Economic Geography, Trade, and War
DAVID H. BEARCE
Department
of
Political Science, University
of
Pittsburgh
ERIC
O'N.
FISHER
Department
of
Economics. The Ohio State University
An agent-based model
in
which economic exchange and military conflict are emergent processes
is
used
to explore the relationship between trade and war. The model
of
exchange
is
an
applied analysis
of
the eco-
nomics
of
trading networks. The model
of
conflict treats war as a breakdown
in
interstate bargaining due to
incomplete information. The simulations explore how initial economic geography, state revisionism, defen-
sive advantage, and technological advancement akin to globalization affect both trade and war. The results
show that the relationship between trade and war depends on third factors, and an inverse relationship
between trade and war emerges from compact geographies with revisionist states.
What is the relationship between international trade and war? Liberal scholars, such
as
Rosecrance (1986), Doyle (1986), Oneal et
al.
(1996), and Oneal and Russett (1997),
generally posit an inverse relationship between international trade and war because
trade links may create a disincentive to engage in military conflict. Realist scholars
sometimes hypothesize a positive relationship; Liberman (1996) argues that military
conquest remains a potentially efficient way to acquire resources, and Hoffman (1963)
shows that increased economic interactions may actually sow the seeds
of
conflict by
creating dependent economjc relationships described by Waltz (1970). Others, such as
Buzan (1984), argue that there should be no real correlation at all between trade and
war because the origins
of
trade are different from the causes
of
war. I
In
examining the empirical implications
of
this debate, scholars have employed a
limited set
of
tools, often using statistical analyses that are not based on
an
underlying
1.
Good recent surveys
of
this broad literature can be found
in
McMillan (1997) and Mansfield and
Poll ins (2001).
AUTHORS' NOTE: The authors are grateful for the comments
of
three anonymous referees, Kathryn
Marshall, Todd Schoellman, Erik Gartzke, Jack Hirshleifer, Earl Thompson, David Hineline, and seminar
participants at the 2000 Mershon Conference on Interdependence and Conflict, the UCLA Computational
Social Sciences Colloquium
in
the winter
of2001,
the Seventh International Conference
of
the Society for
Computational Economics, and the Midwest International Economics Meetings
in
the fall of2001. Fisher's
research was supported by grants from the National Science Foundation and The Ohio State University's
Mershon Center. All the Gauss programs and their output described
in
the simulations below are available at
http://economics.sbs.ohio-state.edu/efisher/egtw.
structural model. One problem with econometric inference is that the observed history
of the world has actually run along only one particular path. Different trade networks
and patterns of military conflict could well have emerged from seemingly random
decisions and small changes in initial conditions—a key insight from the new wave of
counterfactual analysis including Tetlock and Belkin’s (1996) volume. Another prob-
lem is that such statistical models force one to treat either trade or war as an exogenous
variable. This perspective may lose the insight that trade and war are both endogenous
processes. Neither is outside the international system; indeed, each may be an emer-
gent property within a system of states motivated to increase their resources and
power.
Understanding that trade and war are both endogenous leads us to consider whether
the relationship between economic exchange and military conflict may be governed by
third factors. Two quick examples will help illustrate this point. Closer economic
geography makes the growth of trading networks possible, but it can also render mili-
tary conquest and the subsequent extraction of resources easier to achieve. Features of
economic geography that facilitate trade, such as roads, bridges, and railways, can
similarly be used for troop movements and resource extraction. Likewise, the impulse
leading unitary states to invest in trade relationships with other states, namely the
desire to acquire other states’ resources—often called economic revisionism—is
related to the motivation leading them to consider war. Revisionist states can improve
their position within the international system through both war and trade, which, for
example, may account for the distrust of some Western political leaders about China’s
entry into the World Trade Organization.
Scholars who model trade and war have long understood that external factors, such
as economic geography, matter; it is now commonplace to include measures of dis-
tance in gravity models of trade and also to consider contiguity terms in models of
interstate military conflict. Still, these terms function only as very crude approxima-
tions of economic geography. We agree with Gleditsch and Ward (2000, 6) who state,
“The role of geography has been widely ignored.” Our measure of economic geogra-
phy accounts for both physical geography and economic infrastructure. Because of
varying natural features, different regions in the international system have idiosyn-
cratic economic geographies and disparately shaped trading networks. The economic
geography in Western Europe is relatively compact compared with the distended
geography in South America; this difference may help to account for the historically
high levels of both trade and war within the former.
Revisionism can be defined as the desire of certain states in the international system
to acquire additional power, which is usually measured in terms of material resources.
Traditional realist scholars such as Morgenthau (1948) and Schweller (1994) often
identify revisionism as an important source of military conflict. Scholarship on trade
and war has neglected the role of revisionism in generating economic exchange;
indeed, it is worth exploring in detail how a state’s impulse to acquire resources can
affect both war and trade. An important contribution of our work is that we offer an
analytically cogent measure of state revisionism using a simple measure of the elastic-
ity of substitution. The Armington aggregator common in empirical studies of interna-
tional trade implies that a highly revisionist state has strong economies of scope. If
such a state obtains a diverse resource base, it will achieve a large increase in military
power.
We use an agent-based model that treats trade and war as emergent processes in a
system of states. This model allows us in essence to “rerun history,” examining the
impact of random events and changes in initial conditions. Configured in different ini-
tial economic geographies, the states begin in autarky, and each is endowed with a
unique tradable good. A state begins to trade with another state if it can pay the fixed
cost of opening and maintaining commercial relations. A state may also demand
resources from another state; as a result of these threats, costly wars may occur.
This model is novel in its treatment of both trade and war. Emphasizing that trade is
not just a bilateral phenomenon, the model functions as an applied theoretical analysis
of the economics of trading networks. One key feature is the trade-off between the
fixed costs of setting up and maintaining trade links and the benefits of increased diver-
sity in consumption. These fixed costs are endogenous; thus, in an important sense, the
evolution of the economic geography is an emergent property of the opportunities for
exchange.
2
Trading networks arise spontaneously and have an ambiguous effect on the
likelihood of conflict. For example, we show that a state will not go to war with another
state that is fully integrated into its trading group, but it will fight with a near neighbor
that is part of a complementary group. Indeed, the potential gains from conflict will be
higher if the defending state has many different trading partners and thus a rich array of
new goods available for usurpation.
Although there have been several notable agent-based computer models of war,
3
our work also represents one of the first efforts to apply the insights of formal theorists
such as Fearon (1995) and Morrow (1989), who argue that war tends to result from a
breakdown of interstate bargaining due to incomplete information. In our model, when
a state makes a demand for resources, it cannot know whether it faces a weak or strong
defender. Weak defenders generally capitulate to all but the most extreme demands,
but war will occur when a state demands resources from a committed defender who
resists the threat. Wars thus occur randomly, as in Gartzke’s (1999) model.
The remainder of the article is organized into four sections. The second section
describes the model. The third investigates some of its analytical properties. The
fourth section reports the simulation results that are the empirical heart of the model.
The fifth section concludes.
THE MODEL
The world consists of m states. Each state is endowed at time t with an idiosyncratic
good ω
i
(t), and the list of states is the index set i ∈ M ={1,...,m}. A state’s endowment
2. One can think of the current economic geography of the world system as an evolving state variable.
The initial economic geography is clearly exogenous. A good survey of the recent theoretical developments
in economic geography is that of Fujita, Krugman, and Venables (1999). Almost all this literature focuses on
very simple, static, spatial environments.
3. Epstein and Axtell (1996), Cederman (1994), Cusack and Stoll (1990), and Bremer and Mihalka
(1977) are examples.
depends on time because a state that loses a war suffers a diminution of its economic
resources. Also, because each state has its own good, there is a simple relationship
between patterns of trade and the structure of demand in the world economy. We are
using the analytical artifice of a state-specific composite commodity to capture the
notion that every state has comparative advantage in some good.
Each state has preferences that are summarized by a utility function, u
i
:
R
+
m
→ R,
whose rule u
i
(x) also serves as the foundation for an expected utility indicator. For ana-
lytical simplicity, we assume that each state is risk neutral, and we impose further that
m
ρ
i
(1)
ρ
1
i
ux =
∑
x , 0 ≤ ρ ≤ ,
i
()
k
i
1
k
=1
where x
k
is the kth component of the vector x. Thus, states have identical homothetic
preferences, and each state values every good in the world economy.
The parameter ρ
i
merits some discussion. As ρ
i
tends toward zero, the ith state
becomes more desirous of others’ goods. As ρ
i
tends toward unity, it becomes more
content with its own good. We believe this measure offers a useful way to capture the
concept of “revisionism,”
4
with larger ρ
i
values indicating less revisionism. Interna-
tional relations theorists such as Morgenthau (1948), Kissinger (1957), and Carr
(1946) identify revisionist states as those dissatisfied with the status quo, a limited bas-
ket of goods in a model of economic exchange. Schweller and Preiss (1997, 11)
emphasize that such states are often desirous of greater power, which is usually mea-
sured in terms of material resources. Thus, our measure captures aspects of both unit-
level satisfaction and power.
Equation (1) can also be interpreted as an Armington aggregator for an economy’s
productive capacity. Using this interpretation, one can consider the equation as a pro-
duction function, aggregating a potentially diverse resource base into a homogeneous
final good called national output. If ρ
i
is small, then an economy has strong economies
of scope and benefits greatly from a diverse resource base. Thus, when a revisionist
state obtains a diverse basket of goods, it will become substantially more powerful in
military terms.
ECONOMIC EXCHANGE
5
A trading network is a collection of sets
{}
m
=1
that cover M.Each S
i
is the list of
S
ii
trading partners of the ith state. Because this collection covers the index set of states,
m
M ⊆
S
i
. This collection of sets must satisfy two consistency properties. First, i ∈ S
i
i=1
because every state is its own trading partner. Second, if j ∈ S
i
, then it must be the case
4. Economists identify ρ
i
as an elasticity parameter. When ρ
i
is near unity, goods are almost perfect
substitutes, and the desire for variety is attenuated.
5. The model of economic exchange draws heavily on the excellent work of Jodhimani (1999).
that i ∈ S
j
. For example, if
{}
m
{{1}, . . . ,{m}}, then all the states in the world are in
S
ii=1
=
autarky, or if
{}
m
={M,...,M}, then the world economy is completely integrated.
S
ii=1
Trading with another state requires paying fixed costs that depend on the current
(and thus time-dependent) economic distance between trading partners. Consider a
symmetric m × m matrix,
at()
0
1 j
at()
im
At()
=
,
at
1
()
j
at()
0
mj
whose canonical element 0 ≤ a
ij
(t) measures the economic distance between states i
and j at time t. This matrix has two simple properties. First, the distance from a state to
itself is zero. Second, the distance from i to j is the same as the distance from j to i.
6
We follow the tradition in the literatures on international trade and economic geog-
raphy, imposing that economic distance is the fraction of resources that a state pays to
maintain its commercial relations with any trading partner. It is useful to define a trade
indicator function:
1
if j
∈
S
i
χ
ij
()
t
=
0
otherwise.
This function simply tracks who is trading with whom; it takes the value of unity if
states i and j are trading partners and zero otherwise.
Now let the initial economic geography A(0) be given. We assume that economic
distance evolves according to
a
ij
(t +1)= a
ij
(t) exp (–δχ
ij
(t)– θ),
(2)
where δ is the parameter that captures the rate at which trading costs decrease if a trad-
ing link between state i and state j is extant. The parameter θ is the exogenous rate at
which the economic geography becomes more compact in every period. Thus, the eco-
nomic distance between two states shrinks as long as the trading links between them
have not been severed, and it may also shrink if there is exogenous technological prog-
ress that makes trade between all countries easier. Also, in the spirit of the strong sym-
metry of the model, this learning-by-trading parameter δ applies to every link equally.
6. We do not impose the triangle inequality a priori in this matrix because these numbers describe
economic distance, not spatial geography. For example, it is entirely plausible that the economic distance
between Cuba and the United States is greater than the sum of the economic distances from Cuba to Mexico
and then from Mexico to the United States.
