Trade and Politics: A Network Perspective
Trade has increased in every era, resulting in a vastly global exchange network of resources. Certainly, “trade along with war has been central to the evolution of international relations” (Gilpin,1987:171). Realists, neo-realists, and other structural theorists of political hegemony concur when it comes to the role of economic dominance and trade multiplicity in gauging the hegemonic position of a given nation-state.
This essay, using data on trade of miscellaneous manufactures of metal among 80 countries in 1994, aims to contribute to the debate within political economy on whether political stature is synonymous with a country’s economic preponderance. By comparing the networks of the United States (US), Germany, Japan, China, and Brazil, this research will analyse whether a country’s position within the global trade network aligns with its proclaimed political position in the international arena. In all, the findings largely match the theoretical expectations posited by structural theorists.
Power is a central concept in international relations; a ‘goal’ of nations that is used to ‘influence’ others (Morgenthau,1978:4-15). Here, ‘power’ is the probability that an actor within a social relationship will be able to carry out his or her own will notwithstanding opposition and irrespective of the basis on which that probability rests (Weber,1947:152). Translating trade preponderance into political power may seem problematic to some. Here, the propensity to underscore the economic foundations of international politics may face denunciation of being reductionist “neo-Smithian” Marxism (Brenner,1977:86). Alternative interpretations of the global structure also afford agency to military, diplomatic, and cultural determinants of global politics (Snyder & Kick,1979). While these postulations may provide a more nuanced notion of hegemony, the current enquiry nevertheless operationalises an economic analysis that can be defended “with recourse to interpretability and the scientific goal of parsimony” (Smith & White,1992:865) Furthermore, Uzzi (1997) suggests the theoretical likelihood of economic exchange materialising into a multiplex tie, which within the global system, could be construed in political terms. Similarly, relational proximity advocates that egos in a business transaction are enticed into developing reciprocity and building trust in order to maintain trade (Axelrod,1984). Over time, as these functions are reciprocated, actors are “predisposed to interpret their partners’ actions favourably, even in uncertain situations” (Ferriani et al.,2013:11). Similarly, Kuwabara (2011:578) quips that “economic exchange can reinforce bonds of cohesion.” This, for one, might help explain Britain’s defense of Saudi Arabia despite the latter’s human rights violations, given the prevalence of UK-Saudi trade (RT,2017). Likewise, China’s influence in African politics has amplified now that the country has deepened trade ties with the continent (Albert,2017).
Since the Soviet Union’s eventual disintegration in 1991, commentators noted that “American hegemony was complete” (Babones,2015), wherein the US confronted no serious contenders for the vocation of the global policeman (Conry,1997). Analysts purported the nation’s political dominance to its “[s]teady…macroeconomic growth” (Dymski,2002:1).
That said, even in the 1990s, there were other nations that were rising politically. In 1992, for example, Senator Paul Tsongas quipped: “[T]he Cold War is over – Japan won” (cited by Dumbrell,1997:16). Similarly, several authors (Stares,1992;Geipel,1993) prophesied German hegemony after the fall of the Iron Curtain.
Thus, using the hierarchy suggested by neorealist theorists and world-system exponents, this essay postulates that if a country’s position within the global trade regime were to align with its proclaimed political position in the international arena, then the network analysis would show that US would be the most ‘powerful’ nation, with Germany and Japan in close pursuit; followed by the then-emerging China and the developing Brazil.
The dataset used includes statistics on trade among 80 countries in 1994 (see Figure 1). The arcs signify imports into one state from another (in $1,000). Here, miscellaneous manufactures of metal, which exemplifies high-technology merchandises or heavy manufacture is chosen, for it exemplifies the cluster of ‘capital-incentive’ trade that tends to move together in the global economy (Nemeth & Smith,1985). This is particularly helpful in understanding ‘power’ for, according to the ‘world-system theory’, it is primarily the core and dominant countries that export such goods (Wallerstein,1974).
Both, UCINET and NETDRAW programmes are used in this analysis. The former to conduct the statistical analysis, while the latter for its ability in visually depicting networks.
The network approach underlines that “power is inherently relational,” and that “an ego’s power is alter’s dependence” (Hanneman & Riddle,2005). Here, structural analysis “equates the power of a particular node to its position in the network, defined by its persistent relationships with other nodes.” Relatedly, Fernandez and Gould (1994) contend that the configuration in which an ego is embedded in the relational network inflicts ‘constraints’ and bestows ‘opportunities’. Thus, those that encounter lesser constraints and possess greater opportunities than their peers are in advantageous structural positions. Thus, using network theory, we can postulate that for a country to be a global hegemon, it should fulfil the following criteria:
a) A relatively less dense ego network:
Network density is the number of existing arcs within the network divided by the possible number of arcs. In a seminal treatise, Granovetter (1973) contended that densely-connected actors are liable to have access to the same information, thereby leading to redundant connections. Similarly, Hansen (1999) argues that the conformity in alters in such networks reduces an ego’s autonomy and leaves little opportunity for useful information to arise from other cliques.
b) A high degree and closeness centrality:
Hafner-Burton and Montgomery (2006:11) propose that states with high degree centrality can “withhold social benefits such as membership and recognition or enact social sanctions…as a method of coercion.” For instance, the US has continuously denied affording Palestine membership into the United Nations due to its ‘central’ position in the world-system. Similarly, Wasserman and Faust (1994) claim that actors who have a strong in-degree centrality, are said to be ‘prominent’ or have ‘high prestige’; these nations can be construed as important as several nations wish to trade with them and to export to them. Conversely, those with a high out-degree are considered ‘influential’, with Freeman (1979) even going so far as to argue that those actors with the most connections are more likely to be ‘powerful’. Thus, actors having more ties with others may be in strategically influential positions as they may have access to the resources of other nations, akin to Barney and Clark’s (2007) resource-based theory.
This essay will primarily gauge centrality via the Freeman-degree measure; but also utilize the Bonacich and Eigenvector approaches, which circumscribe centrality as a function of an ego’s alters and the alter’s connections.
c) A higher number of structural holes:
States could gain power as ‘brokers’, wherein power is rested in a node that bridges structural holes – caused by an absence of a trade link between other nodes – within the network. Burt (2000:353), for example, argued that structural holes “create a competitive advantage for an individual whose relationships span the holes.” Thus, one’s structural position may “impose constraints on autonomy as well as offer opportunities for influence” (Hafner-Burton,et al.,2009:571). This can be calculated in two ways:
i. High ‘effective size’, which exemplifies the actual size of an ego’s network minus its redundancy.
ii. Less ‘constrain’, which measures the extent to which a country’s trading partners all receive commodities from one another.
Results and Discussion:
a) Ego network density and other basic measures:
As can be seen from Table 1, the German and American networks are the largest. Here, ‘size’ is the ego plus the number of nodes that one-step out of the ego. This shows that Germany trades with all but three other nations within the global network. Conversely, Brazil has only 30 alters. Brazil also has the highest density of the chosen countries – and as Figure 2 illustrates – the country is embedded in a dense local structure. It does not appear to trade with many others; with ties dominant in South America, North America, and Western Europe. This makes intuitive sense; as of 1994, EU membership was constrained to only 12 (predominantly Western European) members. Additionally, it is not only true that 31% of all possible ties amongst its alters are present, but several alters can be seen to trade ‘heavily’ with each other (as evidenced by the tie strength in value of trade).
On the other hand, Germany and the US have many alters, but relatively less density. Additionally, they have a higher ‘brokerage’ value, which perceives an ego as the ‘go-between’ for pairs of other nations. In Brazil’s ego-network, if the other nations are not trading with each other, Brazil has the potential to be a ‘broker’. Moreover, the ‘Normalised Broker’ assesses the extent to which the country’s role is that of a broker by dividing the number of pairs that are not directly connected by the number of ordered pairs. Thus, Germany, which has 2486 unconnected pairs within its network – and therefore as many instances of being the broker – embraces that position 85% of the time. Similarly, the US plays the broker 84% of the time, while having 2140 unconnected trade ties. Chinese and Japanese networks are similar in these measures and lie between the two Western Giants and the Latin American emerging economy.
b) Freeman, Bonacich, and Eigenvector centralities:
As can be seen from Table 2, the Freeman degree measure supports our hypothesis. The measure ranks nations in descending order of degrees – with the US coming on top, followed closely by Germany.
The last two columns deal with the standardised scores of in-and-out degrees, which aid us in comparing across networks of different sizes and densities. Again, the US and Germany have the highest standardised-degree scores.
A quick analysis of the ‘meso’ level is warranted – to see what the distribution of degree centrality looks like across actors in the global sphere. Freeman’s network centralisation measures show the variability in the degrees of actors in our observed network as a percentage of that in a star network of the same size; the latter in effect being a centralised global system encompassing a single hegemon connected to all other nations that do not trade amongst each other. Here, the out-degree graph centralisation score is merely 4.2% and the in-degree is 3.5%, which is not surprising given the preponderance and complexity of international trade, akin to Bhagwati’s (1995) quip of global trade ties exhibiting a ‘spaghetti bowl effect’, wherein the “crisscrossing” network ties are equated with the “strands of spaghetti tangled in a bowl” (Akira,2006). This may lead one to believe that power of individual nations does not vary significantly and, in general, positional advantages are not as unequally allocated as previously theorised. However, considering the ‘macro’ level analysis at hand, suffice to say that 4.2% centralisation is rather high.
Moving on to the Bonacich measure, which gives importance to the networks of the alters too, we notice a familiar scenario (see Table 3).
Looking at the absolute value of the power measures, we notice that the US is clearly the most central, with Japan overtaking Germany. This shows they not only have a high degree, but are also connected to other nations with a high degree. Japan’s overtaking of Germany, despite the latter having a higher Freeman centrality signifies that Japan may not have greater trade connections, but has “the right connections” (Portes,2000:4).
Similarly, by calculating the eigenvector values, Table 4 shows that the US is the most central actor in terms of the ‘global’ or ‘overall’ structure of the network. Japan, once again, surpasses Germany, which may indicate that Germany’s trade network is comparatively more locally concentrated – hinting at the preponderance of German trade within the European Union.
That said, the eigenvector values need to be analysed with great caution as only 43.6% of the global variation in distances is accounted for by the first factor (or the largest eigenvalue). This means that less than half of all the distances among nations are reflective of the main dimension or pattern, and the dominant pattern is not doing a thorough job of relating the data.
c) Effective Size, constrain, and other structural measures:
Table 5 illustrates how Germany has the largest effective size, followed by the US. China and Japan have similar values, whereas Brazil, as usual, is trailing the pack. This can highlight that Germany has less ‘redundant’ actors in its network, in comparison to US and has greater structural holes, which, arguably, would put it in an advantageous position vis-à-vis its other trade (and political) rivals. Thus, countries with networks opulent in structural holes would be those which “know about, have a hand in, and exercise control over, more rewarding opportunities” (Burt,2000:355). This, in turn, would increase Germany’s bargaining positions as the trade partners would have lesser (or non-existent) alternatives. However, once the Effective Size is normed (with its actual size), it leads to the ‘efficiency’ measure; one where the US and Germany have similar outcomes.
Similarly, Germany and the US face the least constrain; indicating that most of these countries’ trading partners do not have trade links with each other. This is an interesting outcome, for Burt (1992) postulates that actors who have several links to others may, in effect, evade autonomy instead of gain power. This, however, is not the case in our global network.
This essay’s more delineated goal was to work within the confines of international political economy and use the results of the network analysis of commodity trade flows to gauge some postulations related to the amalgamation of political hegemony and international trade. In the 1990s, the declinist school asserted that America, vexed with ‘imperial overstretch’ (Kennedy,1987), was hitherto in comparative decline; while others prophesied the second coming of Asia or the ascent of Germany. Our analysis not only supports Krauthammer’s (1990:23) insistence that the decade was America’s “unipolar moment,” but also those structural theorists that equate a nation’s economic exchange to its “given structural position within the world-system” (Evans,1979:15).
Furthermore, network analysis affords an empirical insight into the familiar characteristics of international relations. For instance, several of our measures highlight the export-oriented outlook of the countries analysed. The Freeman-degree particularly shows that the Asian economies had a significantly greater out-degree to in-degree; a crucial point in hindsight, given their contemporary rise. Here, the theory of mercantilism can be alluded to, wherein states are seen to act purely out of self-interest, continually striving to increase their national wealth and power. In an economic setting, nations seek to maximize their net exports and pursue only what is economically feasible for themselves (Gilpin,1987). In this vein, future research could partake in multiple network analyses to empirically expound on qualitative descriptions of change in the international arena.
 A significant complexity with using Bonacich power or eigenvector centrality is the – eventually empirical – difficulty of exactly how much the centrality of alters matters, and if being linked to strong others (beta>0) or weak others (beta<0) increases centrality. This essay will use the former, in line with other network analysts (Hafner-Burton,et al., 2009) and the ‘competitive advantage’ principle of liberal theorists who articulate that trade creates strong, positive ties (Broome,2014).
 This calculates the impact an ego receives for each invested trade tie.
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