Public support for empowering police during the COVID-19 crisis: evidence from London

ABSTRACT

In the face of the COVID-19 pandemic, police services around the world were granted unprecedented new powers to enforce social distancing restrictions. In this paper, we present data from a rolling representative sample survey of Londoners (n = 3,201) fielded during the height of the first wave of the pandemic (April to June 2020). We examine the scale of public support for giving police additional powers to enforce the regulations, whether support for different powers ebbed and flowed over time, and which factors predicted support for police powers. First, we use interrupted time-series analysis to model change over time. Second, we pool the data to test the predictors of support for police powers. Aside from one lockdown-specific temporal factor (the easing of restrictions), we find that even in the midst of a pandemic, legitimacy, procedural justice and affective evaluations of pandemic powers are the most important factors explaining variation in public support for police empowerment.

KEYWORDS:

• COVID-19
• police empowerment
• procedural justice
• legitimacy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 NHS refers to the National Health Service.

2 Wales, Scotland and Northern Ireland have separate regulations.

3 In an online survey experiment, Nix et al. (2021) found a modest effect of providing information about COVID health risks to police on support for less general policing, and no effect on support for more policing of social distancing. We refer here to their modelling of the observational predictors of public support for social distancing policing.

4 Of course, there are a number of other events that occurred during the study period that we could have selected as our points of intervention (e.g. another government advisor resigning on 6 May after breaking lockdown rules, the London BLM protests in early June). We chose our points of intervention due to these events being the most widely publicised in the media and elsewhere.

5 To determine the appropriate lag for the autocorrelation of the adjacent error terms, we used a Cumby-Huizinga test which assesses whether the autocorrelation structure is correctly specified for a model (Baum and Schaffer, 2013). As we fitted a relatively large number of models, there was some variation in the number of suggested lags between 0 and 2. For the sake of consistency, we considered 1 lag for every model. Importantly, this specification did not meaningfully change any of the results presented in this paper.

6 We fitted the SEM using all five measures of public support for police powers and the findings were very similar.

7 These paths are not shown in the figure for visual ease.

8 Using the full dataset, we also tested the interaction between affect and the lockdown vs first easing of lockdown variable (the interaction effect was not statistically significant, p = 0.11) and the interaction between obligation to obey the police and the lockdown vs first easing of lockdown variable (the interaction effect was not statistically significant, p = 0.43).