Abstract
Several scholars argue that state infrastructural power affects the likelihood of civil violence yet make competing claims. Some propose that states with high levels of infrastructural power instigate violence by reducing local autonomy, while others suggest infrastructural power endows states with the capacity to contain civil violence. We test these claims using both qualitative and quantitative methods. Through a pooled time-series analysis of 32 former British colonies, we find that infrastructural power is not significantly related to civil violence, suggesting either that infrastructural power has no effect or no net effect. Then, through case studies of Burma and Botswana, we investigate the impact of infrastructural power on civil violence, focusing on mechanisms and causal conditions. The case studies provide evidence that infrastructural power produces competing mechanisms that negate any net effect and that different conditions and policies affect whether a state’s level of infrastructural power contains conflict or instigates unrest.
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Notes
Notably, the distinction between direct and indirect rule is more of a spectrum than a strict dichotomy, as colonial states were only occasionally complete colonial constructs with integrated systems of rule—the ideal type of direct rule—and were never completely bifurcated systems of domination—the ideal type of indirect rule. Colonial India, for instance, included both directly and indirectly ruled regions. Analyses of British colonialism must therefore focus on the extent of direct and indirect rule.
Of the 39 former British colonies with available data on the extent of indirect rule, only 32 are included in this analysis because seven former colonies do not meet the population requirement (500,000) for MAR data. Other former British colonies lack MAR data because they lack minorities at risk. The latter cases are included in this analysis to limit the dataset’s selection bias and are scored as 0: no communal conflict or rebellion involving minority groups.
Decennial scores are the best estimates that could be obtained for many of the countries in this analysis given missing data.
Countries without values on at least one variable for a given decade are dropped case-wise for that decade.
Two modeling strategies dealing with the pooled time-series data have been considered. The first is the fully robust pooled OLS and logistic results that we present. The second is a random effects pooled time-series analysis. A random effects analysis incorporates unobserved heterogeneity (as in an omitted variable) that may exist at the unit-level or between time-periods that may influence the outcome of interest. As the unobserved heterogeneity approaches 0, the random effects model converges with the pooled OLS model considered above. Employing the Breusch and Pagan (1979) Lagrangian multiplier test, we find that there is in fact significant unobserved heterogeneity (p < 0.001), or random variation. The Hausman (1978) test on the time-variant variables used in this study, furthermore, shows that the random effects model is preferred to that of a fixed effects model, the latter of which allows the unobserved heterogeneity to be correlated with the exogenous predictors to test variation that appears across time periods within panels, the effect of the predictors being fixed across states. Yet we find that both the fully robust pooled OLS and logistic models and the fixed effects models produce virtually identical substantive results, and we choose to concentrate on the former because of their greater simplicity.
Implementing Wooldridge’s (2002) test for serial correlation in pooled time series data shows no significant serial correlation for the rebellion endogenous variable (p > 0.05) but does show significant serial correlation for the communal conflict endogenous variable (p < 0.05).
Diagnostics show tolerable levels of correlation among the predictors (variance inflation factors < 4)
The entire scale for communal conflict is: 0 = none, 1 = acts of harassment, 2 = political agitation, 3 = sporadic violent attacks, 4 = anti-group demonstrations, 5 = communal rioting, 6 = communal warfare.
The entire scale for rebellion is: 0 = no reported rebellion; 1 = political banditry, sporadic terrorism; 2 = campaigns of terrorism; 3 = local rebellion; 4 = small-scale guerrilla activity; 5 = intermediate guerrilla activity; 6 = large-scale guerrilla activity; 7 = protracted civil war.
Both per capita GDP and total population are logged after obtaining their respective decennial means.
The incremental F statistic is calculated as \(F_{\left( {K_2 - K_1 } \right),\left( {N - K_2 - 1} \right)} = \frac{{{{\left( {R_2^2 - R_1^2 } \right)} \mathord{\left/ {\vphantom {{\left( {R_2^2 - R_1^2 } \right)} {\left( {K_2 - K_1 } \right)}}} \right. \kern-\nulldelimiterspace} {\left( {K_2 - K_1 } \right)}}}}{{{{\left( {1 - R_2^2 } \right)} \mathord{\left/ {\vphantom {{\left( {1 - R_2^2 } \right)} {\left( {N - K_2 - 1} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {N - K_2 - 1} \right)}}}}\).
Separate analyses (not reported) suggest that population (logged), fractionalization, and per capita GDP all have the capability of reducing the relationship between indirect rule and rebellion to zero when specified individually with indirect rule.
The likelihood-ratio test is calculated as \(G^{\text{2}} {\text{ = 2}}\left( {{\text{log}}_e L_1 - \log _e L_2 } \right)\) and has a chi-square distribution.
See Lange (forthcoming) for a detailed description of the crisis and the resulting state reforms.
Recent events in Burma—the cyclone of May 2008—show that the state’s infrastructural power has not been deployed to provide even basic security to the population.
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The authors would like to thank Dan Slater, Hillel Soifer, Matthias vom Hau, and Daniel Ziblatt for their constructive comments. Lange’s research for this article was funded by the Social Science and Humanities Research Council of Canada.
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Lange, M., Balian, H. Containing Conflict or Instigating Unrest? A Test of the Effects of State Infrastructural Power on Civil Violence. St Comp Int Dev 43, 314–333 (2008). https://doi.org/10.1007/s12116-008-9025-9
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DOI: https://doi.org/10.1007/s12116-008-9025-9