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Downplaying Extremism? How the State Approaches Right-Wing and Left-Wing Extremist Threats

Published online by Cambridge University Press:  06 July 2026

Jeyhun Alizade
Affiliation:
WZB Berlin Social Science Center
Rafaela Dancygier
Affiliation:
Princeton University
Jonathan Homola
Affiliation:
University of California, Los Angeles

Summary

The rise of political extremism is often attributed to citizens' economic and cultural grievances. Less is known about how the state itself may facilitate extremism in contemporary democracies, despite frequent claims that elected leaders fail to contain it. How valid is this critique? Analyzing thousands of documents on the behavior of political parties, intelligence agencies, and the police in Germany across decades and states, the authors show that blind spots in combating extremism are widespread and deeply partisan. In parliamentary debates and election manifestos, right-wing parties devote less attention to right-wing extremist crimes than their prevalence warrants, while left-wing parties often downplay left-wing extremism. Similar divisions appear within ostensibly neutral intelligence agencies and the police. Across institutions, partisanship and ideology shape how state actors address extremist threats, raising concerns about the state's capacity to safeguard public safety and democracy. This title is also available as Open Access on Cambridge Core.

Information

Figure 0

Figure 1 Media reporting on the downplaying of extremismNote: Source: NexisUni database. See text for search terms.

Figure 1

Figure 2 Trends in extremist crimes in Germany (1969–2024)Note: Numbers are drawn from the annual reports of the Verfassungsschutz, which publishes extremist crime statistics as recorded by the police. The Verfassungsschutz did not publish left-wing extremist crime statistics from 1977 to 1980.

Figure 2

Figure 3 Keyword ratio in speeches and manifestosNote: Plots depict parties’ Positions (first and third row) on left-wing versus right-wing extremism in speeches and manifestos according to the logged ratio scaling as well as Bias (second and fourth row) in relation to reported crimes. Plots aggregated up to center-left versus center-right parties can be found in Online Appendix Figure B.1.

Figure 3

Figure 4 Regression of keywords in speeches on partiesNote: OLS regression coefficients with 95% confidence intervals. CDU/CSU is excluded as the reference category. Position is the logged odds ratio of right-wing and left-wing keywords: log# RWE keywords + 0.5# LWE keywords + 0.5. Bias takes into account the number of RWE and LWE crimes: log# RWE keywords + 0.5# LWE keywords + 0.5−log# RWE Crime + 0.5# LWE Crime + 0.5. The coefficients shown in this figure correspond to Models 1, 3, 4, and 6 in Table B.3, respectively.

Figure 4

Figure 5 Regression of keywords in manifestos on partiesNote: OLS regression coefficients with 95% confidence intervals. CDU/CSU is excluded as the reference category. Position is the logged odds ratio of right-wing and left-wing keywords: log# RWE keywords + 0.5# LWE keywords + 0.5. Bias takes into account the number of RWE and LWE crimes: log# RWE keywords + 0.5# LWE keywords + 0.5−log# RWE Crime + 0.5# LWE Crime + 0.5. The coefficients shown in this figure correspond to Models 1, 4, 5, and 8 in Table B.4, respectively.

Figure 5

Figure 6 Distribution of type of extremism referenced in parliamentary inquiries, by party (1952–2019)

Figure 6

Figure 7 Distribution of types of extremism referenced in parliamentary inquiries by party and decadeNote: The category “unclear” is excluded to increase readability. Inquiries from before the 1970s were excluded due to the low number of observations in those decades. The AfD is excluded because it did not exist until 2013.

Figure 7

Figure 8 Regression of extremism type referenced in parliamentary inquiry on partiesNote: OLS regression coefficients with 95% confidence intervals. CDU/CSU is excluded as the reference category. Controls: state and year fixed effects; RWE/LWE crime ratio. The dependent variable is coded as follows: 0 = LWE; 1 = LWE & RWE; 2 = RWE. The coefficients shown in this figure correspond to Models 1 and 5 in Table C.1, respectively.

Figure 8

Figure 9 Effect of far-right polling on speeches, manifestos, and inquiriesNote: OLS regression coefficients with 95% confidence intervals for the marginal effect of far-right polling on parties’ Position and Bias in speeches and manifestos as well as extremism type referenced in parliamentary inquiries. All models account for time, manifesto and inquiry models also include state fixed effects, and the inquiry model additionally controls for the RWE/LWE crime ratio. Full results can be found in Online Appendix Tables B.7 (Models 3 and 6), B.8 (Models 4 and 8), and C.3 (Model 4).

Figure 9

Figure 10 Relative attention to right-wing and left-wing extremism in intelligence reports (1964–2023)Note: This figure shows the logged ratio of RWE and LWE chapter lengths in pages (log# RWE Pages + 0.5# LWE Pages + 0.5) by the party of the interior minister, aggregated by year. For a corresponding figure showing the Bias, see Figure D.2.

Figure 10

Figure 11 Regression of chapter length in intelligence reports on interior minister partisanshipNote: OLS regression coefficients with 95% confidence intervals. Position is calculated as log# RWE Pages + 0.5# LWE Pages + 0.5. Bias is calculated as log# RWE Pages + 0.5# LWE Pages + 0.5−log# RWE Crime + 0.5# LWE Crime + 0.5. We imputed missing crime values using the value at the federal level, where possible. The SPD is coded as center-left, the CDU/CSU, and FDP as center-right. Fixed effects: state and year. The coefficients shown in this figure correspond to Models 1, 4, 5, and 8 in Table D.1, respectively.

Figure 11

Figure 12 Regression of first chapter content on interior minister partisanshipNote: Average marginal effects with 95% confidence intervals based on logistic regressions of the first chapter content on the party of the interior minister. Controls include state and year fixed effects, and RWE/LWE crime numbers. Missing values for crime numbers were imputed using multiple imputation. The corresponding regression models are in Table D.4 (columns 1, 5, 6, and 10).

Figure 12

Figure 13 Regression of “organization” keywords in intelligence reports on interior minister partisanshipNote: OLS coefficients with 95% confidence intervals. The dependent variable is constructed as log# Org. keywords in RWE Chapter + 0.5# Org. keywords in LWE Chapter + 0.5. Keywords used: “organization”; “organized.” Control variables: state and year fixed effects; ratio # words RWE/LWE chapters; RWE/LWE crime ratio. Missing values in the crime ratio were imputed using multiple imputation. The SPD is coded as center-left and the CDU/CSU and the FDP as center-right. The corresponding regression models are in Table D.8 (columns 1 and 6).

Figure 13

Figure 14 Marginal effect of far-right polling on chapter length by interior minister partisanshipNote: Marginal effects with 95% confidence intervals based on OLS regressions in which Far-Right Polling was interacted with Interior Minister Partisanship. The first outcome variable Position is calculated as log# RWE Pages + 0.5# LWE Pages + 0.5. The second outcome variable Bias is calculated as log# RWE Pages + 0.5# LWE Pages + 0.5−log# RWE Crime + 0.5# LWE Crime + 0.5. Fixed effects: state and year. The corresponding regression models are in Table D.9 (columns 1, 4, 5 and 8).

Figure 14

Figure 15 Regression of keywords in police union journals on keywordsNote: DPolG is the right-leaning union; GdP is excluded as the reference category. OLS regression coefficients with 95% confidence intervals. Position is calculated as log# RWE keywords + 0.5# LWE keywords + 0.5. Bias is calculated as log# RWE keywords + 0.5# LWE keywords + 0.5−log# RWE Crime + 0.5# LWE Crime + 0.5. Missing values in the crime ratio were imputed using the value on the federal level. The coefficients shown in this figure correspond to Models 1, 3, 4, and 6 in Table E.1, respectively.

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