Interaction between trade, conflict and cooperation:
the case of Japan and China
Shiro Armstrong *Crawford School of Economics and Government
Australian National University
September 2007

Abstract
The complex interaction between trade and politics is analysed using Granger
causality tests. The purpose is to determine the presence and direction of causation
between trade and political events, both positive and negative, and to gauge an idea ofthe lag length of causality. The focus of the study is on the Japan-China relationshipwhere trade is growing quickly despite long standing political distance between thetwo countries. The other important political and economic partner for both countries,
the United States, is also examined by way of comparison. Evidence of Granger
causality is found with the presence of lag lengths, and direction of causality being
different for each bilateral relationship. The economic relationship underpins andconstrains the political relationship between Japan and China while an increase inpositive political news and a decrease in negative political news promote trade tosome degree.

JEL Classifications: C32; F10; F59
Keywords: Conflict and Cooperation; Japan; China; Trade; Granger Causality;
Nonlinear Causality
Contact: Crawford School of Economics and Government, Australian National University.
Email: shiro.armstrong@anu.edu.au*

I am grateful to Peter Drysdale, Ligang Song, Jane Golley, Cezary Kapuscinski,
Vikas Godara, Ryan Manuel, Bruce Chapman and Trevor Breusch for valuablecomments and suggestions, and Pim Chanthapun for technical assistance.

CONTENTS
1. INTRODUCTION ....................................................................................................................................... 1
2. TRADE, CONFLICT AND COOPERATION ................................................................................................... 3
Empirical Evidence............................................................................................................................... 6
3. DATA ...................................................................................................................................................... 9
Conflict/Cooperation Events Data........................................................................................................ 9
Trade data........................................................................................................................................... 13
4. GRANGER CAUSALITY .......................................................................................................................... 14
Why Granger Causality and not Cross-Sectional Analysis?............................................................... 16
Results................................................................................................................................................. 17
Net cooperation as the appropriate measure of political distance ..................................................... 26
Nonlinear analysis .............................................................................................................................. 26
5. DISCUSSION .......................................................................................................................................... 27
The Japan-China relationship ............................................................................................................ 28
Further work ....................................................................................................................................... 30
6. CONCLUSION ........................................................................................................................................ 31
REFERENCES............................................................................................................................................. 32
APPENDIX ................................................................................................................................................. 35
Appendix A Conflict data for Japan-China......................................................................................... 35
Appendix B Nonlinear Granger Causality .......................................................................................... 37

1. Introduction
The bilateral relationship between Japan and China is often characterised as one ofcold politics and hot economics; the economic relationship continues to boom while
historical issues and political tensions continue to strain the relationship.
After six years of suspended visits between leaders, the last year has seen an effort byboth sides to mend relations, perhaps in recognition of the growing importance of
their economic relationship. Trade in 2006 was as high as US$211 billion on someestimates. China is Japan’s largest trading partner (measured in terms of the sum of
trade flows both ways) and Japan is China’s third largest partner, after the UnitedStates and Europe.
Do political events have any effect on the economic relationship? Does increased
trade and dependence cause countries to get along better? Or do the increased
interactions through trade, or asymmetry in trade such as that seen in the UnitedStates-China case, cause political conflict to rise?
The view that increased trade and economic interdependence will result inincreasingly cooperative political relations is shared by many scholars and policy
makers (Mansfield and Pollins, 2004). There is also a view that increased imbalancein trade and economic interdependence will cause political tensions to rise.
The purpose of this paper is to investigate the relationship between trade and politicalconflict (or cooperation) between China and Japan. The paper also looks at bothcountries’ relationships with their other major trading partner, the United States. The
expectation is that the economic relationship is interdependent with how wellcountries are getting along. The Japan-China relationship is expected to be different
from both the United States-China and the United States-Japan relationships, the latterof which is a lot more stable politically.
Although the probability of war, or high intensity conflict, between Japan and China
is low, there are occasional flash points in the relationship such as Taiwan, territorial
claims and history that have the potential to escalate. Extreme conflict (economic and1

trade sanctions, or even war) and high level cooperation (such as customs union or
security alliance) are at the opposite ends of the spectrum in a scale of conflict orcooperation events. These extremes do not occur in the China-Japan relationship overthe period analysed in this study but the relationship is one of low intensity conflictand cooperation.
China’s relationship with its other major trading partner, the United States, is also
complicated but does not perhaps share the historical complexity of the Japan-China
relationship. The asymmetry in trade flows, stemming from the growing US bilateral
trade deficit with China, is straining relations. On the other hand, the mutual
recognition of the importance of the relationship, and the increased trade andinterdependence, is causing the two to negotiate their way through the imbalancecarefully and there are substantial efforts to keep relations stable.
Conflict is defined in this paper as an unfriendly or negative political action or stanceof one country towards another and can be thought of interchangeably as the negativeof cooperation.
An index of net cooperation is created. This index is a measure of cooperation minus
conflict, and is used as a single variable to describe the political distance between two
countries. Although not all events or news in the data are created by the actions ofauthorities on either side, and many are instead events that are a product of
independent actors, they all have some positive or negative political impact. Thus theindex of net cooperation derived from the record of these events can serve as ameasure of the political distance between the two countries in the study.
An alternative net conflict variable, a measure of conflict minus cooperation, iscommonly used in the literature. This differencing of the two variables imposes some
limitations and assumptions on the event data and can alter results2. A distinction ismade between trade and interdependence in this study and, unlike previous studies,both are tested for their relationship with net cooperation here.

2

I thank Bruce Chapman for this point.
2

The study uses monthly data up to 2004 whereas previous studies for other countries
have used quarterly data, analysed much earlier periods and not examined therelationship between Japan and China. Sub-periods of the data are analysed for theJapan-China relationship to capture any changing dynamics in the trade-cooperation
relationship. In addition nonlinear causality is tested to find further evidence of theinterdependence of conflict/cooperation and trade.
The next section sets out the main arguments in the trade-conflict debate and thenreviews some of the growing empirical evidence supporting various arguments.
Section 3 provides a description and explanation of the conflict/cooperation and tradedata. Section 4 explains the Granger causality model, presents the results anddiscusses other tests that were carried out. Section 5 discusses implications andSection 6 concludes.

2. Trade, Conflict and Cooperation
There has been a rich debate for centuries about the link between conflict/cooperationand economic interdependence (Mansfield and Pollins, 2003). In the last few decades,
the debate has seen the growth of a vast amount of systematic empirical analysis
which has added to the understanding of the issue but there remain many unansweredquestions (Mansfield and Pollins, 2001). The broad line of thinking associated withthe ‘liberal’ school is that as trade and interdependence between nations grow, there isa greater opportunity cost to conflict and so the chances of conflict are reduced.
Another view point, associated with the realist school, argues that countries also go to
war to acquire resources as an alternative to international trade and that asymmetries
in trade relationships can cause tensions to rise. These views, and the supporting
empirical evidence, is briefly reviewed below, as are other factors affecting the link
between conflict and interdependence such as the proximity of countries and the levelof their political liberalisation.
Interdependence can mean vulnerability towards another nation (for example, throughexposure to a dominant resource or strategic goods supplier) and/or sensitivity todependence (for example, through the effect of economic shocks such as inflation or
exchange rate volatility in one country on another) and quite often trade flows are the3

best quantitative measure of these independencies available 3. There is of course a highcorrelation between trade and interdependence as a large component ofinterdependence is due to trade. But interdependence will generally include otherforms of trade, such as in services, as well as the flow of people and investment.
As early as 1748 Montesquieu, the French social commentator and political thinker,
famously said

Peace is the natural effect of trade. Two nations who traffic with each otherbecome reciprocally dependent; for if one has an interest in buying, the otherhas an interest in selling: and thus their union is founded on their mutual
necessities’ (de Secondat, 1748 [1989], p. 316).
This is an insight that is often quoted in studies of interdependence and cooperation.
The basic idea is that mutual dependence leads to increased cooperation and adecrease in hostilities, or conflict. Cooperation has a positive relationship withinterdependence or trade.
Trade flows are influenced significantly by broad political relations of amity andenmity between nations (Pollins, 1989b). The argument for a positive relationshipbetween trade and cooperation runs both ways: trade fosters peace and peace fosters
trade. As trade increases between two countries, the opportunity cost of conflict rises.
Trade is seen as being driven by politics and the political distance between nations. It
is easy to see that allies may trade more and sign trade agreements (for example, theNorth American Free Trade Agreement) whereas in general countries reduce tradewith their enemies (for example, the United States and Cuba).
Countries, and the actors within those countries, trade because they gain from it and it
is in their interest to do so. Disputes and conflict may lead to a loss in trade andtherefore a loss in welfare. Highly interdependent states rarely engage in full blown
war because the costs of doing so are too high.

3

For a discussion of this see Mansfield and Pollins (2001).
4

Hirschman (1945) describes the politics of foreign trade and notes that there is ‘thepossibility of using trade as a means of political pressure…in the pursuit of power’ (p.
xvi). The gains from trade between nations can have unequal distribution withincountries as well as between countries (Hirschman, 1945/1980). This unequal
distribution of gains from trade can lead to a change in the structure of power within acountry and between countries. Therefore, asymmetry in the gains from trade, which
occurs quite naturally and regularly, especially if it causes a shift in power relations,
can lead to military conflict in extreme cases (Gilpin, 1981; Levy, 1989; Mearsheimer,
1990).
An example of conflict (mainly low intensity conflict) arising from asymmetric tradeis the case of the United States and the trade disputes it has with some of its largest
trade partners – Japan in the 1980s and the current United States-China tensions overa rapidly growing bilateral trade imbalance.
There have been other characteristics identified that influence the direction of conflictand cooperation. Countries with elected democracies, it is argued, do not go to war
with each other 4 (Kant, 1795/1999; Wright, 1942); and there are ambiguous effects ofenduring rivalries on the trade-conflict relationship.
Distance is very important in analysing the link between political distance andeconomic interdependence (O’Loughlin, 1993; Robst et al., 2006). On one hand,
distance is an important determinant of trade and has been used extensively in gravity
models of trade since Tinbergen (1962) to explain trade dependence. On the other
hand, closer countries interact more with each other and neighbours tend to have moredisputes (Vasquez, 1995). Further, neighbours may be more likely to have enduring
rivalries (Stinnett and Diehl, 2001). The origin of such neighbourhood rivalries can
include territorial disputes, disputes from increased trade (the closer countries are, thehigher trade is, on average, as the gravity model attests) and the ability of countries towage war on a neighbour as opposed to a distant country.

4

For a discussion of the literature, including empirical studies, see Polachek andSeiglie (2006).
5

Of course countries that trade often have many interactions and it is easy to see howeven the closest of allies have disputes and low intensity conflict from time to time. If
the relationship of two countries is secure economically, incentives to create low
intensity conflict for domestic political reasons can arise. In fact, trade, or the threat ofrestrictions of trade, can be used as a signal to resolve disputes and avoid more serious
conflict (Gartzke, Li and Boehmer, 2001). So trade can reduce military conflicts but
increase non-military conflict, ‘particularly if one country is more hesitant to fight’(Robst et al., 2006 p. 4). From this perspective, interdependence may foster a highdegree of low-intensity conflict that is less likely to escalate (Gartzke, 1998).
To complicate matters, history is littered with counter-examples of adversaries trading
during war time and countries changing the way they interact with the rest of theworld. At first it is counter-intuitive to think of conflict or a negative action towards
another country causing trade, but there are examples in history such as when
Commodore Perry forced a then closed Japan to open its ports to international trade.
Many studies do not recognize that the nature of the complex interactions between
trade (or interdependence more broadly) and conflict change over time, both inintensity and direction, and that the relationship may depend on both domestic andinternational factors (Mansfield and Pollins, 2001). This dynamic is revealed in the
non linear relationships between these variables but it is beyond the scope of thispaper to shed light on such aspects in the Japan-China-United States traderelationships.

Empirical Evidence
Polachek (1978) was the first to analyse the relationship between trade and conflictwith a cross section study of the affect of trade on conflict. He found that increased
trade reduces net conflict, thus finding evidence of the liberal view. Polachek (1980)
constructed a model from microeconomic foundations in an important paper showing
the negative relationship between conflict and trade. His work generated aproliferation of empirical papers testing both realist and liberal theories.

6

While Polachek (1978) accounted for causality running from trade to conflict, Pollins(1989a, 1989b) is often recognized as the first to show the effect of conflict on trade
in a single equation cross section.
The importance of causality both ways between trade (or interdependence) andconflict has been recognized since Polachek (1980), and two-staged least squares
regression models are used to estimate these relationships. Reuveny and Kang (2003)
and Polachek (1997) have since derived simultaneous equations models.
Barbieri and Schneider (1999) contain a summary table of the main findings of themost significant empirical work in the field. It is clear from their review in the tablethat the results are mixed, methodologies are wide ranging, the countries covered
differ, and in general, the analyses are conducted on periods long before the papers
were written.
The most common conflict data sets used are events data such as the Cooperation andPeace Data Bank (COPDAB), World Events Interaction Survey (WEIS), and war data
sets such as Militarised Interstate Disputes (MID) data, the latter of which arecompiled by the Correlates of War Project. There is a stream in the literature testing
the compatibility of many of these measures, the biases in the coding (King and Lowe,
2003) and whether these measures accurately reflect reality (Pevehouse, 2004). One
such example shows the high level of compatibility between the COPDAB and WEIS
data sets (Reuveny and Kang, 1996).
Barbieri (1996) finds strong evidence of economic interdependence increasing the
likelihood of militarised interstate disputes. She finds this applies to symmetric aswell as asymmetric interdependence between nations. In her study, the only form ofinterdependence that seems to mitigate conflict is low to moderate interdependence.
Her data for conflicts is from 1870 to 1938 and she may have captured the different
nature of countries in a different time from now.
Robst et al. (2006) estimate the effects of distance on cooperation and conflictseparately and find that trade reduces conflict to a greater extent when two countries
are geographically close, but trade has a greater effect on cooperation when countries
7are distant. They also find that proximity increases the number and severity of bothconflict and cooperation events more among non-trading countries than countries withlarge trade. Their second finding does not relate to the study here as Japan and China
are close but trade is large – Robst et al. do find that although ‘proximity provides
incentives for conflict, trade mitigates these incentives’ (p. 5).
The literature suggests that the causality is bilateral-relationship dependent and theexistence of causality often reciprocal – if conflict affects trade from country X to Y,
then often conflict will affect trade from country Y to X (Reuveny and Kang, 1998).
Results in the literature also point to causality running in different directions indifferent bilateral trade relationships, and the interactions depend on the type of traded
good (for example, strategic goods versus non-strategic goods) (Reuveny and Kang,
1998).
Gasiorowski and Polachek (1982) and Reuveny and Kang (1996) test for Granger
causality between conflict and trade using time series data. Gasiorowski and Polachek
analyse Warsaw pact countries’ trade with the United States and find relatively little
evidence of Granger causality. Reuveny and Kang look at some of the most importantand politically and economically significant relationships from the 1960s to early
1990s, concluding that the Granger causality is dependent on the relationships
analysed and tends to be reciprocal. Similar methodology is used here for the Granger
causality tests but with monthly data instead of quarterly data, with fewer
relationships covered but in more detail.
To better understand how politics or conflict can affect trade, we take a look at theextreme case. Conflict leading to a trade embargo or war will obviously affect trade
adversely. The other extreme is that a customs union or high level economic
cooperation will increase trade. How does low level political conflict or cooperation
affect trade relations? A one-off territorial dispute may have little direct effect on
trade but it adds to perceived trade risk and increases overall country risk. The
cumulative effects of continued low intensity conflict will add to negative perceptionsof, and attitudes towards, that country. At the margin it is to be expected that this can8make a significant difference in the decision to undertake trade and other economictransactions.
Increased trade can foster cooperation and peace. Increased imbalances in trade or ashift in power relations sometimes due to trade can cause tensions 5. Hence there is
feedback relationship from trade to conflict and cooperation and vice versa.
3. Data
Conflict/Cooperation Events Data
The conflict/cooperation data used in this study is from King’s (2003) 6 dataset ofIntegrated Data for Events Analysis (IDEA) which is an extension and refinement of
the WEIS data set. There are more categories of conflict and cooperation in IDEA
than in WEIS or COPDAB. Monthly bilateral conflict and cooperation variables forJapan-China, Japan-United States and United States-China were extracted for theperiod 1990-2004. A net cooperation variable constructed by differencing conflictfrom cooperation and results below show that this practice, which is common in theliterature, places too much of a restriction on the data, when compared to results ofanalysing conflict and cooperation separately.
Cooperation is represented by positive political or non-political event in a relationship,
generally from one country towards another. A report of ‘Japan increasing overseas
development aid (ODA) to China’ would be a cooperative event from Japan towards
China. Conflict is represented by a negative political or non-political event such as‘nation-wide protests in China against Japanese interests’ or ‘Japanese Prime
Minister’s visit to Yasukuni shrine angers China’. For the net cooperation variable, avalue of zero means no event or the weighted positive event (cooperation) has
cancelled out the equally weighted negative event (conflict).
The events are machine coded from Reuters Business Briefs using Virtual Research
Associates (VRA) software and the results are shown to be more accurate and5

A good example of the comfortable co-existence of the liberal and realist schools is
Richard Nixon’s opening up to China. Nixon was known as a realist but his opening
up of relations with China was a liberal prescription.
6
Available at http://gking.harvard.edu/events/
9consistent than high skill human coders (King and Lowe, 2003). The events covered
report most actions from one country towards another including such events
categorised as comment, consult, approve, promise, grant, reward, agree, request,
propose, reject, accuse, protest, deny, demand, warn, threaten and demonstrate. All
events are given weights consistent with Goldstein (1992) to capture severity andextended from WEIS.
Net cooperation is used in this study. The assumption here is that a positive event will
to some extent cancel out, or have the opposite effect on, a negative effect. The
variables are analysed separately to confirm the importance of a net measure. Net
conflict (conflict minus cooperation as opposed to the other way around) is used inother studies (Polachek, 1980; Pollins 1989a).
Figures 1 and 2 show the net cooperation indexes between Japan and China, andChina and the United States for the sub period 2002-2004 to illustrate what the data
picks up and what the data looks like. The fifteen years from 1990 to 2004 covers
many events and the sub period is used here purely for illustration. Cooperation ispositive and conflict is negative on the vertical axes in Figures 1 and 2.
Some of the peaks and troughs that can be noted from Figure 1 include:
Cooperation•

April 2002: Chinese leader Li Peng meets Prime Minister Koizumi in Japan.

•

August 2003: China and Japan participate and work together in 6 party talks tosolve the North Korean nuclear problem. Also there is news of foreign direct
investment (FDI) hitting record highs.

Conflict•

May 2002: diplomatic rift over North Korean asylum seekers•

July 2002: sunken North Korean ship in East China Sea creates tension
between Japan and China•

December 2003: 400 Japanese businessmen organise an orgy in South China
with 500 prostitutes. This is significant and shows up as such a large event
because of ongoing discomfort over the war time sex slavery.

10•March 2004: Japan cuts aid to China by 20 per cent. There are also disputes
over Senkaku/Diaoyu islands. These events lead to a cancellation of bilateraltalks on a maritime treaty.

Figure 1 Net Cooperation and Conflict between Japan and China, 2002-2004
40

30

Cooperation→

20

10←Conflict

0

-10

-20

China towards Japan

October

July

April

2004

October

July

April

2003

October

July

April

2002

-30

Japan towards China

Note: Calculated as cumulative positive news scores less cumulative negative news scores.
Source: King, Gary and Will Lowe, (2003) ‘10 Million International Dyadic Events’.

Figure 1 shows that the net cooperation index does not appear to pick up one of themost important and significant events between the two countries: the Yasukuni shrine
visits by Koizumi. The annual visits to the shrine between 2001 and 2006 created agreat deal of tension as the shrine is supposed to house the spirits of 14 class A war
criminals and Koizumi’s visits were very public and were seen by the Chinese public
as honouring these war criminals. The visits during this sub period took place on 21
Apr 2002, 14 Jan 2003 and 1 January 2004 but do not show up in the net cooperationindex. The reason the shrine visits do not register in Figure 1 or in the net cooperation
variable used in this study, is that the negative reportage of the shrine visit iscancelled out by positive cooperation news (such as record high FDI or trade numbers
being reported). Indeed, if the conflict variable is viewed independently, the shrine
visits do show up as significant conflict events (see Appendix A). The 2002 and 2003
visits are the most significant and the reasons for the 2001 and 2004 visits not11

showing up strongly even in the conflict data in those exact months are explained in
the Appendix.
Also common in the literature is the use of relative conflict/cooperation rather thanabsolute conflict/cooperation to control for some countries having more events
reported than others. Data sets such as COPDAB which use the New York Times
exclusively might bias the reporting of US events upwards, and no news source is bias
free. Countries like the United States will naturally have more events as well, as they
are engaged in more international activities and have more interactions with othercountries. Converting the events into a relative measure by dividing the scale by the
total number of events is not appropriate for this study because it will negate the factthat some countries interact more with other countries.
Some of the peaks and troughs from the United States-China relationship in Figure 2
include:Cooperation•

October 2002: China and the United States agree to work together on nuclear
disarmament of North Korea.

•

April 2004: Trade talks result in satisfactory outcome for both countries.

Conflict•

November 2003: Trade dispute leading to US antidumping measures being put
in place on Chinese TVs, textiles, steel and soy beans (this is shown only as
conflict from China towards the United States).

•

July 2004: reports of avian influenza in China; China announces military
manoeuvres in the Taiwan Straits and presses the United States to stop arms
sales to Taiwan.

The conflict/cooperation data are not perfect and the creation of a net cooperation
variable may be inappropriate as note previously, as some important events do notshow up. Also, large events only show up as spikes in the data whereas quite often
they have lasting consequences. Relying on one news source, as the data set used here
does, misses news from some countries and over-reports from others. This is adifficult problem as Chinese events perhaps have better coverage, than other sources,12

in news sources such as the China Daily, but then this data source is also far from bias
free. Reuters is seen as acceptable, more international, and bias free than other news
sources traditionally used such as the New York Times which is the exclusive source
for the COPDAB database. WEIS and KEDS use a number of different sources andare flexible as they allow coders to specify the data sources. There is also difficulty inisolating reporting bias for countries such as the United States as they have moreinternational interactions and hence appear in the news the most.

Figure 2 Net Cooperation and Conflict between China and the US, 2002-2004
100

80

Cooperation→

60

40

20

←Conflict

0

-20

-40

US towards China

October

July

April

2004

October

July

April

2003

October

July

April

2002

-60

China towards US

Note: Calculated as cumulative positive news scores less cumulative negative news scores.
Source: King, Gary and Will Lowe, (2003) ‘10 Million International Dyadic Events’.

Trade data
Trade data are monthly and from the IMF’s Direction of Trade Statistics (2007). As iscommon practice, import data are used as there is incentive to under-report exports.
The data are adjusted for seasonality and de-trended to make them into stationary
series where necessary and the appropriate tests for unit roots carried out.

13

Two variables are used for trade and compared: nominal, absolute US dollar value
trade flow from one country to another and a trade index constructed by Hirschman(1945), which isTradeij =(X + M )ij

ij( X iw + M iw )where subscripts i and j on Trade indicate trade from country i to j, Xij is exports fromcountry i to j, Mij is imports of country i from j and subscript w indicates world (so Xiw
is total exports of country i). This shows the importance of country j to country i.
Here, Hirschman’s (1945) index of trade is used as it captures both types ofinterdependence: vulnerability (Keohane and Nye, 1977) and dependence (Hirschman,1945). This measure is used in other studies (Reuveny and Kang, 1996 and Barbieri,
1996). Although this index is useful for capturing aspects of interdependence, aflaw in its use is discussed below. The results from using both trade variables arecompared.

4. Granger Causality
Granger causality is defined as the past values of one variable being useful inexplaining the current value of another, given an information set that includes pastvalues of both variables (Granger, 1977). A variable X is said to Granger cause avariable Y if lagged values of X help explain values of Y. Granger causality is not deepcausality but a method that can show there exists a relationship between variables.
The availability of rich monthly data is taken advantage of here and Granger causality
is used to test the hypothesis that there is no Granger causality between trade and thenet cooperation index. The direction of causality and the lag lengths are expected tobe different across country pairs (Gasiorowski and Polachek, 1982; Reuveny andKang, 1996).
The model is a vector autoregressive (VAR) model which looks like14

Tt = ∑ α iTt −i + ∑ β i Ct −i(1)

Ct = ∑ α 'i Tt −i + ∑ β 'i Ct −i

(2)

i =1

i =1

i =1

i =1

where T is trade (the index or exports) and C is net cooperation, with the subscript
indicating time.
There is Granger Causality if we reject the null hypothesis of
βi’s = 0 in Equation 1[conflict or cooperation will help forecast trade in
future]or
α’i’s = 0 in Equation 2[trade helps forecast conflict or cooperation]

The existence of a third variable which Granger causes both net cooperation and trade
will give the result that net cooperation Granger causes trade or vice versa when arelationship does not exist (Sims, 1980; Granger 1980). Sims found causality is
unidirectional from money to income but not vice versa in his famous 1972 paper andlater showed the addition of the interest rate to the vector autoregression effectively
explains away his earlier finding (1980). Here it is difficult to think of anothervariable that could be explaining both variation in the political distance between two
countries and variation in their trade volumes. A third country’s influence or presence
could affect the results from time to time during shocks but this would be difficult tocapture consistently over time and to isolate. The Japan-China relationship could be
affected by the involvement of European countries, the United States, or even themultilateral landscape in general. However, it is a stretch to think of the general trend
in news items between two countries to be driven by a third country. This study does
not analyse the effects of external factors on the trade-cooperation/conflict
relationship.
Another potential problem that Sims points out is the existence of serial correlation
which can cause problems in this sort of estimation when ‘some elements of optimal
control enter’ the model (Sims, 1972 pp. 542). If one variable in a bivariate system ischosen optimally, the values of that variable become structural elements of the system

15

(Sims, 1972). The only sense where that could potentially be a problem in this case iswhen conflict or cooperation is controlled to influence trade. This is somewhat
plausible but the conflict and cooperation variables include actions by many actors ineach country and events out of control of leaders, governments and authorities.
Sims (1972) points out that presence of uni-directional Granger causality can bethought of as causality but bi-directional Granger causality shows a feedback
mechanism and cannot be called causality. As discussed above, it is assumed that
there is a bi-directional relationship between trade and conflict and cooperation andthe results below would seem to confirm this. Causality is used to refer to thedirection of influence but as in the case of Granger causality analysis, it is not deepcausality.

Why Granger Causality and not Cross-Sectional Analysis?
It is common in the literature to find single equation cross-sectional analysis toestimate the effect of various measures of trade or interdependence on conflict andcooperation. Pollins (1989a, 1989b) is generally given credit for popularizing theestimation of the effect of conflict and cooperation on trade. The recognition of
causality running both ways between conflict/cooperation and trade led to manystudies estimating simultaneous equations. The problem with two stage least squares
or three stage least squares estimation in analysing the relationship between
conflict/cooperation and trade simultaneously is that exogenous variables are needed
to identify the equations. Defence expenditure has been used to identify conflict andcooperation and development indicators such as education levels and highway
vehicles per capita have been used to identify trade (Polachek, 1992). Using defence
expenditure may be useful when analysing high intensity conflicts with data sets such
as MID, but in this study it is problematic for the analysis of China-Japan relations.
China’s defence expenditure is unclear and Japan’s defence expenditure is uniquely
constrained constitutionally. Also, there have not been any significant military related
conflicts and nothing worthy of showing up in the MID data set. China also trades alot more than its level of development would suggest, if such indicators were used to
identify trade. For these reasons, the most common variables used to allow asimultaneous equation to work do not perform adequately for the case of Japan-China.
16

The results below here suggest that cross section analysis, often only possible withannual data and depending on the country pairs, may not capture the true dynamics ofinternational trade and political interactions, as reflected in the differing lag structuresin the results.
The advantage of time series methodology is that there is greater freedom in terms offinding different lag structures among different bilateral relationships. There are someproblems with this methodology that Kim and Rousseau (2005) summarise. First, theresults seem to be sensitive to the number of lags included (Geweke, 1984). Secondly,
as mentioned, the inclusion of a third variable can alter the results (Granger, 1980).
Finally, de-trending a series (in this case the trade data) may lead to different causality
conclusions (Kang, 1985). Moreover, Granger causality is not deep causality.
Results
The main results of the Granger causality tests are shown in Tables 1 to 3. An increasein the net cooperation index in the tables is either an increase in cooperation or adecrease in conflict relative to the other variable. ‘Japanese net cooperation’ in thetables means a rise, on balance, in reports of positive over events that relate to country
Y, where Y is the other country in the bilateral analysis. There is no strong evidence ofincreased conflict causing trade to increase (which does not make sense, except in theevent of some countervailing action) but there is evidence of cooperation (or areduction in conflict) causing trade to increase.
Two tests are carried out. The first is for Granger causality: that the coefficients arejointly statistically different to zero. This is testing the joint significance of all β’s inEquation 1 or joint significance of all α’’s in Equation 2 (null: β1= β2= β3=…=0). The
statistical significance is shown with a star next to the number of lags. The second test
is whether the sum of the coefficients is statistically different to zero (null: β1+ β2+
β3+…=0). This is a test of wether we can confidently claim the long run multipliereffect of one variable on the other is positive, negative, or indeed whether there is any
overall statistical effect at all. This statistical significance is denoted with a star next
to the sum of coefficients.

17

The results in Tables 1 to 3 show the sum of coefficients even if they are notstatistically different to zero as in many cases, the sign is consistent over a number oflags where Granger causality was found, and it will show the general trend and istherefore useful in interpreting the results. The magnitude of the sum of coefficients is
not of great value in interpreting the results as they are coefficients between detrended and differenced trade values and an index. In Table 2 they are of lessimportance as they reflect the correlations between two indices.

Table 1 Trade and net cooperation, 1990-2004
Japan-China

Lags

Sum of coefficientsa. Japanese exports to China= f(Chinese net cooperation)

7***
8***
9**
10***
11**
12**

13.55
8.72
6.88
12.80
5.87
0.55

b. Japanese net cooperation= f(Chinese exports to Japan)

12*
13*
14*

-0.024-0.019-0.023China- USLags

Sum of coefficientsc. US net cooperation= f(Chinese exports to US)

11*
12*
13**
14**
15**
16**
17**

-0.036**-0.043**-0.032*-0.028-0.035*-0.044**-0.038d. Chinese exports to US= f(Chinese net cooperation)

6*
9*

-2.48
-3.57

e. US net cooperation= f(US exports to China)

2**
3**
4*
5***
6**
7**
8**
13**
19**

-0.014**-0.012*-0.014**-0.017***-0.018***-0.018**-0.013*-0.008-0.00418

Japan-US

Lags

Sum of coefficientsf. Japanese net cooperation= f(Japanese exports to US)

3*
4*
10*
11*
12**
13*

0.005
-0.001-0.023-0.026-0.020-0.015g. US exports to Japan= f(Japanese net cooperation)

14**
15**
16**
17**
18**

3.60*
3.61*
2.37
2.17
1.81

h. US exports to Japan= f(US net cooperation)

3**
4*
5*
6*

2.31
2.54
2.89
3.47*

Notes: * = 10% level of significance, ** = 5% level of significance and *** = 1% level ofsignificance. Statistical significance on the lag number signifies joint statistical significanceof all the lags from one lag up to that number (presence of Granger causality) and significanceon the sum of coefficients is a test whether the sum of all the lags are significant.

The lag lengths differ for each country pair in the study and this is consistent withresults in other studies (Reuveny and Kang, 1996). Trade contracts are in generallonger term than monthly or quarterly, and the effects of large or significant events,
good or bad, take time to affect trade and investment numbers. The cancellation ofmost contracts cannot be effected immediately. Similarly, an increase in trade is notfollowed immediately by cooperative actions or a diminution in negative actions
because it takes time to initiate trade transactions. There is a lag in causality and it
varies for each relationship.
The first result in Table 1 shows an increase in the index of Chinese net cooperation
towards Japan (Granger) causes Japanese exports to China. This can be thought of in a
number of ways. As the political climate in China towards Japan improves, China islikely to import more from Japan. Another interpretation is that as the political climate

19

in China improves towards Japan, Japanese companies are likely to be more confident
and inclined to sign contracts to supply to the Chinese market 7.
The second Japan-China result (Table 1b) is that increasing Chinese exports to Japan
help explain a fall in the index of cooperation from Japan towards China. Increased
Japanese imports of Chinese goods after 12 months can be seen to worsen the climate
in Japan towards China. This is consistent with what the media often refers to as the
China fear in Japan of industry hollowing out and jobs being lost to China as well asJapan’s fear of losing its economic dominance in the region. Table 3 below shows
stronger evidence of this result.
The United States-China results show strong evidence that trade flows reduce net
cooperation in the United States towards China. The sum of the coefficients aremostly statistically significant and both an increase in Chinese exports to the UnitedStates and United States exports to China cause the United States’ stance towards
China to worsen.
Finally for Table 1, that Japanese exports to the United States help explain a reductionin the Japanese net cooperation index towards the United States is unexpected (Table
1f). The sum of the lag coefficients are very small and closer inspection of the datashows a mix of positive and negative coefficients for different lags 8. The other results(Table 1g and h) show increases in the index of cooperation in both directions help
explain increased United States trade to Japan.

7

It could be argued that the political climate in China towards Japan will be affectedby the political climate in Japan towards China.
8
The coefficients of each lag are not reported here as they are in some other papers(Sims, 1972) because the sheer amount of data and the relationships for which they
may be interesting (Japan-United States) are not the main aim of this paper.
20

Table 2 Interdependence and net cooperation, 1990-2004
Japan-China

Lags

Sum of coefficientsa. Japanese net cooperation= f(Chinese dependence on Japan)

5**
6**
7**
8***
9**
10**
11**
12**
13**

810
1264**
1516**
2244***
2323***
1983**
2719**
2025*
2735*

b. Japanese net cooperation= f(Japanese dependence on China)

12*
13*
14*
15*

-752
-180
286
622

c. Japanese dependence on China= f(Japanese net cooperation)

2*-0.00012

Japan-USLags

d. Japanese net cooperation= f(Japanese dependence on US)

6**
7**
8**
9**

5313***
6173***
7790***
8320***

e. US net cooperation= f(Japanese dependence on US)

6**
18**
19**
20**
21**
22***

2918
15380***
14835**
18568***
24198***
28415***

f. US net cooperation= f(US dependence on Japan)

2*
3***
4***
5*
6*

418**
581***
602***
542***
517**

Sum of coefficients

Notes: * = 10% level of significance, ** = 5% level of significance and *** = 1% level ofsignificance. Statistical significance on the lag number signifies joint statistical significanceof all the lags from one lag up to that number (presence of Granger causality) and significanceon the sum of coefficients is a test whether the sum of all the lags are significant.

21

In Table 1b Chinese exports to Japan were explaining falls in Japanese net
cooperation (or increasing tensions) towards China; in Table 2a an increase in China’s
dependence on Japan helps explain the opposite effect, a positive movement on the
net cooperation scale. The results are not inconsistent as the dependence index usedhere captures the increasing importance of Japan for China, reflected in its risingdependence and vulnerability in relation to Japan. This nuanced story is only picked
up with the use of both the nominal export variable and the trade index of dependence.
The results of increased Japanese dependence on China are mixed (Table 2b).
Although one could interpret the change in sign after 13 months, from a negative to a
positive effect on Japan’s stance towards China, as the change over time of the effectof Japan’s integration with China on the country as a whole, the sum of thecoefficients are not statistically significant and the results cannot be interpreted asconfidently as other results. All that can be said is that Japanese dependence on China
Granger causes a movement in the index of Japanese net cooperation towards China.
The Granger causality estimations carried out on interdependence and cooperation
using the trade index do not include the United States-China case because the highly
unbalanced trade relationship distorts the trade index 9.
The results showing a positive relationship between net cooperation and dependence
for Japan and the United States are strong (Table 2d, e and f). There is unidirectionalcausality from dependence to net cooperation showing that mutual interdependence
fosters cooperation – the classic liberal hypothesis.
As the causality, direction of causality and lag lengths vary by country pair, it isreasonable to assume that even within a country pair the dynamics and interactions
change over time. To take account of, and to test for this, two additional steps aretaken. The first is to test whether the longer term relationships in Tables 1 and 2 are
9

Hirschman’s index of trade dependence does not perform well for highly unbalanced
trade or rapidly growing imbalances in trade. The direction of trade is irrelevant in theindex so it does not treat China’s growing surplus (the United States’ growing deficit)
as trade increasing in one direction. As the theories and literature tell us, it is theasymmetry in trade, or the growing imbalance that is the driver of conflict and so theindex would only appear to perform well for trade that is relatively even.
22consistent over time, or whether the nature of the interactions changes over time, thefifteen year period was split roughly into half. In order to keep the number ofobservations high, the eight and seven year split represents the only sub-periods
analysed. Table 3 shows that the results are significantly different between the twotime sub-periods.
The other method used is the nonlinear Granger causality which is detailed inAppendix B and discussed below.

Table 3 Japan-China Trade/Interdependence and Net Cooperation by sub
periods, 1990-1997 and 1998-2004
1990-1997

1998-2004

Trade and Net Cooperation
coeffsLags

Sum of coeffs Lags

Sum ofa. Japanese exports to China= f(Chinese net cooperation)

7**
8**
9**
10***
16*

6.30
-1.35
-0.73
6.48
-12.0

2*
3*
7*
12**
13*

-0.79
-0.54
22.12
8.85
7.44

b. Japanese net cooperation= f(Chinese exports to Japan)

12*
13*
14**
15**
16*

-0.07***-0.06**-0.08***-0.08***-0.07**

c. Japanese exports to China= f(Japanese net cooperation)

12***
13**
14**
15**
16*

24.7
25.4
20.5
22.4
22.5

d. Japanese net cooperation= f(Japanese exports to China)13*-0.16*

2*
4*
5**
6*

0.003
-0.0060.003-0.002e. Chinese net cooperation= f(Japanese exports to China)

22**
23*
24*

0.317
0.220
0.358

f. Chinese exports to Japan= f(Japanese net cooperation)

11*

26.7***

23

g. Chinese net cooperation= f(Chinese exports to Japan)

6**
7**
8*
12*
13*
15*

0.030
0.027
0.034
0.111**
0.126**
0.181***

h. Chinese exports to Japan= f(Chinese net cooperation)

6*

0.002

17**
18*
19*
20*

5.44
6.37
11.18
10.59

1990-1997
1998-2004
Lags Sum of coeffs Lags Sum of

Dependence and Net Cooperation
coeffsi. Japanese net cooperation= f(Chinese dependence on Japan)

2*
3**
4*
5**
6***
7**
8**
9**
10*
11*
12*

-374
-866
-466
54
985
1069
1719
1696
1733
2279
1672

j. Chinese dependence on Japan= f(Chinese net cooperation)

3**-0.0002k. Chinese net cooperation= f(Chinese dependence on Japan)

20**
21**
22***
23***
24**

3942*
4140
5678*
6397*
6198

l. Chinese net cooperation
8*= f(Japanese dependence on China) 20**
21*
22*
23*

1991*
4851*
3985
4201
3738

Notes: * = 10% level of significance, ** = 5% level of significance and *** = 1% level ofsignificance. Statistical significance on the lag number signifies joint statistical significanceof all the lags from one lag up to that number (presence of Granger causality) and significanceon the sum of coefficients is a test whether the sum of all the lags are significant.

24

Table 3 reveals more evidence of the existence of a trade-cooperation nexus which isperhaps hidden in the longer series. It is interesting to note there is a stronger link
between trade and net cooperation in the period 1998-2004 while there is a stronger
dependence-net cooperation link in the earlier period 1990-1997. For example, there
is no evidence found of Granger causality between trade dependence and net
cooperation in the 1998-2004 period. The result from Table 1a that Chinese net
cooperation towards Japan Granger causes Japanese exports to China is present inboth periods in Table 3a but with ambiguity of the sign of the effect. Analysis of thelonger time period may better capture the direction of the effect, if there is one at all.
The most significant result is that an increase in Japanese net cooperation appears tolift Japanese exports to China (Table 3c). This is the same as saying an increase in
conflict from Japan towards China Granger causes a reduction in Japanese exports to
China. Again, the sum of coefficients is not significantly different to zero but areconsistently positive and well above the magnitude of the individual lag coefficients
which range from 1.8 to 13 in this case.
The other result from Table 1b, that Chinese exports to Japan are causing the measureof conflict from Japan to China to rise, is confirmed in Table 3b but with evidence
only found in the latter period. It is reasonable to expect that the China fear that is a
product of China’s rise is more pronounced in the second period analysed as China
became a much bigger player internationally and in trade with Japan, and the direct
threat to Japan’s regional dominance became more apparent.
The main Japan-China result from Table 2a, that growing Chinese dependence on
Japan is causing Japanese net cooperation to rise towards China, is also found in
Table 3i but is only apparent in the earlier period.
The nuanced story from Table 1 can be extended to: Japanese net cooperation towards
China is positively affected in the earlier period by Japan’s growing importance toChina and negatively affected in the latter period by the growing exports of China toJapan, as China’s dominance grows and becomes more visible.

25

Net cooperation as the appropriate measure of political distance
The net cooperation variable used in this study follows the seminal papers of Polachek(1980) and Pollins (1989a). Other studies use only conflict and it could be argued aratio of cooperation to conflict could pick up different dynamics between conflict andcooperation and not restrict it to one functional form. It is reasonable to assume that it
is not conflict (negative news) alone, or cooperation (positive news) that drives trade,but the net effect of both. To subtract conflict from cooperation imposes theassumption that they are additive and that the reporting of certain events cancels out
the reporting of others. One approach might be to test the robustness of the resultswith the ratio of cooperation to conflict. This, however, is difficult as there are
frequent zero values in the news events where no positive or negative news item wasreported in that month for the bilateral relationship. Also, despite the fact that thiswould test another shape of the relationship between cooperation and conflict, and its
relationship with trade, it seems more appropriate to think of cooperation and conflict
as additive, as positive and negative utility towards another country.
The Granger causality tests from Tables 1 and 2 were repeated but with conflict andcooperation separately. While some results confirm the results in Tables 1 and 2, andeven some in Table 3, a majority of results were not consistent and did not make sensewith what is observed in reality.

Nonlinear analysis
Since the nature of interactions, the structure of interactions and the time taken for
actors in economies to respond to interactions change over time, and some events
cause quicker responses than others, a non parametric analysis is also carried out forthe study of trade and conflict.
Traditional Granger causality methods find correlations between conditional means of
two variables. Nonlinear Granger causality finds relationships between conditional
distributions of two variables. The linear VAR causality tests strip out the linear
causality and the non parametric methods test whether the remaining VAR residuals
have any remaining structural relationship. So although useful in finding a
relationship between two variables, the exact nature of the relationship is difficult to26

determine. It is unclear whether it is the variance (volatility) of one series, or any
other aspect of that series, affecting the mean, variance or other characteristics of theother variable. In that sense the methodology is a black box. Perhaps the biggest
weakness of the nonlinear tests is that the presence of a relationship between two
variables does not say anything about the effect of the relationship. That is, if itappears trade causes a movement in the net cooperation variable, it cannot be
determined whether it is a positive or negative movement.
Nevertheless, the results show there is further evidence, in addition to the linear
results, of trade affecting conflict/cooperation and vice versa. The methodology andresults are presented in the Appendix.
Although the signs of the relationships are unclear, the existence of causality is asignificant result and means that:
1. Japan’s stance towards China has implications for its trade relationship (tradein both directions) with China.
2. Japan’s trade flows to China cause potentially mixed reactions from both sides.
The first of these conclusions would appear more important as it shows the political
relationship is constrained by the economic relationship. This result confirms thefindings from Table 3 (results c and f). Japanese leaders have to be aware of theramifications of this constraint when taking actions that could raise political tensions.
The United States-China and United States-Japan nonlinear test results also show
evidence of a nonlinear relationship between trade and net cooperation and causality
in more directions, compared to just the linear results.

5. Discussion
The three relationships covered here include neighbours (Japan and China), rivals (theUnited States and China; China and Japan), allies (the United States and Japan),democracies and a one-party state. No generalisations are made here about thecharacteristics of the countries and their effect on the trade-conflict/cooperation
relationship as is done elsewhere – there is often a lack of sensible bounds on
27

conclusions and results are generalised across time and countries (Mansfield andPollins, 2001).
The results in this study are not completely consistent with Reuveny and Kang (1996).
Reuveny and Kang find that causality tends to be reciprocal whereas in this study
causality seems to be unidirectional when only applying the linear results that they use.
The conclusion Reuveny and Kang draw that the nature of the interaction between
politics and economics differs for each country relationship is, however, supported bythis study.
There is evidence in the present study that supports both the liberal and realist schools.
The findings are consistent with the theories described previously of low intensity
conflict in a relationship underpinned by strong economics, of enduring rivalries, andneighbourly disputes that are mitigated by trade (Robst et al., 2006). The structure ofJapan-China relations at this point makes them relatively low intensity in conflict.

The Japan-China relationshipThis analysis leads to a number of important conclusions. The economic relationshipunderpins the relationship between China and Japan, and the results here show thatthe economic relationship constrains the political relationship.
Yasukuni Shrine is the most talked about issue and is most symbolic of the difficulties
between Japan and its largest neighbours, China and South Korea. In a sign of therecognition of the importance of the relationship, Prime Minster Abe did not visit theshrine while he was leader and in the contest for leadership after Abe, Fukuda who
eventually won the contest, gave a clear message that in order to mend relations with
Asian neighbours, a Yasukuni Shrine visit will not take place10. Recognition of theimplications of shrine visits is now clear and even if future leaders do seek to increase
political tensions for domestic political reasons, it would appear these actions would
be taken in a manner so as to minimise the damage to Japan-China relations. As the10

There has been increasing talk of either removing the souls of the 14 class A war
criminals from Yasukuni, or failing that technically, creating another memorial for
Japan’s war dead that does not include the war criminals.
28

results here suggest, politics or variations in political do distance appear to have aneffect on the bilateral trade relationship and trade affects political distance (See Table
1a and 1b; Table 3a, b and c).
There is some evidence of increased low intensity conflict from Japan towards China
as Chinese trade to Japan increases, but this can be explained by the underlying
economic relationship’s being strong enough that the Japanese leaders and someactors in the economy can cause low intensity conflict for domestic political reasons.
This is consistent with the media reports of China fear and with the findings of Robst
et al. (2006) and Gartzke (1998). Tensions may rise significantly if trade started to
become unbalanced like in the United States-China case. The Japan-China traderelationship is currently relatively balanced and a growing Japanese deficit with China
would have to be managed carefully to dampen tensions and rising nationalism
between the two countries.
Thus, these low intensity conflicts between Japan and China would seem unlikely toescalate as the countries become more integrated economically, unless trade becomeslargely lopsided. Increased trade, and also investment, have meant that both forms ofinterdependence, vulnerability and dependence, have grown. The increased
vulnerability of one country towards the other and the unresolved historical issues
complicate and fuel domestic sentiments in some segments of both countries and fromtime to time it would appear that both governments play these up for domestic
political gain.
The results of this study suggest that Japanese exports to China are increasingly
affected by Japanese political distance from China (Tables 1a, 3a and 3c). A rise innegative Chinese sentiments towards Japan and increasingly a rise in Japan’s negative
sentiments towards China adversely affect Japanese exports. Japan’s politicians are
increasingly constrained in their actions that might affect China and Chinese
sentiments.
The economic relationship is now being driven by market forces and not significantly
directed by policy intervention (Drysdale, 2007) as the highly complementaryindustrial structures and proximity of both countries drives the growth in trade. And
29

the politics affect the economic relationship in some areas more than others as someJapanese companies feel they are global brands that can de-link themselves from theircountry of origin (Armstrong, 2007). But the Chinese economy, especially after WTO
accession, is much more of a level playing field for companies from Japan, the UnitedStates and Europe and Japan’s advantage of being in China early is diminishingrapidly. The evidence of Japanese net cooperation affecting Japanese exports to China
after 1998 shows that it is important for Japanese actors in the Chinese economy not
to have to carry Japanese political baggage in competing with others in that market(Armstrong, 2007). The interaction between politics and trade would appear to be
more pronounced in the later period analysed.
As the bilateral economic relationship between the two countries becomes even more
important than it is already is, its effect on the politics between the two countries islikely to grow. The last year has seen political tensions ease with visits by bothleaders to the other country and efforts from both governments to resolve some
important issues. Significant examples include the joint experts commission set up toagree on disputed interpretations of history and an agreement to pursue jointly, andeventually commit to, a bilateral trade agreement (free trade agreement or a broader
economic partnership agreement). A trade agreement of the kind that is being talked
about between Japan and China would be a significant political commitment.

Further work
Economic relationships between countries involve a lot more than just trade in goods.
An obvious next step would be to undertake a similar systematic study that included
services trade. Also of particular importance in the China-Japan relationship is foreign
direct investment (FDI) as it is a big part of the relationship and is likely to be equally
responsive to the political climate, if not more so.
Another extension the analysis is to take account of multilateral, or third party, effects
of political distance and trade. It is not difficult to see why the influence of the UnitedStates could have an effect on Japan-China trade, or that as the political distance
between Japan and China increases, trade through Hong Kong or other countries will
increase.
30

The results show evidence of nonlinear Granger causality and an obvious extension is
to find the shape and nature of this nonlinearity. The methodology of Baek and Brock(1992) is limited to finding evidence of nonlinear causality only, not whether thecausality has a positive or negative effect and where these turning points may be.
Quadratic and other shaped functional forms could be tested in future to see whether
trade interacts differently to low intensity conflict and higher intensity conflict. The
effects of volatility in the political relationship on the trade relationship are also ofinterest. The structure of the non linear relationships is potentially important inunderstanding the dynamics of the relationships between trade and politics andpolitics and trade.

6. Conclusion
The purpose of this paper was to test whether there is any relationship between tradeand political conflict or cooperation, and the direction of its causality in the JapanChina relationship. Both countries’ interactions with the United States were analysedby way of comparison. The study uses the latest events data available (up to 2004),
uses monthly data and extends the analysis from testing the null hypothesis that there
is no linear Granger causality by testing the null hypothesis of no nonlinear Granger
causality.
The main results are that an increase in China’s level of cooperation helps explain an
increase in Japanese exports to China; in the 1990-1997 period, growth in Japan’s
importance to China increases Japanese cooperation towards China; and in the 19982004 period an increase in Japanese cooperation towards China helps explain an
increase in Japanese exports to China.
Chinese imports of Japanese products do not appear to be independent of how well
the countries are getting along.
Growing Chinese exports to Japan and the United States are causing a rise in themeasure of negative sentiment towards China, but the growth of Japanese trade toChina dampens this effect. The large imbalance in the trade relationship between
31China and the United States is causing tensions to rise in the United States fromincreased trade in both directions.
The stable and rapidly growing economic relationship, of which trade flows are a bigpart, constrain political behaviour between China and Japan. The risinginterdependence between the nations and concomitant opportunity cost of serious
conflict has led to an easing of political tensions and even some movement towards
increased cooperation. The structure of the political relationship appears likely, fromthis analysis, to be increasingly affected by the economic relationship.

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34

Appendix
Appendix A Conflict data for Japan-China
Table 4 Conflict data from China towards Japan
Month→
Year↓

Jan

Feb

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

0
4
0
0
0
8.8
4.9
0
0
0
5
0
9.9
11.2
0

5
0.1
0
11.1
0
0
0
4
0
0
3
9
4.9
0
0

Mar
0
0
9.2
9.3
0
17.7
0
0
0
0
4
3
0
0
14.2

April

May

June

July

Aug

Sept

Oct

Nov

Dec

0
0
5
0
0
4.9
6
2.3
0.1
0.1
6.2
13
19.9
4.9
2.2

10.4
0
0
0
0
0
0
9.1
5.2
2.3
2.2
21.2
12.4
0
0

2.2
9.8
0
0
0
2.2
2.2
12
17.2
0
0
7.6
0
0
4.4

5.1
0
0.1
0.1
0
0
12
0.1
0
3
0
9.4
0
0
4.6

0
0.1
0
0
0
4.9
12.3
9.3
5
0
0
0.2
4.9
14.2
3

0
0
0.1
0
20.5
8.7
36.2
8.5
2.3
0.1
2.2
4.9
4
8.8
0

9.9
0
0
0
7.1
0
7.4
0
8
0
0
0
0
4.9
8

0
0
0
0
0
9.3
0
0
10.3
4.9
0
4
7
0
2.2

0
0
0
0
0
0
6.7
0
0
0
6.4
8.2
0
0.1
10.6

Total
32.6
14
14.4
20.5
27.6
56.5
87.7
45.3
48.1
10.4
29
80.5
63
44.1
49.2

Notes:• shaded months indicate a Yasukuni shrine visit by Prime Minister Koizumi. The first visit, in 2001 was proceeded by an announcementin May that year which got all the press. The visit itself was not reported so highly and did not cause as much negative news as in May
perhaps because Koizumi gave in to Chinese pressure in August and did not visit the shrine on the highly significant anniversary of theend of the war, 15 August, but instead on 13 August. The visit on 2004 happened on New Year’s day when it is tradition for Japanese tovisit shrines and temples and this could be the reason it was not reported as a negative event. Indeed, there is much less news coverage ofthis visit then the others.

35

•The largest conflict month is September 1996 with a score of 36.2. This is when Japan, China and Taiwan all claimed ownership ofuninhibited islands in the East China Sea, thought to have oil. From the International Herald Tribune: ‘Japan's persistent moves to assert
sovereignty over the islands, known as the Senkakus in Japanese and the Diaoyus in Chinese, have whipped up a storm of nationalist
fervor by angering Chinese in China, Taiwan, Hong Kong and Macau — despite their political differences. But analysts say that themain reason Japan and China will continue to press their claims, even at the risk of confrontation, is that both countries want access toenergy resources close to their shores.’ (September 17, 1996) 11.

Table 5 Conflict data from Japan towards China
Month→
Year↓
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

Jan
0.1
0.2
9.8
12.2
0
0
0
0
0
0.2
0
0
9
4.9
0

Feb
0
0
2.4
0
0
0
5.9
8.9
0
5
0
0
0
0
0

Mar
3
0
0
0
0.1
0
18.8
0
0
0
0.1
12.8
8.1
4
26.8

April
0
0
4.4
0
0
4
0
0
0
0
0
10.4
4
0
5

May
0.1
2.4
4.4
0
4.9
4.6
2.3
0
7.6
0
0
0
27.6
10.3
0

June
0.1
4.9
2.4
4.9
0.1
2.3
6.3
4.4
9.3
4.9
0.1
4.3
0
4.4
0

July
8.8
0
4.4
0
2.2
4.2
4.6
4.9
0
0.1
0
0
16.3
0
2.2

Aug
0
0
14.4
0
4.9
22.4
0.1
4
0
2.4
9.3
0
0
0
0.3

Sept
0
0
0
0
4.1
0.1
8.3
0
0
4
0
0
2.3
0
0

Oct
14
0
0
0
15.5
0
1
0
0
0
14.8
4.9
0
0
0

Nov
0
0
0
0.1
0
0
0
4.9
0
0.1
0
8
0
0
4.8

Dec
0
0
0
0
0
6.6
4.4
9.2
0
0
0
12.5
0
18.1
14

Total conflict from China towards Japan was 623 units and total conflict from Japan towards China was 530 units over the period.
11

http://www.iht.com/articles/1996/09/17/isles.t_4.php

36

Total
26.1
7.5
42.2
17.2
31.8
44.2
51.7
36.3
16.9
16.7
24.3
52.9
67.3
41.7
53.1

Appendix B Nonlinear Granger Causality
There is recognition that linear Granger causality tests may only capture some of thecausality relationship between two variables depending on the nature of interactions
between the two variables (Baek and Brock, 1992; Hiemstra and Jones, 1993;
Hiemstra and Jones, 1994; Diks and Panchenko, 2006). The linear VAR causality
tests strip out the linear causality and the non parametric methods test whether theremaining VAR residuals have any remaining structural relationship. The
methodology is predominantly used in finance literature where futures prices, trading
volumes and other time series show nonlinear causal relationships due to complex
feedback and feed-forward loops, as well as the fact that they are mostly self
regulating systems generally characterised by nonlinear processes (Fujihara andMougoue, 1997). The appropriateness of the methodology for finding causal
relationships in the complex nexus between cooperation and trade is discussed in
Section 6.
The test as first developed by Baek and Brock in 1992 was modified by Hiemstra andJones in 1994, and the Hiemstra and Jones version of the test appears to be the mostwidely used since. Diks and Panchenko (2006) have made some improvements on thetest statistic after showing that the Hiemstra and Jones test statistic over-rejects thenull hypothesis of ‘no nonlinear Granger causality’ with larger numbers ofobservations. The over-rejection is particularly important in most uses of themethodology, such as in daily financial data, where sample sizes are large, but would
not appear to be a problem in monthly data over a period of 15 years as is the casehere 12.
The following nonparametric methodology is based mainly on Hiemstra and Jones(1994).

12

Diks and Panchenko (2006) show the over rejection becomes an issue when thenumber of observations is 1,000 and serious when the number starts to reach 10,000.
This study has less than 200 observations. The relatively small sample size here mean
the Hiemstra and Jones test is appropriate.
37

Using the two stationary time series of exports (Xt) and net cooperation (Ct) where t =1, 2, … n, let the m-length lead vector of Xt be designated Xtm and denote the Lxlength and Lc-length lag vectors of Xt and Ct by XtLxLx and CtLcLc.
For given values of m, Lx, Lc ≥ 1 and for e > 0, C does not strictly Granger cause X if:(

Pr X tm − X sm < e X tLx−Lx − X sLx− Lx < e, CtLc− Lc − CsLc−Lc < e(= Pr X tm − X sm < e X tLx−Lx − X sLx− Lx < e

)

)
(3)

where Pr(.) denotes probability and ⋅ denotes maximum norm.

Hiemstra and Jones (1994) explain the Baek and Brock (1992) formula:
The first line in Equation 3 is the conditional probability that two arbitrary mlength lead vectors of {Xt} are within a distance e of each other, given that thecorresponding Lx-length lag vectors of {Xt} and Lc-length lag vectors of {Ct}are within e of each other. In other words, it is the probability measure of

X tm − X sm < e (the difference between the two vectors is less than a certain
bandwidth, e), given the pair of Lx-histories of X differ by less than e and thepair of Lc-histories of C also differ by less than e (Baek and Brock, 1992).
The second line is the conditional probability that two arbitrary m-length lead vectorsof {Xt} are within e of each other given that their corresponding Lx-length lag vectors
are within e of each other.
From Baek and Brock (1992) the above equation can be explained: given an arbitrary
bandwidth, e, Lc lags of C does not incrementally help predict the next period’s valueof X given Lx lags of X. If Xt = G(Xt-Lx, Ct-Lc) for some deterministic continuousfunction G, then it is easy to see why the event “Ct-Lc close to Cs-Lc” would help
nonlinearly incrementally predict “Xt close to Xs”. How ‘close’ one vector is toanother is defined by e.

38

To test Equation 3 we can rewrite the conditional probabilities in Equation 3 ascorresponding ratios of joint probabilities using the law of total probability:(

Pr X tm − X sm < e, X tLx− Lx − X sLx− Lx < e, CtLc− Lc − CsLc− Lc < e

=

(

(

Pr X tLx− Lx − X sLx− Lx < e, CtLc− Lc − CsLc− Lc < e

Pr X tm − X sm < e, X tLx− Lx − X sLx− Lx < e(

Pr X tLx− Lx − X sLx− Lx < e

)

)

)

)
(4)

which then simplifies to(
Pr ( X−X< e, C − C
Pr ( X−X< e)=
Pr ( X
−X
< e)

)
< e)

Pr X tm−+LxLx − X sm−+LxLx < e, CtLc− Lc − C sLc− Lc < eLxt − LxLx
s − Lxm + Lxt − Lxm + Lxs − LxLx
t − LxLx
s − LxLc
t − LcLc
s − Lc(5)

Equation 5 can be rewritten as

C1 (m + Lx, Lc, e) C3 (m + Lx, e)=
C2 (Lx, Lc, e)
C4 (Lx, e)(6)

where C1, C2, C3 and C4 are the correlation-integral estimators of the joint
probabilities which are discussed in detail in Hiemstra and Jones (1994). The basic
idea of the test is that if Equation 6 holds, that is, the left hand side is equal to theright hand side, one residual series has predictive power over the other.
The test statistic and test is: for given values m, Lx, Lc ≥ 1 and for e > 0 under theassumption that {Xt} and {Ct} are strictly stationary and weakly dependent, if {Ct}does not strictly Granger cause {Xt} then,⎡ C (m + Lx, Lc, e, n ) C3 (m + Lx, e, n ) ⎤2
n⎢ 1−⎥ → N 0, σ (m, Lx, Lc, e )
(
)
(
)
C
Lx
,
Lc
,
e
,
n
C
Lx
,
e
,
n
2
4
⎣
⎦

(

39

)

(7)

where σ 2 (m, Lx, Lc, e ) is an estimator discussed in detail in Hiemstra and Jones(1994) and Æ means has an asymptotically normal distribution.
The point of the test is that after removing the linear predictive power with a linear
VAR model (as described in Section 4), any remaining incremental predictive powerof one residual series over another can be considered nonlinear predictive power(Baek and Brock, 1992; Hiemstra and Jones, 1994). One problem is that the directionof causality is undeterminable.
Consistent with other instances of using the nonlinear Granger causality test of Baek
and Brock, only the case where m = 1 (m is the lead lag length) and Lx = Lc (laglengths of {Xt} and {Ct} are equal) is considered. The tests are carried out fordifferent values of e as the choice tends to be arbitrary.

Results
The results for Japan and China are presented in Table 5. The results for the UnitedStates and China, as well as for Japan and the United States are presented in Appendix
E due to the large number of results.

Table 6 Nonlinear Causality between Net Cooperation and Trade for China andJapan
a. Net cooperation from China toJapan causes trade from China toJapan
e = 1.46
Lx = Lc
CS
t-value
1
0.48
0.515
2
0.3
0.22
3
0.95
0.506
4
0.255
-1.98 **
5
0.19
0.63
6
0.174
0.481
7
0.19
0.497
8
0.22
0.474
9
0.248
0.485
10
0.166
0.245
11
0.168
0.225
12
0.496
0.441
13
0.1402
1.57 *
14
0.10790.731
150.25234.581 ***
16
0.25
3.767 ***

b. Trade from Japan to China causes net
cooperation from China to Japane = 1.25
Lx = Lc
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

40

CS
0.65
0.24
0.6
0.117
0.192
0.401
0.649
0.868
0.888
0.543
0.13610.16420.17690.17590.17920.1856
0.1898t-value
0.851
0.168
0.298
0.431
0.591
1.18
1.332 *
1.485 *
1.21
0.52
1.582 *
4.77 ***
4.667 ***
4.143 ***
3.854 ***
3.812 ***
3.501 ***

c. Net cooperation from Japan to
China causes trade from China toJapan
e = 1.25
Lx = LyCS
t-value
1
0.134
0.961
2
0.28
0.155
3
0.12
0.55
4
0.291
-1.1665
0.324-1.47 *
6
0.564
-1.447 *70.897-1.858 **80.1015-1.348 *
9
0.534
0.451
10
0.11960.659

e. Trade from Japan to China causes net
cooperation from Japan to Chinae = 1.42
Lx = Lc

CS

1
2
3
4
5
6
7
8
9
10
11
12
13

0.3
0.57
0.48
0.111
0.4
0.105
0.348
0.389
0.479
0.10850.1806
0.21330.1821

d. Trade from China to Japan causes
net cooperation from Japan to Chinae = 0.96
Lx = Ly

CS
1
2
3
4
5
6

0.158
0.73
0.403
0.633
0.666
0.2428t-value-1.404 *0.352-1.428 *-1.412 *0.896-2.264 **f. net cooperation from Japan to China
causes trade from Japan to Chinae = 1.25

t-value
0.341
0.373
0.296
0.648
0.165
0.371
-1.69 **
-1.35 *
-1
-1.597 *-1.831 **-1.684 **-1.542 *Lx = Lc

CS

1
2
3
4
5
6
7
8
9
10
11
12

0.1
0.335
0.306
0.123
0.371
0.657
0.669
0.12410.20050.21590.19650.1376t-value
0.81
1.661 **
1.382 *
0.438
1.84 **
1.584 *
1.344 *
2.151 **
3.297 ***
2.997 ***
2.326 **
0.994

* indicates 10% level of significance, ** indicates 5% level of significance and *** indicates 1%
significance.
Notes: Lx=Lc are the lag lengths. CS is the difference between the left hand side and the righthand side in Equation 6. The t-ratios are asymptotically standard normal. The bandwidth values, e,were chosen where the most causality was found.

Panels (d) and (e) are consistent with the linear causality tests in the cases of tradefrom Japan to China and in general causing change in the net cooperation index fromJapan to China (from the linear results it is safe to assume that the sign is mostly
negative even though nonlinear overall, and that the trade causes cooperation toincrease) and trade from China to Japan causing net cooperation to decrease fromJapan to China. The other results are additional evidence of the relationship between
trade and net cooperation. Although we cannot tell the signs of the causality
41

relationships, the linear causality results are consistent with what we would expect
and there is no reason to expect unusual results here. It is tempting to interpret theresults in panel (c) as net cooperation causing trade but the interpretation is that the
net cooperation index, positive or negative, has a causal relationship with the tradevariable of unknown sign. It would make sense to interpret this as a rise in net
cooperation increases trade or a fall in net cooperation reduces trade – the other way
around does not make sense as there is no realist theory that states an increase in
conflict, or a reduction in cooperation, causes trade.
The cases for which there was no causality present were trade from China to Japancausing net cooperation to change from China to Japan, a change in net cooperationfrom China towards Japan having an affect on trade from Japan to China and net
cooperation from Japan to China causing a change in trade from Japan to China.
The China-United States relationship as shown in Appendix E has many more lag
structures statistically significant and the causal relationship appears to be more robust.
Again, the lag lengths differ between country pairs.

Table 7 Nonlinear Results for China-US
a. Trade from China to US causes net

b. Trade from China to US causes net

conflict from China towards US

conflict from US to Chinae = 1.46

e = 1.25

Lx = Lc

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

0.19
0.122
0.322
0.249
0.273
0.201
0.214
0.5
0.153
0.2
0.192
0.617
0.634
0.622
0.624
0.625
0.65
0.661
0.659
0.654

t-value

Lx = Lc

0.237
0.797
-1.575 *-1.169-1.77 **
0.722
0.706
0.16
0.344
0.6
0.601
3.16 ***
2.98 ***
2.853 ***
2.814 ***
2.786 ***
2.791 ***
2.833 ***
2.868 ***
2.858 ***

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

42

0.26
0.45
0.99
0.391
0.655
0.794
0.1041
0.399
0.206
0.748
0.1167
0.38
0.101
0.978
0.102
0.10420.10470.10110.1
0.1014t-value
0.276
0.309
0.44
-1.653 **-2.51 ***
-2.351 ***-2.82 ***
0.599
0.333
0.978
-1.65 **
0.37
3.308 ***
3.219 ***
3.23 ***
3.156 ***
3.39 ***
2.891 ***
3.6 ***
2.97 ***

c. net conflict from US to Chin causesd. Trade from US to China causes net

trade from US to China

conflict from China to US

e = 1.46

e = 1.125

Lx = Lc

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

t-value

0.83
0.235
0.152
0.275
0.325
0.435
0.38
0.606
0.799
0.10710.1153
0.945
0.954
0.732
0.534
0.501
0.366
0.335
0.36
0.265

Lx = Lc

0.798
1.603 *
1.67 **
1.809 **
1.714 **
2.85 ***
1.829 **
2.428 ***
2.952 ***
3.345 ***
3.186 ***
3.884 ***
3.303 ***
2.906 ***
2.311 **
2.51 ***
1.87 **
1.728 **
1.684 **
1.316 *

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

e. Trade from US to China causes net
conflict from US to Chinae = 1.5
Lx = Lc

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

0.85
0.144
0.195
0.285
0.306
0.264
0.363
0.469
0.411
0.47
0.445
0.398
0.462
0.48
0.471
0.444
0.433
0.379
0.36
0.393

CS

t-value
1.475 *
1.779 **
1.794 **
2.91 ***
1.879 **
1.429 *
1.883 **
2.278 **
1.657 **
1.973 **
1.796 **
1.935 **
2.744 ***
3.31 ***
2.879 ***
2.762 ***
2.688 ***
2.457 ***
2.333 ***
2.441 ***

43

0.53
0.244
0.322
0.325
0.849
0.74
0.579
0.815
0.11770.13840.15220.14390.13610.12590.12980.13120.12630.13130.13620.1424t-value
0.481
1.315 *
1.82 **
0.836
2.132 **
1.586 *
0.941
1.498 *
2.527 ***
3.106 ***
3.87 ***
3.634 ***
3.401 ***
3.192 ***
3.97 ***
3.29 ***
2.906 ***
2.885 ***
2.901 ***
2.867 ***

Table 8 Nonlinear Results for Japan-US
a. Net conflict from US to Japanb. Trade from Japan to US causes net

causes trade from Japan to US

conflict from Japan to US

e = 1.5

e = 1.46

Lx = Lc

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

t-value

0.9
0.109
0.74
0.97
0.32
0.65
0.309
0.106
0.139
0.45
0.185
0.486
0.67
0.10630.10160.13170.13210.13780.156
0.1286

Lx = Lc

0.162-1.77 **
0.503
0.571
-1.266
0.212
0.797
0.198
0.24
0.71
0.284
0.724
1.24
1.592 *
1.231
1.697 **
1.545 *
1.45 *
1.434 *
1.39 *

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

t-value

0.12
0.58
0.152
0.386
0.238
0.276
0.11
0.339
0.475
0.10310.14280.12630.14950.15440.11120.24490.2
0.24590.2128

0.175
0.545
0.907
1.818 **
0.852
0.817
0.294
0.684
0.85
1.549 *
2.55 ***
1.434 *
1.74 **
1.42 *
0.82
3.369 ***
2.927 ***
2.921 ***
2.88 ***

c. Trade from Japan to US causes net

d. Net conflict from US to Japan

conflict from US to Japancauses trade from US to Japane = 1.5

e = 1.25

Lx = Lc

CS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

0.83
0.198
0.105
0.147
0.206
0.378
0.352
0.596
0.463
0.711
0.11060.975
0.10550.714
0.1128t-value

Lx = Lc

1.29 *
1.675 **
0.702
0.804
0.889
1.233
0.955
1.559 *
0.953
1.416 *
1.976 **
1.622 *
1.614 *
0.854
1.402 *

CS
1
2
3
4
5
6
7
8
9
10
11
12
13

e. Trade from US to Japan causes net
conflict from US to Japane = 1.38

44

0.23
0.38
0.19
0.21
0.316
0.901
0.507
0.2
0.14470.26460.255
0.24350.2333t-value
0.3
0.308
0.87
0.67
0.697
-1.346 *0.561
0.2
1.357 *
6.857 ***
5.872 ***
5.126 ***
4.606 ***

Lx = Lc

CS
1
2
3
4
5
6

t-value

0.114
0.192
0.367
0.337
0.516
0.244

1.81 **
1.286 *
2.68 ***
1.529 *
1.884 **
0.702

* indicates 10% level of significance, ** indicates 5% level of significance and ***indicates 1% significance.

45