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Dynamic Democratic Backsliding

Published online by Cambridge University Press:  19 November 2025

Eddy S. F. Yeung*
Affiliation:
Bush School of Government and Public Service, Texas A&M University, College Station, TX, USA Department of Politics and International Relations, University of Oxford, Oxford, UK
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Abstract

Democratic backsliding occurs incrementally, but the empirical study of how citizens respond to undemocratic politicians has been predominantly static. I formulate and test predictions about how different sequences of backsliding shape accountability. Using a novel preregistered experiment ($N = 4,234$) capturing the reality that democratic transgressions are committed by elected officials step by step, I find that a majority of American respondents – against the backdrop of partisan and policy interests – are willing to electorally remove the incumbent as episodes of democratic backsliding unfold. Moreover, incumbents who incrementally decrease the severity of democratic transgressions are held accountable more promptly than incumbents who incrementally increase or sporadically vary the severity. By establishing a new experimental framework to study democratic backsliding, my dynamic approach not only paints a more nuanced picture of Americans’ willingness to defend democracy, but also demonstrates that sequence matters in shaping voter behavior amid incremental transgressions of democracy.

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Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press
Figure 0

Table 1. Undemocratic governor actions in the experiment and their mapping onto the concept of democratic transgressions

Figure 1

Figure 1. Survival curves by experimental condition and treatment effects estimated from the preregistered Cox model.Note: in Panel A, Kaplan–Meier survival curves for each experimental condition are plotted, summarizing the proportion of respondents insisting on not recalling the governor for each of the six experimental periods. In Panel B, positive coefficients indicate a greater likelihood of voting to recall the governor, compared to the No Transgressions condition. The Cox model with covariates is preregistered; the one without is not. Full information on the regression estimates is available in Table S3 in Supplementary Material Section F. For exploratory analysis of heterogeneous treatment effects, see Table S4 in Supplementary Material Section F.

Figure 2

Figure 2. Survival curves and log-rank tests for pairs of experimental conditions.Note: shaded areas indicate 95 per cent confidence intervals. Log-rank tests formally compare the statistical difference between two Kaplan–Meier survival curves, with the null hypothesis being that the two curves are equivalent. This non-parametric analysis is not preregistered.

Figure 3

Figure 3. Coefficient plot of estimated relationship between affective polarization and loyal support for the governor across experimental conditions.Note: positive coefficients indicate a greater likelihood of refusing to vote to recall the governor in all periods. Because the measure of partisan affect is standardized and the dependent variable is binary, an estimate of 0.05, for example, means that a one standard-deviation increase in affective polarization predicts a five percentage-point increase in the likelihood of being a loyal supporter of the governor (that is, not voting to recall the governor in all periods). This analysis is exploratory. Full information on the regression estimates is available in Table S8 in Supplementary Material Section G.

Figure 4

Figure 4. Survival curves by experimental condition and party identification.Note: shaded areas indicate 95 per cent confidence intervals. Log-rank tests formally compare the statistical difference between the three Kaplan–Meier survival curves in each experimental condition, with the null hypothesis being that the three curves are equivalent. This analysis is exploratory.

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