Economic Hardship Increases Justification of Political Violence

ABSTRACT

Acts of political violence have reached a new level of intensity with the storming of the United States Capitol in 2021. In this paper, I investigate whether and how economic hardship affects political violence justification. Using an original two-wave panel survey around the 2020 US Presidential election, two studies from the American National Election Survey (ANES), and an original study from Germany, the results show substantial support for political violence justification among respondents. In both countries, political violence justification is strongly and consistently associated with economic hardship. The study thus shows that political violence justification is widespread, not just in polarised two-party systems, but also in other, less polarised multiparty systems.

KEYWORDS:

• Political violence
• economic hardship
• polarisation
• United States
• Germany

Acknowledgements

The author would like to thank Paul Marx and the anonymous reviewers for very helpful feedback. Furthermore, the author is very grateful to Airo Hino and Rob Fahey (Waseda University) who co-designed and conducted the data collection of Study 1.

Data Availability Statement

All data and replication materials will be made available in the author’s Harvard dataverse (https://dataverse.harvard.edu/dataverse/sjungkunz).

Data Deposition

Data will be deposited in the author’s Harvard Dataverse.

Disclosure Statement

No potential conflict of interest was reported by the author.

Notes

1 This distinguishes political violence justification from other forms of discrimination like xenophobia and racism that are more strongly based on intergroup disparities (Bauer et al., 2023; Stephan et al., 2016).

2 In a different perspective, the justification of political violence can also be regarded as an ultima ratio measure to voice personal grievances and an outcry for politicians to tackle the improvement of the economic situation of the poor (see further Lawless & Fox, 2001).

3 All studies, their fieldwork periods, question wording, coding, and summary statistics can be found in detail in the Supplementary Material.

4 In the US, I capture polarisation similar to Kalmoe and Mason (2019, 2022b) through the strength of partisanship. In Germany, I use a measure for affective polarisation in multiparty systems developed by Wagner (2021). For more information on question wording and coding see Supplementary Material.

5 Since the German data contained a variation in question wording for both items, I can assess the intensity of violence justification even further. Hence, 18 percent of respondents strongly consider ‘harassment’ justified, but only nine percent consider the ‘threatening’ of politicians justified. There are, however, basically no differences between asking about the use of ‘violence against property’, ‘violence’, and ‘physical violence’ to pursue political goals (all around one to two percent strong support). For more information, see Supplementary Material.

6 Across countries and models I further find that political violence justification is higher among men and younger respondents, whereas there are mixed results for educational degree. Future work could thus explore the role of these socio-demographics with regards to the development of political violence justification.

7 Fixed-effects models are a standard tool in panel data analysis to assess the effect of change in a predictor on change in a dependent variable (see further Allison, 2009). The standard fixed-effects estimator is based on a demeaning of each indicator of interest at each time point from its intertemporal mean ($y_{it}-{\overline{y}}_i=\text{β}\times \left(x_{it}-{\overline{x}}_i\right)+(u_{it}-{\overline{u}}_i)$) which cancels out unobserved heterogeneity and omitted variable bias by holding heterogeneity constant over time. In this special two-wave panel case, the fixed effects estimator is also numerically equal to the first-difference estimator which calculates the effect of changes in a predictor compared to the previous period ($y_{it}-y_{i,t-1}=\text{β}\times \left(x_{it}-x_{i,t-1}\right)+(u_{it}-u_{i,t-1})$).

8 I recoded the equivalised household income into income deciles for working respondents and created a ‘low income’ dummy that refers to respondents in the bottom income decile.

9 Full models can be found in the Supplementary Material.

Additional information

Funding

This research was part of the project ‘The influence of socio-economic problems on political integration’ (PI: Paul Marx), funded by the Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen, the project ‘The micro and macro dynamics of political inequality’ (PI: Paul Marx), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation – Project no. 490987363), and the project ‘Growing into politics under material hardship: The impact of socio-economic problems on political socialization’, funded by the Deutsche Forschungsgemeinschaft (DFG – Project no. 505158386). The data collection of Study 1 was further funded by the Japan Society for the Promotion of Science (PI: Airo Hino, Kakenhi Grant 20KK0026).

Notes on contributors

Sebastian Jungkunz

Sebastian Jungkunz is a Post-Doctoral Researcher at the Institute of Political Science and Sociology at the University of Bonn, Germany. His research focuses on (youth) political participation, populism, and political extremism. He published his work, among others, in the European Journal of Political Research, European Sociological Review, PLOS ONE, Political Research Quarterly, and West European Politics. Email: [email protected], [email protected]