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Market Structure, Power Asymmetries, and Credible Commitments: Alliances and Major-Power Trade, 1907-1991
Joanne Gowa Edward D. Mansfield Department of Politics Department of Political Science Princeton University University of Pennsylvania Princeton, NJ 08544 Philadelphia, PA 19104 609-258-5831 (phone) 215-898-7657 (phone) 609-258-5349 (fax) 215-573-2073 (fax) [email protected] [email protected]
Market Structure, Power Asymmetries, and Credible Commitments: Alliances and Major-Power Trade, 1907-1991
Abstract
In this paper, we examine the political economy of trade flows among major
powers during the twentieth century. In the aftermath of World War II, scale economies
motivated an increasing proportion of trade and the size distribution among the major
powers became severely skewed. As a result, free trade became a risky strategy for
smaller major powers. Reducing this risk required the larger states to make a credible
commitment to keep their markets open. Alliances enabled them to do so. As such,
military coalitions became especially important determinants of trade flows after World
War II.
Our empirical analysis supports this argument. We find that alliances exert a
strong and sizeable effect on trade throughout the twentieth century. Their impact,
however, is larger in the bipolar system than in its multipolar predecessor. Furthermore,
relative to their prewar counterparts, post-World War II alliances exert more powerful
effects on trade between states of disparate size than on trade between more symmetrical
partners. Thus, our empirical analysis suggests that alliances do help resolve the problem
of dynamic inconsistency that power asymmetries and scale economies create.
The argument and evidence we present suggests that the international political
economy resembles other political arenas in which welfare-enhancing outcomes require
the nominally dominant power to tie its own hands. In the international system, we
argue, an alliance can help a large state to do so, just as merchant guilds enabled
medieval rulers to achieve efficient trade levels and a stronger parliament in the
seventeenth century helped the English crown to obtain loans at less than prohibitive
rates.
1
Introduction
Existing studies advance two important arguments about the political foundations
of major-power trade. According to the literature on international regimes, Prisoner’s
Dilemma (PD) preference rankings characterize the trade policy preferences of large
states. Thus, each state’s dominant strategy is to impose tariffs. As is true of PD games
more generally, however, states can achieve the Pareto-optimal free-trade equilibrium if
their interactions continue indefinitely and a credible punishment mechanism anchors
each state’s pledge to keep its markets open (e.g., Axelrod 1984; Keohane 1984;
Yarbrough and Yarbrough 1986).
More recent studies argue that regime theory neglects the impact on major-power
trade of concerns about security produced by the anarchic international system (e.g.,
Gowa 1994; Gowa and Mansfield 1993; Kim 1998; Mansfield and Bronson 1997a,
1997b; Pahre 1999). Some argue, for example, that the “security externalities” of trade
make open markets more likely between allies than between adversaries. Variations in
the expected duration of alliances, they add, make the impact of alliances on trade
stronger in bipolar than in multipolar systems (Gowa and Mansfield 1993; Gowa 1994).
In this paper, we extend the existing research program on the political economy of
trade in two ways. First, we argue that the conditions under which states preferred free
trade shifted after World War II. In the postwar period, scale economies motivated an
increasing proportion of trade and the size distribution among the major powers was
severely skewed. As a result, free trade became a risky strategy for relatively small
major powers. To reduce this risk, larger states had to make a credible commitment to
keep their markets open. It is because alliances allowed the nominally dominant states to
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tie its own hands, we argue, that they became especially important determinants of trade
flows after World War II.
Next, we analyze the effects of alliances on major-power trade flows between
1907 and 1991. We do so in two ways. First, recent empirical research has not generated
any consensus on the relationship between alliances and foreign commerce. Because our
study is predicated on the argument that alliances promote open trade, we begin by re-
examining this key issue. Our findings indicate that alliances do exert a large and
statistically significant effect on trade throughout the twentieth century. We also find that
their impact is larger in the bipolar system than in its multipolar predecessor.
Second, we test the particular hypothesis this paper advances about the role that
alliances play when markets are imperfect and a marked size asymmetry exists between
prospective trading partners. The evidence is consistent with our argument. Relative to
their prewar counterparts, post-World War II alliances exert more powerful effects on
trade between states of disparate size than on trade between more symmetrical partners.
Thus, the second part of our empirical analysis suggests that alliances do help states
resolve the problem of dynamic inconsistency that power asymmetries and scale
economies create.
Immediately below, we briefly review the existing literature on the political
foundations of open international trade. Then, we explain why shifts in market structure
and in the distribution of power generate a problem of time inconsistency. Next, we
show how intra-alliance trade can mitigate this problem. Finally, we examine the data
and present our findings.
3
The Existing Literature
Existing literature about the political determinants of international trade is based
on standard trade theory. Thus, it assumes that a state’s optimal trade policy is a function
only of its world market power—i.e., of its ability to affect the price of its exports relative
to its imports. Whether a country can affect its terms of trade depends upon its relative
size.1 Standard theory defines a country as “small” if its trade policy cannot increase its
terms of trade—i.e., a small country, by definition, faces an infinitely elastic export
supply curve. It is because small countries are price takers that free trade maximizes their
real income regardless of the policies other states adopt.
In contrast, changes in the trade volume of a “large” country can improve its
terms of trade. As a result, its optimal policy depends upon which of three possible
distributions of market power exist. If only one large country exists, its first-best
alternative is an “optimal” tariff—i.e., a tax on trade that maximizes the net gain from the
improved terms of trade and decreased trade volume the tariff generates (Conybeare
1984).2 An optimal tariff remains a large country’s most attractive policy option even if
another relatively large state exists, as long as it has more market power than does its
prospective trading partner.3 For reasons that will become clear below, it is important to
note that a tariff is in either case a Pareto-inferior policy option: the income transfer it
1 Market power can also accrue because a country has a monopoly over the supply of a particular product (e.g., oil). We do not explicitly examine this case, but the argument we advance also applies to this source of market power. 2 Setting the tariff equal to the reciprocal of the elasticity of its trading partner’s export supply curve accomplishes this goal (Ethier 1983, 192). This is the reason that a small country’s welfare-maximizing tariff level is zero. 3 This is so even if a trade war ensues between them. Johnson (1953/54) and Kennan and Riezman (1988) discuss the conditions of power asymmetry under which this conclusion holds.
4
induces is inefficient, as the large country gains less than the target country loses.4
Finally, a uniform distribution of market power among large countries generates
preference rankings that conform to a PD game.
As such, this third situation is the canonical case of international regime theory
(e.g., Axelrod 1984; Keohane 1982, 1984). Drawing on studies of the PD game,
proponents of regime theory show that free trade can emerge as an equilibrium outcome
of an infinitely repeated game. The game-theoretic set up in this literature assumes that
the conditions of standard trade theory hold. However, asymmetric information exists
about whether states shirk. As a result, decentralized action cannot secure liberal trade. A
regime can do so, however, insofar as it enables its members to detect and punish
attempts to cheat.5
Because regime theory emphasizes the role of market failures, it does not consider
the impact of security concerns on trade. Not surprisingly, studies that take these
concerns explicitly into account generate more qualified predictions about free-trade
prospects. Robert Pahre (1999, 123-48), for example, argues that whether open markets
prevail, even among allies, depends on the distribution of military burdens among them.
His argument assumes that security and trade are public goods, although others maintain
that both can often be privatized (e.g., Conybeare 1984, 1992).
4 That is, the gain to the large country is less than the sum of its loss of consumer surplus and the small country’s loss of producer surplus. A Pareto-superior alternative is free trade plus a side payment from the smaller country to the larger one. This solution is of more interest in theory than in practice, however, given the familiar problems associated with arranging and enforcing side payments. See, e.g., Acemoglu 2002, 3. 5 The regime literature, however, does not identify the mechanism that allows a trade regime to extract information from states with incentives to misrepresent it.
5
Assuming that free trade is excludable, Joanne Gowa and Edward D. Mansfield
(1993; Gowa 1994) contend that regime theory is incomplete. Trade increases a state’s
real income, they argue, bolstering its political-military capacity. Because this “security
externality” is positive in the case of allies and negative in the case of adversaries, allies
have stronger incentives than other states to trade freely with each other.6 As in the case
of regime theory, Gowa and Mansfield assume that the conditions of standard theory
hold.
In the next section of this paper, we discuss the effects of scale economies and
power asymmetries on the incentives of major powers to trade freely with each other.
Doing so makes it clear that PD preference rankings do not always accurately describe
the incentive structures of major powers. This implies that neither repeated interaction
nor monitoring mechanisms will sustain free trade. Imperfect markets and power
asymmetries create a credible-commitment problem. As such, whether the dominant
power is willing and able to tie its own hands determines whether international markets
will be open.
Market Structure and Free Trade
Neoclassical trade theory assumes that differences among countries drive trade.
In its Heckscher-Ohlin variant, differences in relative factor endowments motivate trade.
6 Powell (1999) maintains that concerns about security need not deter trade irrespective of the distribution of the gains that ensue. He acknowledges that a state that receives a disproportionately large share of the gains might attempt to shift the prevailing balance of power in its favor. He argues, however, that its trading partner can neutralize its effort simply by allocating a larger share of its returns to its own military sector. Even under this scenario, however, states are better off trading with their allies than with their adversaries. If the state that receives the larger share of the gains from trade increases its allocation to guns, its ally need not respond in kind. Evidence of free riding among allies suggests the empirical relevance of this argument.
6
All else equal, gains from trade increase with the disparity between the capital/labor
ratios of trading partners. As such, Heckscher-Ohlin theory generates predictions about
both the direction and composition of international trade. Because relative factor
endowments diverge most sharply between rich and poor countries, neoclassical trade
theory predicts that most trade will occur between them. It also predicts that trade will
take the form of exchanges of perfectly homogenous goods across different industries.
Thus, for example, Germany will export widgets to and import cocoa beans from the
Cote dIvoire.
Different predictions, however, arise from what has come to be called the “new”
trade theory, which accords central roles to product differentiation and scale economies
(e.g., Krugman 1980; Helpman and Krugman 1985; Helpman 1999).7 As their incomes
increase, consumers begin to demand goods that reflect their taste for variety. Hence, if
production exhibits increasing returns to scale (i.e., unit costs that fall as output expands),
an incentive to engage in product differentiation exists. Firms within the same industry
will therefore specialize in the production of differentiated goods—i.e., they will produce
functionally similar goods that differ from each other with respect to quality or style
(Grubel and Lloyd 1975, 95).8 In the presence of scale economies and consumer
demands for variety, the new trade theory predicts that countries with relatively similar
factor endowments will engage in intraindustry trade.
7 It is important to note, however, that some observers dispute the role of scale economies in motivating trade (e.g., Davis and Weinstein 2001). 8 Scale economies can exert large effects on production costs: some studies show that a one percent increase in output leads to 0.15 to 0.20 percent fall in unit production costs (Helpman 1999, 140). As Antweiler and Trefler point out (2002, 107), if costs decrease by 0.13 percent for every one percent increase in output, a firm operating at 10 percent of the output level of a U.S. firm faces 55 percent higher average costs.
7
In general, existing evidence is consistent with the idea that the foundations of
trade have shifted over time. Kevin H. O’Rourke and Jeffrey G. Williamson (1999a, 7;
see also 1999b), for example, observe that “the correct trade model may vary with the
period being studied.”9 They describe the nineteenth century as the “classic” era of
Heckscher-Ohlin trade (O’Rourke and Williamson 1999a, 4). Trade in that period, as A.
G. Kenwood and A. L. Lougheed (1992, 91) point out, involved the “exchange of
manufactured goods for raw materials and foodstuffs between the rapidly industrializing
countries of Europe and North America and primary producing countries.” The interwar
pattern also conformed to neoclassical trade theory, as manufactures “dominated” the
export shares of the industrialized countries, while the less developed countries exported
primary products (Kenwood and Lougheed 1992, 213-14).
In contrast, post-World War II trade seems to fit the new trade theory more
closely. Intraindustry trade rose steadily from 36 percent of global trade in 1959 to 42
percent in 1964 and 48 percent in 1967 (Grubel and Lloyd 1975, 41). The share of
intraindustry trade in total British trade was about 53 percent in 1970. Ten years later, it
had increased to about 75 percent. By 1990, almost 85 percent of British trade occurred
within rather than across industries. German trade displays a similar pattern: the
corresponding figures are 56 percent in 1970, 57 percent in 1980, and 72 percent in 1990
(Helpman 1998, 581). The British and German data, as Elhanan Helpman (1999, 134)
9 It is important to note that differences in relative factor endowments can also generate intra-industry trade, as factor intensities can vary as much across goods within the same industry as they do in the case of goods that different industries produce. Nonetheless, differences in factor proportions can explain increases in intraindustry trade only if the factor endowments that characterize the principal trading countries have become more disparate across time. This, however, seems to be exactly the opposite of what has occurred in the postwar period (Deardorff 1984, 502).
8
notes, are consistent with “a general trend of rising shares of intraindustry trade” among
the advanced industrialized countries.
The distribution of trade also reflects the growing importance of trade within
rather than across industries. Thus, an increasing proportion of world trade occurs
between the members of the Organization for Economic Cooperation and Development
(OECD), despite their increasingly similar per capita incomes and factor endowments
(Deardorff 1984, 502). More than half of all merchandise trade now occurs among the
advanced countries, while North-South trade accounts for only about 30 percent of
international trade in goods (Helpman 1998, 573).
Thus, the new trade theory seems to apply with particular force to the post-World
War II period. It is important to note, however, that neither neoclassical nor the new trade
theory alone can explain trade either before or after 1945. Scale economies drove some
trade prior to World War II (e.g., Brown 1995) and differences in factor endowments
continue to motivate a sizeable portion of contemporary world trade. Nonetheless, it
seems clear that scale economies matter a great deal more in the post-1945 period than
they did previously. In the next section of this paper, we examine how changes in the
basis of trade can affect the prospects for open international markets.
Scale Economies, Sunk Costs, and Free Trade
Increasing returns to scale often arise as a consequence of fixed costs—i.e., the
costs that a firm must bear in order to operate and that are independent of its level of
output (Tirole 1988, 307). When production requires the expenditure of fixed costs, entry
9
is not free and markets will tend to be imperfectly competitive.10 We focus here on the
case in which fixed costs take the form of irreversible investments or sunk costs,
expenditures “that cannot be recouped if the action is reversed at a later date” (Dixit
1992, 108).11
Irreversible investments, as Caroline Freund and John McLaren (1999, 19) note,
are a “routine part of everyday international business.” Standard business manuals, they
observe, contains “long lists of adjustments that must be made in approaching a new
market.” Even Canadian exports to the United States, they point out, require substantial
amounts of legal research. A very familiar example that illustrates the role of sunk costs
in international trade is that of Japanese auto producers. To export to the U.S. market,
these producers had to invest in specialized equipment to meet safety and pollution
standards specific to the United States (Yarbrough and Yarbrough 1992, 71).
It seems clear that overseas trade can create opportunities for profitable
investments in IRS industries that would not otherwise exist. A relatively small country
can exploit scale economies if it invests in the production of goods designed for export to
a larger country. Exercising this option is risky, however. If the small country does so, it
will endow its larger trading partner with the power to appropriate the resulting surplus
ex post (Yarbrough and Yarbrough 1992). As a result, sinking costs into an IRS industry
can leave a relatively small state worse off than it would have been otherwise.
10 Scale economies that are external to the plant but internal to the industry can also exist. In this case, markets remain perfectly competitive. Thus, we limit our analysis to cases in which IRS arise at the firm level. Internal economies of scale give a cost advantage to large firms and create an imperfectly competitive market structure (Krugman and Obstfeld 2000, 122). 11 In practice, the period of time in which costs are sunk varies, so irreversibility endures for the period in “which the cost of being freed from the commitment…is sufficiently high that it does not pay to be freed” (Tirole 1988, 308).
10
Indeed, when exporting requires assets that are specific to the large state’s market,
even the prospect of free-trade negotiations can degrade the welfare of a small state.12
The expectation that the larger country’s market will remain open can stimulate an
allocation of resources within the smaller state that vitiates its bargaining power ex post,
rendering autarky its ex ante welfare-maximizing choice (McLaren 1997).13 Moreover,
as the Japanese auto case suggests, the absolute size of the “small” country can be quite
large by world standards; what matters is the relative size of the two countries’ markets.
As a result, when large disparities in market size exist and markets deviate from
the perfectly competitive structure that standard trade theory assumes, free trade is no
longer a dominant strategy for any state. Small countries that condition their domestic
resource allocation on the presumption that the markets of their larger trading partner will
remain open can endanger rather than enhance their welfare. If imperfect markets and
power asymmetries exist, free trade will maximize the expected national income of the
relatively small state only if the large power is willing and able to make a credible
commitment to keep its markets open.14 We address the implications of this problem of
dynamic inconsistency for liberal international trade next.
12 These investments, of course, can also increase the dependence of the large country on trade. However, the effect on the small country is much greater, because the induced change in its resource allocation is proportionately much larger. In addition, in a trade war, it is in the interest of the large country to impose a tariff, while the small country’s interest dictates adherence to free trade. As a result, increasing trade dependence “improves the trade war outcome for the former and worsens it for the latter,” thereby decreasing the threat point of the smaller state relative to that of its larger counterpart (McLaren 1997, 403, n. 9). 13 This is so as long as the small country’s exports are “poor” substitutes for its imports or if countries do not differ much with respect to labor productivity across sectors (McLaren 1997, 403). 14 See Lapan (1988) for a more general discussion of the time-inconsistency problem in trade.
11
Credible Commitments and Free Trade
Regime theory, as we noted above, assumes that the conditions of standard trade
theory hold and that state preferences conform to those of a PD game. As such, it
assumes that each state can affect its terms of trade. By definition, therefore, it applies
only to the behavior of large states. Studies that focus on the impact of security concerns
on major-power trade are also based on standard trade theory and a PD framework. The
explanatory power of the existing literature, therefore, varies with the foundations of
trade and the extent to which power asymmetries exist among major-power allies. For
reasons we explain below, its explanatory power varies across international systems.
During the multipolar era, relative symmetry prevailed within great-power
alliances. In 1907, for example, the ratio of British to French gross domestic product and
that of Britain to Russian GDP were both about 1.5:1.15 The corresponding French-
Russian ratio stood at 1.03:1. In 1913, the ratio of British to French economic output was
1.40, and the corresponding ratios for Britain and Russia and France and Russia were
0.85 and 0.86, respectively. In 1936, these ratios ranged between 0.39 for France and
Russia to 1.71 for Britain and France. In addition, as we noted above, trade patterns were
consistent with the predictions of the Heckscher-Ohlin model. Thus, major-power trade
during the multipolar era is generally consistent with the iterated PD game that is the
foundation of existing theories about the political economy of trade.
A marked gap between these theories and the real world, however, opens up after
World War II. During the Cold War, the distribution of market power among the major-
power allies was highly skewed. In 1950, U.S. economic output was about eight times as
12
large as that of Britain and about 10 times as large as that of France. The U.S.-British
ratio remains about 8:1, regardless of whether 1950, 1960, or 1970 data are used to
calculate it. The U.S.-French ratio ranges between about 10:1 in 1950 and 3.5:1 in 1970.
U.S. economic output exceeds that of Germany by a factor of 12 in 1950, 7 in 1960; and
5.5 in 1970. The ratio of U.S. to Japanese economic output ranges between 26:1 in 1950
and 5:1 as of 1970. Thus, marked asymmetries characterize the major powers during the
bipolar period. This period is also one in which, as we noted above, scale economies
motivate a sizeable share of trade among the advanced countries and a disproportionately
large share of world trade occurs among them.
Thus, the straightforward application of existing theories to major-power trade
during the bipolar era seems problematic. Power asymmetries and scale-economy based
trade creates a credible-commitment problem in this period that had not existed
previously. As a result, whether relatively small states choose to assume the risk free
trade creates depends on whether the dominant state is willing to make a credible
commitment to keep its markets open. However, even the willingness of the large state
to tie its own hands may not suffice. The crisis-bargaining literature shows, for example,
that states may not be able to peacefully resolve a dispute even if they are eager to do so.
Because any exogenous shock to the balance of power can generate a demand for future
revisions, no state can credibly commit to adhere to a peace accord (e.g., Fearon 1994).
The same type of problem can impede efforts to conclude a free-trade pact when
the conditions of standard trade theory no longer hold. A large state cannot solve the
problem of time inconsistency with a promise to adhere to free trade, because promising
15 The statistics in this and the next paragraph are calculated using Correlates of War data, described below.
13
to keep its markets open is in its interest regardless of the strategy it actually intends to
follow. As an announcement does not raise the costs of reneging, it does not affect the
incentive to do so; an announcement is, therefore, “cheap” talk. Consequently, no
prospective trading partner will alter its ex ante beliefs about the large state’s preferences
on the basis of an announcement alone. Instead, the large state must send a “costly”
signal—i.e., it must take an action that is too expensive for its opportunistic counterpart
to mimic.
A political-military alliance can serve as such a signal. This is so even though the
trade signal can be an unintended consequence of alliance formation. Regardless of its
size, a state typically joins an alliance in order to enhance its security. Especially for
small states, any ensuing military force integration allows them to protect themselves
more effectively and more efficiently than if each relied only on its own resources. The
value added of an alliance to a large state more typically lies elsewhere—in, for example,
the access it gains to the territory of its smaller allies or its ability to influence them
(Morrow 1991).
The formation of an alliance can help to secure the large state’s commitment to
keep its markets open for two reasons. First, the decision of the large state to become a
member makes clear its belief that the alliance enhances its welfare. Relative to the
status quo ex ante, therefore, an alliance endows its smaller members with some leverage
over the large state. In addition, the potential military strength of any alliance increases
with the aggregate economic resources of its members.16 As a result, an alliance affects
16 This suggests that it is misleading to represent security concerns as limited to the effects of trade on current military expenditures. The division of the gains from trade also affects security because of its effect on productive capacity in general (cf. Morrow 1997; Powell 1999).
14
the trade policy incentives of all its members. In the case of the large state, joining an
alliance decreases its incentive to exploit its market power. If its relatively small ally
engages in IRS investments and the large state expropriates the resulting ex post surplus,
the deadweight loss falls on the alliance as a whole. Thus, the large state not only
imposes costs on the small state but also shoots itself in the foot if it does so.17 This, in
turn, makes its smaller members willing to sink costs into the production of exports for
the large country’s markets.
In sum, major-power alliances should exert a positive impact on trade between
their members in both multipolar and bipolar systems. Moreover, as others have argued,
differences in the expected duration of alliances suggest that their effect should be larger
in the more recent period (Gowa and Mansfield 1993; Gowa 1994). The argument we
advance here, however, implies that other sources of cross-systemic variation exist.
When power distributions are skewed and scale economies motivate trade, alliances
should exert particularly powerful effects on trade. An alliance serves as the credible
commitment necessary to induce its smaller members to adopt free-trade strategies.
Thus, bipolar alliances should exert a stronger effect than multipolar alliances on trade
between disparately sized states.
Testing the Argument
To test our argument, we conduct two sets of analyses. Building on recent studies
of the political economy of trade between major powers during the twentieth century, we
17 If the smaller state invests all of its resources in the scale economy sector and the elasticity of substitution between exports and imports is weakly less than one, the resource transfer a tariff induces will be Pareto-optimal, as no consumption distortion occurs in the smaller country. However, complete specialization is specific to the assumption that trade is based only on differences in labor productivity (i.e., on the Ricardian model) (McLaren 1997, 414, n. 41).
15
estimate the impact of alliances on commerce and assess whether this impact varies
between bipolar and multipolar systems (Morrow, Siverson, and Taberes 1998, 1999).
Next, we examine whether the effect of alliances on trade also depends on market
structure and the distribution of power between economic partners.
In both sets of analyses, we seek to explain bilateral trade flows between states
that were major powers during some part of the twentieth century. We focus on trade
relations between major powers for three reasons. First, our argument applies only if at
least one trading partner can affect its terms of trade. Major powers are more likely than
other states to be able to do so, because both their economies and their trade volumes are
generally sizeable (e.g., Dornbusch 1993). Second, reliable economic data do not exist
for other states, especially before 1945. Finally, since many extant studies of the impact
of alliances on trade include only major powers, analyzing a sample composed of these
states allows us to directly compare our findings with existing results in the literature
(Gowa 1994; Gowa and Mansfield 1993; Morrow, Siverson, and Taberes 1998, 1999).
Further, we analyze aggregate bilateral trade flows. We do so because systematic
data on bilateral tariffs and other trade barriers do not exist for most of the twentieth
century and because an inverse relationship exists between trade barriers and trade flows.
Tariffs raise the price of imports; as such, they typically depress the volume of foreign
commerce.18 In addition, although a conclusive test of our argument requires data on
intraindustry trade and irreversible investments that are not yet available for the countries
and time period in our sample, the model of aggregate bilateral trade flows used in the
following analysis provides a useful first cut at the problem of whether the relationship
18 Of course, trade barriers will not affect trade volumes if demand is completely inelastic.
16
between alliances and trade varies across systems as well as with market structure and
power distributions.19
To analyze the effects of alliances on bilateral trade flows, we rely on the gravity
model, the workhorse for empirical research on overseas commerce. The gravity model
includes the gross national product (GNP) and population of both the importing and
exporting countries, as well as the geographic distance between them. The theoretical
foundations of the model predict that GNP will be directly related to trade, while both
population and distance will be inversely related to it (Anderson 1979; Bergstrand 1985,
1989; Deardorff 1984, 1998).20 In order to test our theory, we include alliances and the
structure of the international system in the model. As in earlier studies of the political
economy of trade, we also control for regime type, the existence of a military dispute
between states, and the extent to which trading partners have similar foreign policies
(e.g., Bliss and Russett 1998; Duffield 2002; Gowa 1994; Gowa and Mansfield 1993;
Kim 1998, chap. 8; Mansfield, Milner, and Rosendorff 2000; Morrow, Siverson, and
Taberes 1998, 1999).
Since the underlying form of the gravity model is multiplicative, it is estimated
after taking the natural logarithm of each variable.21 Thus, we analyze the following
model:
19 For some recent empirical studies of the role of scale economies in trade, see Antweiler and Trefler (2002) and Everett and Keller (2002). For discussions of the extent to which it is necessary or possible to differentiate between comparative-advantage and scale-economy based trade, see Helpman (1999) and Davis and Weinstein (2001). 20 Anderson (1979), Bergstrand (1985, 1989), and Deardorff (1998) have shown that the gravity model is consistent with a variety of different theories of international trade. 21 The gravity model’s multiplicative form has been justified in a number of ways. One intuitively appealing rationale is that as the national income or population of either i or j approaches zero, so should the amount of trade between them (Deardorff 1998).
17
logEXPORTij = log β0 + β1logGNPi + β2logGNPj + β3logPOPi +
β4logPOPj + β5logDISTANCEij + β6logBIPOLAR ALLYij +
β7logMULTIPOLAR ALLYij + β8logDISPUTEij + β9logDEMOCRACYij +
β10logSIMILARITYij + β11logWWI + β12logWWII + log εij. (1)
The dependent variable – logEXPORTij – is the natural logarithm of the value of
exports from state i to state j in year t, expressed in constant U.S. dollars.22 The
independent variables include logGNPi and logGNPj, the natural logarithms of the gross
national products of state i and state j, respectively, in year t-1, also expressed in constant
U.S. dollars; logPOPi and logPOPj, the natural logarithms of their respective national
populations in t-1; and logDISTANCEij, the natural logarithm of the shortest geographical
distance between i’s and j’s capital cities.
To assess the effects of alliances, we also include logBIPOLAR ALLYij and
logMULTIPOLAR ALLYij. The former variable indicates whether i and j are political-
military allies during a given year, t-1, in the bipolar era, which began in 1946. The latter
variable indicates whether i and j are allied during a given year, t-1, in the multipolar
period. To code both variables, we use the list of political-military alliances that the
Correlates of War (COW) Project (1993) compiled. We add to that list any states that
fought together in an international war and the Japanese-American Security Agreement,
an issue discussed at greater length below.
22 In light of our argument, it would be useful to exclude trade in military goods. However, data limitations make this impossible. Nonetheless, there are data on the total amount of trade in military goods between most country-pairs in our sample for four periods covered here: 1967-1976, 1976-1980, 1981-1985, and 1987-1991 (United States Arms Control and Development Agency 1983, 1994, 1999). On average, military trade was only about one percent of total dyadic trade during these periods; only rarely did it approach five percent. Thus, little reason exists to believe that trade in military goods accounts for the results reported below.
18
Next, logDISPUTEij indicates whether i and j are engaged in a militarized dispute
(MID) in year t-1. A MID exists when i and/or j threatens, displays, or uses force against
the other. We code disputes using data taken from Daniel M. Jones, Stuart A. Bremer,
and J. David Singer (1996) and from Zeev Maoz (2001). Further, logDEMOCRACYij
indicates whether both i and j are democracies in t-1. States are coded as democratic if
they are assigned a score of 6 or higher on Ted Robert Gurr’s index of institutionalized
democracy, an 11-point scale ranging from 0 (least democratic) to 10 (most democratic)
(Gurr, Jaggers, and Moore 1989; Jaggers and Gurr 1995). Note that each of the four
dummy variables just described takes on values of 1 and 0; in antilogarithmic form they
take on values of e (the base of the natural logarithms) and 1.
As we explain further below, our analysis builds on a recent set of studies in
which James D. Morrow, Randolph M. Siverson, and Tressa Tabares (1998, 1999)
analyze annual trade flows from 1907 to 1990 among France, Germany/West Germany,
Great Britain, Italy, Japan, Russia/Soviet Union, and the United States. As in their work,
we define SIMILARITYij as the taub correlation between the alliance portfolios of states i
and j in year t-1. This variable can range from –1 to 1; but since its lowest observed
value in our data is -0.475, we add 1.475 to SIMILARITYij so that the minimum value of
logSIMILARITYij is 0. Further, Morrow, Siverson, and Tabares (1998, 653) omit from
their sample the periods when World Wars I and II were waged on the grounds that little
trade data are available for these years. However, we have been able to collect a fair
amount of data on major-power trade flows in both periods. We have also collected data
for 1991, the last year of the bipolar era. Consequently, we examine all years (t) from
19
1907 to 1991.23 To ensure that our findings do not depend on wartime trade, however,
we include one dummy variable for the years when World War I was fought and a second
variable for the years when World War II was underway.
In addition, we make three other changes to the model and data that Morrow,
Siverson, and Tabares use. First, we correct various coding errors in their data set.
Second, where it is possible to do so, we replace missing data with data described in the
appendix to this paper, markedly increasing the number of observations for which
complete information exists. Third, Morrow, Siverson, and Tabares measure both
logEXPORTij and the independent variables described earlier in the same year, t. In
contrast, we measure each independent variable (except logWWI and logWWII) in year t-
1 to reduce the possibility of any simultaneity bias and to take into account the fact that
trade patterns are unlikely to respond immediately to changes in political conditions.
Finally, Morrow, Siverson, and Tabares maintain that the Japanese-American
Security Agreement is a unilateral rather than a mutual security guarantee. Thus, in
contrast to previous work (e.g., Gowa and Mansfield 1993; Gowa 1994), they do not
consider it an alliance. A uniform application of this standard, however, would dictate
the recoding of various other security agreements as well. For example, the North
Atlantic Treaty Organization (NATO) was, for all practical purposes, a unilateral U.S.
guarantee of European security throughout the Cold War. Therefore, we code the
Japanese-American Security Agreement as an alliance, but we also assess the robustness
of our findings to this coding decision.
23 Note that it is not possible to extend the analysis beyond 1991 because data on both MIDs and the similarity of alliance portfolios are unavailable.
20
The Statistical Results
As noted earlier, our sample is made up of country-pairs composed of France,
Germany/West Germany, Great Britain, Italy, Japan, Russia/Soviet Union, and the
United States. Each dyad, i and j, is measured twice in every year from 1907 to 1991,
once where exports from i to j are evaluated, the other where exports from j to i are
analyzed. The analysis of time-series cross-sectional data poses various well-known
statistical problems. Particularly important is the danger that the error term (log εij) will
be heteroskedastic as well as serially and contemporaneously correlated. Recent work
suggests that the following procedure can be used to address these problems: estimate the
parameters using ordinary least squares, purge the errors of serial correlation, and
generate “panel-corrected” standard errors that account for any heteroskedasticity and
contemporaneous correlation of the errors across dyads (Beck and Katz 1995).24 Like
Morrow, Siverson, and Taberes (1998, 1999), we use this estimation procedure and
assume that the errors for each dyad follow a first-order autoregressive process common
to all country-pairs in the sample.25
24 Note that the coefficient of variation based on this procedure tends to be much lower than that based on least squares regression. Thus, whereas the adjusted R2 is .61 when our baseline model is estimated using ordinary least squares – a figure that is typical for the gravity model – the adjusted R2s in Tables 1 and 2 are less than half that size. 25 An alternative method to model dynamics in the data is to include a lagged endogenous variable (Beck and Katz 1996). To take into account the concerns expressed by various observers about this method (Achen 2000; Maddala 1998) and to maintain consistency with Morrow, Siverson, and Taberes’s (1998, 1999) study, we model the errors as being serially correlated (AR1). However, we have also examined whether our results are robust to our modeling decision, partly because some previous studies of alliances and trade do include (an instrument for) a lagged endogenous variable (Mansfield and Bronson 1997a). To this end, we created an instrument for the lagged value of trade by regressing logEXPORTij in year t-1 on logGNPi, logGNPj, logPOPi, logPOPj, and logDISTANCEij in year t-2, logWWI, logWWII, and a dummy variable for each country in the sample. When we add to our model the predicted value of logEXPORTij in year t-1, the estimates of the alliance terms are much the same as those shown in
21
The results, which we present in the first column of Table 1, are consistent with
our argument. The effects of alliances on trade are both positive and statistically
significant, regardless of the structure of the international system. Moreover, alliances do
exert a larger effect on trade when the system is bipolar rather than multipolar. On
average, allies conduct over 60 percent more trade than non-allied countries after World
War II. The corresponding increase during the multipolar era is about 12 percent.26 The
difference between these estimated effects is highly significant (χ2 = 10.88; p = 0.001).
Most of the other variables in our model also exert a strong impact on bilateral
trade flows. As expected, trade is directly related to the national income of both states i
and j; it is inversely related to the distance between them; and each of these relationships
is statistically significant. As in Morrow, Siverson, and Taberes’s (1998, 1999) research,
however, the effect of population on trade is less consistent. Although the estimate of
logPOPj is negative and statistically significant, the estimate of logPOPi is positive and
insignificant.
In addition, we find considerable evidence that military disputes decrease trade,
while democracy and similar alliance portfolios increase it. The estimate of
logDISPUTEij is negative; the estimates of logDEMOCRACYij and logSIMILARITYij are
positive; and all three of them are statistically significant. Finally, and not surprisingly,
Tables 1 and 2. Thus, the effects of alliances do not depend on how we model dynamics in the data. The effects of national income, population, and distance, however, do change in unexpected ways when we include the predicted value of lagged trade. Similar problems have contributed to a general concern about using a lagged endogenous variable to model dynamics (Achen 2000). 26 The quantitative effect of alliance membership in a bipolar system, based on the estimates in the first column of Table 1, is given by eβ6 - 1β6 = e.481 – 1 = .62. The corresponding effect for alliances in a multipolar system is eβ7 - 1β7 = e.114 – 1 = .12.
22
less trade occurred during the world wars than at other times. The effects of each world
war on trade are negative, quantitatively large, and statistically significant.
It is important, of course, to assess the robustness of these results. First, some
observers argue that China was also a major power during much of the twentieth century.
We did not include China in our sample, because data about its economy and trade
patterns are sparse, especially prior to World War II. However, reliable Chinese
economic data are available for the period since 1960. We use this information to check
whether our findings are robust to the inclusion of China as a major power. As the
second column of Table 1 shows, adding this country to the sample has virtually no effect
on the impact of alliances or any other variable in equation (1).
Second, we address whether our results are robust to the use of a different
measure of foreign-policy similarity. Although Morrow, Siverson, and Taberes rely on
the taub correlation between alliance portfolios to measure SIMILARITYij, an alternative
measure of preference similarity – referred to as “S” – has been used with increasing
regularity in research on international relations (e.g., Gartzke 1998, 2000; Signorino and
Ritter 1999). The third column of Table 1 presents the results when we replace taub with
this index. In general, the results do not depend on which measure is used. It is worth
noting, however, that the estimate of logMULTIPOLAR ALLYij is about three-quarters
larger and much more highly significant in the third column than the first, providing even
stronger evidence that multipolar alliances influence trade flows. Nonetheless, their
impact continues to be significantly smaller than that of bipolar alliances. Since various
studies argue that S is a better index of foreign policy similarity than taub, the fit of our
model improves somewhat when S is used, and S is replacing taub with increasing
23
frequency in the literature, we will use it in the remaining analyses (Gartzke 1998, 2000;
Signorino and Ritter 1999). Nonetheless, the results rarely depend much on the specific
SIMILARITYij measure.
Third, recall that we added the Japanese-American Security Agreement to the
COW Project’s list of alliances and that we also code states that fought together in a war
as allies. Further analysis provides little indication that these coding decisions have any
substantial effect on the results. As the fourth column of Table 1 shows, the coefficient
of each alliance term remains positive and statistically significant even if we do not code
the U.S.-Japan Agreement as an alliance. In addition, alliances continue to have a larger
effect on trade during the bipolar than the multipolar period, although omitting this
agreement clearly reduces the impact of alliances somewhat in the post-World War II era.
Further, the fifth column of Table 1 indicates that the results are largely unaffected if we
no longer regard states that fought together in a war as allies.
Fourth, we analyze whether the results are robust with respect to the inclusion of a
number of variables that are omitted from equation (1). For example, many preferential
trading arrangements (PTAs) strongly influence trade flows (Frankel 1997; Mansfield
and Bronson 1997a, 1997b). To ensure that the effects of alliances on trade are not
artifacts of PTA membership, we add a dummy variable that indicates if states i and j are
European Community (EC) members in year t-1. The EC is the only PTA relevant to the
states in our sample. Including this variable is also important because its members are
allies. The results in the sixth column of Table 1 indicate that trade between EC
members is significantly greater than trade between other states, but including this
variable does not affect the coefficients of the other variables in the model. Of particular
24
importance for our purposes is that the effects of bipolar alliances and multipolar
alliances in this specification are virtually identical to those in the third column of Table
1.
We also account for the General Agreement on Tariffs and Trade (GATT), since
it is widely recognized that this institution fostered open trade among its members, many
of which were allied. The seventh column presents the results when a dummy variable
indicating whether states i and j are GATT members in year t-1 is included in the model.
The final column of Table 1 shows the results when we account for the EC and GATT.
The estimates of both variables are positive and statistically significant, indicating that
parties to GATT and the EC conduct more trade than other states. Moreover, the effects
of the remaining variables in the model – including logBIPOLAR ALLYij and
logMULTIPOLAR ALLYij – are quite robust with respect to whether we account for the
influence of these international economic institutions.
Next, it is useful to ensure that the observed relationship between alliances and
trade is not an artifact of any secular trend in both trade flows and the propensity of major
powers to ally with each other. The results provide no evidence that our results are
attributable to the effects of time. Including a trend in the model has very little affect on
signs, sizes, or significance levels of the coefficients in equation (1), and the estimate of
this variable is not statistically significant.
In addition, for each country in our sample, we have included all available data
from 1907 to 1991. However, the COW data set does not code occupied Germany (1946-
1954), Japan (1946-1951), or France (1943) as members of the interstate system (Small
and Singer 1982; Singer and Small 1994). To determine if foreign occupation influences
25
our results, we introduce a dummy variable that equals 1 if either state i or state j is
occupied in year t, and 0 otherwise. The estimate of this variable is neither statistically
significant nor sizeable. Moreover, including it has no bearing on the other coefficients
in the model.
Finally, it is important to ensure that our results do not reflect the effects of trade
flows on alliances. We therefore estimate a logistic regression in which the observed
value of the dependent variable equals 1 if states i and j are allies in year t, and 0
otherwise. We regress this variable on logEXPORTij, logGNPi, logGNPj, logPOPi,
logPOPj, logDISTANCEij, and logDEMOCRACYij – all of which are measured in year t –
as well as a natural spline function (with three knots) of the length of time since i and j
were last allied to account for any temporal dependence in the data (Beck, Katz, and
Tucker 1998). We then estimate the same model after estimating logEXPORTij in t-1
rather than t. In neither case is the estimate of logEXPORTij statistically significant.
Thus, consistent with the findings of other studies (Mansfield and Bronson 1997a), we
find no evidence that trade flows influence alliances. Consequently, there is little reason
to believe that problems of endogeneity afflict our results.
Scale Economies, Polarity, and Alliances
It seems clear, then, that alliances exert a positive and statistically significant
effect on major-power trade and that their influence is larger during the bipolar era, when
scale-economies motivated trade to a greater extent than during the multipolar era. In
this section, we examine whether the evidence is consistent with our argument that
alliances reduce the risk that a relatively small state confronts in liberalizing trade with a
larger counterpart when markets are imperfect. As we argued above, free trade will
26
maximize the real income of a small state under these conditions only if its large trading
partner can make a credible commitment to keep its markets open. An alliance enables
the large state to do so by making its adoption of a free-trade strategy incentive
compatible. Empirically, this implies that alliances should exert particularly powerful
effects on foreign commerce when scale economies motivate trade between states of
highly disparate size.
In a preliminary effort to test this argument, we amend equation (1) in the
following way:
logEXPORTij = log β0 + β1logGNPi + β2logGNPj + β3logPOPi +
β4logPOPj + β5logDISTANCEij + β6logALLYij + β7logBIPOLAR +
β8logDISPARITYij + β9(logALLYij × logBIPOLAR) + β10(logALLYij ×
logDISPARITYij) + β11(logBIPOLAR × logDISPARITYij) + β12(logALLYij ×
logBIPOLAR × logDISPARITYij) + β13logDISPUTEij +
β14logDEMOCRACYij + β15logSIMILARITYij + β16logWWI + β17logWWII
+ log εij. (2)
In equation (2), logALLYij is a dummy variable that equals 1 if states i and j are allied in
year t-1 and 0 otherwise. We include logBIPOLAR to capture the fact that scale
economies motivated trade to a greater extent during the period after World War II than
beforehand. It equals 1 during the bipolar period and 0 during the multipolar period.
Further, we include logALLYij × logBIPOLAR to capture the different effects of alliances
on trade in the bipolar and the multipolar eras.
Finally, DISPARITYij is a widely used measure of the relative size of trading
partners (e.g., Egger 2000; Helpman 1987). It is defined as follows:
27
DISPARITYij = 1 – [GNPi / (GNPi + GNPj)]2 - [GNPj / (GNPi + GNPj)]2
The larger the value of this index, which ranges from 0 to 0.5, the more similar are the
national incomes of states i and j. Our argument about credible commitments implies
that bipolar alliances should exert a particularly powerful effect on trade between states
of markedly different sizes. Hence, we also include the interactions between
logDISPARITYij and logALLYij, logBIPOLAR, and logALLYij × logBIPOLAR,
respectively.
To test this hypothesis, equation (2) is estimated using the same statistical
procedure we employed earlier. The results – which are shown in the first column of
Table 2 – indicate that each variable in the model except logPOPi and logPOPj has a
statistically significant effect on trade. They also provide support for our argument.
Bipolar alliances do exert a larger impact on trade as the distribution of economic power
between trading partners becomes more skewed.
To more fully interpret these results, it is useful to compare the influence of
bipolar alliances on the predicted volume of foreign commerce when states i and j are
about the same size to their influence when the gap between i and j widens. We begin by
expressing equation (2) in antilogarithmic form:
Xij(t) = C × [ALLYij exp(β6 + β9logBIPOLAR + β10logDISPARITYij +
β12(logBIPOLAR × logDISPARITYij))] × [BIPOLAR exp(β7 + β11log
DISPARITYij)] × [DISPARITYij exp(β8)], (3)
where
C=β0GDPiβ1GDPj
β2POPiβ3POPj
β4DISTijβ5DISPUTEij
β13DEMOCRACYijβ14 ×
SIMILARITYijβ15WWIβ16 WWIIβ17εij.
28
For the purposes of this analysis, C can be treated as a constant. We use equation (3) and
the estimates of β6-β12 in the first column of Table 2 to generate predicted values of Xij(t).
Since our principal interest is in the effect of bipolar alliances on trade as the size
distribution between states i and j becomes more skewed, we start by setting BIPOLAR to
e (and logBIPOLAR to 1). We then compare the effects of a change from the absence to
the presence of alliance on the predicted volume of trade when DISPARITYij at its mean
(.39) to the effects of such a change when the value of this variable is reduced by one
standard deviation (to .28). (Recall that the value of DISPARITYij falls as the trade
partners become increasingly unequal in size.) When DISPARITYij is evaluated at its
mean, an alliance increases the predicted value of exports by about 60 percent. As our
argument predicts, the impact of an alliance rises considerably – yielding more than a 110
percent increases in the predicted volume of trade – when the value of DISPARITYij is
decreased by a standard deviation (i.e., when the difference in the size of the trading
partners increases).
In contrast, multipolar alliances exert a far smaller impact on trade. To address
this issue, we set BIPOLAR to 1 (and logBIPOLAR to 0) and then vary the extent of
disparity in the same way as we did earlier. The results indicate that an alliance increases
the predicted volume of trade by roughly 15 percent when DISPARITYij is evaluated at its
mean, but has virtually no effect on trade when DISPARITYij is reduced by a standard
deviation. Thus, alliances during the bipolar period not only increase trade flows
between their members, but they also do so in a way that suggests that military coalitions
enable large states to tie their own hands with respect to trade policy.
Finally, we analyze whether these results are robust to: (1) the inclusion of
dummy variables for EC and GATT membership, and (2) the use of a modified gravity
model. First, the findings in the second column of Table 2 indicate that accounting for
29
the EC and GATT has little bearing on the effects of the remaining variables in equation
(2). Moreover, we continue to find that the impact of bipolar alliances on trade increases
as the difference widens between the GNPs of states i and j. During the bipolar era, an
alliance generates an 80 percent increase in the predicted volume of trade when the value
of DISPARITYij is one standard deviation below its mean and only about a 40 percent
increase when DISPARITYij is at its mean.
Second, a number of recent economic studies have modified the standard gravity
model in the following ways (e.g., Egger 2000; Helpman 1987). First, they replace
logGNPi and logGNPj with log(GNPi + GNPj). Second, they replace logPOPi and
logPOPj with a measure of the difference in the relative factor endowments of states i and
j that is expressed as follows:
RFLACij = │log(GNPi/POPi) – log(GNPj/POPj)│
The expectation of this model is that the estimates of log(GNPi + GNPj) and RFLACij will
be positive. Third, these studies include DISPARITYij. Finally, they introduce country-
specific fixed effects into the model. We modify equation (2) in the same way (of course,
DISPARITYij was already in the model) in a final effort to assess the robustness of the
results. We also include the dummy variables for EC and GATT membership since our
earlier analyses indicate that they are strongly related to trade.
The third column of Table 3 displays the results, although we do not report the
country-specific fixed effects (which we include for every state in the sample except the
United States, the country that we arbitrarily designate as the reference category) to
conserve space.27 As expected, the estimates of log(GNPi + GNPj) and RFLACij are
27 Various observers have argued against including fixed effects in models like ours (e.g., Beck and Katz 2001). For that reason and because gravity models rarely include such effects, we have
30
positive and statistically significant. Moreover, with a few exceptions (specifically,
BIPOLAR and DISPARITYij), the remaining results are quite similar to the previous
findings. Of particular importance is that a bipolar alliance produces about a 40 percent
rise in the predicted volume of exports when the gap in the trade partners’ national
incomes is relatively large and only about a 10 percent increase when the gap narrows.
Although the absolute size of the increases in trade stemming from an alliance is clearly
reduced when we control for the EC and GATT and when the modified gravity model is
analyzed, these results continue to provide strong evidence for our argument that
alliances have a larger influence on trade when markets are imperfect and the distribution
of market power between trade partners is skewed.
Conclusion
The relationship between security and trade has been a longstanding source of
controversy in the field of international relations. In recent years, various studies have
argued that allies trade more freely with each other than do other countries. Some of
these studies maintain that alliance volatility explains the stronger effect of alliances on
trade in bipolar than in multipolar systems. The findings of our empirical analysis of
major-power trade during the twentieth century are consistent with this argument.
However, our analysis also casts new light on the way in which alliances
influence trade. From a theoretical standpoint, we argue that the existing literature on the
not included them thus far. If, however, fixed effects are included in a gravity model, it is preferable to do so on a country-specific rather than a dyad-specific basis (Beck and Katz 2001, 493; Egger 2000; Mansfield and Bronson 1997a). As Antoni Estevadeordal, Brian Frantz, and Alan M. Taylor (2002, 15, n. 37, emphasis original) point out, including dyadic fixed effects to explain bilateral trade leads to the absorption of “any time-invariant pair characteristics,” which represents a “complete rejection of the gravity model framework.” As such, it is “completely detached from any theoretical model of trade.”
31
political foundations of international trade has been tied for far too long to standard trade
theory.28 Economists have turned to models based on scale economies with increasing
regularity to explain recent trends in both the composition and distribution of trade. It is
time for political scientists to follow suit.
As we show here, doing so can have significant implications with respect to the
analysis of how relatively small states operate in international markets. The literature on
the political economy of international trade has simply assumed that they will adopt free
trade. However, if firms cannot enter and exit markets freely, smaller states will be
unwilling to condition their production on the availability of export markets in the large
state. Inducing them to do so requires the large state to make a credible commitment to
adhere to free trade.
Thus, the international political economy resembles other political arenas in
which marked power asymmetries exist – e.g., cases in which a monopoly of power
threatened efficient trade levels in the medieval era (Greif, Milgrom, and Weingast 1994)
or jeopardized the ability of seventeenth-century monarchs to obtain loans at less than
prohibitive rates (Weingast and North 1988). In each case, the most powerful actor in the
system needed to find a way to tie its own hands. In the international system, we argue,
an alliance can help its dominant member to do so. Moreover, we find that the existing
data support this argument.
28As the references in the text suggest, Yarbrough and Yarbrough (1992) represents an important exception.
32
Appendix
The purpose of this Appendix is to describe the data on trade, national income,
population, and distance used in our analyses. For each of these variables, we began with
Morrow, Siverson, and Taberes’s (1998) data, which are described in the appendix to
their article. We filled in missing trade data using Mitchell (1992, 1995), with the
following exceptions. First, French exports to Japan from 1907-1908 and from 1914-
1921 were taken from Japan (1918, 1924). French exports to Russia during World War I
were taken from France (1919), Nolde (1928), and Clarke and Matko (1983). French
exports to the Soviet Union during World War II were taken from Clarke and Matko
(1983).
Second, German exports to Japan were taken from Japan (1918, 1924,1939) and
Supreme Command for the Allied Powers (1948). German exports to the United States
were taken from the U.S. Department of Commerce, Monthly Summary of Foreign
Commerce of the United States, from 1914-1922 and 1942-1945. Third, Italian exports
to Japan were taken from Japan (1918, 1924, 1939). Italian exports to Russia were taken
from Italy (1916) and from Clarke and Matko (1983). Italian exports to the United States
from 1942-1946 were taken from the U.S. Department of Commerce, Monthly Summary
of Foreign Commerce.
Fourth, Japanese exports not found in Mitchell were taken from Japan (1918,
1924, 1939), Clarke and Matko (1983), U.S. Department of Commerce (1942), and U.S.
Department of Commerce, Monthly Summary of Foreign Commerce of the United
States. Japanese trade data for the period immediately after World War II are taken from
the Supreme Command for the Allied Powers (1948-1953). Fifth, Russian exports to
33
France in 1914, 1920, and 1921 were taken from France (1919) and Clarke and Matko
(1983). Russian/Soviet exports to Italy were taken from Italy (1916) and Clarke and
Matko (1983). Russian/Soviet exports to Japan were taken from Japan (1918, 1924,
1939), U.S. Department of Commerce (1942), and Clarke and Matko (1983).
Russian/Soviet exports to the U.S. were taken from U.S. Department of Commerce,
Monthly Summary of Foreign Commerce of the United States. Sixth, data on United
States exports were taken from U.S. Department of Commerce, Monthly Summary of the
Foreign Commerce of the United States. Data for China – which we analyze for the
period after 1960 to address the robustness of our results – are taken from the
International Monetary Fund’s Direction of Trade. Finally, for all countries in the sample
except China, missing trade data from 1946 and 1947 were filled in using U.S.
Department of Commerce (1950).
Note that, in cases where exports from state i to state j were not available but j’s
imports from i were available, the latter figure was used. In all cases, trade data
denominated in foreign currencies were first translated into current dollars. The primary
source for exchange rates was the United States Bureau of the Census (1924, 1934,
1947). Most missing values were filled in with data from Bidwell (1970), United Nations
(1951), and Japan (1918). In some remaining cases during World War II, exchange rate
data were unavailable (for example, when the United States government ordered that
currency markets in the New York stop trading the currencies of Axis states during
World War II). In these cases, the final quote available before the war was used. These
cases are as follows. For the ruble in 1939 and 1940, a spot quote in January of 1939
from Bidwell (1970) was used. The 1942 exchange rates for marks were taken from the
34
last quote in the U.S. Census Bureau (1947) for 1941. For Japan, the exchange rate from
1942 was used for 1943 and 1944. To convert trade in current dollars to constant dollars,
we followed Morrow, Siverson, and Taberes in using data on the Consumer Price Index
drawn from the U.S. Census Bureau (1962, 1996) and Liesner (1989).
To fill in data on national income, we relied primarily on Maddison (1995).
Additional data on Russian/Soviet national income in 1907-1912 and 1914-1927 were
taken from Gregory (1982) and Clarke and Matko (1983). Data on Chinese national
income were taken from the World Bank Development Indicators.
To fill in data on national population, we again relied on Maddison (1995).
However, he does not include data on Soviet population for 1907-1912, 1914-1919, and
1941-1945. For 1907-1912, data were taken from Gregory (1982). For 1914-1919, we
relied on the Correlates of War national material capabilities data set. For 1941-1945,
Soviet population was taken from Clarke and Matko (1983). For a few years
immediately after World War I, Maddison (1991) disagrees with Maddison (1995) on
Germany’s population. In these cases, we rely on the earlier study.
Morrow, Siverson, and Taberes measure the distance between capital cities.
When filling in missing observations, we rely on the values they report, except in the case
of Germany. Berlin was treated as its capital from 1907 to 1948 and from 1990 to 1991.
Bonn was treated as its capital from 1949 to 1989.
Finally, all of the data we added to Morrow, Siverson, and Taberes’s data set on
alliances and the similarity of states’ foreign policy preferences (both taub and S) were
taken from Eugene v2.001 (Bennett and Stam 2000). All of the data we added on
military disputes were taken from Maoz (2001). All additional data on regime type were
35
taken from the Polity98 data set. Like Morrow, Siverson, and Taberes, we code states for
which data on regime type is missing in a given year as non-democratic.
36
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Table 1. Estimated Effects of Gross National Product, Population, Distance, Alliances, Military Disputes, Regime Type, Similarity of Relations, World War, the EC, and GATT on Trade Flows Between Major Powers, 1907-1991. Variable Model 1.1 Model 1.2a Model 1.3 INTERCEPT -2.140** -2.608** -3.457**
(.737) (.629) (.727) GNPi .533** .538** .513**
(.056) (.038) (.053) GNPj .401** .369** .374**
(.046) (.032) (.043) DISTANCE -.396** -.436** -.418**
(.041) (.022) (.041) POPi .027 .079 .139 (.128) (.051) (.128) POPj -.210* -.019 -.089
(.110) (.073) (.107) BIPOLAR ALLY .481** .465** .531**
(.100) (.075) (.097) MULTIPOLAR ALLY .114* .100** .189**
(.068) (.054) (.059) DISPUTE -.315** -.307** -.295** (.043) (.032) (.041) DEMOCRACY .740** .759** .858**
(.089) (.075) (.096) SIMILARITY (TAU) .408** .478**
(.151) (.123) SIMILARITY (S) 1.555** (.230) WWI -.823** -.820** -.763**
(.140) (.122) (.131) WWII -.997** -1.004** -.809** (.144) (.110) (.140) N 3346 3774 3209 R2 .20 .20 .23
44
Table 1 – continued Variable Model 1.4b Model 1.5c Model 1.6 INTERCEPT -3.813** -3.331** -3.739**
(.725) (.728) (.749) GNPi .536** .514** .500**
(.053) (.053) (.053) GNPj .397** .375** .360**
(.043) (.043) (.043) DISTANCE -.435** -.416** -.384**
(.040) (.041) (.041) POPi .139 .129 .177 (.128) (.128) (.130) POPj -.086 -.098 -.050
(.108) (.108) (.109) BIPOLAR ALLY .325** .534** .475**
(.092) (.097) (.097) MULTIPOLAR ALLY .203** .191** .192**
(.060) (.072) (.058) DISPUTE -.301** -.303** -.296** (.042) (.041) (.041) DEMOCRACY .915** .857** .848**
(.098) (.097) (.095) SIMILARITY (S) 1.585** 1.456** 1.511**
(.237) (.245) (.229) WWI -.771** -.757** -.760**
(.132) (.131) (.130) WWII -.841** -.796** -.809** (.141) (.140) (.139) EC .401** (.120) N 3209 3209 3209 R2 .23 .23 .23
45
Table 1 – continued Variable Model 1.7 Model 1.8 INTERCEPT -3.248** -3.849**
(.711) (.741) GNPi .490** .482**
(.053) (.053) GNPj .348** .340**
(.046) (.045) DISTANCE -.431** -.403**
(.040) (.040) POPi .179 .202 (.126) (.128) POPj -.042 -.018
(.111) (.113) BIPOLAR ALLY .406** .372**
(.094) (.095) MULTIPOLAR ALLY .191** .194**
(.060) (.059) DISPUTE -.302** -.303** (.041) (.041) DEMOCRACY .762** .762**
(.092) (.092) SIMILARITY (S) 1.581** 1.547**
(.231) (.230) WWI -.753** -.751**
(.134) (.133) WWII -.827** -.827** (.140) (.139) EC .305** (.117) GATT .408** .376** (.114) (.113) N 3209 3209 R2 .25 .25
46
Table 1 – continued Note: Entries are Prais-Winsten regression estimates, with panel corrected standard errors in parentheses. a Results are generated after including dyads involving China for the period 1960-1991. b Estimates are generated after excluding the Japanese-American Security Agreement from the list of alliances. c States that fought together in a war but did not have a formal alliance are excluded from the list of alliances. ** p < .01; * p < .05. One-tailed tests of statistical significance are conducted for the estimates of GNPi, GNPj, DISTANCE, POPi, POPj, BIPOLAR ALLY, and MULTIPOLAR ALLY since their signs are specified by the gravity model and by our theory, respectively. Two-tailed tests are conducted for the remaining estimates.
47
Table 2. Estimated Effects of Alliances, Polarity, and the Similarity of Economic Size on Trade Flows Between Major Powers, 1907-1991. Variable Model 2.1 Model 2.2 Model 2.3a INTERCEPT -4.936** -5.095** -.522
(.769) (.779) (1.277) GNPi .577** .545** (.056) (.056) GNPj .435** .401**
(.046) (.048)
GNPi + GNPj .972** (.089) DISTANCE -.525** -.531** -.732**
(.039) (.039) (.049) POPi .151 .262
(.125) (.128) POPj -.067 .047
(.103) (.112)
RFLAC .369** (.080) ALLY .618** .603** .622**
(.124) (.123) (.121) BIPOLAR .757** .522 1.031**
(.272) (.271) (.290) DISPARITY -.838** -.822** .341**
(.116) (.114) (.165) ALLY × BIPOLAR -.790** -.973** -1.232** (.232) (.235) (.230) ALLY × DISPARITY .502** .503** .374** (.108) (.107) (.100) BIPOLAR × DISPARITY 1.242** 1.160** 1.116** (.186) (.183) (.181) ALLY × BIPOLAR × -1.229** -1.258** -1.109** DISPARITY (.204) (.203) (.190) DISPUTE -.294** -.293** -.332** (.043) (.042) (.041) DEMOCRACY .975** .871** .531**
(.097) (.093) (.094)
48
Table 2 – continued Variable Model 2.1 Model 2.2 Model 2.3a SIMILARITY (S) 1.750** 1.709** 1.792** (.228) (.224) (.223) WWI -.715** -.711** -.817**
(.127) (.125) (.122) WWII -1.024** -1.071** -1.074** (.157) (.154) (.157) EC .347** .310* (.120) (.127) GATT .460** .273* (.116) (.112) N 3209 3209 3209 R2 .27 .27 .32 Note: Entries are Prais-Winsten regression estimates, with panel corrected standard errors in parentheses. a Results are generated after including country-specific fixed effects. **p < .01; * p < .05. One-tailed tests of statistical significance are conducted for the estimates of GNPi, GNPj, DISTANCE, POPi, POPj, GNPi + GNPj, and RFLAC since their signs are specified by the gravity model. Two-tailed tests are conducted for the remaining estimates.
49