Abstract
Comparing cases of democracies induced by nonviolent resistance (NVR) with democracies which were installed through other means, this chapter finds that the former are much more resilient than the latter. NVR-induced democrackies, on average, survive longer, are more likely to pass the two-turnover test of democratic consolidation, and score higher on key indicators of democratic quality. These results are robust to different model specifications and to alternative measurements of democratic transition. This supports our argument about the path-dependent effect of NVR that the mode of transition determines the trajectories of democratic transition and its subsequent consolidation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We only consider cases of endogenous democratic transition. Cases of foreign powers imposing democratization and cases where democratization occurred as part of decolonization are not included in the analysis.
- 2.
Note that we excluded campaigns whose goals were secession or policy change.
- 3.
These are, of course, ideal-types. NAVCO distinguishes campaigns as ‘primarily’ violent or nonviolent so the differences between NVR and VR may well be a matter of degree.
- 4.
We opted for the Ulfelder (2012) data for three reasons. First, it explicitly specifies transition events while the other two datasets are mainly interested in specifying the presence of democratic and autocratic regimes. Moreover, the Ulfelder data contains detailed descriptions of transition events which makes the coding of NVR influence more accurate and valid. Given our focus on transition events, we prefer the Ulfelder dataset as our main measure for political regimes. Second, the Ulfelder data uses a less demanding definition of democratization and therefore includes many cases not covered by the other two datasets. Third, in the subsequent empirical analysis we utilize matching procedures which try to optimize balance of covariance to advance the causal interpretation of the estimated effects of NVR. Among the three datasets used in the analysis, we achieved the best covariate balance in the Ulfelder dataset. Complete lists of cases included in all three datasets along with the respective coding decisions on NVR influence on democratic transitions are provided in the appendix section A1.
- 5.
We use a tenfold politico-geographic classification of world regions from the V-DEM dataset (Coppedge et al. 2015).
- 6.
Descriptive statistics for all variables are reported in the appendix section A2.
- 7.
Most frequently scholars point to issues of extrapolation and model dependence. Extrapolation refers to the assumption that the results generalize beyond the range of the available data. Model dependence means that the functional form of the regression model parameters impacts the estimated effects.
- 8.
Additional details of the matching procedures are described in the appendix section A3.
- 9.
Detailed results are reported in the appendix section A4. Also note that the results on the effect of NVR on democratic survival draw upon an earlier study conducted by the authors (Bayer et al. 2016).
- 10.
For each of the three datasets, we estimated Cox models with robust standard errors clustered by regime and Cox models with shared regime frailties. For the Geddes data, some of the models yielded a potential violation of the proportional hazard assumption and subsequent analysis indicated that the effect of NVR on democratic survival could be diminishing over time.
- 11.
The model estimates a separate logit for the first and second turnover, accounting for the characteristic of the data that while all regimes in the sample can experience the first turnover of power, only those regimes that had a successful first turnover may achieve the second turnover of power.
- 12.
Detailed results are reported in the appendix section A5. Note that preliminary results on this topic were also reported by Bethke (2017).
- 13.
Detailed results are reported in the appendix section A6. Also note that this analysis of the effect of NVR on democratic quality draws upon an earlier study conducted by Bethke and Pinckney (2019).
- 14.
For instance, one potential unmeasured factor which we can account for with this procedure is democratic culture or, more specifically, the differences in the predisposition of elites and the population towards democratic values. Countries with positive attitudes towards democracy among elites and the population should be more likely to experience the occurrence of NVR-induced transitions and attain a higher level democratic quality after transition. The DiD approach accounts for such confounding factors by comparing differences in improvement of democratic quality.
- 15.
This also contradicts Gasiorowski and Power (1998, p. 133, Fn. 116), who argue that the two-turnover test is ‘excessively confining’ for Third World countries who have a mixed record of democracy. It turns out that the second peaceful turnover reveals a crucial difference between NVR-induced democracies and those that came about through other modes of transition.
References
Aronow, P. M., & Samii, C. (2016). Does Regression Produce Representative Estimates of Causal Effects? American Journal of Political Science, 60(1), 250–267. https://doi.org/10.1111/ajps.12185.
Bayer, M., Bethke, F. S., & Lambach, D. (2016). The Democratic Dividend of Nonviolent Resistance. Journal of Peace Research, 53(6), 758–771. https://doi.org/10.1177/0022343316658090.
Bethke, F. S. (2017). Nonviolent Resistance and Peaceful Turnover of Power. Peace Economics, Peace Science and Public Policy, 23(4).
Bethke, F. S., & Pinckney, J. (2019). Nonviolent resistance and the quality of democracy. Conflict Management and Peace Science. https://doi.org/10.1177/0738894219855918.
Boix, C., Miller, M., & Rosato, S. (2013). A Complete Data Set of Political Regimes, 1800–2007. Comparative Political Studies, 46(12), 1523–1554.
Boix, C., & Stokes, S. C. (2003). Endogenous Democratization. World Politics, 55(4), 517–549.
Buis, M. (2013). SEQLOGIT: Stata Module to Fit a Sequential Logit Model. EconPapers. https://econpapers.repec.org/software/bocbocode/s456843.htm.
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.
Cheibub, J. A. (2007). Presidentialism, Parliamentarism, and Democracy. Cambridge University Press.
Chenoweth, E., & Lewis, O. A. (2013). Unpacking Nonviolent Campaigns: Introducing the NAVCO 2.0 Dataset. Journal of Peace Research, 50(3).
Chenoweth, E., & Stephan, M. J. (2011). Why Civil Resistance Works: The Strategic Logic of Nonviolent Conflict. New York: Columbia University Press.
Coppedge, M., Gerring, J., Lindberg, S. I., Skaaning, S.-E., & Teorell, J. (2015). V-Dem Codebook v5. Gothenburg: Varieties of Democracy (V-Dem) Project.
Gasiorowski, M. J., & Power, T. J. (1998). The Structural Determinants of Democratic Consolidation: Evidence from the Third World. Comparative Political Studies, 31(6), 740–771.
Geddes, B., Wright, J., & Frantz, E. (2014). Autocratic Breakdown and Regime Transitions: A New Data Set. Perspectives on Politics, 12(2), 313–331.
Gleditsch, K. S. (2002). Expanded Trade and GDP Data. Journal of Conflict Resolution, 46, 712–724.
Gleditsch, K. S., & Ward, M. D. (2006). Diffusion and the International Context of Democratization. International Organization, 60(4), 911–933.
Guo, S., & Fraser, M. W. (2010). Propensity Score Analysis: Statistical Methods and Applications. Thousand Oaks: SAGE Publications.
Guo, S., & Stradiotto, G. A. (2014). Democratic Transitions: Modes and Outcomes. New York: Routledge.
Haggard, S., & Kaufman, R. R. (2016). Dictators and Democrats: Masses, Elites and Regime Change. Princeton: Princeton University Press.
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3), 199–236.
Huntington, S. P. (1968). Political Order in Changing Societies. New Haven: Yale University Press.
Lipset, S. M. (1959). Some Social Requisites of Democracy: Economic Development and Political Legitimacy. American Political Science Review, 53(01), 69–105.
Schneider, B. R. (1995). Democratic Consolidations: Some Broad Comparisons and Sweeping Arguments. Latin American Research Review, 30(2), 215–234.
Sekhon, J. S. (2009). Opiates for the Matches: Matching Methods for Causal Inference. Annual Review of Political Science, 12, 487–508.
Singer, J. D. (1987). Reconstructing the Correlates of War Dataset on Material Capabilities of States, 1816–1985. International Interactions, 14, 115–132.
Teorell, J. (2010). Determinants of Democratization: Explaining Regime Change in the World, 1972–2006. Cambridge University Press.
Teorell, J., Coppedge, M., Gerring, J., & Lindberg, S. (2016). Measuring Electoral Democracy with V-Dem Data: Introducing a New Polyarchy Index (Working Paper No. 25). Varieties of Democracy Institute.
Ulfelder, J. (2010). Dilemmas of Democratic Consolidations. A Game-theory Approach. Boulder, CO: First Forum Press.
Ulfelder, J. (2012). Democracy/Autocracy Data Set. Harvard Dataverse, V1(hdl:1902.1/18836).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Lambach, D., Bayer, M., Bethke, F.S., Dressler, M., Dudouet, V. (2020). Statistical Analysis. In: Nonviolent Resistance and Democratic Consolidation. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-39371-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-39371-7_3
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-39370-0
Online ISBN: 978-3-030-39371-7
eBook Packages: Political Science and International StudiesPolitical Science and International Studies (R0)