Published online by Cambridge University Press: 13 June 2011
Despite substantial evidence that international trade has promoted peace in the post—World War II era, the commercial peace research program still faces an important historical challenge. Dramatic economic integration in the nineteenth century failed to prevent the increasing interstate hostilities that culminated in the outbreak of war in 1914. This article uses a theoretical revision grounded in standard trade theory to reexamine the relationship between commerce and peace in the fifty years before World War I, a period often referred to as the first era of globalization. The article focuses on domestic conflict over commercial policy rather than on interdependence to understand the conditions under which globalization promotes peace. In a sample dating from 1865 to 1914, the authors find that lower regulatory barriers to commerce reduce participation in militarized interstate disputes. Contradicting conventional wisdom, this evidence affirms a basic premise of commercial liberalism during the first era of globalization—free trade promotes peace.
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6 We define the level of free trade in an economy as inversely related to the extent of government regulation of international commerce. As aggregate tariff levels rise, the quantity of imports entering the country duty free should decline.
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21 Different predictions about the distributional effects of globalization between the Heckscher-Ohlin and Specific Factors model of trade derive largely from the different assumptions about the costs of moving a factor from one sector to another. The Heckscher-Ohlin model assumes zero costs and has been used to predict that societal cleavages created by globalization depend on class. Owners of abundant factors favor globalization, while owners of scarce factors oppose it. However, the specific factors model becomes more appropriate as these costs become positive. Under these circumstances, societal cleavages are more likely to be based on industry differences. For an example of class-based analysis, see Rogowski, Ronald, Commerce and Coalitions: How Trade Affects Domestic Political Alignments (Princeton: Princeton University Press, 1989)Google Scholar. For a test of these distinctions between class and industry, see Hiscox, Michael, “Commerce, Coalitions, and Factor Mobility: Evidence from Congressional Votes on Trade Legislation,” Commerce, Coalitions, and Factor Mobility: Evidence from Congressional Votes on Trade Legislation, 96 (September 2002)Google Scholar. We will not explore these distinctions any further here. Rather we wish to exploit the simple insight that globalization produces societal conflict and not all domestic groups will necessarily support the further integration of national markets into the global economy. Our analysis is similar to that of Solingen (fn. 15) who focuses on the domestic struggle between internationalist and statist-nationalist-confessional coalitions.
22 Again, this depends on the assumptions made about the costs of moving across industries. As costs approach zero, owners of abundant factors employed in the exporting industry will see income rise. As the costs of redeployment increase, the income of factors specific to the exporting industry will rise.
23 We distinguish between export-oriented firms and firms that are competitive in international markets without state assistance for the following reason: Firms or industries may orient their sales to international markets but rely on state assistance to be competitive there. For reasons discussed elsewhere in this article, such firms have been captured by the state and are less likely to support restrained national interests and peace. Prussian landowners prior to World War I are an example of this dynamic. This group relied on subsidies to export grain to western Russia and supported an aggressive stance against Russia during the press war in the six months prior to July 1914.
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45 Bairoch (fn. 44), 57.
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51 Blattman, Clemens, and Williamson (fn. 41).
52 The eight deleted colonies from the Williamson data set are: Australia, Burma, Canada, Ceylon, India, Indonesia, New Zealand, and the Philippines.
53 Mitchell's data allowed us to add Belgium, Bulgaria, the Netherlands, Romania, and Switzerland to the sample. These cases account for 168 cases in the baseline model sample of 1,293. Additionally, data from both Williamson and Mitchell is available for 519 of these 1,293 cases. The bivariate correlation between these two data sources is 0.9851. Brian Mitchell, International Historical Statistics (New York: Palgrave Macmillan, various years).
54 While annual counts ranged between thirty-three and forty-four states in the international system during this period, there were on average thirty-nine. The following thirty-two states are included in our sample: Argentina, Austria-Hungary, Belgium, Brazil, Bulgaria, Chile, China, Colombia, Cuba, Denmark, Egypt, France, Germany, Great Britain, Greece, Italy, Japan, Mexico, the Netherlands, Norway, Peru, Portugal, Romania, Russia, Serbia, Spain, Sweden, Switzerland, Thailand, Turkey, the United States, and Uruguay.
55 We utilize version 2 of the Polity IV, which converts regime scores previously classified as “interregnum” or “transition” to the twenty-one-point scale. For a discussion of these coding rules, see http://www.cidcm.umd.edu/inscr/polity/convert.htm; and Monty G. Marshall and Keith Jaggers, Polity IV Project: Political Regime Characteristics and Transitions, 1800–2002. Available at http:// www.cidcm.umd.edu/polity.
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60 While we do not include these coefficient estimates in our tables for reasons of space, this technique includes one counter and three cubic splines in each of the models. Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard, “Taking Time Seriously in Binary Time-Series-Cross-Section Analysis,” Taking Time Seriously in Binary Time-Series-Cross-Section Analysis, 42 (October 1998)Google Scholar. To counter within-panel heteroskedasticity we employ Huber-White standard errors, which are reported in parentheses below the parameter estimates.
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65 Oneal and Russett (fn. 4), 24. In a series of regressions where the sample was restricted to the observations in a given year, our tests show a positive coefficient on democracy until 1905. In the years 1906 to 1914, the coefficient is negative in 1906,1907,1912, and 1914.
66 These suggestions about the foreign-policy implications of limited political participation in nineteenth-century democracies are consistent with the arguments of the selectorate model found in Bueno de Mesquita et al. (fn. 34). They argue that the proportion of society responsible for selecting political leaders is the crucial component of democracy that shapes decisions for war and peace. The need to cultivate political support from a majority of the population leads democratic officials to be concerned about public-policy failures. They avoid entering war when the probability of victory is low. However, when entering war, they devote more resources to the effort to ensure victory than do autocratic regimes. Peace thus emerges from democratic hesitation to wage war against powerful adversaries and the unwillingness of many states to attack democracies because of the latter's tendency to contribute more resources to victory.
67 The median value for Protect is 0.105. Its 90th percentile value is 0.313. In these simulations we hold all other variables constant at their medians. King, Gary, Tomz, Michael, and Wittenberg, Jason, “Making the Most of Statistical Analysis: Improving Interpretation and Presentation,” Making the Most of Statistical Analysis: Improving Interpretation and Presentation, 44 (April 2000)Google Scholar.
68 The numbers in parentheses immediately below the shift in the likelihood of conflict are a 95 percent confidence interval on that substantive result.
69 In these 939 cases, the bivariate correlation between openness and protection is -0.155. We relied on three sources to construct this openness indicator. First, we used Oneal and Russett's (fn. 4) bilateral-trade to GDP ratios that measure interdependence between two states in a dyad. This trade series begins in 1885. To construct a monadic openness measure, we totaled up these bilateral openness measures for all potential trading partners for each state in the international system. For example, imagine there were four states in the international system in 1890: the United States, France, Great Britain, and Spain. Then imagine that the bilateral trade ratios between the United States and these three trading partners were 0.05,0.03, and 0.02. We would create a total openness score for the United States in 1890 by totaling these three scores and getting a value of 0.10. This strategy created fairly good data coverage for the period from 1885 to 1914. Second, we relied on Mitchell (fn. 53) to add pre-1885 data for nine countries to the analysis: Brazil, Denmark, France, Germany, Italy, Spain, Sweden, Great Britain, and the United States. While Mitchell has strong data coverage on imports and exports for most countries during the pre-1885 period, national income data that matches in terms of currency and price indexing (current prices) is available only for the above nine countries. Third, we relied on Maddison for GDP indices that were matched with trade data from Mitchell to construct openness indicators for the following states in the pre-1885 period: Argentina, Austria-Hungary, Belgium, Chile, Japan, the Netherlands, and Spain. Maddison, Angus, Monitoring the World Economy 1820—1992 (Paris: Development Center for Organisation for Economic Cooperation and Development, 1995)Google Scholar.
70 Keshk, Pollins, and Reuveny (fn. 7); and Maddala (fn. 60).
71 This estimation technique is called two-stage probit least squares (2SPLS). 2SPLS estimates the coefficients very similarly to two-stage least squares. All of the right-hand variables are used to estimate predicted values of both conflict and protection levels. These predicted values are then utilized as the instrumental variables in the final estimations of the respective regressions that use MILCONFLICT and Protect as the dependent variables. We estimated the 2SPLS model using the STATA command “cd-simeq.”The program was written by Keshk. Keshk, Omar M. G., “CDSIMEQ; A Program to Implement Two-Stage Probit Least Squares,” The STATJ Journal 3 (2003Google Scholar).
72 Mansfield, Milner, and Rosendorff (fn. 46) find that democratic regimes trade more with each other. These findings point to the inclusion of a control for regime type in the protection equation, with the expectation that democracies have lower tariffs.
73 Because large economies have large internal markets that reduce integration with the global economy, we also control for economic size with a variable measuring a country's population and expect it to be positively correlated with protection levels. Together the lagged protect variable and the population variable satisfy the exclusion restriction for this two-stage approach. The population variable carries the extra virtue of offering an operationalization of economic size that is not correlated with our key dependent variable of interest—military conflict. In bivariate regressions with conflict as the dependent variable (not shown), the coefficient on population was not significant. For a study that examines the relationship between economic openness and economic size (as measured by population), see Alesina, Alberto and Spolaore, Enrico, The Size of Nations (Cambridge, Mass.: MIT Press, 2003)Google Scholar.
74 Much of the identification of the protection effect comes from the lagged Protect variable. Because Protect is highly autocorrelated, endogeneity bias may not be completely purged from the analysis. Future research could usefully identify other exogenous variation in trade protection.
75 While not described in any more detail, we also checked whether an assumption of unit homogeneity across countries was valid for our sample by incorporating fixed and random effects models. Hausman tests on both of these models revealed that the probit specification without country-specific coefficients or country-specific error terms was appropriate.
76 Blattman, Clemens, and Williamson (fn. 42).
78 These regressions suppress a regional dummy for North America and thus make it the reference category for the other regional variables.
79 We obtained similar results in the single equation framework. The coefficient on Protect was still positive and significant.
80 A single equation framework using probit produced the same primary conclusions.
81 Mansfield (fn. 8).
82 The purpose of this section is not to identify a new explanation for the origins of World War I. Given this conflict's status as perhaps the most heavily studied war ever, a huge volume of research identifying multiple causes for war suggests that the conflict was in many ways overdetermined. In part, the multicausal nature of this single event underscores the importance of assessing the links between free trade and peace over this entire fifty-year period of globalization with techniques, like statistical analysis, suitable to examining a large number of cases. At the same time, the centrality of this single case to realist critiques of commercial liberalism creates a need to examine it within the context of the theoretical framework presented here.
83 Russett and Oneal (fn. 4).
85 Writing about the transition in Russian foreign policy as the Three Emperors' League expired and Russia moved closer to France in the late 188O's, Geyer notes, “Protectionism was clearly the Achilles' heel of the Russo-German Entente. . . . There was an inevitability in the mounting economic antagonism because neither Germany nor Russia could renounce protectionism.” Geyer, Dietrich, Russian Imperialism: The Interaction of Domestic and Foreign Policy, 1860—1914, trans. Little, Bruce (Leamington Spa, U.K.: Berg, 1987), 152Google Scholar.
86 For a good summary of this press war and its implications for great power diplomacy, see Fischer (fn. 35), 370–88.
93 Spring (fn. 38), 68.
94 Fischer (fn. 35), 367.
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