GoKawiilAbstract Introduction: While corruption exists in both democracies and autocracies, its social consequences may differ fundamentally across regime types. Democratic norms of equality and impartiality make trust highly sensitive to institutional failure. We theorize two mechanisms—normative amplification and representative contagion—by which corruption erodes trust more in democracies. In democracies, corruption violates core fairness norms and implicates the citizenry that elected corrupt officials. In autocracies, corruption is expected and elites are seen as separate from ordinary citizens. Methods: To test this theory, we perform multilevel analysis of data from 62 countries combining individual-level survey responses with country-level democratic quality indicators. Results: We first demonstrate that perceiving corruption predicts lower generalized trust almost universally across individuals. We then show this individual-level psychological mechanism is considerably stronger in democracies than in autocracies, even controlling for inequality and country-level corruption. Discussion: These findings reveal an asymmetric vulnerability: the accountability structures that make democracies function also make their social capital fragile. This has important implications for understanding democratic resilience, as corruption threatens the social trust necessary for democratic cooperation differently across regime types.
1 Introduction Democracy may be uniquely sensitive to certain threats. Recent scholarship on democratic backsliding reveals how democracies can erode from within when norms decay and institutions weaken ( Levitsky and Ziblatt, 2018). In this article, we identify a specific sensitivity: in democracies, social capital appears to be particularly responsive to corruption. We theorize that this sensitivity arises from democracy’s foundational commitments to equality and impartiality. These commitments may create two psychological mechanisms that amplify corruption’s impact on social trust. First, normative amplification: in democracies, where universalism is the professed ideal, corruption may signal a breach of the social contract. Citizens may infer that if the institutions meant to embody fairness are compromised, the wider society is untrustworthy. In autocracies, by contrast, where particularism is expected, corruption confirms business as usual rather than signaling societal rot. Second, representative contagion: in democracies, corrupt officials are viewed as emanating from “the people” through elections, potentially implicating the citizenry itself. In autocracies, predatory elites are seen as a distinct class, quarantining interpersonal trust from elite malfeasance. If these mechanisms operate as theorized, then the individual-level psychological process linking corruption perceptions to social distrust should be regime-dependent—strong in democracies, weak in autocracies. A study by You (2018) provides suggestive evidence for our thesis. Using country-level data on social trust and corruption, and studying democracies and autocracies separately, he demonstrated that more corruption is strongly associated with weaker social trust among democracies—but not among autocracies. This striking pattern is consistent with our theory. However, as the finding was obtained at the aggregate level, it leaves open whether it reflects genuine differences in how individuals psychologically process corruption, or whether it is an artifact of other phenomena. The present paper aims to provide individual-level evidence for how trust among people in democracies may be especially sensitive to corruption. After replicating You’s country-level findings in more recent data from 62 countries—covering the full spectrum from autocracies like Russia and Iran to stable liberal democracies like New Zealand and Netherlands—we use multilevel modeling to test whether a corresponding individual-level pattern exists. We find that individuals’ perceptions of corruption are associated with lower generalized trust in democracies, while this same individual-level association is substantially weaker or absent in autocracies. These findings suggest an asymmetry in how corruption relates to social trust across regime types. While democracies foster high social trust through their institutions, they may simultaneously make that social capital more vulnerable to perceptions of institutional failure. This may be the price of accountability: the very norms that make democracies function—equality, representation, transparency—may also ensure that institutional failures resonate in citizens’ social worldviews.
3 Methods 3.1 Data We combine individual-level data from the most recent wave (2017–2022) of the World Values Survey (WVS; Haerpfer et al., 2022) with country-level indicators (averaged across the same period) of democratic quality from the Varieties of Democracy (V-Dem) project ( Coppedge et al., 2025; Pemstein et al., 2025). Our analysis includes 62 countries for which we have complete data on all variables of interest. We use WVS Wave 7 (2017–2022) because it contains the corruption perception module required for our analysis. Although a Joint EVS/WVS dataset exists with 92 countries, the European Values Survey does not include the corruption perception items, making it unsuitable for our purposes. Our 62 countries therefore represent the full set of countries with complete data on perceived corruption, generalized trust, and democratic quality indicators. The WVS provides our key individual-level measures. Generalized trust is measured by the standard question: “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?” Responses are coded 1 for “most people can be trusted” and 0 otherwise. Perceived corruption is measured by asking respondents how widespread they believe corruption to be among public officials, on a scale from 1 (there is no corruption in my country) to 10 (there is abundant corruption in my country). While corruption perceptions may not perfectly align with objective corruption levels ( Charron, 2016), perceptions are presumably what directly affect individual trust judgments. We include standard individual-level controls: age (five categories: 18–29, 30–39, 40–49, 50–59, 60+), gender (male/female), education (three levels based on ISCED categories: low, medium, high), household income (three levels based on the WVS 10-point scale: low [1–3], medium [4–7], high [8–10]), and employment status (three categories: employed [full-time, part-time, or self-employed], not in labor force [retired, homemaker, or student], and unemployed/other). From V-Dem, we use two measures of democratic quality: the Regimes of the World (RoW) classification and the Liberal Democracy Index . The RoW ( Lührmann et al., 2018) is a categorical measure distinguishing closed autocracies (no multiparty elections), electoral autocracies (multiparty elections that are not free and fair), electoral democracies (free elections but limited liberal protections), and liberal democracies (free elections with strong liberal protections). Following our theoretical framework—which emphasizes that the mechanisms of normative amplification and representative contagion require genuine electoral accountability—we create a binary classification: democracies (electoral and liberal democracies, RoW = 2–3) versus autocracies (closed and electoral autocracies, RoW = 0–1). Electoral autocracies are classified as autocracies because, despite having multiparty elections, these elections lack the competitive integrity necessary for the representative contagion mechanism to operate. In contrast to the categorical RoW measure, the Liberal Democracy Index is a continuous measure, which captures both electoral and liberal dimensions of democracy, including the quality of elections, checks on executive power, equality before the law, and individual liberties. This index ranges from 0 (least democratic) to 1 (most democratic). We use the Liberal Democracy Index rather than the Electoral Democracy Index (also known as Polyarchy) because our theoretical mechanisms—normative amplification and representative contagion—depend on features beyond electoral procedures. The liberal component of the Liberal Democracy Index captures the rule of law, checks on executive power, and equality before the law, which are central to our argument that corruption in democracies violates norms of impartiality. In robustness analyses, we also test whether results hold using the Electoral Democracy Index as an alternative moderator. 3.2 Analysis plan Our research design tests three progressively refined hypotheses outlined above. 3.2.1 Country-level analysis (H1) To test H1, we calculate country-level aggregates of perceived corruption and generalized trust, then examine whether their correlation differs when calculated separately among democracies and autocracies as defined by the RoW categorization. H1 predicts a strong negative correlation between perceived corruption and generalized trust among democracies but a weaker correlation among autocracies. The above dichotomous analysis matches the original approach of You (2018). As a continuous alternative, we also examine the interaction between perceived corruption and the Liberal Democracy Index in a multiple regression analysis of country-level generalized trust. H1 predicts a negative interaction, representing a stronger negative effect of corruption in more democratic countries. 3.2.2 Multilevel analysis (H2, H3) To test whether the aggregate pattern reflects genuine individual-level mechanisms (H2) and whether these mechanisms vary by regime type (H3), we estimate a random-intercept, random-slope multilevel logistic regression model. This approach models the hierarchical structure of the data, with individuals nested within countries. Standard errors appropriately reflect uncertainty at both levels. At Level 1 (individual), generalized trust is modeled as a function of perceived corruption, controlling for demographic characteristics (age, gender, education, income, and employment status). At Level 2 (country), we allow both the intercept and the slope for perceived corruption to vary across countries. Crucially, we include a cross-level interaction between perceived corruption and the Liberal Democracy Index (treated as a continuous variable), which directly tests H3: whether the individual-level corruption-trust relationship varies with democratic quality. In other words, the cross-level interaction estimates whether the psychological mechanism linking corruption perceptions to trust operates differently depending on institutional context. Formally, the model can be expressed as follows: Level 1 (Individual): Level 2 (Country): where is the probability of expressing trust for individual i in country j; is perceived corruption (grand-mean centered); is a vector of demographic controls; is the Liberal Democracy Index (grand-mean centered); is the cross-level interaction coefficient testing H3; and , are country-level random effects assumed to follow a bivariate normal distribution. For computational efficiency with large sample sizes (>85,000 individuals), we use an aggregated binomial approach. Observations are grouped by country, corruption level, and demographic categories, and trust incidence is modeled using a binomial distribution. This yields estimates identical to individual-level analysis but with substantially improved computational performance. Both perceived corruption and the Liberal Democracy Index are grand-mean centered to facilitate interpretation of main effects. We also conduct robustness checks including: (1) adding competing cross-level moderators to test whether these factors can account for the democracy moderation; (2) testing press freedom, the Electoral Democracy Index, and state resilience ( Travaglino et al., 2025) as alternative moderators in separate models (as their high correlations with liberal democracy, r = 0.90 and 0.78 respectively, preclude simultaneous estimation); and (3) leave-one-out analyses to ensure no single country drives the results. For competing moderators, we include economic inequality (Gini coefficient from SWIID; Solt, 2020), political polarization (from V-Dem), and measures of digital information access. We include both social media use as a self-reported news source (country-level mean from WVS item on frequency of obtaining political information from social media) and internet penetration (percentage of population using the internet; World Bank, 2024). If our theory is correct, we should observe a negative main association between corruption perceptions and trust at the individual level (H2) and a negative cross-level interaction, indicating that the corruption-trust relationship is stronger (more negative) in more democratic countries (H3).
4 Results Table 1 presents the 62 countries, ordered by the Liberal Democracy Index, with their results for generalized trust and perceived corruption. Table 1 Country Regime type (RoW) Liberal democracy index N Generalized trust (%) Perceived corruption M (SD) New Zealand Democracy 0.83 1,057 59.5 5.52 (2.37) Germany Democracy 0.83 1,528 46.0 5.58 (2.22) Netherlands Democracy 0.82 2,145 61.2 6.20 (2.16) Uruguay Democracy 0.81 1,000 14.9 7.70 (2.29) Australia Democracy 0.80 1813 54.0 6.65 (2.28) United Kingdom Democracy 0.79 3,056 45.8 7.10 (2.25) Chile Democracy 0.79 1,000 14.3 7.10 (2.11) Korea South Democracy 0.78 1,245 32.9 6.51 (1.59) Canada Democracy 0.76 4,018 49.5 6.73 (2.00) Japan Democracy 0.75 1,353 35.6 6.88 (2.06) United States Democracy 0.74 2,596 39.7 7.83 (2.10) Slovak Republic Democracy 0.74 1,200 21.6 7.81 (1.91) Czech Republic Democracy 0.73 1,200 37.3 7.06 (2.00) Taiwan Democracy 0.72 1,223 31.0 7.61 (2.10) Greece Democracy 0.70 1,200 8.4 8.37 (1.70) Cyprus Democracy 0.70 1,000 8.0 8.23 (1.91) Peru Democracy 0.68 1,400 5.3 9.51 (1.21) Argentina Democracy 0.64 1,003 20.7 8.51 (1.63) Brazil Democracy 0.57 1762 6.6 9.45 (1.57) Romania Democracy 0.56 1,257 11.9 8.73 (1.85) Tunisia Democracy 0.53 1,208 14.2 8.16 (2.41) Colombia Democracy 0.53 1,520 4.5 9.48 (1.48) Mongolia Democracy 0.51 1,638 27.5 7.60 (2.20) Armenia Democracy 0.47 1,223 8.1 7.55 (2.70) Ecuador Democracy 0.45 1,200 5.9 8.88 (1.84) Indonesia Democracy 0.44 3,200 5.2 8.38 (2.51) Kenya Autocracy 0.41 1,266 9.6 8.46 (2.36) Mexico Democracy 0.41 1741 10.3 8.87 (2.05) Guatemala Democracy 0.39 1,229 18.0 9.14 (1.67) Nigeria Democracy 0.36 1,237 12.7 8.74 (2.18) Maldives Democracy 0.34 1,039 21.3 9.27 (1.45) Singapore Autocracy 0.33 2012 34.0 3.52 (1.99) India Autocracy 0.32 1,692 17.7 7.77 (2.27) Bolivia Democracy 0.32 2067 8.6 8.63 (1.94) Philippines Autocracy 0.30 1,200 5.3 6.73 (2.71) Malaysia Autocracy 0.30 1,313 19.6 8.00 (2.00) Ukraine Autocracy 0.28 1,289 30.7 8.41 (1.88) Lebanon Autocracy 0.28 1,200 9.9 7.83 (2.03) Kyrgyzstan Autocracy 0.28 1,200 11.8 8.90 (2.09) Serbia Autocracy 0.27 1,046 16.6 8.39 (1.92) Pakistan Autocracy 0.25 1995 23.5 8.70 (2.06) Jordan Autocracy 0.24 1,203 16.0 8.20 (2.27) Iraq Autocracy 0.24 1,200 11.2 8.78 (1.73) Morocco Autocracy 0.24 1,200 16.5 7.70 (2.00) Hong Kong SAR Autocracy 0.22 2075 39.5 5.44 (2.03) Zimbabwe Autocracy 0.20 1,215 2.1 8.55 (2.57) Myanmar (Burma) Autocracy 0.17 1,200 15.1 7.38 (2.56) Thailand Autocracy 0.16 1,500 31.4 6.97 (2.35) Libya Autocracy 0.14 1,196 9.3 9.12 (1.55) Ethiopia Autocracy 0.14 1,230 11.9 8.65 (2.21) Egypt Autocracy 0.12 1,200 7.4 8.52 (1.81) Kazakhstan Autocracy 0.12 1,276 23.9 6.98 (2.27) Vietnam Autocracy 0.11 1,200 27.7 7.37 (2.13) Turkey Autocracy 0.11 2,415 14.3 6.57 (2.22) Iran Autocracy 0.11 1,499 14.8 6.77 (3.13) Bangladesh Autocracy 0.10 1,200 12.9 7.75 (2.06) Russia Autocracy 0.09 1810 23.9 7.66 (2.00) Uzbekistan Autocracy 0.07 1,250 34.7 6.71 (2.56) Venezuela Autocracy 0.06 1,190 14.2 8.66 (1.80) Nicaragua Autocracy 0.05 1,200 4.2 7.87 (2.79) China Autocracy 0.04 3,036 65.4 6.49 (2.37) Tajikistan Autocracy 0.04 1,200 20.6 5.62 (2.58) Country-level summary statistics. Figure 1 tests H1 by showing how country-level generalized trust varies with perceived corruption, separately for democracies and autocracies. In support of H1, the pattern strikingly differs between regime types. Among democracies, there is a strong negative relationship: countries with higher perceived corruption have substantially lower generalized trust. Among autocracies, this relationship is considerably weaker—replicating You's (2018) finding in more recent data and with a theory-driven operationalization of regime type based on the Regimes of the World classification. The alternative analysis using the continuous Liberal Democracy Index as a moderator of the effect of perceived corruption on generalized trust confirms this pattern: the country-level interaction between perceived corruption and liberal democracy is negative (B = −12.07, 95% CI [−22.36, −1.77], p = 0.022). Figure 1 The country-level association between corruption and trust differs between regime types. Scatter plot of country-level perceived corruption (x -axis) and generalized trust (y -axis). Each point represents a country, with shape and color indicating regime type based on the Regimes of the World classification. Regression lines are shown separately for democracies (electoral and liberal democracies) and autocracies (closed and electoral autocracies). The negative relationship between corruption and trust is strong among democracies and much weaker among autocracies. To test H2 and H3 we now turn to the multilevel analysis, which models how individuals’ corruption perceptions are associated with their trust while allowing this relationship to vary across countries. In support of H2, the main association between perceived corruption and generalized trust is negative (B = −0.12, 95% CI [−0.14, −0.11], p < 0.001). Thus, on average, individuals who perceive higher corruption exhibit lower generalized trust. Additionally, the main effect of liberal democracy is positive (B = 0.79, 95% CI [0.01, 1.56], p = 0.047), indicating that at average levels of perceived corruption, individuals in more democratic countries exhibit higher generalized trust. In support of H3, the cross-level interaction between perceived corruption and liberal democracy is negative (B = −0.16, 95% CI [−0.22, −0.10], p < 0.001), indicating that the negative association between corruption perceptions and trust is stronger in more democratic countries. Figure 2 illustrates this pattern by plotting country-specific corruption-trust slopes against democratic quality. The slopes are extracted from a model without the cross-level interaction, showing the empirical variation that the interaction term captures. The blue line represents the predicted slope from the main model’s interaction term. Corruption slopes tend to be strongly negative in countries with high democratic quality while being close to zero in countries with low democratic quality. Figure 2 Liberal democracy and the individual-level corruption-trust relationship. Each point represents one country. The x -axis shows the V-Dem liberal democracy index; the y -axis shows the country-specific corruption slope extracted from a random-slopes model without the cross-level interaction. More negative values indicate stronger negative associations between perceived corruption and trust. The blue line shows the predicted relationship from the main model’s cross-level interaction. The strong negative slope demonstrates that as democratic quality increases, the individual-level psychological mechanism linking corruption perceptions to social distrust becomes substantially stronger. To translate these results into substantive terms, we calculated predicted probabilities of expressing trust at different levels of corruption perception and democracy, illustrated in Figure 3. For a highly democratic country (90th percentile of the Liberal Democracy Index, the solid line in Figure 3), moving from low perceived corruption (4 on the 1–10 scale, which is the 10th percentile of observed values) to high perceived corruption (10 on the scale, 90th percentile) is associated with a decrease in the probability of trusting others from approximately 34 to 14%. The same change in corruption perception is associated with a much smaller decrease, from approximately 17 to 11% in a highly autocratic country (10th percentile of the Liberal Democracy Index, the dashed line in Figure 3). Figure 3 Predicted probability of trusting others by perceived corruption and democratic quality. Lines show model-predicted probabilities at the 10th (low democracy), 50th (medium democracy), and 90th (high democracy) percentiles of the Liberal Democracy Index. The steeper slope for high-democracy countries illustrates the stronger corruption-trust link in democratic contexts. We conducted several robustness checks. First, we added economic inequality (Gini coefficient) as a competing cross-level moderator. The democracy × corruption interaction remains essentially unchanged in this model (B = −0.16, 95% CI [−0.22, −0.1], p < 0.001). Similarly, when political polarization was added as a competing moderator, the democracy × corruption interaction remained robust B = −0.16, 95% CI [−0.22, −0.09], p < 0.001. Controlling for country-level social media use and internet penetration, our main finding also persisted (B = −0.18, 95% CI [−0.26, −0.11], p < 0.001). Second, we tested whether alternative country-level characteristics could serve as moderators. Press freedom showed the same moderating effect to liberal democracy (B = −0.16, 95% CI [−0.22, −0.1], p < 0.001), consistent with its close conceptual and empirical overlap with democracy. Similarly using the Electoral Democracy Index instead of the Liberal Democracy Index, results remained unchanged (B = −0.16, 95% CI [−0.22, −0.1], p < 0.001). State resilience showed a weaker, though directionally consistent, moderating effect (B = −0.02, 95% CI [−0.03, −0.01], p = 0), suggesting that democratic institutions rather than state capacity drive the moderation. Third, we conducted leave-one-out analyses, re-estimating the model 62 times, each time excluding one country. The cross-level interaction coefficient ranged from −0.17 to −0.15 across these analyses, with no single country driving the results.
5 Discussion This article provides systematic individual-level evidence that the corruption-trust association differs across regime types. Previous research documented this pattern at the aggregate level ( You, 2018) but could not determine whether it reflected genuine differences in individual-level psychological processes or merely compositional effects. By measuring country-specific individual-level coefficients and showing they vary systematically with democratic quality, we provide evidence consistent with the view that regime type shapes how corruption perceptions relate to social trust. Our findings suggest an asymmetry in how corruption relates to trust across regime types. In autocracies, the individual-level association between corruption perceptions and social distrust appears weak or absent, which may help explain how some autocratic regimes combine high corruption with relatively high generalized trust ( Figure 1). In democracies, this association is substantially stronger. This pattern suggests that government quality in democracies may matter not merely for administrative efficiency or economic performance, but potentially for the social conditions that support democratic governance. Institutional integrity may affect the social trust that facilitates democratic cooperation—from voluntary tax compliance to electoral participation to civic engagement. These findings speak to contemporary debates about democratic backsliding and resilience. They suggest that corruption scandals in established democracies should perhaps not be viewed merely as criminal justice matters or administrative failures, but as potential threats to social cohesion. This may help explain a puzzling feature of contemporary politics: why relatively minor corruption scandals can generate significant political crises in established democracies, while autocracies weather far more egregious corruption with limited social consequence. The difference may lie not in the severity of the corruption per se, but in how institutional frameworks shape how citizens interpret and respond to corruption. Our results also have potential implications for anti-corruption efforts. Standard approaches focus on technical reforms: strengthening audit institutions, improving procurement transparency, raising civil servant salaries, and enhancing criminal enforcement. While these measures may reduce corruption levels, our findings suggest they may be insufficient to address the social consequences of corruption in democracies. If corruption perceptions are indeed associated with reduced social trust in democratic contexts, then anti-corruption strategies in democracies may need to be accompanied by efforts to rebuild and maintain social trust: swift, visible accountability when corruption is discovered; symbolic reaffirmation of democratic equality norms; and frank public discourse about how corruption relates to democratic values. These findings also suggest that government communication about transparency and anti-corruption efforts may matter for social trust. Because perceptions of corruption, and not merely actual corruption, appear to drive the trust erosion we document, proactive communication about institutional integrity may be valuable. Democratic governments could invest in publicizing accountability measures, successful prosecutions of corrupt officials, and ongoing institutional reforms. Such communication campaigns would not substitute for substantive anti-corruption work but could complement it by ensuring that citizens are aware of their government’s commitment to impartiality. This may be especially important in democracies, where our findings suggest that trust is particularly sensitive to perceived corruption. More broadly, these findings contribute to understanding potential micro-foundations of regime stability. While much scholarship focuses on how institutions shape elite behavior, our results suggest that institutions may also shape mass psychology in ways relevant for regime dynamics. Democratic institutions may create citizens whose social trust is more responsive to perceived institutional failure than citizens in autocracies. 5.1 Limitations We acknowledge several limitations of this study. First, and most importantly, we theorize but do not directly test the specific mechanisms we propose—normative amplification and representative contagion. Our data show that the corruption-trust association varies by regime type, but we cannot observe the psychological processes that produce this variation. Future experimental research could directly manipulate normative frames (e.g., presenting corruption as violating equality norms versus as typical elite behavior) and representative connection (e.g., emphasizing that officials were elected by citizens versus appointed by elites) to test whether these factors moderate how corruption information affects trust. Survey research could also measure perceived norm violation and representative identification as mediators. Until such studies are conducted, our mechanistic account remains theoretical. Second, our cross-sectional individual-level data cannot establish the causal direction from corruption perceptions to trust. The association we observe is consistent with corruption perceptions reducing trust, but reverse causality is also plausible: individuals with generally low trust may be more inclined to perceive corruption. For evidence supporting the corruption-to-trust direction, we rely on the experimental literature ( Rothstein and Eek, 2009; Martinangeli et al., 2024), which demonstrates that exposure to information about corruption causally reduces generalized trust. Our contribution is to show that this association varies systematically by regime type, but we cannot rule out that regime type also moderates reverse-causal processes. Third, our cross-sectional design cannot capture within-country change over time. Longitudinal analysis tracking how the within-country corruption-trust relationship shifts after prolonged democratization or backsliding would provide stronger evidence for our theoretical account. Fourth, we focus on liberal democracy as the primary moderator, and do not examine the separate roles of different institutional features such as judicial independence, press freedom, and electoral integrity. These features are highly correlated in our data, making it difficult to isolate their independent contributions.
6 Conclusion Ultimately, our findings point to a potential fragility in democracies. If democracy is built on a social contract that requires mutual trust among citizens, and if corruption perceptions undermine that trust more strongly in democratic contexts, then democracies may face a vulnerability that autocracies do not. When citizens perceive corruption, they may lose faith not only in their leaders but also in each other. As democracies worldwide face challenges from polarization, populism, and institutional decay, understanding how institutional context shapes the social consequences of corruption may be important for preserving democratic resilience.
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Data availability statement Publicly available datasets were analyzed in this study. Data, codebook, and replication instructions are available at: https://doi.org/10.17605/OSF.IO/A8M4R.
Author contributions KE: Conceptualization, Methodology, Writing – original draft. IV: Data curation, Formal analysis, Visualization, Writing – review & editing.
Funding The author(s) declared that financial support was received for this work and/or its publication. Support for this research was provided by the Knut and Alice Wallenberg Foundation (grant no. 2022.0191).
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