Silviase/jgov_v1.4_full_ann huggingface dataset:
practice, we can divide our sample roughly in half by defining high concentration as $ HHI \geq 0.2 $ .

Then, as discussed, industry growth reflects life cycle patterns, and the turnover of market leaders depends on the industry life cycle. As Kato and Honjo (2006, 2009) argued, high industry growth may provide potential entrants more opportunities for new entry. At the same time, high industry growth may provide market leaders' competitors with more opportunities to increase their market shares. Thus, it is hypothesized that the turnover of market leaders is more likely to occur in growing industries. Moreover, the more volatile market demand is, the higher is the likelihood of the turnover of market leaders. Market leaders face difficulties in maintaining their market shares in demand-volatile industries because they tend to be particularly affected by demand fluctuations regardless of positive or negative growth. In addition, as Ghemawat and Nalebuff (1985) suggested, firms with larger market shares face difficulties surviving in declining industries. In fact, Davies and Geroski (1997) found that both positive and negative growth in demand bring greater uncertainty for leading firms. Thus, it is hypothesized that the turnover of market leaders is more likely to occur in not only growing industries but also declining industries.

To identify the effect of demand volatility on the turnover of market leaders, we use industry growth, GROW. In addition, as the relationship may be non-linear, its squared term, GROW_SQ, is also included in the regression model. Additionally, the impact of positive industry growth may differ from that of negative industry growth on the turnover of market leaders. For this reason, we use GROW_P and GROW_N, instead of GROW and GROW_SQ, to identify the difference in the effect of industry growth between growing and declining industries.

While, as already argued, concentration and industry growth affect the turnover of market leaders, the interaction effects of concentration and industry growth may be significantly associated with market mobility. The turnover of market leaders may occur in highly concentrated industries because of the intense competition among them. Moreover, even if concentration is high, market leaders may still find survival
difficult in declining industries. As decreasing demand may damage their lion's shares, the turnover of market leaders is more likely to occur in declining industries. Therefore, it is hypothesized that the negative impact of concentration on the turnover of market leaders is mitigated in such industries. The interaction effects of concentration and industry growth provide important insights into how market mobility depends on growth patterns and industry-specific characteristics.

In addition to concentration and industry growth, market mobility may be accelerated by innovations. As discussed, discontinuous innovations often disrupt established market positions, thereby promoting competition in the industry. Indeed, there are distinct patterns in market dynamics between industries with low and high R&D intensities (e.g., Dosi et al., 1997). Further, there may be different life cycle patterns between these industries (e.g., Gort and Klepper, 1982; Klepper, 1997). In this respect, Davies and Geroski (1997) concluded that R&D and innovation play a major role in affecting the dynamics of market shares. Although technological progress may exert a role as a barrier to entry and mobility, it is considered that the turnover of market leaders is more likely to occur in R&D-intensive industries. The variable for R&D intensity, RD, is defined as the ratio of R&D expenditure to sales in the examined industries.

Additionally, the impact of cartels on competition in the industry has attracted attention from the perspective of competition policy. Although it is not easy to identify the presence of a cartel, including unproven cartels, we use CARTEL to capture discovered cartels over time. The traits of a cartel may persist even after its detection.

Furthermore, several controls are required in the regression model. We include a variable for import products, IMPORT, to control for the rate of import products. $ ^{8} $ A variable for industry size is also included in the regression model. We also use a variable for mergers, defined as whether market leaders merge within the industry, because merger may cause the turnover of market leaders. Finally, year dummies are included to control for the macroeconomic conditions associated with the calendar
years in the model.

Table 2 presents the definitions and summary statistics of the variables used in this paper.

### 5. Estimation results

As the dependent variable is a binary response in Eq. (4), we use the logit model. Then, as it is an ordered response in Eq. (7), we use the ordered logit model. The model includes an industry-specific term, which is assumed to be a random effect, to capture heterogeneity in the market structure of industries. That is, we estimate the parameters, using the random-effects model. $ ^{9} $

Tables 3, 4, and 5 show the estimation results when $ Y(1) $ , $ Y(2) $ , and $ Y(3) $ are used as the dependent variable, respectively. In these tables, we provide the estimated coefficients when using the random-effects logit model. $ ^{10} $ We present the results with GROW and GROW_SQ in column (i) and those with GROW_P and GROW_N in column (ii). We also present the results with the interaction term of HHI and GROW_P and the term of HHI and GROW_N in column (iii). Additionally, we use H_HHI in column (iv), instead of HHI. Furthermore, Table 6 shows the estimation results when Z is used as the dependent variable. In Table 6, we provide the estimated coefficients when using the random-effects ordered logit model.

As shown in these tables, HHI has a significantly negative effect on the turnover of market leaders in columns (i), (ii), and (iii) of Tables 3–6. Further, the coefficients of $ H\_HHI $ are negative and significant in column (iv). We also find a negative relationship between the turnover of market leaders and concentration. This relationship is consistent with the findings of Eckard (1987), Doi (2001), Sakakibara and Porter (2001), and Kato and Honjo (2006, 2009). These results indicate that market leaders are less likely to be replaced in highly concentrated industries, suggesting
that concentration leads to less competition between market leaders. We thus provide support for the evidence that market mobility is low in highly concentrated industries.

With respect to industry growth, the coefficients of GROW are insignificant, while those of GROW_SQ are positive at the 1% significance level in column (i) of Tables 3–6. These results reveal that the turnover of market leaders is more likely to occur in demand-volatile industries. The results also indicate that the effect of industry growth on the turnover of market leaders shows a U-shaped relationship, which is consistent with the findings of Davis and Geroski (1997) and Kato and Honjo (2006, 2009). Additionally, the coefficients of GROW_P are positive and significant, while those of GROW_N are negative and significant in column (ii). The results indicate that market leaders are more likely to be replaced by competitors not only in growing industries, but also in declining industries. In developed countries including Japan, several industries may face sluggish demand growth in the declining phase. In declining industries, the likelihood of the turnover of market firms is higher, although market leaders may increase their market shares because their competitors sell out their businesses in such industries. These findings suggest that firms have more opportunities to gain leadership positions in declining industries, indicating the possibility of competition between market leaders in such industries. $ ^{11} $

As for the interaction terms of concentration and industry growth, a positive sign for the interaction terms of HHI and $ GROW\_P $ and a negative sign for the interaction terms of HHI and $ GROW\_N $ are found in column (iii), although the effects are insignificant in Tables 4, 5, and 6. Additionally, the interaction terms of $ H\_HHI $ and $ GROW\_P $ are positive at the 5% significance level in columns (iv) of Tables 3, 5, and 6. In particular, the interaction terms of $ H\_HHI $ and $ GROW\_N $ are negative at the 1% significance level. These results indicate that market leaders are more likely to be replaced by competitors in growing and declining industries,
even if the industries are highly concentrated. These findings suggest that industry growth, rather than concentration, has a more significant impact on the turnover of market leaders and that demand fluctuations play an important role in enhancing market mobility. Given that high concentration and declining industry growth represent the later stages in the industry life cycle, the turnover of market leaders is more likely to occur in such stages through the intense competition between market leaders.

Moreover, the coefficients of RD are positive at the 1% significance level in Tables 3–6. These results reveal that the turnover of market leaders is less likely to occur in R&D-intensive industries. As Davies and Geroski (1997) argued, R&D has a major effect on the dynamics of market shares. The results reveal that innovations lead to intense competition between market leaders, probably because they often disrupt established market positions. The findings also suggest that, as Kato and Honjo (2009) pointed out, the speed of technological progress differs significantly among industries, indicating the presence of different patterns in market dynamics and life cycles between industries with low and high R&D intensities.

The coefficients of CARTEL are negative but insignificant in Table 4. This negative effect indicates that the turnover of market leaders is less likely to occur in industries where a cartel has been exposed. This finding is consistent with that of Kato and Honjo (2009), indicating that the traits of cartels can persist even after their detection. In addition, the coefficients of IMPORT are negative but insignificant in Table 3. Meanwhile, SIZE has a significantly negative effect on the turnover of market leaders, indicating that the turnover of market leaders is less likely to occur in large industries. Finally, the coefficients of MERGE(1), MERGE(2), and MERGE(3) are insignificant. Although mergers are considered to cause the turnover of market leaders, we find little such evidence.

Furthermore, Tables 3, 4, and 5 may indicate the difference in the determinants of turnover among first-, second-, and third-ranked firms. $ ^{12} $ These tables show that the negative impact of concentration, HHI, is larger only for the turnover of first-
ranked firms, while the positive impact of demand fluctuations, GROW_SQ, and R&D intensity, RD, is larger for the turnover of top three-ranked firms. The findings imply that first-ranked firms are more likely to maintain their leadership positions in highly concentrated industries, while second- and third-ranked firms may have opportunities to gain market share in demand-volatile and R&D-intensive industries.

### 6. Conclusions

This paper has explored the turnover of market leaders in the manufacturing and ICT industries in Japan. We proposed indices for market mobility by focusing on the turnover of market leaders and examined how the likelihood of the turnover of market leaders differs across industries. We provided 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 revealed that the turnover of market leaders is more likely to occur in R&D-intensive industries. Furthermore, the interaction effects of industry growth and concentration indicated that the turnover of market leaders is more likely to occur in declining industries with high concentration.

This paper has several limitations. First, we did not take into account the effects of market leaders themselves on the turnover of market leaders because we could not identify the list of market leaders from the data source. Therefore, our analysis did not include firm-specific characteristics, including their strategies and technological levels. Second, the JFTC collects data on market shares for its purpose. Therefore, we could not examine the turnover of market leaders when the JFTC did not survey market shares. At the same time, sample selection bias may be present.

Despite the limitations of our analysis, we provided valuable insights into the impact of industry-specific characteristics on market mobility. In particular, our findings suggest that industry growth is conducive to competition among top-ranked firms, even in concentrated industries. While concentration has traditionally been highlighted from the perspective of competition policy, industry growth, in addition to innovations, may rather have more impact on market mobility, which would
provide a better understanding of industry dynamics.

## Appendix

Table A1 presents the estimated marginal effects for the probability of a positive outcome assuming that $ \mu_{i}=0 $ for $ Y(1) $ , $ Y(2) $ , and $ Y(3) $ .

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Table 1. Distribution of observations by industry

Year	Manufact	ICT	Transport	Who & ret	Services	Others	Total
1991	357	6	3	4	5	4	379
1992	350	6	3	4	5	4	372
1993	367	7	3	6	7	6	396
1994	246	2	3	5	3	6	265
1995	313	2	3	7	4	7	336
1996	270	0	3	7	4	7	291
1997	287	8	6	10	11	11	333
1998	191	4	6	5	5	8	219
1999	267	9	7	6	6	9	304
2000	248	7	7	5	6	9	282
2001	258	9	7	5	7	9	295
2002	190	9	7	4	7	7	224
2003	252	11	7	4	10	7	291
2004	222	11	7	4	8	7	259
2005	258	14	7	4	8	7	298
2006	230	12	7	4	8	7	268
2007	235	13	7	5	8	8	276
2008	227	13	7	5	8	8	268
2009	229	13	8	5	8	8	271
2010	—	—	—	—	—	—	—
Total	4,997	156	108	99	128	139	5,627

Notes: Manufact, ICT, Transport, Who & ret, Services, and Others indicate “Manufacturing,” “Information and communications,” “Transport,” “Wholesale and retail trade,” “Services,” and “Others,” respectively. Others include “Construction,” “Electricity, gas, heat supply, and water,” “Finance and insurance,” “Education, learning support,” and “Eating and drinking places, accommodations.”
Table 2. Definition and summary statistics of variables

Variable	Definition	Mean	S.D.
(Market mobility)
Y(1)	(See the main text.)	0.121	—
Y(2)	(See the main text.)	0.263	—
Y(3)	(See the main text.)	0.395	—
(Concentration)
HHI	Hirschman-Herfindahl index	0.231	0.131
H_HHI	1 if $ HHI \geq 0.2 $ , 0 otherwise	0.517	—
(Industry growth)
GROW	Difference of logarithms of the values of ship-ment between $ t + 1 $ and $ t $ .	-0.024	0.247
GROW_SQ	GROW $ \times $ GROW	0.062	0.427
GROW_P	max $ \{GROW, 0\} $	0.057	0.141
GROW_N	min $ \{GROW, 0\} $	-0.081	0.179
(R&D intensity)
RD	Ratio of R&D expenditures to sales	0.034	0.022
(Cartel)
CARTEL	1 if cartels are exposed in $ t $ or after $ t $ , 0 otherwise	0.031	—
(Import)
IMP	Ratio of imports to domestic total supply	0.048	0.095
(Size)
SIZE	Logarithm of domestic total supply (million yen)	11.774	1.072
(Merger)
MERGE(1)	1 if the first-ranked firm merges, 0 otherwise	0.012	—
MERGE(2)	1 if the first- or second-ranked firm merges, 0 otherwise	0.021	—
MERGE(3)	1 if the first-, second-, or third-ranked firm merges, 0 otherwise	0.029	—
(Year dummies)
Y90, ... Y09	1 if the year is the observation year, 0 otherwise	—	—

Notes: S.D. indicates standard deviation. The number of observations is 5,009. The variables except for year dummies depend on i and t (year dummies depend only on t).
Table 3. Estimation results for the turnover of the top one firm: $ Y(1) $

Variable	(i) Coef.	(ii) Coef.	(iii) Coef.	(iv) Coef.
HHI	-6.615***
(0.760)	-6.812***
(0.754)	-7.957***
(0.888)
H_HHI	-1.010***
(0.159)
GROW	-0.078
(0.175)
GROW_SQ	0.654***
(0.125)
GROW_P	1.824***
(0.295)	1.061**
(0.494)	1.088***
(0.397)
GROW_N	-1.807***
(0.256)	-0.808
(0.523)	-1.060***
(0.319)
HHI $ \times $ GROW_P	2.902*
(1.488)
HHI $ \times $ GROW_N	-4.514**
(2.201)
H_HHI $ \times $ GROW_P	1.185**
(0.579)
H_HHI $ \times $ GROW_N	-1.355***
(0.492)
RD	13.998***
(3.125)	13.105***
(3.055)	13.137***
(3.063)	12.425***
(3.116)
CARTEL	-0.911
(0.555)	-0.974*
(0.549)	-0.885
(0.539)	-0.743
(0.540)
IMPORT	-0.705
(0.616)	-0.945
(0.612)	-0.917
(0.609)	0.504
(0.590)
SIZE	-0.292***
(0.070)	-0.276***
(0.069)	-0.268***
(0.069)	-0.143**
(0.067)
MERGE(1)	0.626
(0.417)	0.621
(0.414)	0.618
(0.416)	0.587
(0.415)
Year dummies	Yes	Yes	Yes	Yes
Number of observations	5,009	5,009	5,009	5,009
Number of industries	594	594	594	594
Log likelihood	-1,612	-1,592	-1,588	-1,619
Wald $ \chi^{2} $	207***
142***	241***
120***	242***
120***	207***
138***
LR $ \chi^{2} $ ( $ \rho=0 $ )

Notes: Standard errors are in parentheses. $ *\*\* $ , $ *\* $ , and $ * $ indicate significance at the 1%, 5%, and 10% levels, respectively. Wald $ \chi^{2} $ is the statistic to test the null hypothesis that all the coefficients equal 0. LR $ \chi^{2} $ is the statistic to test the null hypothesis that $ \rho = 0 $ where $ \rho $ is the proportion of the total variance contributed by the panel-level variance component.
Table 4. Estimation results for the turnover of the top two firms: $ Y(2) $

Variable	(i) Coef.	(ii) Coef.	(iii) Coef.	(iv) Coef.
HHI	-4.338*** (0.511)	-4.430*** (0.504)	-4.755*** (0.572)
H_HHI	-0.925*** (0.122)
GROW	2.2 $ \times $ 10 $ ^{-4} $ (0.154)
GROW_SQ	0.537*** (0.115)
GROW_P	1.752*** (0.280)	1.539*** (0.503)	1.400*** (0.375)
GROW_N	-1.566*** (0.240)	-1.005** (0.483)	-0.950*** (0.292)
HHI $ \times $ GROW_P	0.831 (1.608)
HHI $ \times $ GROW_N	-2.566 (1.981)
H_HHI $ \times $ GROW_P	0.599 (0.550)
H_HHI $ \times $ GROW_N	-1.369*** (0.462)
RD	16.495*** (2.560)	15.776*** (2.489)	15.723*** (2.486)	15.784*** (2.497)
CARTEL	-0.360 (0.333)	-0.380 (0.327)	-0.366 (0.326)	-0.301 (0.327)
IMPORT	-0.850* (0.515)	-1.033** (0.510)	-0.983* (0.508)	-0.325 (0.495)
SIZE	-0.145*** (0.055)	-0.127** (0.054)	-0.123** (0.054)	-0.064 (0.053)
MERGE(2)	0.388 (0.257)	0.371 (0.257)	0.371 (0.258)	0.372 (0.257)
Year dummies	Yes	Yes	Yes	Yes
Number of observations	5,009	5,009	5,009	5,009
Number of industries	594	594	594	594
Log likelihood	-2,528	-2,504	-2,503	-2,515
Wald $ \chi^{2} $	294***	334***	334***	321***
LR $ \chi^{2} $ ( $ \rho = 0 $ )	256***	204***	201***	222***

Notes: Standard errors are in parentheses. $ *\*\* $ , $ *\* $ , and $ * $ indicate significance at the 1%, 5%, and 10% levels, respectively. Wald $ \chi^{2} $ is the statistic to test the null hypothesis that all the coefficients equal 0. LR $ \chi^{2} $ is the statistic to test the null hypothesis that $ \rho = 0 $ where $ \rho $ is the proportion of the total variance contributed by the panel-level variance component.
Table 5. Estimation results for the turnover of the top three firms: $ Y(3) $

Variable	(i) Coef.	(ii) Coef.	(iii) Coef.	(iv) Coef.
HHI	-3.589***
(0.444)	-3.666***
(0.437)	-3.978***
(0.499)
H_HHI	-1.033***
(0.114)
GROW	0.068
(0.154)
GROW_SQ	0.369***
(0.115)
GROW_P	1.847***
(0.318)	1.558***
(0.597)	1.211***
(0.396)
GROW_N	-1.404***
(0.253)	-0.786
(0.487)	-0.716**
(0.301)
HHI $ \times $ GROW_P	1.172
(2.039)
HHI $ \times $ GROW_N	-2.812
(1.965)
H_HHI $ \times $ GROW_P	1.394**
(0.617)
H_HHI $ \times $ GROW_N	-1.689***
(0.489)
RD	17.386***
(2.431)	16.806***
(2.366)	16.750***
(2.364)	17.182***
(2.339)
CARTEL	-0.619**
(0.309)	-0.646**
(0.305)	-0.633**
(0.304)	-0.575*
(0.301)
IMPORT	-0.643
(0.488)	-0.831*
(0.484)	-0.779
(0.483)	-0.430
(0.468)
SIZE	-0.165***
(0.052)	-0.147***
(0.051)	-0.143***
(0.051)	-0.104**
(0.049)
MERGE(3)	0.105
(0.207)	0.083
(0.208)	0.082
(0.208)	0.111
(0.208)
Year dummies	Yes	Yes	Yes	Yes
Number of observations	5,009	5,009	5,009	5,009
Number of industries	594	594	594	594
Log likelihood	-2,940	-2,914	-2,913	-2,909
Wald $ \chi^{2} $	315***
322***	352***
261***	353***
259***	365***
251***
LR $ \chi^{2} $ ( $ \rho=0 $ )

Notes: Standard errors are in parentheses. $ *\*\* $ , $ *\* $ , and $ * $ indicate significance at the 1%, 5%, and 10% levels, respectively. Wald $ \chi^{2} $ is the statistic to test the null hypothesis that all the coefficients equal 0. LR $ \chi^{2} $ is the statistic to test the null hypothesis that $ \rho = 0 $ where $ \rho $ is the proportion of the total variance contributed by the panel-level variance component.