The Coercive Capacity of COVID-19 Policies: Rule of Law and Rule of Fear?

• In Event: Government and Citizen Responses to the COVID-19 Pandemic

Abstract

This paper analyzes the degree to which states with high coercive capacity rely on that capacity in the COVID-19 era. Using a mixed-methods approach that begins with ethnographic observations from Kazakhstan, the paper pans out to analyze a cross-national selection of countries on indicators of state capacity (in particular coercive capacity), the rule of law, COVID-19 policies, and mobility data.
A traditionally held assumption about authoritarian states is that people comply with the regime out of fear. We seek to understand how different types of COVID-19 policies elucidate the preferences for coercive versus information-based strategies for inducing compliance. Early assessments of COVID-19 responses sought to understand whether democracies or autocracies were more well-suited to address the public health crises (Kleinfeld 2020; Berengaut 2020). This paper looks not to regime-level variables, but rather the capacity of states to deploy coercive enforcement for its policies.
In the early days of the COVID-19 pandemic, authoritarian Kazakhstan’s policy orientation seemed to take a page from the police-state playbook. Once lockdowns were announced, signs appeared warning citizens of penalties of fines or administrative arrest for violations, police regularly patrolled both public and residential areas, sometimes announcing the rules over loudspeaker, and the number of arrests was regularly reported. Compliance was high. After the state of emergency was lifted in mid-May, businesses opened and life returned quickly nearly to normal. Despite regular warnings from the government about policies requiring masks and social distancing, compliance became weak and people were openly skeptical about the threat of the virus at the same time that cases began rising sharply. The government’s response has likewise been markedly less enforcement-oriented than during the lockdown, suggesting there may have been a shift from coercively-oriented to information-oriented strategies. On-the-ground observations for the Kazakhstan narrative come in two forms: the ethnographic fieldnotes of one of the authors and a database of around 400 structured public observations made by research assistants in various regions of Kazakhstan. The structured observations capture mask-wearing and social distancing behavior as well as interactions between citizens and law enforcement (public and private, such as security guards). These are supplemented by media and social media analysis to address the main research questions of this paper: what types of policies are governments using to induce compliance? Which of these policies are most effective?
The cross-national analysis looks both at policy types and policy timing related to these questions using data from CoronaNet, an original dataset of over 50000 policy entries from 195 countries. Policy types are divided into those that regulate personal behavior (wearing masks, stay at home orders, and restrictions of movement involving surveillance or requiring permits), those regulating institutions (school and business shut-downs), and public awareness measures that seek to educate the public and produce behavioral compliance. Do these types of policies vary systematically across states with different levels of coercive capacity, suggesting that different strategies are being utilized? The enforcement of individual measures is micro-level, involving one-to-one interactions with law enforcement. The execution of institutional compliance mediates the behavior of the average citizen through the compliance and enforcement of businesses, schools, etc. Public awareness measures, on the other hand, rely on broadly socializing strategies instead of direct enforcement. Therefore, different policy types will logically produce different enforcement strategies that are to varying degrees labor intensive and coercion intensive. To answer the question of effectiveness we turn to an assessment of policy timing. Using geolocation data from Google, we assess the impact of different types of policies and when they were adopted on the movement of people from February – August 2020. Do certain types of policies reduce movement better than others? Do certain types of policies produce more of a movement backlash once relaxed? The answers to these questions will help us to understand if states with coercive capacity are utilizing that capacity effectively.

Authors

• Paula Daniela Ganga, Columbia University

• Caress Rene Schenk, Nazarbaev University