largest market shares in industries, and the first-ranked firms may tend to act as market leaders. In this paper, the market leader is defined as the first-ranked firm, based on firms' market shares in each industry. The CRPS covers data from 1975, and the data set is restricted to industries surveyed in 1975. As a result, we observe market leadership positions over 1975–2002. It is generally recognized that Japan has experienced stable economic growth since the first oil shock of 1973–1974. The observation period corresponds to the phase of stable growth the Japanese economy has entered.

Since data on industry-specific characteristics other than concentration and industry growth were not obtainable from the CRPS, we collected data on advertising, research and development (R&D), exports, imports, and legally sanctioned cartels, using other data sources. The Data Report $ (1) $ of 1975-1980-1985 Linked Input-Output Tables (hereafter, the IO Tables),
which is compiled by the Management and Coordination Agency, was used to obtain data on advertising, exports, and imports classified at the four-digit or six-digit SIC level. In addition, data on R&D intensity was taken from the values estimated by Goto and Suzuki (1989). The R&D intensity was measured as R&D expenditures divided by value added at the roughly three-digit SIC level. Furthermore, data on cartels that had been exempted from the application of the Antimonopoly Act were obtained from the JFTC Annual Reports (Nenji Hokoku).

However, several problems arose when the CRPS was used to construct the data set. First, although we tracked market leaders over a long time, some industrial classification codes were changed or eliminated during the observation period. That is, some observations were occasionally censored. To deal with such cases, we attempt to apply survival data techniques, which will be explained later. Also, the measurement units for concentration and market shares vary across industries—some are measured by unit volume and others by the value. In this paper, we simply calculated concentration and market shares without conversion into unit volume, because of the lack of appropriate deflators. We employed data on concentration and market shares based on production in the analyses, since the sample size obtainable is larger than based on shipment. $ ^{4} $ In addition, there are no differences of data on advertising, R&D, export, and import intensity between some industries, because the industrial classification of the CRPS differs from those of the other data sources.

As a result, our sample consisted of 379 manufacturing industries over the period 1975–2002. Table 1 describes the distribution of industries used
in the sample and the extent of changes in the identity of the first-ranked firm, namely, the turnover of market leaders, by the two-digit SIC level. As is shown in Table 1, the turnover of market leaders was observed in 246 industries during the observation period. In addition, an important finding is that the extent of turnover varies across industries, although it should be noted that the extent of turnover in Table 1 simply indicates the number of industries where the identity of the market leader changed at least once during the observation period regardless of whether the observations are censored. Taking into account industry-specific characteristics, in the following section, we explore industry differences in the turnover of market leaders over time.

### I II. Methods

As already mentioned, while market leaders maintain their positions in some industries, they are displaced from their positions in others. Part of the reason may be due to the differences of market structure between industries. This paper therefore highlights industry differences in the turnover of market leaders. In particular, the paper focuses not only on whether market leaders maintain their positions, but also on how long they maintain their positions in industries. Here, the duration of market leadership is defined as the period from the initial observation year, 1975, to the year when the identity of the first-ranked firm changes. By measuring the duration of market leadership, we will provide evidence that industry-specific characteristics account for the difference in the turnover of market leaders.

Moreover, as mentioned earlier, we track market leaders over a long time, but observations are censored in some industries because the industry classification codes have been changed or eliminated during the observation
period. In these industries, market leaders can be observed only until censored. To take into account the censored observations, we apply survival data techniques, nonparametric and parametric approaches, in order to explore the turnover of market leaders.

### 1. Nonparametric approach

By using a nonparametric method proposed by Kaplan and Meier (1958), we estimate the duration of market leadership. $ ^{5} $ If all the times at which the event occurs in the sample are ordered, and labeled $ t_{j}(j=1,2,\ldots,m) $ , such that $ t_{1}\leq t_{2},\ldots,\leq t_{m} $ , then the Kaplan-Meier estimator, $ \hat{S}(t) $ , which is a standard method to estimate the survival function is given by

$$ \hat{S}(t)=\prod_{j|t_{j}\leq t}\left(1-\frac{d_{j}}{n_{j}}\right), $$

where $ d_{j} $ is the number of individuals who experience the event at time $ t_{j} $ , and $ n_{j} $ is the number of individuals who have not yet experienced the event at that time and therefore still “at risk” of experiencing it (including those censored at $ t_{j} $ ). Here, let $ d_{j} $ denote the number of industries in which the identity of the first-ranked firm changed at time $ t_{j} $ $ ^{6} $ . Also, let $ n_{j} $ denote the number of industries in which the identity of the first-ranked firm has not yet changed at $ t_{j} $ and therefore still ‘at risk’ of experiencing it.

Following the Kaplan-Meier estimator, we show life tables for the duration of market leadership in Table 2. Table 2 indicates that 29% of market leaders in 1975 were observed to maintain their positions throughout the observation period. On the other hand, about 40% of market leaders were displaced from leadership positions within five years. Also, Figure 1 illus-
trates the survival function for the duration of market leadership, by using the Kaplan-Meier estimates in Table 2.

Table 3 shows the Kaplan-Meier estimates calculated by stratification, in order to clarify whether the duration of market leadership varies between industries with different characteristics. The results of significance tests, log-rank and Wilcoxon tests, are also shown in Table 3 to test the equality of survival functions of subsamples. First, Table 3 gives the Kaplan-Meier survival estimates by concentration class. Based on the Hirshman-Herfindahl index in 1975 (HHI), 379 industries in the sample were classified into low concentration (HHI < 0.18) and high concentration subsamples ( $ HHI \geq 0.18 $ ). $ ^{7} $ As shown, it is clearly found that market leaders of the high concentration subsample are more likely to maintain their positions than the counterparts of the low concentration subsample. Also, the estimated survival functions differed significantly between low concentration and high concentration subsamples. This result indicates that market leaders are more likely to maintain their positions in concentrated industries. Also, this is consistent with the findings of some previous studies (e.g., Mueller, 1986; Kato and Honjo, 2005).

We also examine whether the duration of market leadership differs between low advertising intensity and high advertising intensity industries. As is often argued, advertising may encourage product differentiation, which has been viewed as an entry or mobility barrier. In practice, some studies (e.g.,
Sakakibara and Porter, 2001) found that advertising has a negative effect on the mobility of leading firms. Conversely, other studies (e.g., Eckard, 1987; Das et al., 1993) have suggested that advertising does not reduce mobility in an industry. In this respect, more advertising may lead to the turnover of market leaders. On the basis of advertising intensity in 1975 (ADV), the sample was divided into low advertising intensity (ADV < 0.003) and high advertising intensity subsamples ( $ ADV \geq 0.003 $ ) to show the difference in the duration of market leadership. As shown in Table 3, the Kaplan-Meier estimates were almost similar between the two subsamples. The finding suggests that advertising does not affect the turnover of market leaders.

Further, the survival functions of low R&D intensity and high R&D intensity industries are estimated to identify whether the duration of market leadership differs between these subsamples. R&D investment generally plays an important role in innovative activities, and it tends to generate technological progress and new products. Davies and Geroski (1997, p.389) also concluded that R&D and innovation play major roles in affecting the turbulence of market leaders. In this respect, market leaders may be less likely to maintain their positions in R&D-intensive industries. Based on R&D intensity in 1975 (RD), the sample is grouped into low R&D intensity (RD < 0.05) and high R&D intensity subsamples ( $ RD \geq 0.05 $ ) to identify whether the duration of market leadership differs between the two subsamples. The result is shown in Table 3, which indicates that market leaders tend to maintain their positions in the low R&D intensity subsample rather than the high R&D intensity subsample. This suggests that R&D intensity is an important factor in the turnover of market leaders. It also concurs with Davies and Geroski's (1997) argument.

Moreover, we examine whether the duration of market leadership differs between industries with cartels and others. In Japan, as already men-
tioned, several cartels, such as recession cartels, rationalization cartels, export-import cartels, small and medium-sized business cartels, and industry-specific cartels, were exempted from the application of the Antimonopoly Act. These legally sanctioned cartels allow cartel members cooperative arrangements at production, price, investment, capacity, and so on. In case of export-import cartels, for example, a trade association restricts the activities of cartel members in the industry. In practice, legally sanctioned cartels have been seen in some industries, and interfirm competition may be more restricted in industries with legally sanctioned cartels than in those without the cartels. The industries where cartels were legally sanctioned before may still have a historical legacy, even though legally sanctioned cartels have been already abolished. Moreover, Bradburd and Over (1982) suggested that once an industry cooperative equilibrium is allowed to form, it will tend to persist, even if industry concentration subsequently declines substantially. In these respects, market leaders in industries that experienced legally sanctioned cartels may tend to maintain their positions, compared with the others, because of the lack of competition through cooperative arrangements. As shown in Table 3, cartels were legally sanctioned in 93 industries of the sample during the observation period. $ ^{8} $ In Table 3, the survival estimates indicate that market leaders tend to maintain their positions in industries where cartels were legally sanctioned rather than others. This might suggest that legally sanctioned cartels are associated with less turbulence of market leaders.
Finally, the sample was divided into consumer good and producer good industries. As shown in Table 3, market leaders in the consumer good industries appear to maintain their positions than those in producer good industries. The log-rank and Wilcoxon tests verified that the survival estimates are significantly different between consumer good and producer good industries.

### 2. Semiparametric approach

In the previous subsection, the duration of market leadership was examined by applying the Kaplan-Meier method in order to explain industry differences in the turnover of market leaders. In addition, two significance tests, log-rank and Wilcoxon tests, were used to test if the duration of market leadership varies significantly between two subsamples with different characteristics. The results suggest that some industry-specific characteristics affect significantly the turnover of market leaders. Then, by applying a proportional hazards model proposed by Cox (1972), we attempt to identify the effects of industry-specific characteristics on the turnover of market leaders.

The proportional hazards model is well-known as a semiparametric approach for survival data and, particularly, it is useful to show the effects of covariates on the hazard of individuals. In the proportional hazards model, the hazard function that represents the instantaneous failure rate, $ h(t; \boldsymbol{x}_{i}) $ , is assumed to be

$$ h(t;\boldsymbol{x}_{i})=h_{0}(t)\mathrm{e x p}(\boldsymbol{\beta}^{T}\boldsymbol{x}_{i}), $$

where $ h_{0}(t) $ is the baseline hazard function, $ \beta $ is a vector of parameters to be estimated, and $ x_{i} $ is a vector of covariates for individual i. Let $ t_{1} < t_{2} < \cdots < t_{k} $ denote the ordered event times during the observation period, and $ R_{i} $ is a set of individuals who have not yet experienced the event before
just prior to time $ t_{i}. $ ^{9} That is, $ R_{i} $ indicates industries in which the market leader continues to maintain the first-ranked position until the leader was displaced from the position in industry i. To estimate the parameters, the partial likelihood, $ L(\boldsymbol{\beta}) $ , is given by

$$ L(\boldsymbol{\beta})=\prod_{i=1}^{k}\frac{h(t;\boldsymbol{x}_{i})}{\sum_{j\in R_{i}}h(t;\boldsymbol{x}_{j})}=\prod_{i=1}^{k}\frac{\exp(\boldsymbol{\beta}^{T}\boldsymbol{x}_{i})}{\sum_{j\in R_{i}}\exp(\boldsymbol{\beta}^{T}\mathbf{x}_{j})}. $$

The log-likelihood function is written as

$$ \log L(\boldsymbol{\beta})=\sum_{i=1}^{k}\left[\boldsymbol{\beta}^{T}\boldsymbol{x}_{i}-\log\sum_{j\in R_{i}}\exp(\boldsymbol{\beta}^{T}\mathbf{x}_{j})\right]. $$

Maximizing $ \log L(\boldsymbol{\beta}) $ gives the estimated parameters without specifying a function form of $ h_{0}(t) $ .

## (1) Covariates

As some studies suggested, collusion among leading firms may be more likely to occur and their market shares appear to be more stable in highly concentrated industries. Shepherd (1970), for example, argued that successful collusion would tend to hold market shares virtually constant. The stability of market shares, therefore, would tend to occur in oligopolistic industries, since it is more likely to be associated with collusion among leading firms. Kato and Honjo (2006) also found that the market shares of leading firms are more stable in highly concentrated industries. Therefore, it is predicted that concentration is negatively correlated with the turnover of market leaders. In practice, the result of nonparametric approach suggests that market leaders are more likely to maintain their positions in concentrated industries. Thus, concentration would have a positive impact on the duration of
market leadership, and the degree of concentration is here measured by the Hirschman-Herfindahl index (HHI).

In addition, industry growth $ (IG) $ is employed as a covariate. The high growth of market demand presumably provides potential entrants more opportunities for new entry. $ ^{10} $ At the same time, industry growth may accelerate the disequilibrium among incumbents including leading firms within an industry. Moreover, some studies (e.g., Gort and Klepper, 1982; Klepper, 1996) suggested that the market shares of leading firms tend to be less stable in the formative stages of industry life cycle, while they are more likely to be stable in the mature stages. Therefore, more turbulence of market leaders may occur in growing industries. It is predicted that the coefficient of industry growth on the hazard rates has a positive sign.

To control industry-specific conditions associated with nonprice rivalry, advertising intensity is included in the model. As already mentioned, advertising may encourage product differentiation and act as an entry or mobility barrier. If advertising reduces the mobility of leading firms, then it may decrease the probability of turnover of market leaders. On the other hand, since advertising may induce fierce competition, there is the possibility that more advertising leads to the turbulence of market leaders. In addition to advertising intensity, R&D intensity is included in the model in order to take into account the impact of nonprice rivalry. As argued, R&D investment may play a major role in affecting the turbulence of market leaders. In addition, as already shown in Table 3, market leaders are more likely to be displaced in R&D-intensive industries. In these respects, R&D intensity (RD) would have a negative influence on the duration of market leadership.

Export intensity $ (EXP) $ and import intensity $ (IMP) $ are also included in the model. In addition to domestic market conditions, international com-
petition would have some influence on the mobility and turnover of market leaders. In practice, some studies have provided empirical evidence that international competition have significant effects on market mobility. For example, Baldwin and Caves (1998) found that international competition increases the turnover of firms in Canadian manufacturing industries. Doi (2001) also found that exports have a volatility effect on market leadership, whereas Baldwin (1995) suggested that import competition increases mobility in industries. Hence, these covariates are predicted to have positive effects on the hazard rates.

As already explained, several types of cartels have been legally sanctioned in Japanese industries. In addition, as shown in Table 3, the Kaplan-Meier estimates differ between industries with legally sanctioned cartels and others. The result suggests that leadership positions tend to be stable over longer periods in industries where cartels were legally sanctioned. To clarify the effect of cartels on the duration of market leadership, a dummy variable for legally sanctioned cartels (DCAR) is used as a covariate. The dummy variable takes a value of one if cartels are legally sanctioned at any point in the observation period. Furthermore, a dummy for consumer good industries (DCONS) is included in the model. As already shown in Table 3, leadership positions tend to be more stable in consumer good industries than in producer good industries. Therefore, it is predicted that the coefficient of DCONS indicates a negative sign.

Maximizing Equation (4), we estimated the coefficients of the above covariates. The covariates, HHI, ADV, RD, EXP, and IMP are measured by values in 1975. And the covariate, IG, is measured by the difference in the logarithm of domestic production between the initial year, 1975, and the year when the identities of market leaders changed or the observation were
censored, divided by the number of observation years. $ ^{[11]} $ The definitions and summary statistics for these covariates employed in the analysis are given in Table 4.

## (2) Results

By applying the semiparametric approach, Table 5 shows the estimation result using a proportional hazards model. In the analysis, Breslow's (1974) approximation method for tied events is used, and in this approximation each of tied times is treated as though it occurred just before the others.

First, the coefficients of concentration (HHI) were negative and statistically significant in Table 5, suggesting that market leaders are more likely to maintain their positions in concentrated industries. This is also consistent with the result of Table 3. From the perspective of competition policy, these results may indicate that market leaders have less mobility in concentrated industries.

Then, as predicted, industry growth $ (IG) $ had a positive effect on the hazard rates. This indicates that market leaders are less likely to maintain their positions over time in growing industries, which is consistent with the findings of Mueller (1986) and Kato and Honjo (2006). In addition, since the coefficients of IG were statistically significant at the 1% level in any models, industry growth appears to be fairly important in explaining the turnover of market leaders.

With respect to advertising intensity (ADV), any significant coefficients were not found in the analysis, although it was predicted that advertising has some influence on the duration of market leadership. This is consistent with the result of nonparametric approach. On the other hand,