Abstract
Campaigns against authoritarian rule trigger the problems of authoritarian control and power-sharing. Hence, autocrats cannot ignore campaigns, but can they repress them? This chapter hypothesizes that restrictions and violence do just that—if those forms of political repression complement each other. Each variant of political repression has drawbacks: Restrictions dampen, but they do not eliminate interdependent behavior; violence imposes high individual costs on dissent, but it frequently backfires against its originators. Complementarity asserts that those drawbacks matter less when both variants of repression work in tandem. Statistical analysis of 50 campaigns distributed across 112 authoritarian regimes between 1977 and 2001 yields mixed support for the argument. Based on a binary probit model with sample selection correction, the analysis adds a preemptive and a reactive aspect to political repression. The results imply that complementarity matters as long as repression preempts campaigns, but not when it reacts to them. Moreover, once citizens knock at the palace gates, restrictions turn futile. Finally, violence reduces the outlook for successful resistance against authoritarian rule, but it also backfires at all times—preemptive and reactive. By implication, political repression thwarts successful resistance today, but it breeds more resistance tomorrow.
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Notes
- 1.
- 2.
For a general introduction to focal points and to authoritarian regimes see Tucker (2007).
- 3.
For instance, Shiu and Sutter (1996, 332) argue that the Chinese crackdown on the Tienanmen Square protesters partly intended to deter challenges from the provinces amidst an ongoing Chinese center-periphery rivalry.
- 4.
Turkey illustrates the point, but it is not at all a clearcut case of authoritarian rule. Be that as it may, political observers have met President R ‘ecep Tayyip Erdoğan’s recent political course with much concern.
- 5.
Figure 4.2 collapses all campaign observations to a single entry for each year of an authoritarian spell.
- 6.
- 7.
See Appendix 4.8.2 for a slightly more sophisticated argument.
- 8.
See Appendix 4.8.2.
- 9.
Chenoweth and Lewis (2013) are aware of the problem and recommend limited claims of internal validity. However, as Berk (1983) argues, sample selection bias undermines internal and external validity alike. In the presence of sample selection bias, statistical estimates will be unrepresentative of both, the data at hand and the general universe of cases.
- 10.
These parameters are the regression coefficients \(\gamma \), \(\beta \), \(\eta \), \(\theta \), and the error term correlation \(\rho \).
- 11.
The overlap between both equations leads to complications that are dealt with below (see Sigelman and Langche 2000, 177).
- 12.
- 13.
See Edwards (2014) for an introduction.
- 14.
This index sums the binary items on education, social welfare, police, and dispute settlement systems that campaigns establish in parallel to state institutions (Chenoweth and Stephan 2011). Information on traditional and new media as well as campaign militia were disregarded because a scale reliability analysis proved them uninformative.
- 15.
Up to this point, the analysis has consistently supported complementarity in the selection equation. Hence, to remove the interaction amounts to model misspecification (Kam and Franzese 2007).
- 16.
The results average over the interaction between violence and restrictions in the selection equation. One may object the presentation because complementarity in the selection process spills over into the outcome process. Figure 4.6 in the appendix takes that objection into account. The results remain unchanged.
- 17.
- 18.
The overlap between the selection and the outcome equation causes collinearity between the predictors. Therefore, estimates of poorly identified sample selection models will likely be inefficient (Brandt and Schneider 2007, 8). However, since the standard errors in the outcome equation do not change in response to modifications of the selection equation, collinearity turns out to be a minor concern. All evidence so far implies that the selection model and the outcome model tap into different empirical processes.
- 19.
Siegel (2011a) does not constitute an exception to the rule. What his study calls “non-disruptive tactics of suppression” revolves around hearts-and-minds approaches. They reduce individual susceptibility to recruitment attempts and “include institutional and infrastructure development, job creation, and education” (Siegel 2011a, 2). In other words, Siegel studies co-optation as an alternative to violence.
- 20.
To be precise, the proposed test asks whether observations are missing completely at random (MCAR). Should random chance not plausibly account for missingness, then the data could either be missing at random (MAR) or miss for systematic reasons. This analysis assumes the latter.
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4.8 Appendix
4.8 Appendix
4.1.1 4.8.1 Summary Statistics
See Table 4.7.
4.1.2 4.8.2 Difference-In-Means by Campaign Status
Assume that information on dissent is missing from NAVCO 2.0 for reasons that we cannot wholly ignore. Under this assumption, observations included in the data should systematically differ from observations not included in the data. This implication is testable for features, which are available to compare both groups. Restrictions and violence qualify as such features because they are measured annually for all authoritarian regimes. A simple difference-in-means test based on those features gauges the plausibility of sample selection bias (Allison 2002, 3).Footnote 20
Average marginal effects of political repression in Model VII
Table 4.8 shows the results and reinforces the suspicion of sample selection bias. Figures evaluate 5 iterated cluster paired bootstraps with 5,000 iterations each. The means are bias-corrected, and the confidence intervals use a normal approximation at the 95% confidence level.
Restrictions do not seem to be affected, but authoritarian regimes that confront at least one campaign behave much more violently. The corresponding difference is positive and highly statistically significant. In other words, campaigns seem to pose atypical challenges to authoritarian rule, which are sufficiently threatening to justify violence. Models of campaign success should thus account for sample selection bias.
4.1.3 4.8.3 Marginal Effects Accounting for Sample Selection Bias
Figure 4.6 reports the marginal effects of violence and restrictions on the probability of campaign success, as implied by Model VII in Table 4.2. This model removes the interaction between violence and restrictions from the outcome equation, but both types of repression still interact in the selection equation. The figures do not support any sizeable impact the interaction may have on campaign success.
4.1.4 4.8.4 Results for a Graded Measurement of Campaign Success
NAVCO 2.0 codes campaign progress on a five-point scale that ranges from 0 (status quo) to 4 (all campaign goals achieved). For theoretical reasons, outlined earlier, the prior analysis dichotomizes the scale. The table below replaces the binary classification of success and failure with the original coding scheme. Each model in Table 4.9 again uses maximum likelihood, but this time combines a probit selection model with a linear outcome model.
4.1.5 4.8.5 Results for Unique Observations
NAVCO 2.0 nests resistance campaigns by authoritarian spells. Consequently, multiple campaigns may challenge a regime at the same time, creating complex dependencies in the data. The following table excludes such non-unique observations from the analysis, leaving everything else as before. The results lead to the same substantive conclusions (Table 4.10).
4.1.6 4.8.6 Bootstrap Results
Figure 4.7 returns to Model VI in Table 4.2 and compares two distributions.
a Bootstrap results for the outcome equation. b Bootstrap results for the selection equation
Solid lines denote the asymptotical distribution over each coefficient as implied by the reported estimate and its standard error. The support of those density curves is limited to \(\pm 4\) standard errors about the estimate. Dashed lines show the density over the corresponding bootstrap estimates and rug plots give their location.
Both figures evaluate 10 iterated cluster paired bootstraps each containing 500 iterations. As a precaution against samples with non-varying dependent variables, each bootstrap samples independently from (a) the set of authoritarian regimes that never experienced campaigns, and (b) the set of authoritarian regimes that fought at least one campaign.
After removing all models that did not converge about 350 coefficient samples remained for each bootstrap. All bootstrap distributions converged on the same result and were therefore pooled to increase overall precision.
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Tanneberg, D. (2020). Does Repression Prevent Successful Campaigns?. In: The Politics of Repression Under Authoritarian Rule. Contributions to Political Science. Springer, Cham. https://doi.org/10.1007/978-3-030-35477-0_4
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DOI: https://doi.org/10.1007/978-3-030-35477-0_4
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