Skip to main content
Springer Nature Link
Log in

Are Actions Costlier Than Words? Formal Models of Protester-Police Dynamic Interactions and Evidence from Empirical Analysis

  • Original Research
  • Published:
Operations Research Forum Aims and scope Submit manuscript

Abstract

Why do some protests face police repression while others are tolerated? This article formulates signaling game models to analyze the dynamic interaction between police and protesters in autocratic and democratic regimes. The theoretical framework of formal models suggests that low profile protester actions like peaceful marches with shouts avoid repressive police responses as opposed to high profile actions like close proximity contacts with the police. The equilibrium outcomes of our games are analyzed with empirical specifications that draw on two rich datasets. The empirical results of multinomial linear regression (MLR) models support the claim that police are likelier to repress protests which use aggressive actions. These results are one explanation for the Law of Coercive Responsiveness [14]. The robustness analysis of longitudinal data on protest events in the USA from 1960 to 1995 provides additional insights into the mechanism that protests relying on aggressive actions are more likely to face police violence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The author is willing to share the data upon publication.

Code Availability

The author is willing to share the software codes used for empirical analysis upon publication and reasonable request.

Notes

  1. To clarify, while we provide an empirical validity for this logic in our estimation models; however, in the real world, many instances of soft tactics like words and statements do generate violent response by the police.

  2. Disruptive costs are defined as the cumulative function of protest location, duration, and size. Concession cost index is operationalized as the aggregate function of protesters’ demands, protester violence, and recurrent demands.

  3. Klein and Regan [27] suggest that protests which demand the resignation of the state leader are costlier than protests that present other types of demands such as resource distribution and social rights (pg. 489).

  4. We use “Neutral” as our reference category for the multinomial logistic analysis.

  5. In the original NAVCO dataset, there is another category 4 = political engagement: dialogue or negotiations, but we do not report it here since after filtering our dataset had no observations with this category as a tactical option for protesters.

  6. This result of high probability can be affected by the size and specific observations in our dataset.

  7. This result should be taken with some caution because of the lack of data on protests that seek regime change, of which there are only 26 out of 204 observations in our dataset.

  8. The reason is that in the logistical regression context the fixed effects do not control for unobserved time-dependent covariates (see, [55].

References

  1. Allison PD (2005) Fixed effects regression methods for longitudinal data using SAS. SAS Institute, Cary, NC

    Google Scholar 

  2. Angeletos G-M, Hellwig C, Pavan A (2007) Dynamic global games of regime change: learning, multiplicity, and the timing of attacks. Econometrica 75(3):711–756

    Article  Google Scholar 

  3. Asher B (1999) Nonviolent social movements: a geographical perspective. Blackwell, Malden, Mass.

    Google Scholar 

  4. Banks JS, Sobel J (1987) Equilibrium selection in signaling games. Econometrica 50(3):647–661

    Article  Google Scholar 

  5. Bell A, Jones K (2015) Explaining fixed effects: random effects modeling of time-series, cross sectional and panel data. Political Science Research and Methods (PSRM). 3(1):133–153

  6. Boudreau V (2004) Resisting dictatorship: repression and protest in Southeast Asia. Cambridge University Press, New York

    Book  Google Scholar 

  7. Burgess B, Burgess H, Peter W (1994) Justice without violence. Boulder, CO: Lynne Rienner

  8. Butcher C, Svensson I (2016) Manufacturing dissent: modernization and the onset of major nonviolent resistance campaigns. J Conflict Resolut 60(2):311–339

    Article  Google Scholar 

  9. Chang P, Vitale A (2013) Repressive coverage in an authoritarian context: threat, weakness, and legitimacy in South Korea’s democracy movement. Mobilization 18(1):19–39

    Article  Google Scholar 

  10. Chenoweth E, Pinckney J, Lewis OA (2017) Nonviolent and violent campaigns and outcomes dataset, v. 3.0. University of Denver

  11. Chenoweth E, Gallagher Cunningham K (eds) (2013) Understanding nonviolent resistance: an introduction. Special issue. J Peace Res. 50(3)

  12. Croissant A, Kuehn D, Eschenauer T (2018) Mass protests and the military. J Democr 29(3):141–155

    Article  Google Scholar 

  13. Davenport C, Soule S, Armstrong D (2011) Protesting while Black? The differential policing of American Activism, 1960–1990”. Am Sociol Rev 76(1):152–178

    Article  Google Scholar 

  14. Davenport C (2007) State repression and political order. Annu Rev Polit Sci 10:1–23

    Article  Google Scholar 

  15. Dragu T, Lupu Y (2018) Collective action and constraints on repression at the endgame. Comp Pol Stud 51(8):1042–1073

    Article  Google Scholar 

  16. Dudouet V (2013) Dynamics and factors of transition from armed struggle to nonviolent resistance. J Peace Res 50(3):401–413

    Article  Google Scholar 

  17. Earl J, Soule SA, McCarthy John D (2003) Protests under fire? Explaining protest policing. Am Sociol Rev 69:581–606

    Article  Google Scholar 

  18. Earl J, Soule SA (2006) Seeing blue: a police-centered explanation of protest policing. Mobilization 11:145–164

    Article  Google Scholar 

  19. Escriba-Folch A (2013) Repression, political threats, and survival under autocracy. Int Polit Sci Rev 34(5):543–560

    Article  Google Scholar 

  20. Francisco RA (1995) The relationship between coercion and protest: an empirical evaluation in three coercive states. J Conflict Resolut 39(2):263–282

    Article  Google Scholar 

  21. Gartner SS, Regan PM (1996) Threat and repression: the nonlinear relationship between government and opposition violence. J Peace Res 33(3):273

    Article  Google Scholar 

  22. LaGina G (2020) Revealing issue salience via costly protest: how legislative behavior following protest advantages low-resource groups. Br J Polit Sci. 1–21 https://doi.org/10.1017/S0007123420000423

  23. Greene WH (1993) Econometric Analysis, 2nd ed., Macmillan

  24. Greitens SC (2016) Dictators and their secret police-coercive institutions and state violence. New York: Cambridge University Press

  25. Hsiao C (2003) Analysis of panel data. Cambridge University Press

    Book  Google Scholar 

  26. Huang H (2013) Signal left, turn right: central rhetoric and local reform in China. Polit Res Q 66:292–305

    Article  Google Scholar 

  27. Klein GR, Regan PM (2018) Dynamics of political protests. Int Organ 72:485–521

    Article  Google Scholar 

  28. Kuran T (1991) Now out of never: the element of surprise in the East European Revolution of 1989. World Politics 44(1):7–48

    Article  Google Scholar 

  29. Leventoglu B, Metternich NW (2018) Born weak, growing strong: anti-government protests as a signal of rebel strength in the context of Civil Wars. American Journal of Political Science 62(3):581–596

    Article  Google Scholar 

  30. Lohmann S (1993) A signaling model of informative and manipulative political action. American Political Science Review 87(2):319–333

    Article  Google Scholar 

  31. McAdam D, McCarthy J Olzak S, Soule S nd (2019) Dynamics of collective action. http://www.stanford.edu/group/collectiveaction/ (accessed 14 Oct 2019)

  32. Marshall MG, Jaggers K (2016) Polity IV project: political regime characteristics and transitions. Retrieved from http://www.systemicpeace.org/polity/polity4.htm

  33. Mátyás L, Sevestre P (eds) (2008) The econometrics of panel data: fundamentals and recent developments in theory and practice, 3rd edn. Springer, New York

    Google Scholar 

  34. Nepstad SE (2011) Nonviolent revolutions: civil resistance in the late 20th century. Oxford University Press, New York

    Book  Google Scholar 

  35. North D, Wallis J, Weingast B (2009) Violence and social orders: a conceptual framework for interpreting recorded human history. Cambridge University Press, Cambridge

    Book  Google Scholar 

  36. Pierskalla JH (2010) Protest, deterrence, and escalation: the strategic calculus of government repression. J Conflict Resolut 54(1):117–145

    Article  Google Scholar 

  37. Raffalovich LE, Chung R (2014) Models for pooled time-series cross-section data. Int J Confl Violence 8(2):210–221

    Google Scholar 

  38. Reynolds-Stenson H (2018) Protesting the police: anti-police brutality claims as a predictor of police repression of protest. Soc Mov Stud 17(1):14–63

    Google Scholar 

  39. Ritter EH (2014) Policy disputes, political survival, and the onset and severity of state repression. J Conflict Resolut 58(1):143–168

    Article  Google Scholar 

  40. Shadmehr M, Bernhardt D (2011) Collective action with uncertain payoffs: coordination, public signals, and punishment dilemmas. American Political Science Review 105(4):829–851

    Article  Google Scholar 

  41. Shadmehr M ( 2014) Mobilization, repression, and revolutions: grievances and opportunities in contentious politics. J Pol 76(3):621–635

  42. Schock K (2005) Unarmed insurrections: people power movements in non-democracies. University of Minnesota Press, Minneapolis

    Google Scholar 

  43. Schock K (2013) The practice and study of civil resistance. J Peace Res 50(3):277–290

    Article  Google Scholar 

  44. Searle JR (1990) Collective intentions and actions. Cambridge. MA: MIT Press

  45. Sharp G (1973) The politics of nonviolent action. Porter Sargent, Boston, MA

    Google Scholar 

  46. Sharp G (2005) Waging nonviolent struggle: 20th century practice and 21st century potential. Porter Sargent, Boston

    Google Scholar 

  47. Solt F (2015) Economic inequality and nonviolent protest. Social Sciences Quarterly 96(5)

  48. Stephan MJ, Chenoweth E (2008) Why civil resistance works: the strategic logic of nonviolent conflict. Int Secur 33:7–44

    Article  Google Scholar 

  49. Sullivan CM (2016) Undermining resistance: mobilization, repression, and the enforcement of political order. J Conflict Resolut 60(7):1163–1190

    Article  Google Scholar 

  50. Sullivan H (2019) Sticks, stones, and broken bones: protest violence and the state. J Conflict Resolut 63(3):700–726

    Article  Google Scholar 

  51. Tansey, O, Koehler K, Alexander S (2018) Ties to the rest: autocratic linkages and regime survival. Comparative Political Studies, 1–34

  52. Tilly C (2008) Contentious performances. Cambridge University Press, Cambridge, England

    Book  Google Scholar 

  53. Tsebelis G, Sprague J (1989) Coercion and revolution: variations on a predator-prey model. Mathematical Computer Modeling 12(4/5):547–559

    Article  Google Scholar 

  54. Tolstrup J, Seeberg MA, Glavind JG (2019) Signals of support from great power patrons and the use of repression during nonviolent protests. Comp Pol Stud 52(4):514–543

    Article  Google Scholar 

  55. Wilson JR, Lorenz KA (2015) Fixed effects logistic regression model. In: Modeling binary correlated responses using SAS, SPSS and R. ICSA Book Series in Statistics, vol 9. Springer, Cham

  56. Zunes S (1994) Unarmed insurrections against authoritarian governments in the third world: a new kind of revolution. Third World Quarterly 15:403–442

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank Jan Pierskalla, Stergios Skaperdas, David Meyer, Bernard Grofman, Samantha Vortherms, Marek Kaminski, Jeff Kopstein and the editor Panos Pardalos and annonymous reviewers for very useful comments and suggestions on earlier drafts of this paper. The current version of this work has also benefited from commentary received from students and professors alike in Experimental Economics I and II graduate courses taught by John Duffy and Michael McBride, respectively, at UC Irvine. Additionally, I would like to extend my gratitude to PhD student colleagues Avik Sanyal, Andrew Benson, Nishtha Sharma, Mark Hup and Patrick Julius in the Economics department at UCI for tremendously stimulating discussions.

Funding

This research was supported by the Jack W. Peltason Center for the Study of Democracy and the Institute of Humane Studies summer graduate research fellowship Center for Global Peace and Conflict Studies, both at UC Irvine.

Author information

Authors and Affiliations

  1. PhD candidate in political economy, University of California, Irvine, CA, 92697, USA

    Sargis Karavardanyan

Authors
  1. Sargis Karavardanyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding author

Correspondence to Sargis Karavardanyan.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Conflict of Interest

The author declares no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 29 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karavardanyan, S. Are Actions Costlier Than Words? Formal Models of Protester-Police Dynamic Interactions and Evidence from Empirical Analysis. Oper. Res. Forum 2, 54 (2021). https://doi.org/10.1007/s43069-021-00099-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s43069-021-00099-4

Keywords