CESSA WP 2018-02
New Evidence of Market Competition in the Japanese Tire
Industry using Market Share Analysis
Craig R. Parsons, Yokohama National University
Xuan Dong Nguyen, Vietnam National University
Xavier de Vanssay, York University

December 2018

Center for Economic and Social Studies in Asia (CESSA) Working Paper
Downloadable from:http://www.econ.ynu.ac.jp/cessa/publication/workingpaper.html

Center for Economic and Social Studies in Asia, Department of Economics
Yokohama National University

New Evidence of Market Competition in the Japanese Tire Industry using Market
Share Analysis

CRAIG PARSONS 1,
XUAN DONG NGUYEN,andXAVIER DE VANSSAY

Keywords: collusion, Japan, market power, market share, panel unit root, tire

Abstract:
We present a simple, yet powerful, battery of univariate and panel unit-root tests on market sharesto draw some inferences regarding the level of rivalry within an industry. As an application, we
employ them on the Japanese tire industry over a 40-year period. We find that rivalry seems to bepresent among the smaller firms, while at the same time, the dominant firm seems unperturbed by
the competition. We compare these conclusions with a previous study of the industry (Nguyen etal. 2015).

Word Count: 7469

Parsons: Yokohama National University (Japan) email: parsons-craig-gj@ynu.ac.jp; Nguyen Xuan Dong: Vietnam
National
University,
nx.dong@vnu.edu.vn;
deVanssay:York
University(Canada),email:
devanssay@glendon.yorku.ca. This paper was written while de Vanssay was a guest of Yokohama National University
as a Visiting Scholar.
111. Introduction
While the US tire industry has been the subject of economic analysis (see, for example, Klepper
and Simons 2000), little analysis has been done on the Japanese tire market. An exception is Nguyen,de Vanssay, and Parsons (2015), (hereafter: NDP). This is somewhat surprising given the very highlevel of firm concentration and small amount of imports of tires in Japan. Studying the level ofcompetition and rivalry in the Japanese tire industry is even more relevant given that in the recent
past that industry has been the subject of multiple inquests from the Japan Fair Trade Commission(JFTC). (See NDP, p. 6-7.)
In this paper, we make use of recent time series econometric methods to test whether this highly
concentrated and relatively isolated market exhibits cartel-like tendencies. Specifically, we makeuse of unit root and panel unit tests of market shares of the tire firms over time to detect whether
they may be, implicitly or explicitly, colluding.
The use of unit-root testing for market shares is not new. Gallet and List (2001) studied market
shares data for the US cigarette industry over the 1934-1994 period. They employed univariate unitroot testing on (the log of) individual firms’ relative market shares. They found that market sharesin the industry followed a random walk. They conclude that ‘…rivalry in the cigarette industry hasremained strong.’ (p. 473) and that ‘the ability of the industry to sustain cooperation over the longterm is limited.’ (p. 477). Sephton (2008) employs the same data but uses industry panel unit-root
tests. He finds that ‘Competition in the U.S. cigarette market may not have been strong given thatsome firms appear to have enjoyed a steady relative share of the market.’ (p. 421).
Despite the relative ease of the method and the abundance of potentially collusive markets aroundthe world, these methods have, thus far, been seldom used.
Giannetti (2015) uses panel data unit root tests to assess market instability for the Italian retailbanking industry. Her dataset is comprised of 143 banks, over 103 Italian provinces from 1994 to
2006. She finds that ‘the Italian banking industry has experienced movements towards higher levelsof competition.’ (p. 553).
As such, this is the first paper that uses unit-root testing to detect the level of rivalry and competitive
behavior in any Japanese industry. We have chosen the Japanese tire industry, an industry which
has been in the crosshairs of the JFTC for a long time.
We develop a model of conditional convergence along the lines of Carlino and Mills (1993, 1996)
who studied convergence of per capita incomes across US states. We then model the stochastic
process as a possible AR(1). The firm-specific AR(1) methodology seems well justified as it canencompass a random shock (productivity, costs, etc.…). It also allows for a possibly long (infinite)memory with slowly decaying ‘after shocks’ depending on the value of ϕi.

2

At the theoretical level, we examine and discuss the three possible cases: ϕi = 0 (conditional
convergence, stationary), ϕi = 1 (unit-root random walk), and the intermediate case: 0 < ϕi < 1(conditional convergence, with a stationary, long memory, and ‘slow decaying’ stochastic process.)
Our empirical approach is thus informed by our theoretical model. Empirically, we employ both
univariate and panel unit-root tests for reasons explained below.
The rest of this paper is organized as follows. Section 2 gives a brief history and characteristics of
the Japanese automobile tire industry. Section 3 discusses the modeling leading to the use of unitroot testing on market shares. Section 4 describes the firm-level data and analyzes the estimation
results. Finally, Section 5 concludes.

2. The Japanese tire industry
A Japanese subsidiary of the British company Dunlop Rubber was the first company to produce
automobile tires in Japan in 1913. It was then acquired by Sumitomo in 1963. The production
volume of the Japanese automobile tire industry increased steadily until 2008 thanks to a hugedomestic market demand and active exports and has grown as the automobile industry has grown.
The production scale declined sharply in 2009 due to the global economic crisis and recovered
slightly in 2011. Thereafter, the production network of the tire industry expanded globally, and thescale of production in Japan has exhibited a gradually decreasing trend. 2 As shown in Figure 1, theproduction volume of tires in Japan reached 1.08 million tons of rubber in 1996, about twice the1976 volume. Thereafter, it increased steadily at an annual rate of 2 percent to reach 1.34 million
tons of rubber in 2008. The tire production in 2016 was 1.02 million tons of rubber, accounting for
more than 80 percent of the domestic rubber production.
(Refer to Figure 1 at the end of the paper)
The Japanese-made tires are consumed domestically via the original equipment and replacement
channels as well as internationally via the export channel. Figure 2 shows how the Japanese-madetires are distributed from tire makers to consumers. There are two ways in which tires are
distributed: direct and indirect sales. The direct sales refer to the channel under which distributors
sell tires directly to some large users, such as transport, bus, taxi companies, and governmental andmunicipal users. Indirect sales are those under which tire dealers supply tires to end users. There
are about 100 distributors and 120,000 tire dealers on the replacement channel. As seen from Figure
2, the replacement channel has particularly wide-ranging distributors.
(Refer to Figure 2 at the end of the paper)

For more details about the Japanese automobile tire industry, visit the homepage of the Japan Automobile Tire
Manufacturers Association (JATMA) at http://www.jatma.or.jp/media/pdf/tire_industry_2017.pdf.

23

For the last forty years, the (domestic) Japanese tire production has been dominated by
Bridgestone. 3 Yokohama and Sumitomo have, at different times, occupied either the second or third
places. The Toyo Tire & Rubber Company has consistently occupied the fourth place. Two minor
players followed this quartet. The Ohtsu Tire & Rubber Co., Ltd was in fifth place until it mergedwith Sumitomo in 2003, making Sumitomo the second largest tire maker in the industry. Finally, insixth place, Michelin Okamoto Tire (a joint-venture) exited the production market in 2010. Thisapparent market share stability can be seen graphically in Figure 3.
As discussed in NDP (2015), Bridgestone, Yokohama, Sumitomo, Yokohama, Toyo and their sales
agencies developed a collusive, bid-rigging scheme (‘dangou’) in which all present and future
Defense contracts would be shared out over time. Consequently, in fiscal year 2003, most orders
for tires by the Defense Agency went to Bridgestone, Yokohama, Sumitomo, and Toyo under thecollusive bid-rigging scheme. In March 2004, Goodyear cancelled the sales of a contract for tires
to the Defense Agency. Yokohama Tire Tokyo Sales Company knew of this termination of thecontract between Goodyear Wingfoot and thus held a meeting with the other companies in May
2004 to determine how to go about redistributing their portions of the contract abandoned by
Goodyear. This cancellation by Goodyear promptly spurred the Japan Fair Trade Commission(JFTC) to initiate an investigation into this case in June 2004. The results of an investigation carriedby the JFTC uncovered the fact that the largest four firms had, indeed, colluded to determine thecontract price, as prospective contractors in the bid for procurement of automobile tires by theJapanese Defense Agency. Yokohama and Bridgestone were also ordered to pay surcharges
commensurate with the violation period.
(Refer to Figure 3 at the end of the paper)
3. Modeling unit root and hysteresis testing on market shares
3.1 Unit roots
Time series data often exhibit serial correlation. That is to say, the present value of the data is often
dependent on values preceding it. Series in which ‘…the future is like the past’ are called ‘stationary’or I(0). 4 Series that are not stationary (non-stationary or I(1)) are usually characterized by persistentlong run movements, or ‘trends’, or instead may be unstable over time or have breaks. If a particularseries has a unit root, then the series has a non-normal distribution. In this case, standard Least
Squares regression analysis will lead to incorrect t-statistics.
As such, a larger body of the time series and unit root literature is concerned with first determining
whether or not the series or series to be examined contains a unit root. If not, then standard least
Together with the U.S. tire maker Goodyear and French tire maker Michelin, Bridgestone has ranked among the top
three makers in the world. As argued in Guelle (2002), the Japanese tire makers have established strong businessbases in Asia, while foreign makers did not.
4
Stock and Watson (2015, p. 569).
3

4

squares can be used to investigate possible correlation between two or more variables. If one ormore of the series do contain a unit root, however, then other methods (typically cointegration à laJohansen (1995), or in the case of mixed (i.e. two or more series contain both `stationary` (I(0) and‘non-stationary’ I(1)) series, Autoregressive Distributed Lag ARDL) models are employed (e.g.
Pesaran, Shin and Smith, 2001).
However, rather than just a pretesting tool for time series data, economics has had more direct
applications of unit root tests. For example, in testing whether Purchasing Power Parity (PPP) holds
in the real world, international economists often conduct unit root tests on real exchange rates. If
the real exchange series is found to be non-stationary (random walk), this suggests that prices acrosscountries do not move together, thus rejecting the PPP hypothesis (see Rogoff 1996). In finance,
determining whether stock returns are non-stationary (random walk) may tell us whether we can`beat` the market and whether or not markets are efficient (see Lo and MacKinlay 2002).
Along these lines, this paper follows a small, but growing body of literature in which we use unit
root tests to assess whether there may be collusion among a small, concentrated set of firms in asingle industry. The basic idea is that if a firm’s market shares tend to revert to some historical norm
and are ‘stationary’, this may be evidence of cartel-like collusion.
As mentioned earlier, one of the earliest papers was Gallet and List (2001) who used unit root tests
to investigate possible collusion in the US cigarette industry.
This paper follows this literature. In Gallet and List (2001), unit root tests were conducted for eachfirm separately. While in some cases this may be reasonable, in most cases, conducting panel unit
tests makes more sense. The reasons are two-fold. First, it is well-known that unit root tests sufferfrom low power, particularly in small samples (Maddala and Kim 1999). As such, conducting panel
unit root tests (which were only just being developed at the time of the Gallet and List paper) greatly
enlarges the sample and consequentially the power of the test (Pesaran 2015, chapter 31). Thismeans that panel unit root tests have a greater probability to reject a false null hypothesis. In otherwords, panel unit root tests have a lower probability of Type II errors.
The second reason is that for firms in the same industry, errors terms (or ‘innovations’) are mostdefinitely related, especially for an industry with a small number of firms in a single country. As
such, certain panel unit methods allow for ‘cross correlation’ to be incorporated.
3.2 Hysteresis
The basic intuition of this model is to consider the possible existence of hysteresis in (relative)
market shares’ evolution. We define hysteresis as the existence of some permanent effects of atemporary shock (see Göcke 2002 for a detailed discussion). These shocks can be positive or
negative. They can include cost reductions (increases), labor productivity changes, new marketingstrategy, etc.
If we identify the presence of hysteresis for market shares, we can infer that efforts by individual
firms to innovate, produce at lower costs, and generally be more competitive than its rivals, are5

rewarded by an increase in (relative) market shares (domestic production). We can thus describe
the industry as being competitive within Japan. This may be the case even in the presence of a low
number of firms and/or an elevated HHI (e.g. above 0.25).
We envision that each firm likely has its own firm-specific mean (market share) and that there is no
single, common mean for all firms. That is to say, while the market shares may be stable over time,
we do not assume the market shares will be symmetric across firms. There are many reasons why
this may be the case. Early entrant or incumbent effects could be one reason, historical supplier
relationships between tire producers and (Japanese) car makers may be another, possible
geographic-based consumption patterns (Bridgestone and Yokohama have their headquarters inTokyo, while Sumitomo and Toyo are based in western Japan, Osaka and Kobe, respectively) andso on.
If there is instead no hysteresis, then one can infer that there is no lasting effect of the temporaryshock; we return to the (firm-specific) mean. Production market shares do not persistently depend
on a firm’s effort. Under certain assumptions, this implies that the level of competition is weak. To
put it differently, in the absence of hysteresis, it is ‘as if’ (relative) production decisions werecoordinated and no firm could durably deviate from that order/rank despite its individual
circumstances or efforts.
As an approximation and common practice in economics, hysteresis, which is a non-stationary
process, is modeled by a unit-root. 5 In the section below, we show that because we allow for
dynamic conditional convergence, our model guides us to test for the possibility of an intercept anda time-trend.
3.3 A simple model for unit roots in (relative) market shares
To implement the model, we consider the variable for market share, MKSit. To be precise, forreasons that will be come clear later in the text, we shall consider two measures. First, we look at
the log of the production market share for each firm. Then, second, we study the log of the relative
production market share (that is: the log of market share for firm i during year t, divided by theaverage market share for all nt firms during that year t) for firm i during period t. ( 𝑤𝑤ℎ𝑒𝑒𝑒𝑒𝑒𝑒 4 ≤ 𝑛𝑛𝑡𝑡 ≤6)

We assume that there could be a time-invariant equilibrium “idiosyncratic” production market share,that each firm i is moving toward over time∶ 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑒𝑒 . Within the industry, each firm can have a
different market share. Based on 40-plus years of history in the industry, we assume little or no freeentry for the Japanese tire industry.

This is only an approximation. For instance, see Göcke, p. 180-182 for a further distinction between ‘unit root
hysteresis’ and ‘genuine hysteresis’.
5

6

MKSit = 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑒𝑒 + 𝜇𝜇𝑖𝑖𝑖𝑖(1)

where 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑒𝑒 is a time-invariant equilibrium, idiosyncratic, market share for firm i, and where 𝜇𝜇𝑖𝑖𝑖𝑖
is the deviation from that equilibrium at period t for firm i.

At period t, 𝜇𝜇𝑖𝑖𝑖𝑖 is modeled as a linear function of a deterministic “time trend” variable (Tt) and astochastic process (Zit).
𝜇𝜇𝑖𝑖𝑖𝑖 = 𝑣𝑣𝑖𝑖0 + 𝛼𝛼1𝑖𝑖 𝑇𝑇𝑡𝑡 + 𝑍𝑍𝑖𝑖𝑖𝑖(2)This is a form of conditional convergence for (relative) production market shares. 6 Indeed, if firm
i’s market share is initially above its compensating differential — meaning that we have initially
MKSit > 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑒𝑒 — so that 𝜇𝜇𝑖𝑖𝑖𝑖 , the current deviation from equilibrium, is positive.
Eq. (2) describes the corresponding adjustment process.

When 𝜇𝜇𝑖𝑖𝑖𝑖 is positive, then the initial deviation from equilibrium, 𝑣𝑣𝑖𝑖0 , is also positive. To ensure
conditional convergence, when 𝑣𝑣𝑖𝑖0 is positive, then we must have 𝛼𝛼1𝑖𝑖 < 0.

So, in Eq. (2), 𝛼𝛼1𝑖𝑖 is the (deterministic) rate of convergence for firm i. It is negative when a firm’s
market share is above its equilibrium level and is positive when a firm’s market share is below its
equilibrium level.
Substituting (2) in (1): MKSit = 𝛼𝛼0𝑖𝑖 + 𝛼𝛼1𝑖𝑖 𝑇𝑇𝑡𝑡 + 𝑍𝑍𝑖𝑖𝑖𝑖
where 𝛼𝛼0𝑖𝑖 = 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑒𝑒 + 𝑣𝑣𝑖𝑖0 .

(3)(1- ϕiL)Zit = εit(4)

We then turn our attention to the stochastic process 𝑍𝑍𝑖𝑖𝑖𝑖 . For simplicity, we model Zit as an AR(1):with 0 ≤ ϕi ≤ 1, where L is the lag operator, and where εit ~ WN(0, σ2 )So, combining Eq. (3) and Eq. (4),
MKSit = 𝛼𝛼0𝑖𝑖 + 𝛼𝛼1𝑖𝑖 Tt + ϕiZit-1 + εit(5)

From Eq. (3), MKSit - 𝛼𝛼0𝑖𝑖 - 𝛼𝛼1𝑖𝑖 Tt = Zit(6)

MKSit-1 - 𝛼𝛼0𝑖𝑖 - 𝛼𝛼1𝑖𝑖 Tt-1 = Zit-1(7)

MKSit = 𝛼𝛼0𝑖𝑖 + 𝛼𝛼1𝑖𝑖 Tt + ϕi [MKSit-1 - 𝛼𝛼0𝑖𝑖 - 𝛼𝛼1𝑖𝑖 Tt-1] + εit(8)So, changing the ‘time counter subscript’ in Eq. 6,
From Eq. (5) and Eq. (7):Though the notation differs somewhat, these equations (1) and (2) follow Gallet and List (2001, p. 474-5) andCarlino and Mills (1996, p. 600) very closely.
6

7

Re-arranging Eq. (8) and using the lag-operator (L):
MKSit = (1 - ϕi)𝛼𝛼0𝑖𝑖 + 𝛼𝛼1𝑖𝑖 (1 - ϕi L)Tt + ϕi MKSit-1 + εit(9)Eq. (9) is the central equation of the model. It deserves some comments.
First, we observe what happens when we take the limits for ϕi, from Eq. (9),Case a) first, limit to 0:
lim 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 = 𝛼𝛼0𝑖𝑖 + 𝛼𝛼1𝑖𝑖 𝑇𝑇𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖

𝜙𝜙𝑖𝑖 →0(9a)

In this case, Eq. (9a), there is no unit-root, and thus no hysteresis. There is deterministic conditional
convergence to the time-invariant equilibrium idiosyncratic market shares. It follows logically that
this return to conditional convergence signals an absence of serious rivalry within the industry. Low
levels of rivalry or competition generally coincide with less movement in market shares (see Bikker
and van Leuvensteijn 2008). Moreover, in explicit cartels, firms often set certain agreed-uponmarket shares, often based on historical or otherwise entrenched market shares. Such was the casein the JFTC action against certain tire products in 2004 (see section 2 of this paper and in NDP2015). 7
Case b) second, limit to 1:
lim 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 = 𝛼𝛼1𝑖𝑖 + MKSit-1 + εit

𝜙𝜙𝑖𝑖 →1(9b)

With Eq. (9b), we observe that if ϕi →1 (unit root), then indeed Eq. (9) becomes a random walk witha firm-specific drift 𝛼𝛼1𝑖𝑖 . There is hysteresis: the industry exhibits competitive behavior, as it
rewards a firm’s production efforts and rivalry in general. We do not have conditional deterministic
convergence, nor do we have stochastic convergence. In other words, the process is not stationary.
Case c) finally, when 0 < ϕi < 1. This is the intermediate case.
The stochastic process is stationary, and we have a combination of deterministic and stochastic
convergence. That process is actually quite sophisticated and means that, following a firm-specificrandom shock (for example a lowering of the average cost), a firm’s (relative) production market
share will come back slowly over time to its time-invariant equilibrium differential. The difference,with the situation where ϕi = 0, is that, here, this random shock will have long lasting (but decreasingover time, ‘slow-decaying’) effects.
Second, we revisit Eq. (9) as:

In the case of the bid-rigging scheme in 2004 in tires sold to the Japanese Defense Agency, agreed-upon market
shares were very precise. E.g. Bridgestone was to get 35.55% of the market.

7

8

MKSit = 𝛽𝛽0𝑖𝑖 + 𝛽𝛽1𝑖𝑖 Tt + ϕi MKSit-1 + εit(10)where 𝛽𝛽0𝑖𝑖 = (1 - ϕi)𝛼𝛼0𝑖𝑖 and 𝛽𝛽1𝑖𝑖 = 𝛼𝛼1𝑖𝑖 (1 - ϕi L)

Subtracting MKSit-1 on both sides produces the basic Dickey - Fuller (1979) equation:
ΔMKSit = 𝛽𝛽0𝑖𝑖 + 𝛽𝛽1𝑖𝑖 Tt + (ϕi -1)MKSit-1 + εit(11)

ΔMKSit = 𝛽𝛽0𝑖𝑖 + 𝛽𝛽1𝑖𝑖 Tt + ρiMKSit-1 + εit(12)Finally, with (ϕi -1) = ρi

We can estimate Eq. (12), using OLS and test if ρi = 0 or not.
If ρi = 0, we conclude that the time-series, for firm i, has a unit-root.
Derived from Eq. (9), Eq. (12) guides our empirical strategy. Specifically, as we run the various
unit-root tests, first firm-specific, then as panel unit-roots, we allow for an intercept (𝛽𝛽0) and a timetrend (Tt).
Naturally, one can extend Eq. (12) by adding the (multiple) lagged values of the dependent variable
as regressors on the right-hand side, to obtain the Augmented Dickey-Fuller (ADF) equation. 8
4. Application
4.1. The data:
In this section, we present and discuss the statistical properties of our data set. We have also included
the concentration ratios, CR4 and CR5, and the Herfindahl-Hirschman Index (HHI).
(Refer to Table 1 at the end of the paper)
A note about the firms. The sum of all market shares must add to 1. However, like Giannetti (2015),
we consider only the (six, then five, then four) main players. They cover between 95% and 100%of the market over 40 years.
Based on the evolution of these market shares (see Figure 3 in Section 2 above, and descriptive
statistics in Table 2 below), we conclude there is a stability (persistence) of these dominant players.
This means that the Japanese tire industry is not a free entry market. Thus, we do not expect that all
market shares will go down over time. In addition, the Japanese tire market is de facto closed toforeign competitors (except perhaps for very specialised, high-performance tires).

The critical values differ, of course, from those of standard OLS because the ADF statistic does not have a normal
distribution.

89

We present the mean, maximum, minimum, and standard deviation values in Table 2.
(See Table 2 at the end of the paper)
We observe the dominant role played by Bridgestone which, in certain years, has had a production
market share above 50%. However, from a statistical point of view, Sumitomo is more intriguing.
It has, by far, the largest standard deviation (.046) of the group. This explains, in part, why it has
also the largest range (between minimum and maximum values): 16.1 % over the 1976-2015 period.
As we have expressed the production market shares as fractions of the whole market, thecorresponding HHI’s range is between 0 and 1: more precisely, 0 < 𝐻𝐻𝐻𝐻𝐻𝐻 ≪ 1. A monopolistic
industry would have an HHI of 1, while an n-firm industry in perfect competition would have an
HHI of 1/n. Thus, the higher the HHI, the higher the degree of concentration in the market. To
quantify the term ‘high’, we follow the standard United States Department of Justice (2010)
guidelines which state that ‘Unconcentrated Markets’ have HHI less than 0.15, ‘Moderately
Concentrated Markets’ have HHI between 0.15 and 0.25, and finally ‘Highly Concentrated Markets’
have HHI above 0.25.
Refer to Figure 4 at the end of the paper presenting HHI for the industry over time.
Note that the Japan Fair Trade Commission (JFTC) has similar, although more stringent criteria. It
stipulates that “not concentrated markets” have HHI less than 0.1, and that “not highly concentrated
markets” have HHI below 0.18. By extension, ‘highly concentrated markets’ have HHI of 0.18 or
above. For the Japanese tire industry, we observe that the HHI, during the period 1976 to 2015 isuniformly above 0.25. This means that according to both the US Department of Justice’s and the
JFTC’s classifications, the Japanese tire market is ‘highly concentrated’. 9
Of course, the possible non-competitive behavior of an industry is a separate issue, albeit notentirely independent, from the degree of concentration.

4.2 Empirical work
We conduct tests on two sets of data. First, we look at the domestic production market shares (inlogs) for individual firms (4.2a) and for the industry as a whole –as a panel– (4.2b). Second, we
consider the relative domestic production market shares (in log) for individual firms (4.2c) and forthe industry as a whole –as a panel– (4.2d). The study of the relative market shares is more complex
than it appears. Indeed, as mentioned before, two of the six players disappear, one being absorbed

For more details, see Dabbah and Lasok (2008) or visit the JFTC homepage at
http://www.jftc.go.jp/en/pressreleases/yearly_2003/apr/individual_000393.html.

910

in 2003 and the other exiting the market in 2010. So, the number of players is not constant over thedata sample: it decreases over time.
4.2a Unit-root tests on domestic production market shares (in logs)
We are using annual data from 1976 to up to and including 2015. Indeed, as noted before, thenumber of competitors in that industry has shrunk over time. Ohtsu was absorbed by Sumitomo in
2003, and Okamoto ceased production in 2010.
TMKSi = Tire Market Share, where i = 1 to 6.
So, we have:
TMKS1 = Bridgestone Market Share = BRMKSTMKS2 = Sumitomo Market Share = SUMKSTMKS3 = Yokohama Market Share = YOMKSTMKS4 = Toyo Market Share = TOMKSTMKS5 = Ohtsu Market Share = OHMKSTMKS6 = Okamoto Market Share = OKMKS
The corresponding (natural) logs are labelled as follows;
For Bridgestone: LBRMKS, for Sumitomo: LSUMKS, for Yokohama LYOMKS, for
Toyo: LTOMKS, for Ohtsu: LOHMKS, and for Okamoto: LOKMKS.
We conduct the Augmented Dickey-Fuller (ADF), GLS transformed Dickey-Fuller (DFGLS),
Phillips-Perron (PP), Elliot, Rothenberg and Stock (ERS) Point Optimal, and Ng and Perron (NP)tests. We also conduct the Kwiatkowski, et. al. (KPSS) test whose null is `stationarity` rather thanhaving a unit root.
For the unit root tests, we include a constant and a linear time trend. This is the most plausible
representation of our underlying model described in Section 3.
We present, in Table 3, a summary of the results. The detailed test results are available upon request.
(Refer to Table 3 at the end of the paper)

Three main results flow from Table 3. First, four of the five unit-root tests reject the null hypothesis(of a unit root) for the dominant player in the industry (Bridgestone). Second, five out of five unit-

11

root tests do not reject the null hypothesis of a unit root for the smaller firms in the industry. Finally,
regarding stationarity, again, there seems to be a difference between the dominant player, for whom
the stationarity hypothesis is not rejected, and the (three of the five) other firms, for whom thestationarity hypothesis is rejected.
Based on these unit-root tests on individual firms, we can tentatively -and cautiously- conclude that,for that industry and that dataset, the dominant player (Bridgestone) seems to behave as if it wereabove -and undisturbed by- the more intense rivalry and competition apparently taking place
between the five smaller firms.

4.2b Panel Unit-root tests on domestic production market shares (in logs)
In Table 4 (see end of paper), we present a summary of the various panel unit-root tests results.
The result, which indicates that we cannot reject the hypothesis of a [common] unit-root process, israther unsurprising: it was foretold by the tests on individual series (Section 4.2a).
Table 4’s data imply that we do indeed have an overall active rivalry within that industry. However,tests on individual firms have indicated that this rivalry may be more prominent among the smallerfirms than the industry leader.
4.2c Unit root tests on the relative domestic production market shares (in logs)
Here, we are asking a more stringent question: is there is natural tendency for relative market shares
to conditionally converge? In the absence of conditional convergence, we posit there is a form ofhysteresis (as represented by a unit root) and therefore intense levels of competition.
First, we create the series (1976-2015) ATMKS = Average Tire Market Share
1

ATMKSt = 𝑛𝑛 ∑𝑛𝑛𝑖𝑖=1 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 where n = 6 from 1976 to 2002, n = 5 from 2003 to 2009, and n = 4 from2010 to 2015.

We then create the ‘log of relative market shares’ series (1976-2015):
LRMKSit = log(TMKSit/ATMKSt).
These are the six time-series we consider in our unit-root tests.

Comments on Figure 5 (see graph at end of the paper): we observe a clear dichotomy in the relative
market shares. Only Bridgestone has log relative market share values close to 1.16 and declining toaround 0.57. This means that the Bridgestone’s market share has oscillated between 3.19 and 1.86
times the average market share in the industry for that year. Bridgestone has been clearly the12dominant company. Inversely, Sumitomo, Yokohama, Toyo, and Okamoto (until 2009), and Ohtsu(up to 2002) have had relative market share values mostly below zero. This means that their relative
market shares (in log) have been mostly below the average market share in the industry.
The second comment relates to the shape of the graphs. While it appears that the dominant player(Bridgestone) becomes slightly less dominant after 2000 (and until 2015), that result must be
qualified by the fact that the number of competitors has gone down during that period (from six tofour). This has raised the average market share (and, thus, our denominator in our ‘relative market
share’ formula) from around 0.167 to 0.2 in 2003, and to 0.25 in 2010. The point is equally valid
for the firms Sumitomo, Yokohama, and Toyo, and to a lesser extent for Okamoto.
This distinction is due to the fact that we are facing a declining number of firms in that industry.
This drives the ‘average market share’ upwards. The ‘average market share’ is on the denominatorof the ‘relative market share.’ So, as the denominator goes up, the ‘relative market share’ goes down
(ceteris paribus).
In Table 5, (see end of paper) we present a summary of the various unit root tests done on the (logof the) relative market shares of individual firms. The detailed test results are available uponrequest.
The message from Table 5 (see end of paper) is more complicated to interpret. Indeed, there is no
rejection of the unit-root hypothesis in 25 of the 30 possible cases (five 5 tests on six firms). As forthe stationarity KPSS test, the null hypothesis is rejected in four (4) of the six (6) cases.
As mentioned above, the sharp decrease in the number of firms (from six to four) over the period
concerned explains a large part of this instability in the relative market shares.
Viewed for the industry as whole, the analysis is confirmed. The relative market shares in the tire
industry have exhibited instability. Consider Table 6 (at end of paper), which summarizes various
panel unit-root tests for the relative market shares. We see that in four of the five tests, we do not
reject the hypothesis of a unit-root.

Conclusions and Implications
The Japanese tire market has had a small number of players over a long period of time with virtuallyno new entrants. The market is also characterized by a high HHI (though the high HHI is mostly
driven by Bridgestone’s roughly 50% market share). So, it is clear that the market is highly
concentrated. What is not clear is whether there is a high or low degree of competition among thehandful of firms. Indeed. It is well-known from theory that even with a small number of firms,
intense competition is possible. As such, we need empirical tools to help assess the degree ofcompetition.

13

We have presented a relatively simple, yet powerful, way to look at competitive behavior and rivalry
within an industry (or market segment) by testing for the presence of unit-roots (for single series
and in a panel) in market shares. We have applied this approach to the Japanese tire industry.
However, our method can easily be applied to other industries (or market segments) and, as such,should be useful for academics, portfolio managers and regulators.
We also test for unit roots of the relative market shares. The declining number of competitors overtime makes the use of relative market shares complicated, but not un-rewarding. Results obtained
here may be relevant when comparing with other related industries and future work.
The main finding is the dichotomy between the behaviors of the leading/dominant firm
(Bridgestone) and that of the other, much smaller, tire firms.
Based on these unit-root tests on individual firms, it appears that the dominant player (Bridgestone)
seems to behave differently than the five smaller firms. In particular, its market share does not havea unit root which suggests convergence to a historical mean. The other firms all do, however, appear
to have a unit root, which may indicate more competition and rivalry amongst the smaller
competitors.
While empirical work (in English) on the Japanese tire market is scarce, at least one previous work
(NDP 2015) finds a reasonable degree of competition as compared to other industries. Using a very
different methodology here, we again find that the firms appear to be acting competitively. However,as mentioned above, when we break out the individual behaviors of the firms (something that cannot
be done with the method used in NDP), we reveal a more subtle picture of the market. It appears
that the market behemoth, Bridgestone, may be quite stable and may behave as a market-leader,while the other smaller firms fight for market share. While we cannot be certain of this, it doessuggest further avenues for research in this industry.

Appendix: Data
Constructing consistent market shares
Constructing a consistent series of market shares for the six major firms over 40 years presented
certain obstacles. Japan has two books produced annually which report market-shares by firms for
hundreds and even thousands of industries, including tires for automobiles. Those two books are
Yano’s Japan Market Share Dictionary (Nihon Maaketto Shea Jiten) and the Nikkei Market ShareInvestigation (Nikkei Shea Chousa). The Nikkei book only began being published a few years ago,so could not be used.
While the Yano books go back to 1980 and even a little further, there is one critical flaw. Up until
and including 1997, Yano reports market shares based on quantity (tons of rubber). However, from14

1998 and onwards, Yano reports market shares in yen (i.e. value) and calls it ‘value of production’(‘sei-san daka’). (In the data up until 1997 it is not made clear whether this is ‘produced’ or ‘sold’.)
As such, we were forced to construct our own shares for the early years. One promising alternative
was to use the Tire Yearbooks (‘Taiya Nenkan’), published annually by JATMA (Japan Automobile
Tire Manufacturing Association). This, however, presented numerous challenges as well. While
those yearbooks do provide firm-level data for production, such as ‘capacity’ and sales, it is notconsistent across years, nor across firms. For just one of many examples, in more recent years,
Yokohama production data is reported in ‘number of tires’, but for the other firms it is reported in‘tons of rubber’. As such, the Yearbook data was unusable.
Our final market shares series is based in part of the Annual Securities Reports (yuuka-shouken
houkokusho) of Japanese firms’ data obtained by the ‘Eol’ online database. This reports total sales(uri-age daka) in yen, but also reports of sales of just the ‘tires and tubes’ segment of these rubber
products firms. It was this data that was used to construct the early market shares data.
There was a last hurdle, however. Since the 2000s Japan firms began switching to submitting annual
reports on a consolidated basis, in which all sales of tires of the firm worldwide, including foreignsubsidiaries, are reported jointly. As such, we cannot determine the market shares based on Japantires made in Japan from the new, consolidated annual reports.
Fortunately, Yano’s Market shares have always also pertained only to sales of tires produced inJapan. Thus, we did the following. For data up to and including 1997, our market shares are basedon the annual reports’ sales data. From 1998 and onwards, we used the data from Yano, which also
reports sales in yen. There was naturally a concern in splicing data from two different sources.
However, our concerns were allayed when we confirmed that the 1998 market shares which weconstructed from the annual reports were exactly the same to a decimal point as the Yano data. That
is to say, the market shares for all six of the firms were: 15.1% (Yokohama), 9.9% (Toyo), 7.3%
(Ohtsu), 1.6% (Okamoto), 51.8% (Bridgestone) and 14.3% (Sumitomo) from both our calculations
from the annual reports and the market share figures reported in the Yano dictionary for 1998.
A last comment is in order with respect to the global nature of these firms and this market. A
relatively small quantity of tires is imported into Japan (about 10%, of the Japanese market, basedon Japan Customs data and total market sales data from Yano.) The largest share of imports is from
China followed by others such as Thailand, Indonesia, etc. Many of these imports are fromsubsidiaries abroad (e.g. Bridgestone has six plants in China and four in Thailand). On the other
hand, Japanese tires produced in Japan are exported in large amounts. Bridgestone exports a lot
more tires abroad than it sells at home (quantity-based data in 2016, from the Tire Yearbook) share.
Note that its domestic sales in Japan are still massive and dominant. Sumitomo exports a bit moreabroad than it sells at home. Yokohama sells about the same home and abroad. Toyo sells more of
its Japan-made tires in Japan than are exported abroad.

15

In this paper, as in the market shares presented in Yano (and Nikkei), we are looking at shares oftires made in Japan, not only sold in Japan. While precise, consistent market share data on tires soldin Japan only is not available in any of the above sources, we feel comfortable using this broader
definition of market share. If there is any collusion, explicit or implicit, it must certainly be, firstand foremost, among the Japanese firms based in Japan. If the Japanese tire industry is behaving
like a cartel, it seems natural that they would be seeking to stabilizes sales, production, and indeed,
employment, based on total production by its factories in Japan, if possible, rather than ignoring theexport sales, which are sizeable.

References
Bikker, J.A. and van Leuvensteijn, M. (2008). Competition and efficiency in the Dutch life
insurance industry, Applied Economics 40(16): 2063-2084.
Breitung, J. and Meyer, W. (1994). Testing for unit roots in panel data: are wages on different
bargaining levels cointegrated? Applied Economics 26(4): 353-361.
Carlino, G. A. and Mills, L. O. (1996). Testing neoclassical convergence in regional incomes andearnings. Regional Science and Urban Economics26(6): 565–590.
Carrion-I-Silvestre, J. L., Sanso-I-Rossello, A. and Ortuno, M. A. (2001). Unit root and stationarity
tests’ wedding. Economics Letters 70(1): 1-8.
Dabbah, M.M., and Lasok, P. (2008). Merger Control Worldwide: Second Supplement to the First
Edition. Cambridge University Press.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time
Series with a Unit Root. Journal of the American Statistical Association 74(366): 427–431.
Elliot, G., T.J. Rothenberg, and J.H. Stock (1996). Efficient Tests for an Autoregressive Unit Root.
Econometrica 64(4): 813-836.
Gallet, C. A. and List, J. A. (2001). Market share instability: an application of unit root tests to thecigarette industry. Journal of Economics and Business 53(5): 473-480.
Guelle, F. (2002) The Tire Industry: A Comparative Study About the Strategy ofInternationalization of French and Japanese Multinationals. In: Horaguchi H. and Shimokawa K.
(eds.) Japanese Foreign Direct Investment and the East Asian Industrial System. Tokyo: Springer.
Giannetti, C. (2015). Unit roots and the dynamics of market shares: an analysis using an Italianbanking micro-panel. Empirical Economics 48(2): 537–555.

16

Göcke, M. (2002). Various Concepts of Hysteresis Applied in Economics. Journal of Economic
Surveys 16(2): 167-188.
Granger, C. W. J. and Swanson, N. R. (1997). An introduction to stochastic unit-root processes.
Journal of Econometrics 80(1): 35-62.
Im, K.S., Pesaran, M.H. and Shin, Y. (2003). Testing for Unit Roots in Heterogeneous Panels.
Journal of Economics 115(1): 53-74.
Klepper, S. and Simons, K. L. (2000). The Making of an Oligopoly: Firm Survival andTechnological Change in the Evolution of the U.S. Tire Industry. Journal of Political Economy 108(4): 728-60.
Kwiatowski, D. P.C.B. Phillips, P. Schmidt and Y. Shin (1992). Testing the null hypothesis ofstationarity against the alternative of a unit root. Journal of Econometrics 54(1-3): 159-178.
Levin, A., C. F. Lin, and Chu, C. (2002). Unit Root Tests in Panel Data: Asymptotic and FiniteSample Properties. Journal of Econometrics 108(1): 1-24.
Lo, A.W. MacKinlay, A. C. (2002). A Non-Random Walk Down Wall Street. Princeton: Princeton
University Press.
MacKinnon, J. G. (1996). Numerical Distribution Functions for Unit Root and Cointegration Tests.
Journal of Applied Econometrics 11(6): 601-618.
Maddala, G.S. and Kim, I-M (1999). Unit Roots, Cointegration and Structural Change. Cambridge,
UK: Cambridge University Press.
Ng, S., and P. Perron (2001). Lag Length Selection and the Construction of Unit Root Tests with
Good Size and Power. Econometrica 69 (6): 1519-1554.
Nguyen, X. D., de Vanssay, X., and Parsons C. R. (2015). The Japanese Automobile Tire Industry
under Scrutiny. International Journal of the Economics of Business 22(1): 141-162.
Parsons, C. R., Nguyen, X. D. and X. de Vanssay (2018). New Evidence of Market Competition in
the Japanese Tire Industry using Market Share Analysis. CESSA Working Paper 2018-02.
Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica
57(6): 1361-1401.
Pesaran, M. H. (2015). Time Series and Panel Data Econometrics. Oxford University Press.
Phillips, P. C. B. and Perron, P. (1988). Testing for a Unit Root in Time Series
Regression. Biometrika 75(2): 335–346.

17

Rogoff, K. (1996). The Purchasing Power Parity Puzzle. Journal of Economic Literature 34(2):647-688.
Sephton P. (2008). Market shares and rivalry in the U.S. cigarette industry. Applied Economics
Letters 15(6): 417-422.
Tire Yearbook (Taiya Nenkan), 1991~2017, Published annually by Tokyo: RK Tsushinsha.
United States Department of Justice (2010). Horizontal Merger Guidelines (08/19/2010),[PermaLink:
https://www.justice.gov/atr/horizontal-merger-guidelines-08192010#2d
https://perma.cc/SQ9T-9R8V ].

18

16000

1600

14000

1400

12000

1200

10000

1000

8000

800

6000

600

4000

400

2000

200

0

Thousand tons of rubber

Thousand units

Figure 1. Japan’s production volume of tires and automobiles

Automobile Production

2016

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

0

Tire Production (for automobile) tons of rubber

Source: Automotive Yearbook and Tire Yearbook.

19

Auto Makers

Figure 2. Tire Distribution Channels
Original
Equipment

Tyre
MakersReplacement

Exports

Distributors

Tyre Dealers

Tyre
Specialty
Shops
Car
Dealers
Service
Stations
Car Repair
Shops
Auto Parts
Retailers
OthersCars forExportCars for
Domestic
Use

Large
Users

Business
Users
PrivateUsers
DirectExportTradingcompanies

Source: NDP (2015)

20

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

19761978198019821984198619881990199219941996199820002002200420062008201020122014
Bridgestone

Sumitomo

Yokohama

Toyo

Okamoto

Ohtsu

Figure 3. Japanese Tire Manufacturers’ Domestic Production: Market Shares

21

Table 1: Domestic production market shares, concentration ratios, and HHI index
Year

Bridgestone

Sumitomo

Yokohama

Toyo

Ohtsu

Okamoto

CR4

CR5

HHI

1976

0.492

0.121

0.208

0.113

0.055

0.012

0.90

0.98

0.32

1977

0.497

0.119

0.204

0.113

0.054

0.013

0.90

0.98

0.32

1978

0.501

0.124

0.195

0.112

0.054

0.014

0.90

0.98

0.32

1979

0.499

0.131

0.188

0.116

0.051

0.014

0.90

0.98

0.32

1980

0.493

0.140

0.192

0.119

0.042

0.014

0.90

0.98

0.32

1981

0.481

0.155

0.187

0.126

0.039

0.013

0.91

0.98

0.31

1982

0.471

0.161

0.180

0.115

0.059

0.015

0.93

0.97

0.30

1983

0.459

0.165

0.179

0.118

0.064

0.015

0.91

0.96

0.29

1984

0.457

0.161

0.180

0.120

0.066

0.016

0.91

0.96

0.29

1985

0.474

0.149

0.182

0.113

0.064

0.018

0.92

0.97

0.30

1986

0.472

0.149

0.179

0.114

0.066

0.019

0.94

0.98

0.29

1987

0.476

0.148

0.179

0.114

0.067

0.016

0.94

0.98

0.30

1988

0.485

0.148

0.175

0.110

0.067

0.016

0.92

0.98

0.30

1989

0.498

0.141

0.171

0.106

0.064

0.019

0.93

0.99

0.31

1990

0.488

0.135

0.180

0.110

0.069

0.019

0.94

0.99

0.31

1991

0.471

0.142

0.184

0.109

0.073

0.020

0.92

0.97

0.29

1992

0.466

0.147

0.183

0.108

0.079

0.018

0.92

0.97

0.29

1993

0.463

0.150

0.187

0.104

0.082

0.015

0.92

0.98

0.29

1994

0.468

0.148

0.176

0.108

0.085

0.016

0.90

0.97

0.29

1995

0.476

0.143

0.175

0.104

0.085

0.017

0.90

0.97

0.30

1996

0.499

0.144

0.166

0.101

0.070

0.020

0.87

0.95

0.31

1997

0.506

0.144

0.161

0.102

0.071

0.016

0.87

0.95

0.32

1998

0.518

0.143

0.151

0.099

0.073

0.016

0.91

0.98

0.33

1999

0.532

0.139

0.147

0.100

0.068

0.015

0.92

0.99

0.34

2000

0.521

0.148

0.148

0.101

0.069

0.013

0.92

0.99

0.33

2001

0.424

0.190

0.180

0.127

0.063

0.016

0.92

0.98

0.27

2002

0.422

0.194

0.178

0.127

0.063

0.016

0.92

0.98

0.27

2003

0.501

0.189

0.172

0.121

na

0.017

0.98

na

0.33

2004

0.489

0.186

0.176

0.132

na

0.017

0.98

na

0.32

2005

0.495

0.188

0.171

0.125

na

0.021

0.98

na

0.33

2006

0.494

0.200

0.175

0.113

na

0.018

0.98

na

0.33

2007

0.509

0.189

0.180

0.105

na

0.017

0.98

na

0.34

2008

0.498

0.211

0.176

0.102

na

0.013

0.99

na

0.33

2009

0.501

0.201

0.181

0.104

na

0.012

0.99

na

0.34

2010

0.482

0.247

0.179

0.092

na

0.000

1.00

na

0.33

2011

0.486

0.249

0.173

0.092

na

na

1.00

na

0.34

2012

0.464

0.270

0.185

0.081

na

na

1.00

na

0.33

2013

0.449

0.280

0.184

0.087

na

na

1.00

na

0.32

2014

0.444

0.280

0.185

0.093

na

na

1.00

na

0.32

2015

0.440

0.280

0.183

0.097

na

na

1.00

na

0.31

22

Source: Authors’ calculations based on ‘Annual Securities Reports’, Yano (Nihon Maaketto
Shea Jiten). See Appendix for details on the data.

Table 2: Domestic production market shares: Descriptive statistics
Bridgestone

Sumitomo

Yokohama

Toyo

Ohtsu

Okamoto

HHI

Mean

0.481

0.174

0.178

0.109

0.065

0.016

0.312

Median

0.486

0.150

0.180

0.109

0.066

0.016

0.316

Maximum

0.532

0.280

0.208

0.132

0.085

0.021

0.339

Minimum

0.422

0.119

0.147

0.081

0.039

0.000

0.268

Std. Dev.

0.025

0.046

0.012

0.011

0.011

0.004

0.019

Observations

40

40

40

40

27

35

40

23

Figure 4. The Herfindahl-Hirschman Index (HHI) for the Japanese tire industry
1.0

0.8

HHI (based on Japanese Domestic Tire Production Market Shares)

0.6

0.4

0.2

HHI > .25 (“Highly concentrated markets”)

0.0
1980

1985

1990

1995

2000

2005

2010

2015

Sources: Authors’ calculations (see Appendix).

24

Table 3: Unit root and stationary tests (summary results)
Series are the log of the market share
Tests were done with constant and time trend (see equation 12 in Section 3 of the text)
Null:
Series is stationary

Null:
Series has a unit root
ADF
(ERS)

DF-GLS

P-P

Bridgestone

R

R

DNR

R

R

DNR

Sumitomo

DNR

DNR

DNR

DNR

DNR

R

Yokohama

DNR

DNR

DNR

DNR

DNR

R

Toyo

DNR

DNR

DNR

DNR

DNR

DNR

Ohtsu

DNR

DNR

DNR

DNR

DNR

DNR

Okamoto

DNR

DNR

DNR

DNR

DNR

R

Tests
Firms

ERS
Ng-Perron
Point Optimal

KPSS

Notes: `R` indicates that the null hypothesis is rejected and `DNR` that null hypothesis
is not rejected. Significance level (α) at 5%. Small sample critical values are basedon MacKinnon (1996). For the KPSS test, only asymptotical critical values were used.

25

Table 4: Panel (Group) Unit root tests on market shares (in logs)(Summary results)
Series: LOKMKS, LBRMKS, LOHMKS, LSUMKS, LTOMKS, LYOMKSAutomatic lag length selection based on SIC: 0 to 1
Sample: 1976 2015
Exogenous variables: Individual effects, individual linear trends
Metthod of Test

Statistic

Prob. *

Crosssections

Levin, Lin, Chu t-stat-0.5858

0.279

6

214

DNR

Breitung t-stat-0.65637

0.2558

6

208

DNR

Im, Pesaran and Shin W-stat-0.30418

0.3805

6

214

DNR

ADF- Fisher Chi-square

13.30480.3473

6

214

DNR

PP- Fisher Chi-square

9.03548

0.6999

6

215

DNR

Null: Unit RootAssumes common unit root process

Obs Conclusion

Null: Unit RootAssumes individual unit root process

Notes: * Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution.
All other tests assume asymptotic normality. ‘DNR’ stands for ‘do not reject’ the null.

26

Bridgestone relative Market Share (in log)

Yokohama relative Market Share (in log)

1

1

0

0

-1

-1

-2

-2

-3

-3
1980

1985

1990

1995

2000

2005

2010

2015

1980

Sumitomo relative Market Share (in log)

1985

1990

1995

2000

2005

2010

2015

Toyo relative Market Share (in log)

1

1

0

0

-1

-1

-2

-2

-3

-3
1980

1985

1990

1995

2000

2005

2010

1980

2015

1985

1990

1995

2000

2005

2010

2015

Ohtsu relative Market Share (in log)

Okamoto relative Market Share (in log)
1

1

0

0

-1

-1

-2

-2

-3

-3
1980

1985

1990

1995

2000

2005

2010

2015

1980

1985

1990

1995

2000

2005

2010

2015

Figure 5. Relative market shares (in natural logarithms)

27

Table 5: Unit root and stationary tests (summary results)
Series are the log of the RELATIVE market share
Tests were done with constant and time trend (see equation 12 in Section 3 of the text)
Null:
Series is stationary

Null:
Series has a unit root
ADF
(ERS)

DF-GLS

P-P

Bridgestone

R

R

DNR

R

R

DNR

Sumitomo

DNR

DNR

DNR

DNR

DNR

R

Yokohama

DNR

DNR

DNR

DNR

DNR

R

Toyo

DNR

DNR

DNR

DNR

DNR

DNR

Ohtsu

DNR

DNR

DNR

DNR

DNR

DNR

Okamoto

DNR

DNR

DNR

DNR

DNR

R

Tests
Firms

ERS
Ng-Perron
Point Optimal

KPSS

Notes: `R` indicates that the null hypothesis is rejected and `DNR` that null hypothesis
is not rejected. Significance level (α) at 5%. Small sample critical values are basedon MacKinnon (1996). For the KPSS test, only asymptotical critical values were used.

28

Table 6: Panel (Group) Unit root tests on Relative market shares (in logs)(Summary results)
Series: LOKMKS, LBRMKS, LOHMKS, LSUMKS, LTOMKS, LYOMKSAutomatic lag length selection based on SIC: 0 to 1
Sample: 1976 2015
Exogenous variables: Individual effects, individual linear trends
Metthod of Test

Statistic

Prob. *

Crosssections

Levin, Lin, Chu t-stat-1.71505

0.0432

6

214

R

Breitung t-stat-0.27704

0.3909

6

208

DNR

Im, Pesaran and Shin W-stat-0.34241

0.366

6

214

DNR

ADF- Fisher Chi-square

14.79880.2526

6

214

DNR

PP- Fisher Chi-square

8.66892

0.7309

6

215

DNR

Null: Unit RootAssumes common unit root process

Obs Conclusion

Null: Unit RootAssumes individual unit root process

Notes: * Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution.
All other tests assume asymptotic normality. ‘DNR’ stands for ‘do not reject’ the null.

29