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Economic Roots of Civil Wars and Revolutions in the Contemporary World

Published online by Cambridge University Press:  13 June 2011

Carles Boix
Carles Boix
Affiliation:
Princeton University, [email protected].
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Abstract

To explain the distribution of civil wars, guerrilla warfare, and revolutionary outbreaks, the literature on modern political violence has shifted, broadly speaking, from a modernization perspective that emphasized the role of material conflict and of grievances to a more recent research program that stresses the geographical and organizational opportunities that insurgents may have to engage in violence. Drawing on those lines of inquiry equally, this article offers an integrated analytical model that considers both the motives and the opportunities of states and rebels. Civil wars, guerrillas, and revolutionary outbreaks are seen as a result of the nature and distribution of wealth in each country. Systematic and organized violent conflicts are most likely in economies where inequality is high and wealth is mostly immobile, that is, in societies where those worse off would benefit substantially from expropriating all assets. Violence is conditional on the mobilizational and organizational capacity of challengers and on the state capacity to control its territory. The theory is tested on data on civil wars from 1850 to 1999 for the whole world and on data on guerrilla warfare and revolutionary episodes spanning the years from 1919 to 1997 across all countries.

Type
Research Article
Information
World Politics , Volume 60 , Issue 3 , April 2008 , pp. 390 - 437
Copyright
Copyright © Trustees of Princeton University 2008

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References

1 On inequality and violence, see Russett, Bruce M., “Inequality and Instability: The Relation of Land Tenure to Politics,” World Politics 16 (April 1964)CrossRefGoogle Scholar; Paige, Jefferey M., Agrarian Revolution (New York: Free Press, 1975)Google Scholar; Midlarsky, Manus I., “Rulers and the Ruled: Patterned Inequality and the Onset of Mass Political Violence,” American Political Science Review 82 (June 1988)CrossRefGoogle Scholar; Muller, Edward N., “Income Inequality, Regime Repressiveness, and Political Violence,” American Sociological Review 50 (February 1985)CrossRefGoogle Scholar. On the effects of development, see Huntington, Samuel, Political Order in Changing Societies (New Haven: Yale University Press, 1968)Google Scholar; Wolf, Eric R., Peasant Wars in the Twentieth Century (New York: Harper and Row, 1969)Google Scholar; Gurr, Ted, “The Revolution-Social Change Nexus,” Comparative Politics 5 (April 1973)CrossRefGoogle Scholar.

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8 In part, these conditions can be traced back to the more structural theories developed so far. On the one hand, the article brings back in to the discussion the initial literature on political violence and economic inequality. On the other hand, it integrates work by Collier and Hoeffler (fnn. 3, 7), who, at least initially, explained the occurrence of civil wars as a function of greed. In their account greed is fueled by the abundance of natural resources (measured through the percentage of primary products) and by the relatively low life chances of potential rebels (proxied by rates of secondary school enrollment for males). These two latter factors can be easily folded into the model as follows. The presence of abundant natural resources (rather than all sorts of resources, which, prima facie, could also finance any type of illegal activity and therefore should lead us to expect violence everywhere) fits squarely with the idea that only fixed assets can be easily expropriated and controlled by the rebels. Educational attainment also points to the type of assets and to the underlying income distribution in society.

9 Collier and Hoeffler (fn. 7); Fearon and Laitin (fn. 4).

10 This model builds on previous work published in Boix, Carles, Democracy and Redistribution (New York: Cambridge University Press, 2003)CrossRefGoogle Scholar. But it differs mainly in two respects. First, it explores in more detail the use of violence in the choice of institutions. Second, it extends the model to examine the effects of democracy in the use of violence and to allow for open warfare within the wealthy elite.

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12 This formalization (particularly the constraint) assumes that the timing of the political process is such that each individual wealthy voter can choose to move his income abroad and still receive a transfer. This is a Nash equilibrium assumption: the deviation by each voter, in deciding to carry her capital abroad takes the transfers in the economy as given. Altering this assumption so that exiting the country must be done before obtaining transfers slightly complicates the algebra but does not change any of the analysis that follows.

13 For the sake of simplicity I disregard the possibility of collecting revenue to fund some level of public goods.

14 In the model, agents live for only one period and do not care about leaving a bequest to their children. Hence, they undertake a sequence of one-period optimizations. The only links between the different periods is the state of the political system at the start of the period and the capital stock at the start of the period. In each period wealthy and poor agents observe the political system inherited, the distribution of wealth, and its specificity, and they play a game that determines the choice of political regime and, given the latter, the tax rate. The solution concept used is perfect Bayesian equilibrium, as agents play what is essentially a different game in each period.

15 I discuss the choice of political regimes (under peaceful conditions) very briefly to focus instead on the causes of violence.

16 If those costs are inversely related to inequality, then intraelite conflict will be more frequent in highly unequal places.

17 The likelihood of war also goes up when the imbalance of wealth within the elite grows. This requires relaxing the model's assumption that all wealthy individuals have the same assets.

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27 It varies from countries with 0 percent of family farms to nations where 94 percent of the agricultural land is owned as family farms: the mean of the sample is 30 percent with a standard deviation of 23 percent. A detailed discussion and description of the data can be found in Vanhanen (fn. 26), 49–51 and the sources quoted therein.

28 Socialist economies are excluded from this calculation because most of them nationalized all or most of agrarian property, therefore driving the percentage of family farms to 0 (equivalent to an extremely unequal landowning economy).

29 Vanhanen (fn. 26). This average has a mean of 35 percent and varies from 3 to 99 percent.

30 Tearon and Laitin (fn. 4).

31 The use of Correlates of War data reduces the danger of missing data bias considerably. For the period from 1800 to 1999 there are 14,792 country-year observations of sovereign states. For the period from 1850 to 1999 there are 12,972 country-years. The Correlates of War data set covers 14,147 country-years and 12,289 country-years, respectively. The Vanhanen data include 10,462 country-years since 1850 or about 85 percent of the data. Two-thirds of the data not covered by the Vanhanen data belong to small countries (those with fewer than six million inhabitants). The fall to less than 8,900 observations in Table 1 results from employing income, population, and political regime data.

32 I have also used each variable (industrialization and urbanization) separately without any changes in the results I reproduce below.

33 Deininger, Klaus and Squire, Lyn, “A New Data Set Measuring Income Inequality,” World Bank Economic Review 19 (September 1996)Google Scholar. The cross-national variation is a function of the choice of the recipient unit (individual or household), the use of gross versus net income, and the use of expenditure or income. Following the suggestions of Deininger and Squire, the adjusted Gini is equal to the Gini coefficient plus 6.6 points in observations based on expenditure (versus income) and 3 points in observations using net rather than gross income. The results reported do not vary if I use unadjusted Gini coefficients. The year-country adjusted Gini coefficient employed in the sample is a five-year average of adjusted Gini coefficients. This procedure minimizes the volatility in the inequality measures and maximizes the number of observations (approximately doubling them).

34 Banks (fn. 22).

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36 Carles Boix and Sebastian Rosato, “A Complete Data Set of Political Regimes, 1800–1999” (Manuscript, University of Chicago, 2001).

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40 Ibid.

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42 Alternatively, I have coded war onset as 1 at the start of a war, 0 if there is no war, and missing for all observations of ongoing war after the first observation. These alternative specifications do not alter the results in any substantive manner.

43 Logit analysis does not change any of the results.

44 For the period 1850 to 1997, the interactive term is very similar in substantive terms to the coefficient in column 1, close to statistical significance (p=0.137) alone and fully significant in a joint test with the separate terms of the interaction.

45 Dropping income, population, and democracy as control variables, the number of observations rises to 10,462 (or about 85 percent of all sovereign country-years) and the coefficients of the variables of interests remain statistically significant and similar in size.

46 Sambanis (fn. 19); Sambanis, Nicholas and Hegre, Havard, “Sensitivity Analysis of Empirical Results on Civil War Onset,” Journal of Conflict Resolution 50 (August 2006)Google Scholar. This point is corroborated by the fact that if we run the same model excluding large states (for example, the upper half of the sample), the coefficient of population becomes much larger (four times bigger for the regression ran using the lower half). Conversely, excluding the smaller states makes the coefficient smaller and in fact statistically not significant if we only use the upper third of the sample.

47 Substituting democracy as defined in Polity IV for the Boix-Rosato variable does not change the results.

48 For the estimation and properties of the dynamic probit model, see Amemiya, Takeshi, Advanced Econometrics (Cambridge: Harvard University Press, 1985), chap. 11Google Scholar.

49 Results can be obtained from the author.

50 In model 4 oil drops out because it predicts all failures perfectly. Employing the variables of fuel : production per capita and fuel reserves per capita does not change the results. These variables are taken from Humphreys (fn. 37).

51 For a discussion of the effect that oil may have in strengthening states and therefore “offset[ing] increased possibilities for rebels,” see Fearon, James D., “Primary Commodity Exports and Civil War,” Journal of Conflict Resolution 49 (August 2005), 487CrossRefGoogle Scholar.

52 The variable of anocracy or semidemocracy (any case that scores between -5 and 5 when we substract the measure of democracy from the measure of autocracy in Polity IV) has no statistical significance and has been dropped from the estimations. Similarly, a variable measuring “years since independence” (under the assumption that states gain in stability over time) is not statistically significant and does not change any of the results presented in this article.

53 On economic crises and violence, see Collier and Hoeffler (fn. 7); Miguel, Edward, Satyanath, Shanker, and Sergenti, Ernest, “Economic Shocks and Civil Conflict: An Instrumental Variables Approach,” Journal of Political Economy 112 (August 2004)CrossRefGoogle Scholar.

54 Banks (fn.22).

55 In a probit model with guerrilla onsets as the dependent variable and the lagged value of ongoing guerrilla as an independent variable, the latter predicts failures perfectly and drops out of the estimations jointly with a large number of observations.

56 Again, all these comments are mostly based on models 1 and 2, which are based on large data sets.

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58 See also Carles Boix and Frances Rosenbluth, ”Bones of Contention: The Political Economy of Height Inequality” (Manuscript, Princeton University, Yale University, 2006).

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60 To put it differently, this article does not deny that the construction of the state was intertwined with the use of violence. What it simply does is to focus in the analysis of violence after a given political and economic arrangement had been constituted.

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63 An alternative variable, the rate of European settler mortality, offers a much lower number of observations (forty versus more than sixty). Djankov and Reynal-Querol also choose the percentage of Europeans in 1900 as an instrument to assess the impact of colonial instutions on the likelihood of civil war onsets. See Simeon Djankov and Marta Reynal-Querol, “The Colonial Origins of Civil War” (Manuscript, May 2007), http://ssrn.com/abstract=1003337.

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66 It is less clear how political violence may shape inequality. It is not violence itself but the outcome resulting from violence (the victory of one party, the change of regime) that may alter the distribution of wealth. Still, it is true that, by leading to economic stagnation or collapse, violence may depress the wages of certain economic sectors and exacerbate economic inequalities.