We Define a Resistance Campaign as a Series of Observable Continuous Tactics

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.

Notes

1. 1.

   Kurzman refers to political opportunity structures, a central concept in political process theory. See Tarrow (1998), McAdam (1999), McAdam et al. (2001), Kriesi (2004), Tilly and Tarrow (2015).

2. 2.

   For a general introduction to focal points and to authoritarian regimes see Tucker (2007).

3. 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. 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. 5.

   Figure 4.2 collapses all campaign observations to a single entry for each year of an authoritarian spell.

6. 6.

   Berk (1983) provides a general introduction to sample selection bias. Hug (2003) brings key aspects of the debate into the context of comparative politics.

7. 7.

   See Appendix 4.8.2 for a slightly more sophisticated argument.

8. 8.

   See Appendix 4.8.2.

9. 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. 10.

    These parameters are the regression coefficients \(\gamma \), \(\beta \), \(\eta \), \(\theta \), and the error term correlation \(\rho \).

11. 11.

    The overlap between both equations leads to complications that are dealt with below (see Sigelman and Langche 2000, 177).

12. 12.

    On grievances see Cederman et al. (2011), Gurr (1970), Muller and Weede (1994).

13. 13.

    See Edwards (2014) for an introduction.

14. 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. 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. 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. 17.

    Since executive constraints appear in either equation neither the coefficient's sign nor its magnitude nor statistical significance can be read from Table 4.2 alone (Greene 2003, 783). The average marginal effect of executive constraints in columns VI and VII is 0.04 with standard error 0.03.

18. 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. 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. 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.

References

• Allison, P. D. (2002). Missing data. Thousand Oaks: SAGE.

  CrossRef  Google Scholar

• Aytaç, S. E., Schiumerini, L., & Stokes, S. (2017). Why do people join backlash protests? Lessons from Turkey. Journal of Conflict Resolution, 41, 1–24. https://doi.org/10.1177/0022002716686828.

  CrossRef  Google Scholar

• Beck, N., & Katz, J. N. (1995). What to do (and not to do) with time-series cross-section data. American Political Science Review, 89(3), 634–647. https://doi.org/10.2307/2082979.

  CrossRef  Google Scholar

• Bellin, E. (2012). Reconsidering the robustness of authoritarianism in the Middle East: Lessons from the Arab Spring. Comparative Politics, 44(2), 127–149. https://doi.org/10.5129/001041512798838021.

  CrossRef  Google Scholar

• Berk, R. (1983). An introduction to sample selection bias in sociological data. American Sociological Review, 48(3), 386–398. https://doi.org/10.2307/2095230.

  CrossRef  Google Scholar

• Boix, C., & Svolik, M. (2013). The foundations of limited authoritarian government: Institutions, commitment, and power-sharing in dictatorships. The Journal of Politics, 75(2), 300–316. https://doi.org/10.1017/S0022381613000029.

  CrossRef  Google Scholar

• Boudreau, V. (2004). Resisting dictatorship: Repression and protest in Southeast Asia. Cambridge: Cambridge University Press.

  CrossRef  Google Scholar

• Brancati, D. (2016). Democracy protests: Origins, features, and significance. Cambridge: Cambridge University Press.

  CrossRef  Google Scholar

• Brandt, P. T., & Schneider, C. J . (2007). So the reviewer told you to use a selection model? Selection models and the study of international relations. http://pages.ucsd.edu/~cjschneider/working_papers/pdf/Selection-W041.pdf.

• Brownlee, J. (2007). Authoritarianism in an age of democratization. New York: Cambridge University Press.

  CrossRef  Google Scholar

• Bueno de Mesquita, B., & Smith, A. (2010). Leader survival, revolutions, and the nature of government finance. American Journal of Political Science, 54(4), 936–950. https://doi.org/10.1111/j.1540-5907.2010.00463.x.

  CrossRef  Google Scholar

• Butcher, C., & Svensson, I. (2016). Manufacturing dissent: Modernization and the onset of major nonviolent resistance campaigns. Journal of Conflict Resolution, 60(2), 311–339. https://doi.org/10.1177/0022002714541843.

• Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90(3), 414–427. https://doi.org/10.1162/rest.90.3.414.

  CrossRef  Google Scholar

• Carey, S. C. (2006). The dynamic relationship between protest and repression. Political Research Quarterly, 59(1), 1–11. https://doi.org/10.1177/106591290605900101.

  CrossRef  Google Scholar

• Carter, D. B., & Signorino, C. S. (2010). Back to the future: Modeling time dependence in binary data. Political Analysis, 18(3), 271–292. https://doi.org/10.1093/pan/mpq013.

  CrossRef  Google Scholar

• Cederman, L. E., Weidmann, N. B., & Gleditsch, K. S. (2011). Horizontal inequalities and ethnonationalist civil war: A global comparison. American Political Science Review, 105(3), 478–495. https://doi.org/10.1017/S0003055411000207.

  CrossRef  Google Scholar

• Celestino, M. R., & Gleditsch, K. S. (2013). Fresh carnations or all thorn, no rose? Nonviolent campaigns and transitions in autocracies. Journal of Peace Research, 50(3), 385–400. https://doi.org/10.1177/0022343312469979.

  CrossRef  Google Scholar

• Cheibub, J. A., Gandhi, J., & Vreeland, J. (2010). Democracy and dictatorship revisited. Public Choice, 143(1/2), 67–101. https://doi.org/10.1007/s11127-009-9491-2.

  CrossRef  Google Scholar

• Chenoweth, E., & Lewis, O. A. (2013). Unpacking nonviolent campaign: Introducing the NAVCO 2.0 dataset. Journal of Peace Research, 50(3), 415–423. https://doi.org/10.1177/0022343312471551.

• Chenoweth, E., & Stephan, M. J. (2011). Why civil resistance works: The strategic logic of nonviolent conflict. New York: Columbia University Press.

  Google Scholar

• Chenoweth, E., & Ulfelder, J. (2017). Can structural conditions explain the onset of nonviolent uprisings? Journal of Conflict Resolution, 61(2), 298–324. https://doi.org/10.1177/0022002715576574.

  CrossRef  Google Scholar

• Chenoweth, E., Perkoski, E., & Kang, S. (2017). State repression and nonviolent resistance. Journal of Conflict Resolution, 16(2), 1950–1969. https://doi.org/10.1177/0022002717721390.

  CrossRef  Google Scholar

• Clark, A. M., & Sikkink, K. (2013). Information effects and human rights data: Is the good news about increased human rights information bad news for human rights measures? Human Rights Quarterly, 35(3), 539–568.

  Google Scholar

• Coppedge, M., Gerring, J., Lindberg, S. I., Skaaning, S. E., Teorell, J., Altman, D., et al. (2016). V-dem country-year dataset v6.2: Varieties of democracy (v-dem) project. https://www.v-dem.net/en/data/data-version-6-2/.

• Davenport, C. (2007a). State repression and political order. Annual Review of Political Science, 10(1), 1–23. https://doi.org/10.1146/annurev.polisci.10.101405.143216.

• Davenport, C. (2007b). State repression and the tyrannical peace. Journal of Peace Research, 44(4), 485–504. https://doi.org/10.1177/0022343307078940.

• Davenport, C., & Loyle, C. (2012). The states must be crazy: Dissent and the puzzle of repressive persistence. International Journal of Conflict and Violence, 6(1), 75–95.

  Google Scholar

• Della Porta, D. (2014). Mobilizing for democracy: Comparing 1989 and 2011. Oxford: Oxford University Press.

  CrossRef  Google Scholar

• DeMeritt, J. H. (2016). The strategic use of state repression and political violence: Oxford research encyclopedia of politics. New York: Oxford University Press. https://doi.org/10.1093/acrefore/9780190228637.013.32.

  CrossRef  Google Scholar

• DeNardo, J. (1985). Power in numbers: The political strategy of protest and rebellion. Princeton: Princeton University Press.

  CrossRef  Google Scholar

• Dubin, J. A., & Rivers, D. (1989). Selection bias in linear regression, logit and probit models. Sociological Methods and Research, 18(2–3), 360–390. https://doi.org/10.1177/0049124189018002006.

  CrossRef  Google Scholar

• Edwards, G. (2014). Social movements and protest. New York: Cambridge University Press.

  CrossRef  Google Scholar

• Egorov, G., Guriev, S., & Sonin, K. (2009). Why resource-poor dictators allow freer media: A theory and evidence from panel data. American Political Science Review, 103(4), 645–668. https://doi.org/10.1017/S0003055409990219.

  CrossRef  Google Scholar

• Enzmann, B. (Ed.). (2013). Handbuch Politische Gewalt: Formen - Ursachen - Legitimation - Begrenzung. Wiesbaden: Springer VS.

  Google Scholar

• Escriba-Folch, A. (2013). Repression, political threats, and survival under autocracy. International Political Science Review, 34(5), 543–560. https://doi.org/10.1177/0192512113488259.

  CrossRef  Google Scholar

• Fariss, C. J., & Schnakenberg, K. E. (2014). Measuring mutual dependence between state repressive actions. Journal of Conflict Resolution, 58(6), 1003–1032. https://doi.org/10.1177/0022002713487314.

  CrossRef  Google Scholar

• Fearon, J. D., & Laitin, D. (2003). Ethnicity, insurgency, civil war. American Political Science Review, 97(1), 75–90. https://doi.org/10.1017/S0003055403000534.

  CrossRef  Google Scholar

• Fortin-Rittberger, J. (2014). Time-series cross-section. In H. Best & C. Wolf (Eds.), The SAGE handbook of regression analysis and causal inference (pp. 387–408). London: SAGE. https://doi.org/10.4135/9781446288146.n17.

• Francisco, R. A. (2004). After the massacre: Mobilization in the wake of harsh repression. Mobilization: An International Journal, 9(2), 107–126.

  Google Scholar

• Gandhi, J. (2008). Dictatorial institutions and their impact on economic growth. European Journal of Sociology, 49(1), 3–30. https://doi.org/10.1017/S0003975608000015.

  CrossRef  Google Scholar

• Gandhi, J., & Przeworski, A. (2006). Cooperation, cooptation, and rebellion under dictatorships. Economics and Politics, 18(1), 1–26. https://doi.org/10.1111/j.1468-0343.2006.00160.x.

  CrossRef  Google Scholar

• Gehlbach, S., Sonin, K., & Svolik, M. (2016). Formal models of nondemocratic politics. Annual Review of Political Science, 19(1), 565–584. https://doi.org/10.1146/annurev-polisci-042114-014927.

  CrossRef  Google Scholar

• Gerschewski, J., Merkel, W., Schmotz, A., Stefes, C. H., & Tanneberg, D. (2012). Warum überleben Diktaturen? Politische Vierteljahresschrift, 53, 106–131. https://doi.org/10.5771/9783845244655-111.

  CrossRef  Google Scholar

• Goldstone, J., & Tilly, C. (2001). Threat (and opportunity): Popular action and state response in the dynamics of contentious action. In R. R. Aminzade, J. A. Goldstone, D. McAdam, E. J. Perry, W. H. Sewell, S. Tarrow, & C. Tilley (Eds.), Silence and voice in the study of contentious politics (pp. 170–194). Cambridge: Cambridge University Press.

  Google Scholar

• Greene, W. H. (2003). Econometric analysis (5th ed.). Upper Saddle River: Prentice Hall.

  Google Scholar

• Greenhill, B., Ward, M. D., & Sacks, A. (2011). The separation plot: A new visual method for evaluating the fit of binary models. American Journal of Political Science, 55(4), 991–1002. https://doi.org/10.1111/j.1540-5907.2011.00525.x.

  CrossRef  Google Scholar

• Gurr, T. R. (1970). Why men rebel. Princeton: Princeton University Press.

  Google Scholar

• Guyot, J. F., & Badgley, J. (1990). Myanmar in 1989: Tatmadaw V. Asian Survey, 30(2), 187–195. https://doi.org/10.2307/2644897.

  CrossRef  Google Scholar

• Heckman, J. T. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. In S. V. Berg (Ed.), Annals of economic and social measurement (pp. 475–492). Elmont: Publications Expediting.

  Google Scholar

• Heckman, J. T. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161. https://doi.org/10.2307/1912352.

  CrossRef  Google Scholar

• Henderson, C. W. (1991). Conditions affecting the use of political repression. Journal of Conflict Resolution, 35(1), 120–142. https://doi.org/10.1177/0022002791035001007.

  CrossRef  Google Scholar

• Hill, D. W., & Jones, Z. M. (2014). An empirical evaluation of explanations for state repression. American Political Science Review, 108(3), 661–687. https://doi.org/10.1017/S0003055414000306.

  CrossRef  Google Scholar

• Huang, H., Boranbay-Akan, S., & Huang, L. (2016). Media, protest diffusion, and authoritarian resilience. Political Science Research and Methods, 1–20. https://doi.org/10.1017/psrm.2016.25.

• Hug, S. (2003). Selection bias in comparative research: The case of incomplete data sets. Political Analysis, 11(3), 255–274. https://doi.org/10.1093/pan/mpg014.

  CrossRef  Google Scholar

• Hyde, S. D., & Marinov, N. (2012). Which elections can be lost? Political Analysis, 20(2), 191–210. https://doi.org/10.1093/pan/mpr040.

  CrossRef  Google Scholar

• Josua, M., & Edel, M. (2014). To repress or not to repress: Regime survival strategies in the Arab Spring. Terrorism and Political Violence, 27(2), 289–309. https://doi.org/10.1080/09546553.2013.806911.

  CrossRef  Google Scholar

• Kam, C. D., & Franzese, R. J. (2007). Modeling and interpreting interactive hypotheses in regression analysis. Ann Arbor: University of Michigan Press.

  Google Scholar

• Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The worldwide governance indicators: A summary of methodology, data and analytical issues. World Bank Policy Research Working Paper (5430) (pp. 1–31). https://openknowledge.worldbank.org/handle/10986/3913.

• King, G., Pan, J., & Roberts, M. E. (2013). How censorship in China allows government criticism but silences collective expression. American Political Science Review, 107(2), 326–343.

  CrossRef  Google Scholar

• Klandermans, B. (2015). Motivations to action. In D. D. Porta & M. Diani (Eds.), The Oxford handbook of social movements (pp. 215–230). New York: Oxford University Press.

  Google Scholar

• Kricheli, R., Livne, Y., & Magaloni, B. (2011). Taking to the streets: Theory and evidence on protest under authoritarianism. http://stanford.edu/~magaloni/dox/2011takingtothestreets.pdf.

• Kriesi, H. (2004). Political context and opportunity. In D. Snow, S. Soule, & H. Kriesi (Eds.), Blackwell companion to social movements (pp. 67–90). New York: Wiley.

  Google Scholar

• Kuran, T. (1989). Sparks and prairie fires: A theory of unanticipated political revolution. Public Choice, 61(1), 41–74.

  CrossRef  Google Scholar

• Kuran, T. (1991a). The East European revolution of 1989: Is it surprising that we were surprised? American Economic Review, 81(2), 121–125.

  Google Scholar

• Kuran, T. (1991b). Now out of never: The element of surprise in the East European revolution of 1989. World Politics, 44(1), 7–48. https://doi.org/10.2307/2010422.

• Kurzman, C. (1996). Structural opportunity and perceived opportunity in social-movement theory: The Iranian revolution of 1979. American Sociological Review, 61(1), 153–170.

  CrossRef  Google Scholar

• Lehoucq, F. (2016). Does nonviolence work? Comparative Politics, 48(2), 269–287. https://doi.org/10.5129/001041516817037691.

  CrossRef  Google Scholar

• Lintner, B. (1990). Outrage: Burma's struggle for democracy. London: White Lotus.

  Google Scholar

• Lohmann, S. (1994). The dynamics of informational cascades: The Monday demonstrations in Leipzig, East Germany, 1981–1991. World Politics, 47(1), 42–101. https://doi.org/10.2307/2950679.

  CrossRef  Google Scholar

• Lorentzen, P. L. (2013). Regularizing rioting: Permitting public protest in an authoritarian regime. Quarterly Journal of Political Science, 8(2), 127–158. https://doi.org/10.1561/100.00012051.

  CrossRef  Google Scholar

• Marshall, M. G., & Jaggers, K. (2002). Polity IV project: Political regime characteristics and transitions, 1800-2010: Data users' manual.

  Google Scholar

• Martin, B. (2007). Justice ignited: The dynamics of backfire. Lanham: Rowman & Littlefield Publishers.

  Google Scholar

• Mason, T. D., & Krane, D. A. (1989). The political economy of death squads: Toward a theory of the impact of state-sanctioned terror. International Studies Quarterly, 33(2), 175–198. https://doi.org/10.2307/2600536.

  CrossRef  Google Scholar

• McAdam, D. (1999). Political process and the development of black insurgency: 1930–1970 (2nd ed.). Chicago: University of Chicago Press.

  CrossRef  Google Scholar

• McAdam, D., Tarrow, S. G., & Tilly, C. (2001). Dynamics of contention. Cambridge: Cambridge University Press.

  CrossRef  Google Scholar

• Moore, W. H. (1998). Repression and dissent: Substitution, context, and timing. American Journal of Political Science, 42(3), 851–873.

  CrossRef  Google Scholar

• Muller, E., & Weede, E. (1994). Theories of rebellion: Relative deprivation and power contention. Rationality and Society, 6(1), 40–57. https://doi.org/10.1177/1043463194006001004.

  CrossRef  Google Scholar

• Myerson, R. B. (2008). The autocrat's credibility problem and foundations of the constitutional state. American Political Science Review, 102(1), 125–139. https://doi.org/10.1017/s0003055408080076.

  CrossRef  Google Scholar

• Nordås, R., & Davenport, C. (2013). Fight the youth: Youth bulges and state repression. American Journal of Political Science, 926–940. https://doi.org/10.1111/ajps.12025.

• O'Donnell, G., & Schmitter, P. C. (1986). Transitions from authoritarian rule: Tentative conclusions about uncertain democracies. In G. O'Donnell, P. C. Schmitter, & L. Whitehead (Eds.), Transitions from authoritarian rule: Comparative perspectives. Baltimore: The Johns Hopkins University Press.

  Google Scholar

• Opp, K. D. (1994). Repression and revolutionary action. Rationality and Society, 6(1), 101–138.

  CrossRef  Google Scholar

• Opp, K. D., & Rühl, W. (1990). Repression, micromobilization, and political protest. Social Forces, 69(2), 521–547. https://doi.org/10.2307/2579672.

  CrossRef  Google Scholar

• Pierskalla, J. H. (2010). Protest, deterrence, and escalation: The strategic calculus of government repression. Journal of Conflict Resolution, 54(1), 117–145. https://doi.org/10.1177/0022002709352462.

  CrossRef  Google Scholar

• Poe, S. C., & Tate, C. N. (1994). Repression of the human right to personal integrity in the 1980s: A global analysis. American Political Science Review, 88(4), 853–872. https://doi.org/10.2307/2082712.

  CrossRef  Google Scholar

• Przeworski, A. (1992). The games of transition. In S. Mainwaring, G. A. O'Donnell, & J. S. Valenzuela (Eds.), Issues in democratic consolidation (pp. 105–152). Notre Dame: University of Notre Dame Press.

  Google Scholar

• Ritter, E. H. (2014). Policy disputes, political survival, and the onset and severity of state repression. Journal of Conflict Resolution, 58(1), 143–168.

  CrossRef  Google Scholar

• Ritter, E. H., & Conrad, C. R. (2016). Preventing and responding to dissent: The observational challenges of explaining strategic repression. American Political Science Review, 110(1), 85–99. https://doi.org/10.1017/s0003055415000623.

  CrossRef  Google Scholar

• Robertson, G. B. (2011). The politics of protest in hybrid regimes: Managing dissent in post-communist Russia. Cambridge: Cambridge University Press.

  Google Scholar

• Sartori, A. E. (2003). An estimator for some binary-outcome selection models without exclusion restrictions. Political Analysis, 11(2), 111–138. https://doi.org/10.1093/pan/mpg001.

  CrossRef  Google Scholar

• Schedler, A. (2016). The disturbing normality of protest under authoritarianism: Paper prepared for delivery at 24th world congress of political science of the international political science association. http://paperroom.ipsa.org/papers/paper_48097.pdf.

• Schock, K. (2013). The practice and study of civil resistance. Journal of Peace Research, 50(3), 277–290. https://doi.org/10.1177/0022343313476530.

  CrossRef  Google Scholar

• Shiu, G., & Sutter, D. (1996). The political economy of Tiananmen Square. Rationality and Society, 8(3), 325–342.

  CrossRef  Google Scholar

• Siegel, D. A. (2009). Social networks and collective action. American Journal of Political Science, 53(1), 122–138. https://doi.org/10.1111/j.1540-5907.2008.00361.x.

  CrossRef  Google Scholar

• Siegel, D. A. (2011a). Non-disruptive tactics of suppression are superior in countering terrorism, insurgency, and financial panics. PLoS ONE, 6(4), e18545. https://doi.org/10.1371/journal.pone.0018545.

• Siegel, D. A. (2011b). When does repression work? Collective action in social networks. The Journal of Politics, 73(4), 993–1010. https://doi.org/10.1017/S0022381611000727.

• Sigelman, L., & Langche, Z. (2000). Analyzing censored and sample-selected data with tobit and heckit models. Political Analysis, 8(2), 167–182. https://doi.org/10.1093/oxfordjournals.pan.a029811.

  CrossRef  Google Scholar

• Stephan, M. J., & Chenoweth, E. (2008). Why civil resistance works: The strategic logic of nonviolent conflict. International Security, 33(1), 7–44.

  CrossRef  Google Scholar

• Sullivan, C. M. (2016). Undermining resistance: Mobilization, repression, and the enforcement of political order. Journal of Conflict Resolution, 60(7), 1163–1190. https://doi.org/10.1177/0022002714567951.

  CrossRef  Google Scholar

• Sutton, J., Butcher, C., & Svensson, I. (2014). Explaining political jiu-jitsu: Institution-building and the outcomes of regime violence against unarmed protests. Journal of Peace Research, 51(5), 559–573. https://doi.org/10.1177/0022343314531004.

  CrossRef  Google Scholar

• Svolik, M. (2009). Power sharing and leadership dynamics in authoritarian regimes. American Journal of Political Science, 53(2), 477–494. https://doi.org/10.1111/j.1540-5907.2009.00382.x.

  CrossRef  Google Scholar

• Svolik, M. (2012). The politics of authoritarian rule. Cambridge: Cambridge University Press.

  CrossRef  Google Scholar

• Svolik, M. (2013). Contracting on violence: The moral hazard in authoritarian repression and military intervention in politics. Journal of Conflict Resolution, 57(5), 765–794. https://doi.org/10.1177/0022002712449327.

  CrossRef  Google Scholar

• Tanneberg, D., Stefes, C., & Merkel, W. (2013). Hard times and regime failure: Autocratic responses to economic downturns. Contemporary Politics, 19(1), 115–129. https://doi.org/10.1080/13569775.2013.773206.

  CrossRef  Google Scholar

• Tarrow, S. G. (1998). Power in movement: Social movements and contentious politics. Cambridge: Cambridge University Press.

  CrossRef  Google Scholar

• Teorell, J. (2010). Determinants of democratization: Explaining regime change in the world, 1972–2006. Cambridge: Cambridge University Press.

  CrossRef  Google Scholar

• Tilly, C., & Tarrow, S. G. (2015). Contentious politics (2nd ed.). New York: Oxford University Press.

  Google Scholar

• Tindall, D. B. (2015). Networks as constraints and opportunities. In D. D. Porta & M. Diani (Eds.), The Oxford handbook of social movements (pp. 231–245). New York: Oxford University Press.

  Google Scholar

• Toomet, O., & Henningsen, A. (2008). Sample selection models in R: Package sample selection. Journal of Statistical Software, 27(7), 1–23. https://doi.org/10.18637/jss.v027.i07.

• Truex, R. (2016). Focal points, dissident calendars, and preemptive repression. https://doi.org/10.2139/ssrn.2802859.

• Tucker, J. (2007). Enough! Electoral fraud, collective action problems, and post-communist colored revolutions. Perspectives on Politics, 5(3), 535–551.

  CrossRef  Google Scholar

• Weidmann, N. B., & Rød, E. G. (2014). Making uncertainty explicit. Journal of Peace Research, 52(1), 125–128. https://doi.org/10.1177/0022343314523807.

  CrossRef  Google Scholar

• Winship, C., & Mare, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18(1), 327–350. https://doi.org/10.1146/annurev.so.18.080192.001551.

  CrossRef  Google Scholar

• Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.

  Google Scholar

• Wright, J. (2008). To invest or insure? How authoritarian time horizons impact foreign aid effectiveness. Comparative Political Studies, 41(7), 971–1000. https://doi.org/10.1177/0010414007308538.

  CrossRef  Google Scholar

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Authors and Affiliations

1. University of Potsdam, Potsdam, Brandenburg, Germany

   Dag Tanneberg

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Correspondence to Dag Tanneberg .

4.8 Appendix

4.8 Appendix

4.1.1 4.8.1 Summary Statistics

See Table 4.7.

Table 4.7 Summary statistics

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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}

Table 4.8 Difference in mean levels of repression by campaign status

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Fig. 4.6

Average marginal effects of political repression in Model VII

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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.

Table 4.9 Predicting the level of success for resistance campaigns

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Table 4.10 Prediction of successful resistance using unique observations

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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.

Fig. 4.7

a Bootstrap results for the outcome equation. b Bootstrap results for the selection equation

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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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